@inbook{
 type = {inbook},
 year = {2024},
 pages = {83-109},
 volume = {1},
 websites = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781003343783-4/classification-objects-ir-images-using-wavelet-filters-based-lifting-scheme-daniel-trevino-sanchez-vicente-alarcon-aquino},
 month = {2},
 publisher = {CRC Press},
 edition = {1st},
 chapter = {Classification of Objects in IR Images Using Wavelet Filters Based on Lifting Scheme},
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 doi = {https://doi.org/10.1201/9781003343783-4},
 title = {Measurements and Instrumentation for Machine Vision}
}

@article{
 title = {Mejorando la red neuronal convolucional para detectar objetos en imáge
nes infrarrojas},
 type = {article},
 year = {2023},
 pages = {121-129},
 volume = {132},
 websites = {https://elementos.buap.mx/authors_single.php?id=944},
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 author = {Treviño Sánchez, D and Alarcon-Aquino, V},
 journal = {Elementos},
 keywords = {trevino2023mejorando}
}

@article{
 title = {Los sistemas inmunes artificiales en la detección de ciberataques},
 type = {article},
 year = {2023},
 pages = {113-119},
 volume = {132},
 websites = {https://elementos.buap.mx/authors_single.php?id=944},
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 hidden = {false},
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 bibtype = {article},
 author = {Limon-Cantú, David and Alarcon-Aquino, V},
 journal = {Elementos},
 keywords = {limon2023sistemas}
}

@inbook{
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 author = {Treviño Sánchez, D and Alarcon-Aquino, V},
 chapter = {Random Wavelet Coefficients Pooling for Convolutional Neural Networks},
 title = {Technology, Science and Culture: A Global Vision, Volume IV, London, U
nited Kingdom.},
 keywords = {trevino2023random}
}

@article{
 title = {Hybrid pooling with wavelets for convolutional neural networks},
 type = {article},
 year = {2022},
 keywords = {Convolutional neural network,Wavelet transform,feature extraction,lifting scheme,pooling layer},
 pages = {4327-4336},
 volume = {42},
 websites = {https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs219223},
 id = {f71c7952-2a7f-3f19-8739-1b35ba08af97},
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 last_modified = {2022-08-29T17:42:14.853Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art.},
 bibtype = {article},
 author = {Trevino-Sanchez, Daniel and Alarcon-Aquino, Vicente},
 doi = {10.3233/JIFS-219223},
 journal = {Journal of Intelligent & Fuzzy Systems},
 number = {5}
}

The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art.

@article{
 title = {A new machine learning model based on the broad learning system and wavelets},
 type = {article},
 year = {2022},
 keywords = {Algorithm,Artificial intelligence,Broad learning system,Deep learning,Exoplanets,Flat networks,Light curves,Machine learning,Multiresolution analysis,Neural networks,Wavelets},
 volume = {112},
 websites = {https://www.sciencedirect.com/science/article/pii/S0952197622001270?dgcid=author},
 id = {8716ff0e-cfd1-3322-951a-13eb1d567f93},
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 last_modified = {2022-08-29T17:42:16.264Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this work, we present a new neural network named WAvelet-Based Broad LEarning System (WABBLES). WABBLES is based on the flat structure of the broad learning system. Such structure offers an alternative to deep learning models, such as convolutional neural networks. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better classification performance. WABBLES uses wavelets to map the input signal, to obtain more relevant features from it. This is achieved by autonomously learning and adjusting the dilation and translation parameters of a wavelet, which control its shape. In this way, the resulting mapping nodes have a better representation of the most important features for the classification problem. The construction of the model is described here, along with special considerations and algorithms involved. Finally, the proposed model is tested using a database of synthetic astronomical data and a benchmark dataset called the Breast Cancer Wisconsin Dataset (Original). The conducted experiments provide a comparison between the proposed model and several machine learning algorithms with different performance metrics applied to the context of exoplanet identification and breast cancer detection. Our results confirm that the WABBLES model obtains superior accuracy and F-score percentages than the other models.},
 bibtype = {article},
 author = {Jara-Maldonado, M; Alarcon-Aquino, V.; Rosas-Romero, R.},
 journal = {Engineering Applications of Artificial Intelligence},
 number = {June}
}

In this work, we present a new neural network named WAvelet-Based Broad LEarning System (WABBLES). WABBLES is based on the flat structure of the broad learning system. Such structure offers an alternative to deep learning models, such as convolutional neural networks. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better classification performance. WABBLES uses wavelets to map the input signal, to obtain more relevant features from it. This is achieved by autonomously learning and adjusting the dilation and translation parameters of a wavelet, which control its shape. In this way, the resulting mapping nodes have a better representation of the most important features for the classification problem. The construction of the model is described here, along with special considerations and algorithms involved. Finally, the proposed model is tested using a database of synthetic astronomical data and a benchmark dataset called the Breast Cancer Wisconsin Dataset (Original). The conducted experiments provide a comparison between the proposed model and several machine learning algorithms with different performance metrics applied to the context of exoplanet identification and breast cancer detection. Our results confirm that the WABBLES model obtains superior accuracy and F-score percentages than the other models.

@inbook{
 type = {inbook},
 year = {2022},
 volume = {III},
 websites = {http://dx.doi.org/10.5772/intechopen.99973},
 month = {5},
 publisher = {IntechOpen},
 day = {4},
 id = {0a692978-dd2c-3b6d-8ace-086991c2831b},
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 abstract = {The Dendritic Cell Algorithm (DCA) is a bioinspired, population-based, supervised binary classifier, designed for anomaly detection in communication networks. The proposed model is inspired by the behavior of Dendritic Cells and Danger Theory. The main contribution of this research addresses two contemporary challenges of Network-based Intrusion Detection Systems, namely feature selection and generalization capabilities to improve classification performance. Feature selection improvement is achieved by using information gain and mutual information. A Decision Tree model is incorporated as a classification mechanism in order to improve accuracy, as a substitute to the classification threshold of the DCA. The
proposed model is assessed using two publicly available datasets, namely UNSWNB15 and NSL-KDD. Experimental results are compared against state of the art bioinspired and machine learning approaches for binary classification. The proposed approach provides competitive results when compared to other state of the art approaches, such as Support Vector Machines, and Artificial Neural Networks, achieving a 97.25 and 93.28% accuracy for the UNSW-NB15 and NSL-KDD datasets, respectively. Future challenges include multi-class classification, further performance improvements, and online detection.},
 bibtype = {inbook},
 author = {Limon-Cantu, David; and Alarcon-Aquino, Vicente},
 editor = {Hernandez, Luis Ricardo and Serrano-Meneses, Martin},
 doi = {10.5772/intechopen.99973},
 chapter = {Network Intrusion Detection Using Dendritic Cells and Danger Theory},
 title = {Technology, Science and Culture - A Global Vision, Volume III}
}

The Dendritic Cell Algorithm (DCA) is a bioinspired, population-based, supervised binary classifier, designed for anomaly detection in communication networks. The proposed model is inspired by the behavior of Dendritic Cells and Danger Theory. The main contribution of this research addresses two contemporary challenges of Network-based Intrusion Detection Systems, namely feature selection and generalization capabilities to improve classification performance. Feature selection improvement is achieved by using information gain and mutual information. A Decision Tree model is incorporated as a classification mechanism in order to improve accuracy, as a substitute to the classification threshold of the DCA. The proposed model is assessed using two publicly available datasets, namely UNSWNB15 and NSL-KDD. Experimental results are compared against state of the art bioinspired and machine learning approaches for binary classification. The proposed approach provides competitive results when compared to other state of the art approaches, such as Support Vector Machines, and Artificial Neural Networks, achieving a 97.25 and 93.28% accuracy for the UNSW-NB15 and NSL-KDD datasets, respectively. Future challenges include multi-class classification, further performance improvements, and online detection.

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 websites = {http://dx.doi.org/10.5772/intechopen.99973},
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 abstract = {The Dendritic Cell Algorithm (DCA) is a bioinspired, population-based, supervised binary classifier, designed for anomaly detection in communication networks. The proposed model is inspired by the behavior of Dendritic Cells and Danger Theory. The main contribution of this research addresses two contemporary challenges of Network-based Intrusion Detection Systems, namely feature selection and generalization capabilities to improve classification performance. Feature selection improvement is achieved by using information gain and mutual information. A Decision Tree model is incorporated as a classification mechanism in order to improve accuracy, as a substitute to the classification threshold of the DCA. The
proposed model is assessed using two publicly available datasets, namely UNSWNB15 and NSL-KDD. Experimental results are compared against state of the art bioinspired and machine learning approaches for binary classification. The proposed approach provides competitive results when compared to other state of the art approaches, such as Support Vector Machines, and Artificial Neural Networks, achieving a 97.25 and 93.28% accuracy for the UNSW-NB15 and NSL-KDD datasets, respectively. Future challenges include multi-class classification, further performance improvements, and online detection.},
 bibtype = {inbook},
 author = {Jara-Maldonado, Miguel; and Alarcon-Aquino, Vicente; and Rosas-Romero, Roberto},
 editor = {Hernandez, Luis Ricardo; and Serrano-Meneses, Martin},
 doi = {10.5772/intechopen.99973},
 chapter = {Exoplanet Research Using
Machine Learning and
Multiresolution Analysis
Techniques},
 title = {Technology, Science and Culture - A Global Vision, Volume III}
}

The Dendritic Cell Algorithm (DCA) is a bioinspired, population-based, supervised binary classifier, designed for anomaly detection in communication networks. The proposed model is inspired by the behavior of Dendritic Cells and Danger Theory. The main contribution of this research addresses two contemporary challenges of Network-based Intrusion Detection Systems, namely feature selection and generalization capabilities to improve classification performance. Feature selection improvement is achieved by using information gain and mutual information. A Decision Tree model is incorporated as a classification mechanism in order to improve accuracy, as a substitute to the classification threshold of the DCA. The proposed model is assessed using two publicly available datasets, namely UNSWNB15 and NSL-KDD. Experimental results are compared against state of the art bioinspired and machine learning approaches for binary classification. The proposed approach provides competitive results when compared to other state of the art approaches, such as Support Vector Machines, and Artificial Neural Networks, achieving a 97.25 and 93.28% accuracy for the UNSW-NB15 and NSL-KDD datasets, respectively. Future challenges include multi-class classification, further performance improvements, and online detection.

@article{
 title = {Multiresolution dendritic cell algorithm for network anomaly detection},
 type = {article},
 year = {2021},
 pages = {1-32},
 volume = {7},
 websites = {https://peerj.com/articles/cs-749},
 month = {10},
 day = {19},
 id = {c919fe71-9cc4-3e0d-8ece-f2354e03d126},
 created = {2022-08-29T17:42:18.077Z},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.},
 bibtype = {article},
 author = {Limon-Cantu, David and Alarcon-Aquino, Vicente},
 doi = {10.7717/peerj-cs.749},
 journal = {PeerJ Computer Science},
 number = {e749}
}

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

@inproceedings{
 title = {Hybrid Pooling with Wavelets for Convolutional Neural Networks},
 type = {inproceedings},
 year = {2021},
 websites = {https://lke.buap.mx/2021/index.php/oral-presentations/},
 city = {Puebla},
 id = {378788ac-42b9-312a-b0f0-bb9c7ca6953a},
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 bibtype = {inproceedings},
 author = {Trevino-Sanchez, D; Alarcon-Aquino, V.},
 booktitle = {LKE2021 - 8th International Symposium on Language & Knowledge Engineering}
}

@inbook{
 type = {inbook},
 year = {2020},
 pages = {629-655},
 websites = {http://link.springer.com/10.1007/978-3-030-22587-2_19},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {50e004f5-120c-3e7c-a863-bf61e8abae4d},
 created = {2022-08-29T17:42:19.300Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:19.300Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {It is well-known that there has been a considerable progress in multimedia technologies during the last decades, namely TV, photography, sound and video recording, communication systems, etc., which came into the world during at least half of the previous century and were developed as analog systems, and nowadays have been almost completely replaced by digital systems. The aforementioned motivates a deep study of multimedia compression and intensive research in this area. Data compression is concerned with minimization of the number of information carrying units used to represent a given data set. Such smaller representation can be achieved by applying coding algorithms. Coding algorithms can be either lossless algorithms that reconstruct the original data set perfectly or lossy algorithms that reconstruct a close representation of the original data set. Both methods can be used together to achieve higher compression ratios. Lossless compression methods can either exploit statistical structure of the data or compress the data by building a dictionary that uses fewer symbols for each string that appears on the data set. Lossy compression, on the other hand, uses a mathematical transform that projects the current data set onto the frequency domain. The coefficients obtained from the transform are quantized and stored. The quantized coefficients require less space to be stored. This chapter is focused on the recently published advances in image and video compression to date considering the use of the integer discrete cosine transform (IDCT), wavelet transforms, and fovea centralis.},
 bibtype = {inbook},
 author = {Galan-Hernandez, Juan C. and Alarcon-Aquino, Vicente and Starostenko, Oleg and Ramirez-Cortes, Juan Manuel and Gomez-Gil, Pilar},
 doi = {10.1007/978-3-030-22587-2_19},
 chapter = {Advances in Image and Video Compression Using Wavelet Transforms and Fovea Centralis},
 title = {Machine Vision and Navigation}
}

It is well-known that there has been a considerable progress in multimedia technologies during the last decades, namely TV, photography, sound and video recording, communication systems, etc., which came into the world during at least half of the previous century and were developed as analog systems, and nowadays have been almost completely replaced by digital systems. The aforementioned motivates a deep study of multimedia compression and intensive research in this area. Data compression is concerned with minimization of the number of information carrying units used to represent a given data set. Such smaller representation can be achieved by applying coding algorithms. Coding algorithms can be either lossless algorithms that reconstruct the original data set perfectly or lossy algorithms that reconstruct a close representation of the original data set. Both methods can be used together to achieve higher compression ratios. Lossless compression methods can either exploit statistical structure of the data or compress the data by building a dictionary that uses fewer symbols for each string that appears on the data set. Lossy compression, on the other hand, uses a mathematical transform that projects the current data set onto the frequency domain. The coefficients obtained from the transform are quantized and stored. The quantized coefficients require less space to be stored. This chapter is focused on the recently published advances in image and video compression to date considering the use of the integer discrete cosine transform (IDCT), wavelet transforms, and fovea centralis.

@article{
 title = {Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey},
 type = {article},
 year = {2020},
 keywords = {Artificial intelligence,Deep learning,Discrete wavelet transform,Exoplanets,Light curves,Machine learning,Multiresolution analysis,Transits},
 pages = {573-600},
 volume = {13},
 websites = {https://link.springer.com/10.1007/s12145-020-00464-7},
 publisher = {Springer},
 id = {46622163-c757-358e-8a12-fbc7e61797c7},
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 source_type = {Article},
 private_publication = {false},
 abstract = {Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.},
 bibtype = {article},
 author = {Jara-Maldonado, Miguel and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto and Starostenko, Oleg and Ramirez-Cortes, Juan Manuel},
 doi = {10.1007/s12145-020-00464-7},
 journal = {Earth Science Informatics},
 number = {3}
}

Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.

@inbook{
 type = {inbook},
 year = {2020},
 keywords = {Breast cancer,Classification,Deep convolutional neural networks (CNN),Image processing,Regions-CNN,Thermography},
 pages = {255-267},
 volume = {862},
 websites = {http://link.springer.com/10.1007/978-3-030-35445-9_21},
 id = {b63fe7b4-5e88-33ea-9cc3-72fd5352c977},
 created = {2022-08-29T17:42:20.515Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:20.515Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {During 2016, breast cancer was the second cause of death among women within ages of 24–30. This makes mandatory to find reliable strategies that support physicians and medical services for early diagnosis of such disease. Currently, mammography is considered the gold instrument for assessing the risk of having breast cancer, but other methods are being analyzed, looking for systems that may be cheaper and easier to apply, including the use of thermographic images. In this paper, we present an analysis of the performance of a system based on a “Feed-Forward Neural Network” (FFNN), for the identification of two and three levels of risk cancer. Indeed, a system based on a “Regions-Convolutional Neural network” (R-CNN) for automatic segmentation of the breast is proposed. Both systems were tested in a private database developed by the “Center for Studies and Cancer Prevention, A.C.” located in Oaxaca, Mexico, which presents important challenges as class unbalances, a slack recording with respect to application of the protocol and noise. The systems were evaluated using three subsets of the database, built using images with different levels of challenges. Our results showed that a FNNN classifier performed well only with data strictly following the protocol, while the levels of performance with noisy data are not yet acceptable for real applications. In the other hand, the results obtained by the automatic segmentation based on R-CNN were competitive, encouraging for more research in this area.},
 bibtype = {inbook},
 author = {Gomez-Gil, Pilar and Reynoso-Armenta, Daniela and Castro-Ramos, Jorge and Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente},
 doi = {10.1007/978-3-030-35445-9_21},
 chapter = {Segmentation and Classification of Noisy Thermographic Images as an Aid for Identifying Risk Levels of Breast Cancer},
 title = {Studies in Computational Intelligence}
}

During 2016, breast cancer was the second cause of death among women within ages of 24–30. This makes mandatory to find reliable strategies that support physicians and medical services for early diagnosis of such disease. Currently, mammography is considered the gold instrument for assessing the risk of having breast cancer, but other methods are being analyzed, looking for systems that may be cheaper and easier to apply, including the use of thermographic images. In this paper, we present an analysis of the performance of a system based on a “Feed-Forward Neural Network” (FFNN), for the identification of two and three levels of risk cancer. Indeed, a system based on a “Regions-Convolutional Neural network” (R-CNN) for automatic segmentation of the breast is proposed. Both systems were tested in a private database developed by the “Center for Studies and Cancer Prevention, A.C.” located in Oaxaca, Mexico, which presents important challenges as class unbalances, a slack recording with respect to application of the protocol and noise. The systems were evaluated using three subsets of the database, built using images with different levels of challenges. Our results showed that a FNNN classifier performed well only with data strictly following the protocol, while the levels of performance with noisy data are not yet acceptable for real applications. In the other hand, the results obtained by the automatic segmentation based on R-CNN were competitive, encouraging for more research in this area.

@article{
 title = {A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG},
 type = {article},
 year = {2020},
 keywords = {EEG analysis,Epileptic seizure detection,Machine learning classification,Wavelet selection,Wavelet-based neural networks},
 pages = {187-211},
 volume = {92},
 websites = {http://link.springer.com/10.1007/s11265-019-01456-7},
 month = {2},
 day = {17},
 id = {1fbc70b1-99e2-337b-afcd-9db2a3e7c397},
 created = {2022-08-29T17:42:21.104Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:21.104Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present a novel neural network able to classify epileptic seizures using electroencephalogram (EEG) signals, called “Multidimensional Radial Wavelons Feed-Forward Wavelet Neural Network” (MRW-FFWNN). The network is part of a classification system, which distinguishes among three brain states related to epilepsy namely ictal, interictal and healthy. Efficient methods for pre-processing EEG’s, extracting features and getting the final class decisions were selected using a statistical three-fold cross-validation method, which assures the robustness of the system and its generalization ability. The following methods were systematically analyzed to find the most appropriate for this problem: 1) Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filters for noise reduction; 2) discrete Wavelet Transform (DWT) and Maximal Overlap Discrete Wavelet Transform (MODWT) for frequency decomposition of the EEG signals; 3) average correlation and maximum voting correlation for selecting a suitable mother wavelet for frequency decomposition; 4) Binary-tree and one-vs-one (OVO) decomposition strategies for primary binary classification; 5) voting and weighted-voting strategy aggregation strategies for the final classification. The integrated system was assessed using a three-fold cross validation, applied to a benchmark provided by the University of Bonn, getting an average accuracy of 93.33% when tested using sets Z, S and F and 95.0% when sets Z, S, F and O were used. The proposed network got competitive accuracy, compared with other state-of-the art classifiers, training in almost a half of the time than the ones with similar accuracy.},
 bibtype = {article},
 author = {Juárez-Guerra, E. and Alarcon-Aquino, V. and Gómez-Gil, P. and Ramírez-Cortés, J. M. and García-Treviño, E. S.},
 doi = {10.1007/s11265-019-01456-7},
 journal = {Journal of Signal Processing Systems},
 number = {2}
}

In this paper, we present a novel neural network able to classify epileptic seizures using electroencephalogram (EEG) signals, called “Multidimensional Radial Wavelons Feed-Forward Wavelet Neural Network” (MRW-FFWNN). The network is part of a classification system, which distinguishes among three brain states related to epilepsy namely ictal, interictal and healthy. Efficient methods for pre-processing EEG’s, extracting features and getting the final class decisions were selected using a statistical three-fold cross-validation method, which assures the robustness of the system and its generalization ability. The following methods were systematically analyzed to find the most appropriate for this problem: 1) Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filters for noise reduction; 2) discrete Wavelet Transform (DWT) and Maximal Overlap Discrete Wavelet Transform (MODWT) for frequency decomposition of the EEG signals; 3) average correlation and maximum voting correlation for selecting a suitable mother wavelet for frequency decomposition; 4) Binary-tree and one-vs-one (OVO) decomposition strategies for primary binary classification; 5) voting and weighted-voting strategy aggregation strategies for the final classification. The integrated system was assessed using a three-fold cross validation, applied to a benchmark provided by the University of Bonn, getting an average accuracy of 93.33% when tested using sets Z, S and F and 95.0% when sets Z, S, F and O were used. The proposed network got competitive accuracy, compared with other state-of-the art classifiers, training in almost a half of the time than the ones with similar accuracy.

@inbook{
 type = {inbook},
 year = {2020},
 keywords = {Empirical Mode Decomposition,Exoplanets,Light curves,Machine learning,Multiresolution Analysis,Synthetic transits},
 pages = {50-64},
 volume = {12468 LNAI},
 websites = {http://link.springer.com/10.1007/978-3-030-60884-2_4},
 id = {c8bbabc0-5e1e-31c2-8242-e8beda5e9121},
 created = {2022-08-29T17:42:21.735Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:21.735Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2020, Springer Nature Switzerland AG. The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.},
 bibtype = {inbook},
 author = {Jara-Maldonado, Miguel and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto},
 doi = {10.1007/978-3-030-60884-2_4},
 chapter = {A Multiresolution Machine Learning Technique to Identify Exoplanets},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

© 2020, Springer Nature Switzerland AG. The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.

@inproceedings{
 title = {A Multiresolution Machine Learning Technique to Identify Exoplanets},
 type = {inproceedings},
 year = {2020},
 websites = {https://dblp.org/db/conf/micai/micai2020-1.html},
 id = {30b0434f-b67d-349b-8993-c514336db09f},
 created = {2022-08-29T17:42:22.460Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:22.460Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.},
 bibtype = {inproceedings},
 author = {Jara-Maldonado, M.; Alarcon-Aquino, V; Rosas-Romero, R.},
 booktitle = {19th Mexican International Conference on Artificial Intelligence, MICAI}
}

The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.

@inproceedings{
 title = {A Multiresolution Analysis Technique to Improve Exoplanet Identification},
 type = {inproceedings},
 year = {2020},
 websites = {https://drive.google.com/file/d/1AbRZv9mVjYXiWrvLR_z1AMUoY-boQG6g/view?usp=sharing},
 id = {4a2339b2-fc4a-3266-a817-05633ab8b4ce},
 created = {2022-08-29T17:42:23.066Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:23.066Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Jara-Maldonado, M.; Alarcon-Aquino, V; Rosas-Romero, R.},
 booktitle = {Exoplanets III, Heidelberg University}
}

@article{
 title = {An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks},
 type = {article},
 year = {2019},
 keywords = {Broken bar,fault detection,independent component analysis (ICA),induction motor (IM),motor current signature analysis (MCSA),neural network (NN)},
 pages = {1353-1361},
 volume = {68},
 websites = {https://ieeexplore.ieee.org/document/8667659/},
 month = {5},
 id = {7f756918-21d5-3a0e-8e9b-4fc0c057b296},
 created = {2022-08-29T17:42:23.678Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:23.678Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a novel approach on motor current signature analysis (MCSA) for broken bar fault detection of induction motors (IMs), using as input the current signal measured from one of the three motor phases. Independent component analysis (ICA) is used over the Fourier-domain spectral signals obtained from the input and its autocorrelation function. The standard deviation of spectral components within a region of interest (ROI) of an ICA signal output was found to exhibit substantial differences between damaged and healthy motors. Separation of the ROI in one, two, and three sectors leads to an improved extraction of feature vectors, which are further fed into a neural network for classification purposes. The assessment of the proposed method is carried out through several experiments using two damage levels (broken bar and half broken bar) and two load motor conditions (50% and 75%), with a classification accuracy ranging from 90% to 99%. The contribution of this paper lies in a new technique of signal processing for ICA-based feature extraction in a 3-D feature space for IM fault diagnosis.},
 bibtype = {article},
 author = {Garcia-Bracamonte, Juan Enrique and Ramirez-Cortes, Juan Manuel and de Jesus Rangel-Magdaleno, Jose and Gomez-Gil, Pilar and Peregrina-Barreto, Hayde and Alarcon-Aquino, Vicente},
 doi = {10.1109/TIM.2019.2900143},
 journal = {IEEE Transactions on Instrumentation and Measurement},
 number = {5}
}

This paper presents a novel approach on motor current signature analysis (MCSA) for broken bar fault detection of induction motors (IMs), using as input the current signal measured from one of the three motor phases. Independent component analysis (ICA) is used over the Fourier-domain spectral signals obtained from the input and its autocorrelation function. The standard deviation of spectral components within a region of interest (ROI) of an ICA signal output was found to exhibit substantial differences between damaged and healthy motors. Separation of the ROI in one, two, and three sectors leads to an improved extraction of feature vectors, which are further fed into a neural network for classification purposes. The assessment of the proposed method is carried out through several experiments using two damage levels (broken bar and half broken bar) and two load motor conditions (50% and 75%), with a classification accuracy ranging from 90% to 99%. The contribution of this paper lies in a new technique of signal processing for ICA-based feature extraction in a 3-D feature space for IM fault diagnosis.

@article{
 title = {The radial wavelet frame density estimator},
 type = {article},
 year = {2019},
 pages = {111-139},
 volume = {130},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0167947318302044},
 month = {2},
 id = {d8acfe5a-dbd7-3cde-ac9e-0a36159db6d2},
 created = {2022-08-29T17:42:24.288Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:53:48.678Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {The estimation of probability densities is one of the fundamental problems in scientific research. It has been shown that Wavelet Density Estimators, which are a well-documented nonparametric approach, outperform other nonparametric estimators in problems involving densities with discontinuities and local features. However, the use of this type of estimators is not widely extended in the scientific community mainly because of their heavy computational complexity and their difficult algorithmic implementation. A novel multidimensional Wavelet Density Estimator approach based on new multidimensional scaling functions with analytic closed-form expressions is proposed. The key advantages of the proposed estimator are its simpler multidimensional algorithmic implementation and its significant reduction in computational complexity. Algorithmic formulations for four different data analysis scenarios are presented: (1) batch processing of input data, (2) online estimation for stationary process, (3) online estimation for non-stationary contexts and (4) batch estimation of high-dimensional data. The assessment results show that the proposed approach reduces the computational time of the estimation process while maintaining competitive estimation errors.},
 bibtype = {article},
 author = {García Treviño, E.S. and Alarcon-Aquino, V. and Barria, J.A.},
 doi = {10.1016/j.csda.2018.08.021},
 journal = {Computational Statistics & Data Analysis}
}

The estimation of probability densities is one of the fundamental problems in scientific research. It has been shown that Wavelet Density Estimators, which are a well-documented nonparametric approach, outperform other nonparametric estimators in problems involving densities with discontinuities and local features. However, the use of this type of estimators is not widely extended in the scientific community mainly because of their heavy computational complexity and their difficult algorithmic implementation. A novel multidimensional Wavelet Density Estimator approach based on new multidimensional scaling functions with analytic closed-form expressions is proposed. The key advantages of the proposed estimator are its simpler multidimensional algorithmic implementation and its significant reduction in computational complexity. Algorithmic formulations for four different data analysis scenarios are presented: (1) batch processing of input data, (2) online estimation for stationary process, (3) online estimation for non-stationary contexts and (4) batch estimation of high-dimensional data. The assessment results show that the proposed approach reduces the computational time of the estimation process while maintaining competitive estimation errors.

@article{
 title = {Bernoulli-Euler finite-element modelling of vibration modes on axisymmetric containers for level measurement},
 type = {article},
 year = {2019},
 keywords = {Acoustic resonance,Astrophysics,Bernoulli–Euler,COMSOL,Containers,FEM,Finite element analysis,Level measurement,Liquid level,Liquids,Optics,Vibration modes,Vibrations},
 pages = {330-337},
 volume = {17},
 websites = {https://ieeexplore.ieee.org/document/8863180/},
 month = {2},
 id = {81dc1888-1493-3e01-8d1e-eab680f45997},
 created = {2022-08-29T17:42:24.881Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:52:37.078Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Liquid level measurement inside closed containers is an essential process in control systems, quality control, and many real scenarios in the daily life. In this work, an analysis of vibration modes behavior of two axisymmetric containers and an immersed beam, aiming to liquid level measurement using acoustic resonance, is presented. In acoustic resonance, the vibration pattern produced in the container when it is excited by an external force is related to the liquid level and its physical characteristics. Vibration modes are analyzed using Bernoulli-Euler approach and finite-element modelling (FEM) in COMSOL. Results show a good correspondence of normalized frequency vibration modes shifting vs liquid level. Experimental results are consistent with theoretical and simulated FEM analysis, with an error lower than 1% in the first longitudinal vibration mode.},
 bibtype = {article},
 author = {Sanchez Diaz, Juan Carlos and Ramirez Cortes, Juan Manuel and Gomez Gil, Pilar and Rodriguez Montero, Ponciano and Alarcon-Aquino, Vicente and Escamilla Ambrosio, Ponciano Jorge},
 doi = {10.1109/TLA.2019.8863180},
 journal = {IEEE Latin America Transactions},
 number = {02}
}

Liquid level measurement inside closed containers is an essential process in control systems, quality control, and many real scenarios in the daily life. In this work, an analysis of vibration modes behavior of two axisymmetric containers and an immersed beam, aiming to liquid level measurement using acoustic resonance, is presented. In acoustic resonance, the vibration pattern produced in the container when it is excited by an external force is related to the liquid level and its physical characteristics. Vibration modes are analyzed using Bernoulli-Euler approach and finite-element modelling (FEM) in COMSOL. Results show a good correspondence of normalized frequency vibration modes shifting vs liquid level. Experimental results are consistent with theoretical and simulated FEM analysis, with an error lower than 1% in the first longitudinal vibration mode.

@article{
 title = {Wavelet-based frame video coding algorithms using fovea and SPECK},
 type = {article},
 year = {2018},
 keywords = {Fovea,HEVC,SPECK,Video compression,Wavelet compression},
 pages = {127-136},
 volume = {69},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0952197617303032},
 month = {3},
 id = {b2c36493-9d07-3d4d-abbc-b10abac834f5},
 created = {2022-08-29T17:42:29.476Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:49.865Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Current video coding standards such as the H.264 or Advanced Video Coding (AVC) and the H.265 or High Efficiency Video Coding (HEVC) are based on the Integer Discrete Cosine Transform (IDCT). However, it has been shown that wavelet-based algorithms have better performance than IDCT-based algorithms for still images. Also, exploiting the human visual system characteristics in stationary point of view image and video transmissions like the fovea aliasing can improve the quality of the reconstructed image. In this paper, two wavelet-based video coding approaches called SPECK-based Codec (SP-Codec) and Adaptive Wavelet/Fovea-based Codec (AWFV-Codec) are proposed. The proposed SP-Codec approach applies the Set Partitioned Embedded Block Codec (SPECK) wavelet-based compression algorithm in order to increase the intra-frame coding quality. The second proposed approach AWFV-Codec uses the Adaptive Fovea Set Partitioned Embedded Block Codec (AFV-SPECK) fovea wavelet-based compression algorithm for intra-frame coding. Fovea based compression allows to increase the quality of the reconstructed frames over regions of interest (ROI). A comparison of the proposed algorithms against the HEVC based on IDCT approach shows how the proposed wavelet-based schemes achieve higher compression ratios and higher reconstruction quality. When fixation point of view is stationary, the proposed wavelet/fovea-based algorithm achieves better quality at lower compression ratios than the wavelet-based only proposed algorithm.},
 bibtype = {article},
 author = {Galan-Hernandez, J.C. and Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J.M. and Gomez-Gil, Pilar},
 doi = {10.1016/j.engappai.2017.12.008},
 journal = {Engineering Applications of Artificial Intelligence}
}

Current video coding standards such as the H.264 or Advanced Video Coding (AVC) and the H.265 or High Efficiency Video Coding (HEVC) are based on the Integer Discrete Cosine Transform (IDCT). However, it has been shown that wavelet-based algorithms have better performance than IDCT-based algorithms for still images. Also, exploiting the human visual system characteristics in stationary point of view image and video transmissions like the fovea aliasing can improve the quality of the reconstructed image. In this paper, two wavelet-based video coding approaches called SPECK-based Codec (SP-Codec) and Adaptive Wavelet/Fovea-based Codec (AWFV-Codec) are proposed. The proposed SP-Codec approach applies the Set Partitioned Embedded Block Codec (SPECK) wavelet-based compression algorithm in order to increase the intra-frame coding quality. The second proposed approach AWFV-Codec uses the Adaptive Fovea Set Partitioned Embedded Block Codec (AFV-SPECK) fovea wavelet-based compression algorithm for intra-frame coding. Fovea based compression allows to increase the quality of the reconstructed frames over regions of interest (ROI). A comparison of the proposed algorithms against the HEVC based on IDCT approach shows how the proposed wavelet-based schemes achieve higher compression ratios and higher reconstruction quality. When fixation point of view is stationary, the proposed wavelet/fovea-based algorithm achieves better quality at lower compression ratios than the wavelet-based only proposed algorithm.

@inbook{
 type = {inbook},
 year = {2018},
 pages = {1985-2012},
 websites = {https://www.igi-global.com/chapter/machine-vision-application-on-science-and-industry/161990,http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5204-8.ch086},
 month = {9},
 publisher = {IGI Global},
 day = {12},
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 abstract = {Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.},
 bibtype = {inbook},
 author = {Starostenko, Oleg and Cruz-Perez, Claudia and Alarcon-Aquino, Vicente and Melnik, Viktor I and Tyrsa, Vera},
 doi = {10.4018/978-1-5225-5204-8.ch086},
 chapter = {Machine Vision Application on Science and Industry},
 title = {Computer Vision}
}

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

@article{
 title = {Advances in Security and Privacy of Multimodal Interfaces},
 type = {article},
 year = {2018},
 pages = {338-340},
 volume = {24},
 websites = {http://www.jucs.org/jucs_24_4/advances_in_security_and/abstract.html},
 id = {0f6ee24e-4a71-3328-a26c-cdcac5d58a8f},
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 read = {false},
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 abstract = {With the rapid development and increasing complexity of communication systems and interfaces using multiple modalities of communication in human-computer systems such as speech, tactile, gestures, gaze, head and body movements, facial expressions, gait, electroencephalogram (EEG) and electromyogram (EMG) signals, the user requirements for trust, security, and privacy are becoming more demanding. The main objective of this special issue was to provide a forum for researchers interested in the latest research results in the rapidly developing field of security and privacy in multimodal interfaces, therefore providing a valuable information venue to researchers as well as practitioners.},
 bibtype = {article},
 author = {Damaševičius, Robertas and Woźniak, Marcin and Alarcon-Aquino, Vicente and Ganchev, Ivan and Wei, Wei},
 journal = {Journal of Universal Computer Science},
 number = {4}
}

With the rapid development and increasing complexity of communication systems and interfaces using multiple modalities of communication in human-computer systems such as speech, tactile, gestures, gaze, head and body movements, facial expressions, gait, electroencephalogram (EEG) and electromyogram (EMG) signals, the user requirements for trust, security, and privacy are becoming more demanding. The main objective of this special issue was to provide a forum for researchers interested in the latest research results in the rapidly developing field of security and privacy in multimodal interfaces, therefore providing a valuable information venue to researchers as well as practitioners.

@inproceedings{
 title = {Real-time facial expression recognition using local appearance-based descriptors},
 type = {inproceedings},
 year = {2018},
 keywords = {Affective computing,Facial expression recognition,Fuzzy inference engine,Local face feature descriptors},
 pages = {5037-5049},
 volume = {36},
 issue = {5},
 websites = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-179049},
 month = {5},
 day = {14},
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 read = {false},
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 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {In human-computer interaction the automatic face sensing and recognition of facial expressions is still a challenging task of affective computing, psychology and biomedical applications. The main goal of this paper is to increment a recognition rate of approaches for unobtrusive face sensing and automatic interpretation of emotions. The proposed approach explores local scale invariant feature transform descriptors for extraction of face key points used for face detection, recognition and then for encoding facial deformations in terms of Ekman´s Facial Action Coding System (FACS). Real-time face tracking and recognition is provided by quadratic discriminant analysis and Bayesian approaches as classification tools. Based on detected fiducial points, the accurate automatic recognizing six prototypical human facial expressions as well as detecting affective states in real-time scenes is provided by fuzzy inference system based on the proposed reasoning model. Carried out experiments demonstrate that Ekman’s FACS traditionally used in affective computing may be extended to interpretation of nonprototypical compound emotions using Plutchik psychological model of emotional responses. Conducted tests with faces from standard databases confirm that the proposed approaches for analysis of local image features provide robust, quite accurate, fast and low computational cost face sensing and facial expression interpretation.},
 bibtype = {inproceedings},
 author = {Starostenko, Oleg and Cruz-Perez, Claudia and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto},
 editor = {Pinto, David and Singh, Vivek},
 doi = {10.3233/JIFS-179049},
 booktitle = {LKE2018 - 6th International Symposium on Language & Knowledge Engineering}
}

In human-computer interaction the automatic face sensing and recognition of facial expressions is still a challenging task of affective computing, psychology and biomedical applications. The main goal of this paper is to increment a recognition rate of approaches for unobtrusive face sensing and automatic interpretation of emotions. The proposed approach explores local scale invariant feature transform descriptors for extraction of face key points used for face detection, recognition and then for encoding facial deformations in terms of Ekman´s Facial Action Coding System (FACS). Real-time face tracking and recognition is provided by quadratic discriminant analysis and Bayesian approaches as classification tools. Based on detected fiducial points, the accurate automatic recognizing six prototypical human facial expressions as well as detecting affective states in real-time scenes is provided by fuzzy inference system based on the proposed reasoning model. Carried out experiments demonstrate that Ekman’s FACS traditionally used in affective computing may be extended to interpretation of nonprototypical compound emotions using Plutchik psychological model of emotional responses. Conducted tests with faces from standard databases confirm that the proposed approaches for analysis of local image features provide robust, quite accurate, fast and low computational cost face sensing and facial expression interpretation.

@article{
 title = {Advances in security and privacy of multimodal interfaces: J.UCS special issue},
 type = {article},
 year = {2018},
 volume = {24},
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 journal = {Journal of Universal Computer Science},
 number = {4}
}

@article{
 title = {Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments},
 type = {article},
 year = {2017},
 pages = {1-15},
 volume = {2017},
 websites = {https://www.hindawi.com/journals/jece/2017/4613628/},
 id = {e7e6124b-e576-3261-a8d8-a335238fa4a0},
 created = {2022-08-29T17:42:33.844Z},
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 abstract = {<p>Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.</p>},
 bibtype = {article},
 author = {Lopez-Rincon, Omar and Starostenko, Oleg and Alarcon-Aquino, Vicente and Galan-Hernandez, Juan C.},
 doi = {10.1155/2017/4613628},
 journal = {Journal of Electrical and Computer Engineering}
}

Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

@inbook{
 type = {inbook},
 year = {2017},
 keywords = {Computer vision,Morphological filtering,Pattern recognition and tracking,Steel manufacturing on rolling mill},
 pages = {297-307},
 volume = {10267 LNCS},
 websites = {https://link.springer.com/10.1007/978-3-319-59226-8_29},
 id = {df667022-6cd0-3d5f-bd5a-fc88021af87f},
 created = {2022-08-29T17:42:34.550Z},
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 read = {false},
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 abstract = {In steel production the finishing process on hot rolling mill includes a set of essential operations managed by complex control mechanical, electrical and hydraulic equipment. However, accuracy of mill automating mechanisms and sensors is still low due to hot hostile environment with strong vibration and shock. The proposed solution is a computer vision application that exploits morphological filtering and discontinuity masks for detection and separation of rods on rolling mill and provides fast recognition and tracking rod front ends during their deceleration on cooler. The proposed algorithm has been implemented and evaluated in real time conditions achieving precision of rod front end recognition in range of 90–98% on artificial and daylight illumination, respectively.},
 bibtype = {inbook},
 author = {Starostenko, Oleg and Trygub, Irina G. and Cruz-Perez, Claudia and Alarcon-Aquino, Vicente and Potap, Oleg E.},
 doi = {10.1007/978-3-319-59226-8_29},
 chapter = {Visual Remote Monitoring and Control System for Rod Braking on Hot Rolling Mills},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

In steel production the finishing process on hot rolling mill includes a set of essential operations managed by complex control mechanical, electrical and hydraulic equipment. However, accuracy of mill automating mechanisms and sensors is still low due to hot hostile environment with strong vibration and shock. The proposed solution is a computer vision application that exploits morphological filtering and discontinuity masks for detection and separation of rods on rolling mill and provides fast recognition and tracking rod front ends during their deceleration on cooler. The proposed algorithm has been implemented and evaluated in real time conditions achieving precision of rod front end recognition in range of 90–98% on artificial and daylight illumination, respectively.

@inbook{
 type = {inbook},
 year = {2017},
 pages = {159-173},
 websites = {http://link.springer.com/10.1007/978-3-319-47054-2_10},
 publisher = {Springer, Cham},
 id = {df91b958-a9b5-3177-a0a8-c034de257136},
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 confirmed = {true},
 hidden = {false},
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 private_publication = {false},
 abstract = {In the past years, efforts have been made to improve the efficiency of long-term time series forecasting. However, when the involved series is highly oscillatory and nonlinear, this is still an open problem. Given the fact that signals may be approximated as linear combinations of sine functions, the study of the behavior of an adaptive dynamical model able to reproduce a sine function may be relevant for long-term prediction. In this chapter, we present an analysis of the modeling and prediction abilities of the “Long Short-Term Memory” (LSTM) recurrent neural network, when the input signal has a discrete sine function shape. Previous works have shown that LSTM is able to learn relevant events among long-term lags, however, its oscillatory abilities have not been analyzed enough. In our experiments, we found that some configurations of LSTM were able to model the signal, accurately predicting up to 400 steps forward. However, we also found that similar architectures did not perform properly when experiments were repeated, probably due to the fact that the LSTM architectures got over trained and the learning algorithm got trapped in a local minimum.},
 bibtype = {inbook},
 author = {Jiménez-Guarneros, Magdiel and Gómez-Gil, Pilar and Fonseca-Delgado, Rigoberto and Ramírez-Cortés, Manuel and Alarcon-Aquino, Vicente},
 doi = {10.1007/978-3-319-47054-2_10},
 chapter = {Long-Term Prediction of a Sine Function Using a LSTM Neural Network},
 title = {Nature-Inspired Design of Hybrid Intelligent Systems}
}

In the past years, efforts have been made to improve the efficiency of long-term time series forecasting. However, when the involved series is highly oscillatory and nonlinear, this is still an open problem. Given the fact that signals may be approximated as linear combinations of sine functions, the study of the behavior of an adaptive dynamical model able to reproduce a sine function may be relevant for long-term prediction. In this chapter, we present an analysis of the modeling and prediction abilities of the “Long Short-Term Memory” (LSTM) recurrent neural network, when the input signal has a discrete sine function shape. Previous works have shown that LSTM is able to learn relevant events among long-term lags, however, its oscillatory abilities have not been analyzed enough. In our experiments, we found that some configurations of LSTM were able to model the signal, accurately predicting up to 400 steps forward. However, we also found that similar architectures did not perform properly when experiments were repeated, probably due to the fact that the LSTM architectures got over trained and the learning algorithm got trapped in a local minimum.

@inbook{
 type = {inbook},
 year = {2017},
 pages = {113-162},
 volume = {14},
 publisher = {NOVA Publishers},
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 abstract = {Biometrics refers to the identification of both physical and behavioral human characteristics that, under observation, can be subject to quantification. The statistical analysis of the aforementioned quantified observations can be used as a means to verify the personal identity of individuals. Although the verification of the personal identity by means of the study of quantified traits is the subject of multiple disciplines, in the information security access control area this element represents the foundation for identification and authentication. Access Control is one of the core services that have to be present when there exists any need to provide security services to information management. On the other hand, biometric authentication is considered as an access control enabler factor both where enhanced certainty is required for authentication and where an homogeneous approach is needed to avoid the intrinsic limits found by individuals to the use of the more traditional authentication methods. This survey chapter is focused on the recently published advances in biometrics to date, considering them not only in terms of their relative contributions but also in referral to relevant government and industry authentication frameworks and guidelines. The first part of the chapter presents a survey of the biometrics areas and techniques where most recent advances have been published to date. A common ground for biometric systems is presented here first, including a generic model characterization, a description of the main measurable magnitudes commonly used for acquiring relevant biometric data and the considered approaches to engage the matching problem. After basic concepts are described, the actual survey follows, referring specific works representative for the progress they achieve. Particular mention is done to relevant current areas of study as the multimodal biometric systems, adaptive biometrics, biometric cryptosystems, cancelable biometrics and protection of biometric templates. The latter part of the chapter presents national governments and industry initiatives that have substantiated into actual specifications for authentication frameworks and guidelines that involve the use of biometrics. A presentation is done of significant standards and recommendations, focusing on those parts that specifically enable for the adoption and management of biometric technologies: ISO/IEC and NIST standards as well as the FIDO Universal Authentication Framework. The topics mentioned in the former part of the chapter are then reconsidered in the light of the cited standardization initiatives. This chapter is intended for an audience willing to acquire a high-level view of the relevant current topics in biometrics as well as needing a starting point reference to get a deeper knowledge of those topics by means of the presented references to relevant and updated research works.},
 bibtype = {inbook},
 author = {Meltzer-Camino, D and Alarcon-Aquino, V},
 chapter = {Recent Advances in Biometrics and its Standardization: A Survey},
 title = {Advances in Engineering Research}
}

Biometrics refers to the identification of both physical and behavioral human characteristics that, under observation, can be subject to quantification. The statistical analysis of the aforementioned quantified observations can be used as a means to verify the personal identity of individuals. Although the verification of the personal identity by means of the study of quantified traits is the subject of multiple disciplines, in the information security access control area this element represents the foundation for identification and authentication. Access Control is one of the core services that have to be present when there exists any need to provide security services to information management. On the other hand, biometric authentication is considered as an access control enabler factor both where enhanced certainty is required for authentication and where an homogeneous approach is needed to avoid the intrinsic limits found by individuals to the use of the more traditional authentication methods. This survey chapter is focused on the recently published advances in biometrics to date, considering them not only in terms of their relative contributions but also in referral to relevant government and industry authentication frameworks and guidelines. The first part of the chapter presents a survey of the biometrics areas and techniques where most recent advances have been published to date. A common ground for biometric systems is presented here first, including a generic model characterization, a description of the main measurable magnitudes commonly used for acquiring relevant biometric data and the considered approaches to engage the matching problem. After basic concepts are described, the actual survey follows, referring specific works representative for the progress they achieve. Particular mention is done to relevant current areas of study as the multimodal biometric systems, adaptive biometrics, biometric cryptosystems, cancelable biometrics and protection of biometric templates. The latter part of the chapter presents national governments and industry initiatives that have substantiated into actual specifications for authentication frameworks and guidelines that involve the use of biometrics. A presentation is done of significant standards and recommendations, focusing on those parts that specifically enable for the adoption and management of biometric technologies: ISO/IEC and NIST standards as well as the FIDO Universal Authentication Framework. The topics mentioned in the former part of the chapter are then reconsidered in the light of the cited standardization initiatives. This chapter is intended for an audience willing to acquire a high-level view of the relevant current topics in biometrics as well as needing a starting point reference to get a deeper knowledge of those topics by means of the presented references to relevant and updated research works.

@inbook{
 type = {inbook},
 year = {2016},
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 abstract = {Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/ shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.},
 bibtype = {inbook},
 author = {Starostenko, O. and Cruz-Perez, C. and Alarcon-Aquino, V. and Melnik, V.I. and Tyrsa, V.},
 doi = {10.4018/978-1-5225-0632-4.ch005},
 chapter = {Machine Vision Application on Science and Industry: Real-Time Face Sensing and Recognition in Machine Vision -Trends and New Advances},
 title = {Developing and Applying Optoelectronics in Machine Vision}
}

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/ shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

@inbook{
 type = {inbook},
 year = {2015},
 keywords = {Automatic image annotation,Color and texture based image feature Extraction,Image processing},
 pages = {139-147},
 volume = {313},
 websites = {http://link.springer.com/10.1007/978-3-319-06773-5_20},
 id = {46723af0-8270-3f29-88fa-f4637db3f5ba},
 created = {2022-08-29T17:42:38.264Z},
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 last_modified = {2022-08-29T17:42:38.264Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a novel approach for automatic annotation of objects or regions in images based on their color and texture. According to the proposed generalized architecture for automatic generation of image content descriptions the detected regions are labeled by developed cascade SVM-based classifier mapping them to structure that reflects their hierarchical and spatial relation used by text generation engine. For testing the designed system for automatic image annotation around 2,000 images with outdoor-indoor scenes from standard IAPR-TC12 image dataset have been processed obtaining an average precision of classification about 75% with 94% of recall. The precision of classification based on color features has been improved up to 15 ± 5% after extension of classifier with texture detector based on Gabor filter. The proposed approach has a good compromise between classification precision of regions in images and speed despite used considerable time processing taking up to 1 s per image. The approach may be used as a tool for efficient automatic image understanding and description.},
 bibtype = {inbook},
 author = {Cruz-Perez, Claudia and Starostenko, Oleg and Alarcon-Aquino, Vicente and Rodriguez-Asomoza, Jorge},
 doi = {10.1007/978-3-319-06773-5_20},
 chapter = {Automatic Image Annotation for Description of Urban and Outdoor Scenes},
 title = {Lecture Notes in Electrical Engineering}
}

In this paper we present a novel approach for automatic annotation of objects or regions in images based on their color and texture. According to the proposed generalized architecture for automatic generation of image content descriptions the detected regions are labeled by developed cascade SVM-based classifier mapping them to structure that reflects their hierarchical and spatial relation used by text generation engine. For testing the designed system for automatic image annotation around 2,000 images with outdoor-indoor scenes from standard IAPR-TC12 image dataset have been processed obtaining an average precision of classification about 75% with 94% of recall. The precision of classification based on color features has been improved up to 15 ± 5% after extension of classifier with texture detector based on Gabor filter. The proposed approach has a good compromise between classification precision of regions in images and speed despite used considerable time processing taking up to 1 s per image. The approach may be used as a tool for efficient automatic image understanding and description.

@article{
 title = {Unobtrusive emotion sensing and interpretation in smart environment},
 type = {article},
 year = {2015},
 keywords = {Affective computing applications,Facial expression recognition,Sensing basic and non-prototypical emotions},
 pages = {59-83},
 volume = {7},
 websites = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/AIS-140298},
 id = {618217e6-8aef-31c2-a28f-4c772b8129c4},
 created = {2022-08-29T17:42:38.879Z},
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 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:38.879Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Currently, a particular focus of human centered technology is in expanding traditional contextual sensing and smart processing capabilities of ubiquitous systems exploiting user's affective and emotional states to develop more natural communication between computing artefacts and users. This paper presents a smart environment of Web services that has been developed to integrate and manage different existing and new emotion sensing applications, which working together provide tracking and recognition of human affective state in real time. In addition, two emotion interpreters based on the proposed 6-FACS and Distance models have been developed. Both models operate with encoded facial deformations described either in terms of Ekman's Action Units or Facial Animation Parameters of MPEG-4 standards. Fuzzy inference system based on reasoning model implemented in a knowledge base has been used for quantitative measurement and recognition of three-level intensity of basic and non-prototypical facial expressions. Designed frameworks integrated to smart environment have been tested in order to evaluate capability of the proposed models to extract and classify facial expressions providing precision of interpretation of basic emotions in range of 65-96% and non-prototypical emotions in range of 55-65%. The conducted tests confirm that such basic as non-prototypical expressions may be composed by other basic emotions establishing in this way the concordance between existing psychological models of emotions and Ekman's model traditionally used by affective computing applications.},
 bibtype = {article},
 author = {Starostenko, Oleg and Cortés, Ximena and Sánchez, J. Afredo and Alarcon-Aquino, Vicente},
 doi = {10.3233/AIS-140298},
 journal = {Journal of Ambient Intelligence and Smart Environments},
 number = {1}
}

Currently, a particular focus of human centered technology is in expanding traditional contextual sensing and smart processing capabilities of ubiquitous systems exploiting user's affective and emotional states to develop more natural communication between computing artefacts and users. This paper presents a smart environment of Web services that has been developed to integrate and manage different existing and new emotion sensing applications, which working together provide tracking and recognition of human affective state in real time. In addition, two emotion interpreters based on the proposed 6-FACS and Distance models have been developed. Both models operate with encoded facial deformations described either in terms of Ekman's Action Units or Facial Animation Parameters of MPEG-4 standards. Fuzzy inference system based on reasoning model implemented in a knowledge base has been used for quantitative measurement and recognition of three-level intensity of basic and non-prototypical facial expressions. Designed frameworks integrated to smart environment have been tested in order to evaluate capability of the proposed models to extract and classify facial expressions providing precision of interpretation of basic emotions in range of 65-96% and non-prototypical emotions in range of 55-65%. The conducted tests confirm that such basic as non-prototypical expressions may be composed by other basic emotions establishing in this way the concordance between existing psychological models of emotions and Ekman's model traditionally used by affective computing applications.

@article{
 title = {Breaking text-based CAPTCHAs with variable word and character orientation},
 type = {article},
 year = {2015},
 keywords = {Breaking CAPTCHA,Heuristic classifier,Three-color bar character encoding,Word and character straightening,reCAPTCHA Version 2012},
 pages = {1101-1112},
 volume = {48},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0031320314003483},
 month = {4},
 id = {7d205188-d964-359d-b51d-97b780489a73},
 created = {2022-08-29T17:42:39.480Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:39.480Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {A novel approach for automatic segmentation and recognition of CAPTCHAs with variable orientation and random collapse of overlapped characters is presented in this paper. Additionally, the extension of the proposed approach to break reCAPTCHA of version of 2012 is also discussed. The original proposal consists in straightening characters and word in CAPTCHA exploiting then a three-color bar code for their segmentation. The recognition of straightened characters and whole word is provided by the proposed original SVM-based learning classifier. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide an approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts by OCR systems. The designed framework for breaking CAPTCHAs by the proposed approach has been tested achieving average segmentation success rate up to 82% for reCAPTCHA of version 2011 and achieving 95.5% by extended approach for reCAPTCHA of version 2012 with response time less than 0.5 s per two-word reCAPTCHA. The implemented SVM classifier shows a competitive precision about 94%. The obtained very satisfactory results confirm that the proposed approach may be used for development of new security mechanisms to protect users against cyber-criminal activities and Internet threats.},
 bibtype = {article},
 author = {Starostenko, Oleg and Cruz-Perez, Claudia and Uceda-Ponga, Fernando and Alarcon-Aquino, Vicente},
 doi = {10.1016/j.patcog.2014.09.006},
 journal = {Pattern Recognition},
 number = {4}
}

A novel approach for automatic segmentation and recognition of CAPTCHAs with variable orientation and random collapse of overlapped characters is presented in this paper. Additionally, the extension of the proposed approach to break reCAPTCHA of version of 2012 is also discussed. The original proposal consists in straightening characters and word in CAPTCHA exploiting then a three-color bar code for their segmentation. The recognition of straightened characters and whole word is provided by the proposed original SVM-based learning classifier. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide an approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts by OCR systems. The designed framework for breaking CAPTCHAs by the proposed approach has been tested achieving average segmentation success rate up to 82% for reCAPTCHA of version 2011 and achieving 95.5% by extended approach for reCAPTCHA of version 2012 with response time less than 0.5 s per two-word reCAPTCHA. The implemented SVM classifier shows a competitive precision about 94%. The obtained very satisfactory results confirm that the proposed approach may be used for development of new security mechanisms to protect users against cyber-criminal activities and Internet threats.

@inbook{
 type = {inbook},
 year = {2015},
 keywords = {Adaptive systems,Information retrieval,Multi-agent architecture,User preference model},
 pages = {567-574},
 volume = {312},
 websites = {http://link.springer.com/10.1007/978-3-319-06764-3_73},
 id = {319bc34f-83d3-3cac-abe2-f75e9dc081bc},
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 authored = {false},
 confirmed = {true},
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 abstract = {This paper presents the proposed architecture that provides platform for implementation of intelligent software agents (Multi-Agent System) used for information retrieval based on user-oriented model. The existing architectures based on multi-agents as usual lack the ability for adaption to user requirements. Thus, the principal goal is to develop standard reusable scalable infrastructure based on intelligent multi-agents that incorporates particular user model providing relevant information to user queries taking into account his profile, operational patterns and preferences. The evaluation of the proposed architecture has been done comparing it with existing approaches taking into account relevant criteria such as used communication standards for information exchange between agents, available scalable layered structure and ability of adapting information or services to user habits, personal data and requirements. This approach has sufficient merit to be used as a reference for development of applications for user-oriented and adaptive information retrieval systems.},
 bibtype = {inbook},
 author = {Pacheco-Reyes, Juan J. and Starostenko, Oleg and Alarcon-Aquino, Vicente and Rodriguez-Asomoza, Jorge},
 doi = {10.1007/978-3-319-06764-3_73},
 chapter = {Multi-agent Architecture for User Adaptive Information Retrieval Systems},
 title = {Lecture Notes in Electrical Engineering}
}

This paper presents the proposed architecture that provides platform for implementation of intelligent software agents (Multi-Agent System) used for information retrieval based on user-oriented model. The existing architectures based on multi-agents as usual lack the ability for adaption to user requirements. Thus, the principal goal is to develop standard reusable scalable infrastructure based on intelligent multi-agents that incorporates particular user model providing relevant information to user queries taking into account his profile, operational patterns and preferences. The evaluation of the proposed architecture has been done comparing it with existing approaches taking into account relevant criteria such as used communication standards for information exchange between agents, available scalable layered structure and ability of adapting information or services to user habits, personal data and requirements. This approach has sufficient merit to be used as a reference for development of applications for user-oriented and adaptive information retrieval systems.

@article{
 title = {MATLAB and FPGA-based interactive tool for exploring concepts on compressed sensing},
 type = {article},
 year = {2015},
 keywords = {Chebyshev,CoSaMP,FPGA,compressed sensing,compression,signal acquisition},
 pages = {921-930},
 volume = {23},
 websites = {https://onlinelibrary.wiley.com/doi/10.1002/cae.21664},
 month = {11},
 id = {806a864d-b264-3738-9281-bd8f3579ffc7},
 created = {2022-08-29T17:42:40.726Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:40.726Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents an educational platform developed to explore some concepts related to the relatively new signal acquisition paradigm known as Compressed Sensing (CS). CS aims to acquire a signal with sparse or compressible representation in a suitable domain, using a number of samples under the limit established by the Nyquist-Shannon sampling theorem. The application consists of a graphical user interface in MATLAB, and a low-cost FPGA Xilinx Spartan 6, which in that way form a powerful and low-cost design station adequate to perform a number of CS experiments. Reconstruction of signals is carried out using a Greedy algorithm which solves the underdetermined system in real time with a novel Chebyshev-type matrix inversion. Effects related to the number of samples defined in the measurement matrix, and the use of different spaces, such as Discrete Cosine Transform, or Discrete Wavelet Transform, can be easily studied with the described tool. Results derived from its use in graduate courses are discussed.},
 bibtype = {article},
 author = {Rico-Aniles, Daniel and Ramirez-Cortes, Juan Manuel and Rangel-Magdaleno, Jose and Gomez-Gil, Pilar and Peregrina-Barreto, Hayde and Alarcon-Aquino, Vicente},
 doi = {10.1002/cae.21664},
 journal = {Computer Applications in Engineering Education},
 number = {6}
}

This paper presents an educational platform developed to explore some concepts related to the relatively new signal acquisition paradigm known as Compressed Sensing (CS). CS aims to acquire a signal with sparse or compressible representation in a suitable domain, using a number of samples under the limit established by the Nyquist-Shannon sampling theorem. The application consists of a graphical user interface in MATLAB, and a low-cost FPGA Xilinx Spartan 6, which in that way form a powerful and low-cost design station adequate to perform a number of CS experiments. Reconstruction of signals is carried out using a Greedy algorithm which solves the underdetermined system in real time with a novel Chebyshev-type matrix inversion. Effects related to the number of samples defined in the measurement matrix, and the use of different spaces, such as Discrete Cosine Transform, or Discrete Wavelet Transform, can be easily studied with the described tool. Results derived from its use in graduate courses are discussed.

@inbook{
 type = {inbook},
 year = {2015},
 pages = {221-243},
 websites = {https://www.springer.com/gp/book/9783319128160,http://link.springer.com/10.1007/978-3-319-12817-7_10},
 month = {9},
 publisher = {Springer International Publishing},
 day = {12},
 edition = {1st},
 series = {Springer Series in Bio-/Neuroinformatics},
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 abstract = {This chapter presents analysis of emerging mHealth applications as well as the exploration of novel trends supporting healthcare intelligent environments assisted by mobile devices. The case of study is mHealth and remote vital sign monitoring. Particularly, we present a methodology for recollecting, processing and real-time monitoring heart activity with the main purpose to interpret electrocardiogram ECG signals, detect and manage situations of risk and provide the interaction between medical practitioner and patient into smart healthcare environment. The proposed architecture and approach provide continuous detection and interpretation of the patient’s QRS complex. The challenge is to adapt some approaches for data gathering, processing, compression, storage, analysis, and visualization to capabilities of mobile devices. The designed system for monitoring vital signals has been tested using standard MIT-BIH Arrhythmia Database achieving satisfactory ECG interpretation accuracy with relative error in range from 4 % to 10 % for signal sampling frequency of 360 and 128 samples per second respectively. It is important to note that the proposed prototype does not substitute diagnosis by physician. Our intention is to propose methodologies that serve as guide for development of complex health assistance tool expanding coverage of medical services.},
 bibtype = {inbook},
 author = {Starostenko, Oleg and Alarcon-Aquino, Vicente and Rodriguez-Asomoza, Jorge and Sergiyenko, Oleg and Tyrsa, Vera},
 doi = {10.1007/978-3-319-12817-7_10},
 chapter = {Remote Health/Vital Sign Monitoring},
 title = {Mobile Health A Technology Road Map}
}

This chapter presents analysis of emerging mHealth applications as well as the exploration of novel trends supporting healthcare intelligent environments assisted by mobile devices. The case of study is mHealth and remote vital sign monitoring. Particularly, we present a methodology for recollecting, processing and real-time monitoring heart activity with the main purpose to interpret electrocardiogram ECG signals, detect and manage situations of risk and provide the interaction between medical practitioner and patient into smart healthcare environment. The proposed architecture and approach provide continuous detection and interpretation of the patient’s QRS complex. The challenge is to adapt some approaches for data gathering, processing, compression, storage, analysis, and visualization to capabilities of mobile devices. The designed system for monitoring vital signals has been tested using standard MIT-BIH Arrhythmia Database achieving satisfactory ECG interpretation accuracy with relative error in range from 4 % to 10 % for signal sampling frequency of 360 and 128 samples per second respectively. It is important to note that the proposed prototype does not substitute diagnosis by physician. Our intention is to propose methodologies that serve as guide for development of complex health assistance tool expanding coverage of medical services.

@inbook{
 type = {inbook},
 year = {2015},
 pages = {139-147},
 websites = {http://link.springer.com/10.1007/978-3-319-06773-5_20},
 publisher = {Springer International Publishing},
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 abstract = {In this paper we present a novel approach for automatic annotation of objects or regions in images based on their color and texture. According to the proposed generalized architecture for automatic generation of image content descriptions the detected regions are labeled by developed cascade SVM-based classifier mapping them to structure that reflects their hierarchical and spatial relation used by text generation engine. For testing the designed system for automatic image annotation around 2,000 images with outdoor-indoor scenes from standard IAPR-TC12 image dataset have been processed obtaining an average precision of classification about 75 % with 94 % of recall. The precision of classification based on color features has been improved up to 15 ± 5 % after extension of classifier with texture detector based on Gabor filter. The proposed approach has a good compromise between classification precision of regions in images and speed despite used considerable time processing taking up to 1 s per image. The approach may be used as a tool for efficient automatic image understanding and description.},
 bibtype = {inbook},
 author = {Cruz-Perez, Claudia and Starostenko, Oleg and Alarcon-Aquino, Vicente and Rodriguez-Asomoza, Jorge},
 doi = {10.1007/978-3-319-06773-5_20},
 chapter = {Automatic Image Annotation for Description of Urban and Outdoor Scenes},
 title = {Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering}
}

In this paper we present a novel approach for automatic annotation of objects or regions in images based on their color and texture. According to the proposed generalized architecture for automatic generation of image content descriptions the detected regions are labeled by developed cascade SVM-based classifier mapping them to structure that reflects their hierarchical and spatial relation used by text generation engine. For testing the designed system for automatic image annotation around 2,000 images with outdoor-indoor scenes from standard IAPR-TC12 image dataset have been processed obtaining an average precision of classification about 75 % with 94 % of recall. The precision of classification based on color features has been improved up to 15 ± 5 % after extension of classifier with texture detector based on Gabor filter. The proposed approach has a good compromise between classification precision of regions in images and speed despite used considerable time processing taking up to 1 s per image. The approach may be used as a tool for efficient automatic image understanding and description.

@inbook{
 type = {inbook},
 year = {2015},
 pages = {261-269},
 websites = {http://link.springer.com/10.1007/978-3-319-06764-3_33},
 publisher = {Springer, Cham},
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 abstract = {An electroencephalogram (EEG) is a record of the electric signal generated by the cooperative action of brain cells, that is, the time course of extracellular field potentials generated by their synchronous action. EEG is widely used in medicine for diagnostic and analysis of several conditions. In this paper, we present a system based on neural networks and wavelet analysis, able to identify epilepsy seizures using EEG as inputs. This work is part of a research looking for novel models able to obtain classification rates better that the state-of-the-art, for the identification of normal and epileptic patients using EEG. Here we present results using a Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) for feature extraction and Feed-Forward Artificial Neural Networks (FF-ANN) for classification. By using the benchmark database provided by the University of Bonn, our approach obtains an average accuracy of 99.26 % tested using threefold cross-validation, which is better than other works using similar strategies.},
 bibtype = {inbook},
 author = {Juárez-Guerra, E. and Alarcon-Aquino, V and Gómez-Gil, P.},
 doi = {10.1007/978-3-319-06764-3_33},
 chapter = {Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks},
 title = {New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering}
}

An electroencephalogram (EEG) is a record of the electric signal generated by the cooperative action of brain cells, that is, the time course of extracellular field potentials generated by their synchronous action. EEG is widely used in medicine for diagnostic and analysis of several conditions. In this paper, we present a system based on neural networks and wavelet analysis, able to identify epilepsy seizures using EEG as inputs. This work is part of a research looking for novel models able to obtain classification rates better that the state-of-the-art, for the identification of normal and epileptic patients using EEG. Here we present results using a Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) for feature extraction and Feed-Forward Artificial Neural Networks (FF-ANN) for classification. By using the benchmark database provided by the University of Bonn, our approach obtains an average accuracy of 99.26 % tested using threefold cross-validation, which is better than other works using similar strategies.

@article{
 title = {Network Intrusion Detection Using Self-Recurrent Wavelet Neural Network with Multidimensional Radial Wavelons},
 type = {article},
 year = {2014},
 keywords = {Intrusion Detection Systems,Multidimensional Radial Wavelons,Self-Recurrent Wavelet Neural Networks},
 pages = {347-358},
 volume = {43},
 websites = {http://www.itc.ktu.lt/index.php/ITC/article/view/4626},
 month = {12},
 day = {17},
 id = {c239f162-a531-3dbf-a961-cd15a4e3e3bf},
 created = {2022-08-29T17:42:43.745Z},
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 starred = {false},
 authored = {false},
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 abstract = {In this paper we report a novel application-based model as a suitable alternative for the classification and identification of attacks on a computer network, and thus guarantee its safety from HTTP protocol-based malicious commands. The proposed model is built on a self-recurrent neural network architecture based on wavelets with multidimensional radial wavelons, and is therefore suited to work online by analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Six different neural network based systems have been modeled and simulated for comparison purposes in terms of overall performance, namely, a feed-forward neural network, an Elman network, a fully connected recurrent neural network, a recurrent neural network based on wavelets, a self-recurrent wavelet network and the proposed self-recurrent wavelet network with multidimensional radial wavelons. Within the models studied, this paper presents two recurrent architectures which use wavelet functions in their functionality in very distinct ways. The results confirm that recurrent architectures using wavelets obtain superior performance than their peers, in terms not only of the identification and classification of attacks, but also the speed of convergence.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Ramirez-Cortes, J. M. and Gomez-Gil, P. and Starostenko, O. and Garcia-Gonzalez, Y.},
 doi = {10.5755/j01.itc.43.4.4626},
 journal = {Information Technology And Control},
 number = {4}
}

In this paper we report a novel application-based model as a suitable alternative for the classification and identification of attacks on a computer network, and thus guarantee its safety from HTTP protocol-based malicious commands. The proposed model is built on a self-recurrent neural network architecture based on wavelets with multidimensional radial wavelons, and is therefore suited to work online by analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Six different neural network based systems have been modeled and simulated for comparison purposes in terms of overall performance, namely, a feed-forward neural network, an Elman network, a fully connected recurrent neural network, a recurrent neural network based on wavelets, a self-recurrent wavelet network and the proposed self-recurrent wavelet network with multidimensional radial wavelons. Within the models studied, this paper presents two recurrent architectures which use wavelet functions in their functionality in very distinct ways. The results confirm that recurrent architectures using wavelets obtain superior performance than their peers, in terms not only of the identification and classification of attacks, but also the speed of convergence.

@article{
 title = {Interactive educational tool for compensators design in MATLAB® using frequency response analysis},
 type = {article},
 year = {2014},
 keywords = {MATLAB,compensators,control,education,interface},
 pages = {699-707},
 volume = {22},
 websites = {https://onlinelibrary.wiley.com/doi/10.1002/cae.21562},
 month = {12},
 id = {a736c2e7-e544-3e9e-87b0-c1b06d7e14a1},
 created = {2022-08-29T17:42:44.351Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:44.351Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This article presents an educational platform developed to support the teaching of compensators design in a basic control theory course. The application consists of a graphical user interface in MATLAB®, and further connection to the plant under study through the data acquisition toolbox, and a data acquisition card. The developed system allows the students to experiment with parameter changes in the controllers under study, such as gain, overshoot, settling time, and peak time, and visualize results obtained from simulated or real signals. The methodology is based on the frequency response analysis. Typical Bode, root locus, and unit step response plots are easily obtained for a system before and after compensation, in a dynamical way. A modular design allows the students to easily upgrade the application in order to include further methodologies. Results derived from its use in undergraduate and graduate courses are presented. MATLAB is a registered trademark of The MathWorks, Inc.},
 bibtype = {article},
 author = {Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente and Gomez-Gil, Pilar and Diaz-Mendez, Alejandro and Ibarra-Bonilla, Mariana and García-Enriquez, Irma},
 doi = {10.1002/cae.21562},
 journal = {Computer Applications in Engineering Education},
 number = {4}
}

This article presents an educational platform developed to support the teaching of compensators design in a basic control theory course. The application consists of a graphical user interface in MATLAB®, and further connection to the plant under study through the data acquisition toolbox, and a data acquisition card. The developed system allows the students to experiment with parameter changes in the controllers under study, such as gain, overshoot, settling time, and peak time, and visualize results obtained from simulated or real signals. The methodology is based on the frequency response analysis. Typical Bode, root locus, and unit step response plots are easily obtained for a system before and after compensation, in a dynamical way. A modular design allows the students to easily upgrade the application in order to include further methodologies. Results derived from its use in undergraduate and graduate courses are presented. MATLAB is a registered trademark of The MathWorks, Inc.

@inbook{
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 source_type = {CHAP},
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 abstract = {Finding efficient and effective automatic methods for the identification and prediction of epileptic seizures is highly desired, due to the relevance of this brain disorder. Despite the large amount of research going on in identification and prediction solutions, still it is required to find confident methods suitable to be used in real applications. In this paper, we discuss the principal challenges found in epilepsy identification, when it is carried on offline analyzing electro-encephalograms (EEG) recordings. Indeed, we present the results obtained so far in our research group, with a system based on multi-resolution analysis and feed-forward neural networks, which focus on tackling three important challenges found in this type of problems: noise reduction, feature extraction and pertinence of the classifier. A 3-fold validation of our strategy reported an accuracy of 99.26 ± 0.26 %, a sensitive of 98.93 % and a specificity of 99.59 %, using data provided by the University of Bonn. Several combinations of filters and wavelet transforms were tested, found that the best results occurs when a Chebyshev II filter was used to eliminate noise, 5 characteristics were obtained using a Discrete Wavelet Transform (DWT) with a Haar wavelet and a feed-forward neural network with 18 hidden nodes was used for classification.},
 bibtype = {inbook},
 author = {Gómez-Gil, Pilar and Juárez-Guerra, Ever and Alarcon-Aquino, Vicente and Ramírez-Cortés, Manuel and Rangel-Magdaleno, José},
 doi = {10.1007/978-3-319-05170-3_23},
 chapter = {Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks},
 title = {Recent Advances on Hybrid Approaches for Designing Intelligent Systems}
}

Finding efficient and effective automatic methods for the identification and prediction of epileptic seizures is highly desired, due to the relevance of this brain disorder. Despite the large amount of research going on in identification and prediction solutions, still it is required to find confident methods suitable to be used in real applications. In this paper, we discuss the principal challenges found in epilepsy identification, when it is carried on offline analyzing electro-encephalograms (EEG) recordings. Indeed, we present the results obtained so far in our research group, with a system based on multi-resolution analysis and feed-forward neural networks, which focus on tackling three important challenges found in this type of problems: noise reduction, feature extraction and pertinence of the classifier. A 3-fold validation of our strategy reported an accuracy of 99.26 ± 0.26 %, a sensitive of 98.93 % and a specificity of 99.59 %, using data provided by the University of Bonn. Several combinations of filters and wavelet transforms were tested, found that the best results occurs when a Chebyshev II filter was used to eliminate noise, 5 characteristics were obtained using a Discrete Wavelet Transform (DWT) with a Haar wavelet and a feed-forward neural network with 18 hidden nodes was used for classification.

@article{
 title = {Computing polynomial segmentation trough radial surface representation},
 type = {article},
 year = {2014},
 pages = {77-81},
 websites = {http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S1870-90442014000100010&lng=es&nrm=iso},
 id = {0b447a6f-4658-3ae1-af8d-f745c6d78095},
 created = {2022-08-29T17:42:45.563Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:45.563Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {The Visual Information Retrieval (VIR) area requires robust implementations achieved trough mathematical representations for images or data sets. The implementation of a mathematical modeling goes from the corpus image selection, an appropriate descriptor method, a segmentation approach and the similarity metric implementation whose are treated as VIR elements. The goal of this research is to found an appropriate modeling to explain how its items can be represented to achieve a better performance in VIR implementations. A direct method is tested with a subspace arrangement approach. The General Principal Component Analysis (GPCA) is modified inside its segmentation process. Initially, a corpus data sample is tested, the descriptor of RGB colors is implemented to obtain a three dimensional description of image data. Then a selection of radial basis function is achieved to improve the similarity metric implemented. It is concluded that a better performance can be achieved applying powerful extraction methods in visual image retrieval (VIR) designs based in a mathematical formulation. The results lead to design VIR systems with high level of performance based in radial basis functions and polynomial segmentations for handling data sets.},
 bibtype = {article},
 author = {Flores-Pulido, L and Rodriguez-Gomez, G and Starostenko, Oleg and V. Alarcon-Aquino, A Portilla},
 journal = {Research Journal on Computer Science and Computer Engineering},
 number = {49}
}

The Visual Information Retrieval (VIR) area requires robust implementations achieved trough mathematical representations for images or data sets. The implementation of a mathematical modeling goes from the corpus image selection, an appropriate descriptor method, a segmentation approach and the similarity metric implementation whose are treated as VIR elements. The goal of this research is to found an appropriate modeling to explain how its items can be represented to achieve a better performance in VIR implementations. A direct method is tested with a subspace arrangement approach. The General Principal Component Analysis (GPCA) is modified inside its segmentation process. Initially, a corpus data sample is tested, the descriptor of RGB colors is implemented to obtain a three dimensional description of image data. Then a selection of radial basis function is achieved to improve the similarity metric implemented. It is concluded that a better performance can be achieved applying powerful extraction methods in visual image retrieval (VIR) designs based in a mathematical formulation. The results lead to design VIR systems with high level of performance based in radial basis functions and polynomial segmentations for handling data sets.

@article{
 title = {Formalization of Learning Objects for Image-based Language Learning in Mobile Environments},
 type = {article},
 year = {2014},
 pages = {3905-3910},
 volume = {116},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1877042814008817},
 month = {2},
 id = {9d49a10c-ea36-369c-b126-2c315b9a0276},
 created = {2022-08-29T17:42:46.161Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:46.161Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Current mobile learning approaches demand for combining learning media and resources, open communities and customizable tool for individuals. We have focused on the formalization of learning digital resources using the Formal Learning Object Model (FLOM), which describes required components for constructing learning objects, their life cycle from inception to implementation, establishing tasks and roles of all actors involved in the development, learning, interaction, evaluation and feedback processes. The FLOM model has been evaluated on an image-based language learning application, which has been designed for interpretation of Japanese kanji and Mayan glyphs, using emerging data exchange technologies and some approaches for retrieval and visualization of multimedia documents in mobile environments.},
 bibtype = {article},
 author = {Starostenko, Oleg and Perez-Lezama, Claudia and Alarcon-Aquino, V and Sanchez, J Alfredo},
 doi = {10.1016/j.sbspro.2014.01.864},
 journal = {Procedia - Social and Behavioral Sciences}
}

Current mobile learning approaches demand for combining learning media and resources, open communities and customizable tool for individuals. We have focused on the formalization of learning digital resources using the Formal Learning Object Model (FLOM), which describes required components for constructing learning objects, their life cycle from inception to implementation, establishing tasks and roles of all actors involved in the development, learning, interaction, evaluation and feedback processes. The FLOM model has been evaluated on an image-based language learning application, which has been designed for interpretation of Japanese kanji and Mayan glyphs, using emerging data exchange technologies and some approaches for retrieval and visualization of multimedia documents in mobile environments.

@inproceedings{
 title = {Recognizing Actions of Humans in Motion for Smart Environments.},
 type = {inproceedings},
 year = {2014},
 pages = {4-15},
 websites = {https://ebooks.iospress.nl/ISBN/978-1-61499-410-7},
 id = {8c3222b0-8cde-312f-8aec-a501c75dcaef},
 created = {2022-08-29T17:42:46.763Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:55:47.058Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Development of high precision systems for recognition of human actions directly from video records is still open problem. Recently, in smart environments the recognition of dynamic actions of human in motion receives a particular interest. We propose two approaches for human action recognition. In the first approach, the envelope of 30x30 pixels is applied to enclose invariant to dimensions human silhouette separated from background. Once the area with located figure is defined, the image sequence is used as input of convolutional neural network that extracts global figure features without previous image processing. The second proposed approach is based on natural knowledge of the human figure such as proportions of body and position of feet. Together with processing global features, we extract six local features combining in this way the holistic and cluster-based approaches for representation of human figure. The input sub-sequence of previously aligned binary silhouettes from video frames is processed to concatenate local and global features into a single feature vector feeding hierarchical system of three linear support vector machines for human action classification. In order to evaluate the proposed approaches, two frameworks for recognizing human actions such as walk, jump, run, side and skip have been designed and tested on Weizmann standard and proper developed datasets achieving correct classification rate of 97-100%.},
 bibtype = {inproceedings},
 author = {Starostenko, Oleg and Rosas-Romero, Roberto and Martinez-Carballido, Jorge and Alarcon-Aquino, Vicente and Sanchez, J Alfredo},
 doi = {10.3233/978-1-61499-411-4-4},
 booktitle = {Workshop Proceedings of the 10th International Conference on Intelligent Environments}
}

Development of high precision systems for recognition of human actions directly from video records is still open problem. Recently, in smart environments the recognition of dynamic actions of human in motion receives a particular interest. We propose two approaches for human action recognition. In the first approach, the envelope of 30x30 pixels is applied to enclose invariant to dimensions human silhouette separated from background. Once the area with located figure is defined, the image sequence is used as input of convolutional neural network that extracts global figure features without previous image processing. The second proposed approach is based on natural knowledge of the human figure such as proportions of body and position of feet. Together with processing global features, we extract six local features combining in this way the holistic and cluster-based approaches for representation of human figure. The input sub-sequence of previously aligned binary silhouettes from video frames is processed to concatenate local and global features into a single feature vector feeding hierarchical system of three linear support vector machines for human action classification. In order to evaluate the proposed approaches, two frameworks for recognizing human actions such as walk, jump, run, side and skip have been designed and tested on Weizmann standard and proper developed datasets achieving correct classification rate of 97-100%.

@inproceedings{
 title = {A Hybrid algorithm applied to facility location for forest fire fighting considering budget constraints},
 type = {inproceedings},
 year = {2013},
 pages = {262-267},
 websites = {http://ieeexplore.ieee.org/document/6676055/},
 month = {9},
 publisher = {IEEE},
 id = {92f98e75-3593-39c6-ab77-96710d0d13cb},
 created = {2022-08-29T17:42:47.368Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:47.368Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper develops an integrated system for forest fire fighting facility location. We propose a mathematical model to deploy available fire fighting resources in proper positions so that any forest fire can be attacked within a specified response time. The proposed model considers budget constraints. This location problem is a variant of the well-known Maximal Covering Location Problem (MCLP) which is known to be NP-hard. We propose a hybrid algorithm that combines GRASP and Tabu Search procedures. According to preliminary computational experiments the hybrid algorithm provides good quality solutions with a reasonable amount of computer effort. This solution can give support to the decision maker for determining the location of fire-fighting resources. © 2013 IEEE.},
 bibtype = {inproceedings},
 author = {Diaz-Romero, M. A. and Alarcon-Aquino, V. and Diaz-Garcia, J. A.},
 doi = {10.1109/ICEEE.2013.6676055},
 booktitle = {2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)}
}

This paper develops an integrated system for forest fire fighting facility location. We propose a mathematical model to deploy available fire fighting resources in proper positions so that any forest fire can be attacked within a specified response time. The proposed model considers budget constraints. This location problem is a variant of the well-known Maximal Covering Location Problem (MCLP) which is known to be NP-hard. We propose a hybrid algorithm that combines GRASP and Tabu Search procedures. According to preliminary computational experiments the hybrid algorithm provides good quality solutions with a reasonable amount of computer effort. This solution can give support to the decision maker for determining the location of fire-fighting resources. © 2013 IEEE.

@inbook{
 type = {inbook},
 year = {2013},
 pages = {215-236},
 volume = {294},
 id = {fafd298c-f0d3-3e8e-ab52-66296857aa3d},
 created = {2022-08-29T17:42:47.965Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:57:05.599Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Brain Computer Interfaces (BCI) is the generic denomination of systems aiming to establish communication between a human being and an automated system, based on the electric brain signals detected through a variety of modalities. Among these, electroencephalographic signals (EEG) have received considerable attention due to several factors arising on practical scenarios, such as noninvasiveness, portability, and relative cost, without lost on accuracy and generalization. In this chapter we discuss the characteristics of a typical phenomenon associated to motor imagery and mental tasks experiments, known as event related synchronization and desynchronization (ERD/ERS), as well as its energy distribution in the time-frequency space. The typical behavior of ERD/ERS phenomenon has led proposal of different approaches oriented to the solution of the identification problem. In this work, an architecture based on adaptive neurofuzzy inference systems (ANFIS) assembled to a recurrent neural network, applied to the problem of mental tasks temporal classification, is presented. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural network. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network, and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning - extended Kalman filter). A classification rate of 88.12 % in average was obtained through the FCRNN during the generalization stage. © Springer-Verlag Berlin Heidelberg 2013.},
 bibtype = {inbook},
 author = {Morales-Flores, Emanuel and Ramírez-Cortés, Juan Manuel and Gómez-Gil, Pilar and Alarcon-Aquino, Vicente},
 doi = {10.1007/978-3-642-35323-9-9},
 chapter = {Brain computer interface development based on recurrent neural networks and ANFIS systems},
 title = {Studies in Fuzziness and Soft Computing}
}

Brain Computer Interfaces (BCI) is the generic denomination of systems aiming to establish communication between a human being and an automated system, based on the electric brain signals detected through a variety of modalities. Among these, electroencephalographic signals (EEG) have received considerable attention due to several factors arising on practical scenarios, such as noninvasiveness, portability, and relative cost, without lost on accuracy and generalization. In this chapter we discuss the characteristics of a typical phenomenon associated to motor imagery and mental tasks experiments, known as event related synchronization and desynchronization (ERD/ERS), as well as its energy distribution in the time-frequency space. The typical behavior of ERD/ERS phenomenon has led proposal of different approaches oriented to the solution of the identification problem. In this work, an architecture based on adaptive neurofuzzy inference systems (ANFIS) assembled to a recurrent neural network, applied to the problem of mental tasks temporal classification, is presented. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural network. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network, and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning - extended Kalman filter). A classification rate of 88.12 % in average was obtained through the FCRNN during the generalization stage. © Springer-Verlag Berlin Heidelberg 2013.

@article{
 title = {Region-of-Interest Coding based on Fovea and Hierarchical Trees},
 type = {article},
 year = {2013},
 keywords = {Compression techniques,Fovea,Hierarchical trees,Regions-of-interest,Wavelet transforms},
 pages = {343-352},
 volume = {42},
 websites = {http://www.itc.ktu.lt/index.php/ITC/article/view/3076},
 month = {12},
 day = {12},
 id = {c6ef4cda-a805-359d-96c4-bf943bbf8058},
 created = {2022-08-29T17:42:48.588Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:48.588Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Image and video compression exploits the redundancy of data to create a smaller representation. Lossy compression can be considered to be a type of transform coding where the raw data is transformed to a domain. Such a transform coding stores most of image energy into very few coefficients. In this paper we propose a compression algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) that exploits the Human Visual System (HVS) and its fovea. In order to increase the image quality of the reconstructed image, regions of interest (ROI) are defined around a given point of gaze. The use of a fovea combined with ROI for image compression can help to improve the quality of the perception of the image and preserve different levels of detail around the ROI. In the proposed approach, the image is compressed using the Lifting Wavelet Transform and then quantized at multiple compression ratios around the point of fixation of an observer, taking advantage of the natural aliasing of the HVS around the fovea. Such a compression delivers better image or frame reconstruction when a fixation point of an observer is given.},
 bibtype = {article},
 author = {Galan-Hernandez, J. C. and Alarcon-Aquino, V. and Ramirez-Cortes, J. M. and Starostenko, Oleg},
 doi = {10.5755/j01.itc.42.4.3076},
 journal = {Information Technology And Control},
 number = {4}
}

Image and video compression exploits the redundancy of data to create a smaller representation. Lossy compression can be considered to be a type of transform coding where the raw data is transformed to a domain. Such a transform coding stores most of image energy into very few coefficients. In this paper we propose a compression algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) that exploits the Human Visual System (HVS) and its fovea. In order to increase the image quality of the reconstructed image, regions of interest (ROI) are defined around a given point of gaze. The use of a fovea combined with ROI for image compression can help to improve the quality of the perception of the image and preserve different levels of detail around the ROI. In the proposed approach, the image is compressed using the Lifting Wavelet Transform and then quantized at multiple compression ratios around the point of fixation of an observer, taking advantage of the natural aliasing of the HVS around the fovea. Such a compression delivers better image or frame reconstruction when a fixation point of an observer is given.

@inbook{
 type = {inbook},
 year = {2013},
 pages = {135-146},
 volume = {451},
 websites = {http://link.springer.com/10.1007/978-3-642-33021-6_11},
 id = {6ae60941-06a2-394c-afad-4b8cc4b537ff},
 created = {2022-08-29T17:42:49.207Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:57:20.828Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, an architecture based on adaptive neuro-fuzzy inference systems (ANFIS) assembled to recurrent neural networks, applied to the problem of mental tasks temporal classification, is proposed. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural networks. This experiment has been carried out in the context of brain-computer-interface (BCI) systems development. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning - extended Kalman filter). A classification rate of 88.12% in average was obtained through the FCRNN during the generalization stage. © Springer-Verlag Berlin Heidelberg 2013.},
 bibtype = {inbook},
 author = {Morales-Flores, Emmanuel and Ramírez-Cortés, Juan Manuel and Gómez-Gil, Pilar and Alarcon-Aquino, Vicente},
 doi = {10.1007/978-3-642-33021-6_11},
 chapter = {Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks},
 title = {Studies in Computational Intelligence}
}

In this paper, an architecture based on adaptive neuro-fuzzy inference systems (ANFIS) assembled to recurrent neural networks, applied to the problem of mental tasks temporal classification, is proposed. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural networks. This experiment has been carried out in the context of brain-computer-interface (BCI) systems development. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning - extended Kalman filter). A classification rate of 88.12% in average was obtained through the FCRNN during the generalization stage. © Springer-Verlag Berlin Heidelberg 2013.

@article{
 title = {Cancelable biometrics for bimodal cryptosystems},
 type = {article},
 year = {2013},
 keywords = {Biometric Cryptosystem,Cancelable Technique,Hadamard Code},
 pages = {345-352},
 volume = {43},
 websites = {http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932013000400009&lng=es&nrm=iso&tlng=en},
 id = {32fae513-f5ae-303e-983b-cd4b95fbcc21},
 created = {2022-08-29T17:42:49.868Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:49.868Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Biometric-based techniques have recently emerged as a trustworthy and effective approach of user authentication; however, unlike conventional authentication methods such as passwords and tokens, if an enrolled biometric template is compromised, usually it cannot be revoked or re-issued. In this paper, four naive cancelable techniques, namely, shifting, password-dependent shifting, XOR and adding, for a bimodal biometric cryptosystem are presented. The proposed cancelable techniques are designed to be embedded into any bimodal biometric cryptosystem. The bimodal biometric cryptosystem uses speech and electrocardiogram signals as biometric information. The biometric cryptosystem implements an error-correction layer using the Hadamard code. The performance of the four cancelable techniques is assessed using ECG signals from MIT-BIH database and speech signals from a speech database created for testing purposes. The results show that the best performance in terms of FAR and FRR metrics is achieved with XOR and adding techniques.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Gomez-Gil, P. and Ramirez-Cortes, J. M. and Starostenko, O. and Garcia-Baleon, H. A.},
 journal = {Latin American Applied Research},
 number = {4}
}

Biometric-based techniques have recently emerged as a trustworthy and effective approach of user authentication; however, unlike conventional authentication methods such as passwords and tokens, if an enrolled biometric template is compromised, usually it cannot be revoked or re-issued. In this paper, four naive cancelable techniques, namely, shifting, password-dependent shifting, XOR and adding, for a bimodal biometric cryptosystem are presented. The proposed cancelable techniques are designed to be embedded into any bimodal biometric cryptosystem. The bimodal biometric cryptosystem uses speech and electrocardiogram signals as biometric information. The biometric cryptosystem implements an error-correction layer using the Hadamard code. The performance of the four cancelable techniques is assessed using ECG signals from MIT-BIH database and speech signals from a speech database created for testing purposes. The results show that the best performance in terms of FAR and FRR metrics is achieved with XOR and adding techniques.

@article{
 title = {System performance evaluation by combining RTC and VHDL simulation: A case study on NICs},
 type = {article},
 year = {2013},
 keywords = {Analytic model,NIC,Performance evaluation,Real-time calculus,Simulation},
 pages = {1277-1298},
 volume = {59},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1383762113001860},
 month = {11},
 id = {67fae94b-8137-3434-be89-5af44d9c91dd},
 created = {2022-08-29T17:42:50.486Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:57:30.219Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Analytic models allow the performance evaluation of proposed system changes without requiring complex and expensive detailed simulations. In this paper, Real-Time Calculus (RTC), a high-level analysis technique previously proposed for stream-processing hard real-time systems and frequently used to evaluate trade-offs in packet stream processing architectures, has been applied to analyse the behaviour of network interfaces. Moreover, the simulation of HDL (Hardware Description Language) models has been used to build the RTC descriptions required to analyse the behaviour of these relatively complex systems. As a case study of this proposed combination of Real-Time Calculus and HDL simulation, the prediction of buffer-size requirements and bottlenecks on the Network Interface Card (NIC) of a node receiving Ethernet packets, is also provided. This work intends to increase the efficiency of the performance evaluation of systems by providing real-time guarantees on delays and backlogs that are valid up to a certain level of confidence, as opposed to the hard guarantees commonly derived by formal methods. © 2013 Elsevier B.V. All rights reserved.},
 bibtype = {article},
 author = {Garay, Godofredo R. and Ortega, Julio and Díaz, Antonio F. and Corrales, Luis and Alarcon-Aquino, Vicente},
 doi = {10.1016/j.sysarc.2013.09.006},
 journal = {Journal of Systems Architecture},
 number = {10}
}

Analytic models allow the performance evaluation of proposed system changes without requiring complex and expensive detailed simulations. In this paper, Real-Time Calculus (RTC), a high-level analysis technique previously proposed for stream-processing hard real-time systems and frequently used to evaluate trade-offs in packet stream processing architectures, has been applied to analyse the behaviour of network interfaces. Moreover, the simulation of HDL (Hardware Description Language) models has been used to build the RTC descriptions required to analyse the behaviour of these relatively complex systems. As a case study of this proposed combination of Real-Time Calculus and HDL simulation, the prediction of buffer-size requirements and bottlenecks on the Network Interface Card (NIC) of a node receiving Ethernet packets, is also provided. This work intends to increase the efficiency of the performance evaluation of systems by providing real-time guarantees on delays and backlogs that are valid up to a certain level of confidence, as opposed to the hard guarantees commonly derived by formal methods. © 2013 Elsevier B.V. All rights reserved.

@article{
 title = {Lossy Image Compression Using Discrete Wavelet Transform and Thresholding Techniques},
 type = {article},
 year = {2013},
 keywords = {Discrete wavelet transform,Hilbert curve,Huffman coding,Image compression,Thresholding techniques},
 pages = {32-38},
 volume = {7},
 websites = {http://benthamopen.com/ABSTRACT/TOCSJ-7-32},
 month = {10},
 day = {4},
 id = {d4d62af4-004a-3a3a-b53d-3deb0324a04c},
 created = {2022-08-29T17:42:51.117Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:51.117Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Results of lossy image compression using wavelet transforms and several thresholding techniques are presented here. The analyzed image was divided into little sub-images and each one was decomposed in a vector following a Hilbert fractal curve. The wavelet transform was applied to each vector and some of the high frequency components were suppressed based on some threshold criteria. Different levels of wavelet decomposition and wavelet mother functions were assessed. The Huffman coding algorithm was then applied in order to reduce image weight. Simulation results have revealed that high compression ratios were obtained with the mean and the standard deviation thresholding algorithms at different levels of wavelet decomposition. © Alarcon-Aquino et al.; Licensee Bentham Open.},
 bibtype = {article},
 author = {Alarcon-Aquino, V.},
 doi = {10.2174/1874110X01307010032},
 journal = {The Open Cybernetics & Systemics Journal},
 number = {1}
}

Results of lossy image compression using wavelet transforms and several thresholding techniques are presented here. The analyzed image was divided into little sub-images and each one was decomposed in a vector following a Hilbert fractal curve. The wavelet transform was applied to each vector and some of the high frequency components were suppressed based on some threshold criteria. Different levels of wavelet decomposition and wavelet mother functions were assessed. The Huffman coding algorithm was then applied in order to reduce image weight. Simulation results have revealed that high compression ratios were obtained with the mean and the standard deviation thresholding algorithms at different levels of wavelet decomposition. © Alarcon-Aquino et al.; Licensee Bentham Open.

@article{
 title = {FPGA-based educational platform for real-time image processing experiments},
 type = {article},
 year = {2013},
 keywords = {education,filtering,hardware,image,processing},
 pages = {193-201},
 volume = {21},
 websites = {https://onlinelibrary.wiley.com/doi/10.1002/cae.20461},
 month = {3},
 id = {d440e773-8214-3ce5-a97b-eb03134220ac},
 created = {2022-08-29T17:42:51.721Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:51.721Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, an implementation of an educational platform for real-time linear and morphological image filtering using a FPGA NexysII, Xilinx®, Spartan 3E, is described. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station for educational purposes. The FPGA-based system is accessed through a MATLAB graphical user interface, which handles the communication setup and data transfer. The system allows the students to perform comparisons between results obtained from MATLAB simulations and FPGA-based real-time processing. Concluding remarks derived from course evaluations and lab reports are presented. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 193-201, 2013 Copyright © 2010 Wiley Periodicals, Inc.},
 bibtype = {article},
 author = {Ramirez-Cortes, Juan Manuel and Gomez-Gil, Pilar and Alarcon-Aquino, Vicente and Martinez-Carballido, Jorge and Morales-Flores, Emmanuel},
 doi = {10.1002/cae.20461},
 journal = {Computer Applications in Engineering Education},
 number = {1}
}

In this paper, an implementation of an educational platform for real-time linear and morphological image filtering using a FPGA NexysII, Xilinx®, Spartan 3E, is described. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station for educational purposes. The FPGA-based system is accessed through a MATLAB graphical user interface, which handles the communication setup and data transfer. The system allows the students to perform comparisons between results obtained from MATLAB simulations and FPGA-based real-time processing. Concluding remarks derived from course evaluations and lab reports are presented. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 193-201, 2013 Copyright © 2010 Wiley Periodicals, Inc.

@article{
 title = {Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition},
 type = {article},
 year = {2013},
 keywords = {Electroencephalographic signals,Empirical mode decomposition,Gyroscope-driven,Kalman filter},
 pages = {10561-10583},
 volume = {13},
 websites = {http://www.mdpi.com/1424-8220/13/8/10561},
 month = {8},
 day = {14},
 id = {11e9a836-bc49-353c-9ffb-f9a62df4366c},
 created = {2022-08-29T17:42:52.451Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:52.451Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented. © 2013 by the authors; licensee MDPI, Basel, Switzerland.},
 bibtype = {article},
 author = {Rosas-Cholula, Gerardo and Ramirez-Cortes, Juan and Alarcon-Aquino, Vicente and Gomez-Gil, Pilar and Rangel-Magdaleno, Jose and Reyes-Garcia, Carlos},
 doi = {10.3390/s130810561},
 journal = {Sensors},
 number = {8}
}

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

@inbook{
 type = {inbook},
 year = {2013},
 pages = {327-338},
 volume = {152},
 websites = {http://link.springer.com/10.1007/978-1-4614-3535-8_28},
 publisher = {Springer New York},
 id = {cb0dd2cb-83e1-3b81-8e1e-e317b729fd27},
 created = {2022-08-29T17:42:53.073Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CHAP},
 private_publication = {false},
 abstract = {A novel framework on cell phone for recollecting, processing and interpretation of patient’s electrocardiograms ECG as part of development of health care and assisted living environments is presented in this paper. The proposed architecture and algorithm provide continuous detection of the QRS complex during real time ECG monitoring and interaction between doctor and patient expanding coverage of medical services. The developed procedure for heart activity monitoring uses a set of filters for image noise reduction and computes ECG signal gradient for identification of the components with the greatest slope. To highlight the steepest parts of ECG, the absolute value of gradient is averaged over a moving window of 80 ms considered as the minimum duration of QRS complex. In the decision phase, a peak detector is applied. The height of detected peaks is compared to the threshold determined as the signal-to-noise ratio for final definition of heart rate. The designed prototype has been tested using standard MIT-BIH Arrhythmia Database and evaluated confirming that system has good compromise between high transmission and processing speed and satisfactory accuracy, which does not fall below the precision of commercial equipment for heart monitoring.},
 bibtype = {inbook},
 author = {Muñoz-Ramos, O. and Starostenko, O and Alarcon-Aquino, V and Cruz-Perez, C},
 doi = {10.1007/978-1-4614-3535-8_28},
 chapter = {Real-Time System for Monitoring and Analyzing Electrocardiogram on Cell Phone},
 title = {Lecture Notes in Electrical Engineering, Innovations and Advances in Computer, Information, Systems Sciences, and Engineering}
}

A novel framework on cell phone for recollecting, processing and interpretation of patient’s electrocardiograms ECG as part of development of health care and assisted living environments is presented in this paper. The proposed architecture and algorithm provide continuous detection of the QRS complex during real time ECG monitoring and interaction between doctor and patient expanding coverage of medical services. The developed procedure for heart activity monitoring uses a set of filters for image noise reduction and computes ECG signal gradient for identification of the components with the greatest slope. To highlight the steepest parts of ECG, the absolute value of gradient is averaged over a moving window of 80 ms considered as the minimum duration of QRS complex. In the decision phase, a peak detector is applied. The height of detected peaks is compared to the threshold determined as the signal-to-noise ratio for final definition of heart rate. The designed prototype has been tested using standard MIT-BIH Arrhythmia Database and evaluated confirming that system has good compromise between high transmission and processing speed and satisfactory accuracy, which does not fall below the precision of commercial equipment for heart monitoring.

@inproceedings{
 title = {Automatic Breaking reCAPTCHAs Version 2012 by Three-color Bar Character Encoding and Heuristic Recognition},
 type = {inproceedings},
 year = {2013},
 pages = {167-170},
 id = {ab84cd13-ec42-3979-a940-b41f5d3290ff},
 created = {2022-08-29T17:42:53.889Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:53.889Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {C. Cruz-Perez O. Starostenko, V. Alarcon-Aquino, V Shatokha},
 booktitle = {Proceeding of the 6th International Conference Advanced Computer Systems and Networks: Design and Application" (ACSN'2013)}
}

@inproceedings{
 title = {Head movement artifact removal in EEG signals using Empirical Mode Decomposition and Pearson Correlation},
 type = {inproceedings},
 year = {2013},
 websites = {http://world-comp.org/p2013/ICA3048.pdf},
 id = {2f956cc4-ed46-3d93-add5-b9a57d727887},
 created = {2022-08-29T17:42:54.474Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:54.474Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents an approach on head movement artifact removal from EEG signals, in the context of an ongoing brain computer interface project. The proposed artifact removal scheme is based on Empirical Mode Decomposition (EMD) applied to the signals obtained from each electrode. Correlation analysis using Pearson coefficient allows identification of those intrinsic mode functions related to common artifacts, which are associated to head movement. The goal of this experiment is separation of signals corresponding to single and double blinking from head movement artifacts. Once the preprocessing is applied, blinking detection is reduced to threshold operations. Final selection step based on Mahalanobis distance provides a detection rate of 95% in average.},
 bibtype = {inproceedings},
 author = {G. Rosas-Cholula JM. Ramirez-Cortes, J. Rangel-Magdaleno, P. Gomez-Gil, V Alarcon-Aquino},
 booktitle = {Proceedings of the 2013 International Conference on Artificial Intelligence, ICAI 2013}
}

This paper presents an approach on head movement artifact removal from EEG signals, in the context of an ongoing brain computer interface project. The proposed artifact removal scheme is based on Empirical Mode Decomposition (EMD) applied to the signals obtained from each electrode. Correlation analysis using Pearson coefficient allows identification of those intrinsic mode functions related to common artifacts, which are associated to head movement. The goal of this experiment is separation of signals corresponding to single and double blinking from head movement artifacts. Once the preprocessing is applied, blinking detection is reduced to threshold operations. Final selection step based on Mahalanobis distance provides a detection rate of 95% in average.

@inproceedings{
 title = {Design and implementation of the discrete wavelet transform on an FPGA platform to process data sets of up to three dimensions},
 type = {inproceedings},
 year = {2012},
 keywords = {Daubechies,Distributed Arithmetic,Filter Design},
 pages = {333-338},
 websites = {http://ieeexplore.ieee.org/document/6189934/},
 month = {2},
 publisher = {IEEE},
 id = {aadb90d2-efdb-32f2-9c5d-4c345dffde3a},
 created = {2022-08-29T17:42:55.059Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:55.059Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The aim of this work is the design and implementation of a DWT architecture FPGA-based platform for up to three dimensional signal processing. First, filter banks are designed using a distributed arithmetic technique. Then, we design controllers, interfaces and protocols that handle, transmit and sequence all the data during the computing process. Data is sent via USB to the FPGA and the user interface is programmed in MATLAB. A graphical user interface manages the system operation and displays the results on a PC. Designed filters are compared with a fully parallel architecture in relation to the number of gates used, speed, and algorithm performance. © 2012 IEEE.},
 bibtype = {inproceedings},
 author = {Rivera-Juarico, E. A. and Ramirez-Cortes, J. M. and Alarcon-Aquino, V. and Escamilla-Ambrosio, J.},
 doi = {10.1109/CONIELECOMP.2012.6189934},
 booktitle = {CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers}
}

The aim of this work is the design and implementation of a DWT architecture FPGA-based platform for up to three dimensional signal processing. First, filter banks are designed using a distributed arithmetic technique. Then, we design controllers, interfaces and protocols that handle, transmit and sequence all the data during the computing process. Data is sent via USB to the FPGA and the user interface is programmed in MATLAB. A graphical user interface manages the system operation and displays the results on a PC. Designed filters are compared with a fully parallel architecture in relation to the number of gates used, speed, and algorithm performance. © 2012 IEEE.

@inbook{
 type = {inbook},
 year = {2012},
 keywords = {heuristic classifier,reCAPTCHA breaking,segmentation attack,three-color bar character encoding,unpredictable collapse},
 pages = {155-165},
 volume = {7329 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-31149-9_16},
 id = {5b4c7e9b-434f-30a0-bcb7-a32082500917},
 created = {2022-08-29T17:42:55.652Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:55.652Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a novel approach for automatic segmentation and recognition of reCAPTCHA in Web sites. It is based on CAPTCHA image preprocessing with character alignment, morphological segmentation with three-color bar character encoding and heuristic recognition. The original proposal consists in exploiting three-color bar code for characters in CAPTCHA for their robust segmentation with presence of random collapse overlapping letters and distortions by particular patterns of waving rotation. Additionally, a novel implementation of SVM-based learning classifier for recognition of combinations of characters in training corpus has been proposed that permits to increment more than twice the recognition success rate without time extension of system response. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide a novel approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts. Our designed framework implementing the proposed approach has been tested in real-time applications with sites used CAPTCHAS achieving segmentation success rate about of 82% and recognition success rate about of 94%. © 2012 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Cruz-Perez, Claudia and Starostenko, Oleg and Uceda-Ponga, Fernando and Alarcon-Aquino, Vicente and Reyes-Cabrera, Leobardo},
 doi = {10.1007/978-3-642-31149-9_16},
 chapter = {Breaking reCAPTCHAs with Unpredictable Collapse: Heuristic Character Segmentation and Recognition},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

In this paper we present a novel approach for automatic segmentation and recognition of reCAPTCHA in Web sites. It is based on CAPTCHA image preprocessing with character alignment, morphological segmentation with three-color bar character encoding and heuristic recognition. The original proposal consists in exploiting three-color bar code for characters in CAPTCHA for their robust segmentation with presence of random collapse overlapping letters and distortions by particular patterns of waving rotation. Additionally, a novel implementation of SVM-based learning classifier for recognition of combinations of characters in training corpus has been proposed that permits to increment more than twice the recognition success rate without time extension of system response. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide a novel approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts. Our designed framework implementing the proposed approach has been tested in real-time applications with sites used CAPTCHAS achieving segmentation success rate about of 82% and recognition success rate about of 94%. © 2012 Springer-Verlag Berlin Heidelberg.

@inproceedings{
 title = {A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion},
 type = {inproceedings},
 year = {2012},
 keywords = {Adaptive Autoregressive Coefficients (AAR),Autoregressive coefficients (AR),Brain Computer Interfaces (BCI),EEG,Fisher Criterion (FC)},
 pages = {257-261},
 websites = {http://ieeexplore.ieee.org/document/6189920/},
 month = {2},
 publisher = {IEEE},
 id = {7d0f9fce-a433-3e67-b492-f5a405d92716},
 created = {2022-08-29T17:42:56.275Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:56.275Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10-20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented. © 2012 IEEE.},
 bibtype = {inproceedings},
 author = {D'Croz-Baron, David and Ramirez, Juan Manuel and Baker, Mary and Alarcon-Aquino, Vicente and Carrera, Obed},
 doi = {10.1109/CONIELECOMP.2012.6189920},
 booktitle = {CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers}
}

An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10-20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented. © 2012 IEEE.

@inbook{
 type = {inbook},
 year = {2012},
 pages = {75-86},
 websites = {https://hakin9.org/download/dont-be-mocked-secure-your-system-0512/},
 publisher = {Ed. Hakin9 Media 02-682 Warszawa, Poland},
 id = {d1906bf0-d606-34f8-8a7b-516e36e06cf3},
 created = {2022-08-29T17:42:56.899Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:56.899Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CHAP},
 private_publication = {false},
 abstract = {Certain applications, such as online banking sites, are particularly at risk due to the fact that a breach in security would be catastrophic. Several methods have been developed as alternatives solution to the problem of Internet security. It is thus the very dynamic and ever-changing nature of computer network attacks that make an approach based on neural networks an efficient course of action. In this article a step-by-step methodology for detecting and classifying attacks in computer networks by using neural networks is reported.},
 bibtype = {inbook},
 author = {Alarcon-Aquino, V and Gomez-Gil, P and Ramirez-Cortes, J M},
 chapter = {Design of an Intruder Detection System Using Neural Networks},
 title = {Don’t Be Mocked, Secure Your System}
}

Certain applications, such as online banking sites, are particularly at risk due to the fact that a breach in security would be catastrophic. Several methods have been developed as alternatives solution to the problem of Internet security. It is thus the very dynamic and ever-changing nature of computer network attacks that make an approach based on neural networks an efficient course of action. In this article a step-by-step methodology for detecting and classifying attacks in computer networks by using neural networks is reported.

@inproceedings{
 title = {Computational Approach for Mobile Image-Based Language Learning},
 type = {inproceedings},
 year = {2012},
 pages = {292-302},
 websites = {https://library.iated.org/view/STAROSTENKO2012COM},
 publisher = {IATED},
 id = {8b43fc79-bf94-3194-bf12-9c79b64448d0},
 created = {2022-08-29T17:42:57.490Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:57.490Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Most theories of traditional pedagogy require a teacher to assist the learning process in a two-way conversation or through the mediating tools such as reference textual and audiovisual material and lab equipment. However, these theories fail to capture the distinctiveness of mobile learning because they are predicated on the assumption that learning occurs in a classroom environment, mediated by a trained teacher. Actual migration of traditional learning from school to the network now is characterized by using heterogeneous comprehensive, searchable, relevant learning media in social, collaborative learning environment available for open community through personalization of customizable tools, content options and learning objects for particular individual. Taking into account the convergence occurring between new conceptions of learning and new mobile technologies, the main objective of this paper is to evaluate novel pedagogical aspects of mobile learning and to propose appropriate wireless distributed infrastructures, which may support users on the move operating in informal situations considering the ubiquitous nature of learning, its constructive and social activity and its dynamic context providing people with knowledge and skills they need to succeed in a rapidly changing world.},
 bibtype = {inproceedings},
 author = {Starostenko, O and Alarcon-Aquino, V and Sanchez, A and Cruz-Perez, C},
 booktitle = {EDULEARN12 Proceedings}
}

Most theories of traditional pedagogy require a teacher to assist the learning process in a two-way conversation or through the mediating tools such as reference textual and audiovisual material and lab equipment. However, these theories fail to capture the distinctiveness of mobile learning because they are predicated on the assumption that learning occurs in a classroom environment, mediated by a trained teacher. Actual migration of traditional learning from school to the network now is characterized by using heterogeneous comprehensive, searchable, relevant learning media in social, collaborative learning environment available for open community through personalization of customizable tools, content options and learning objects for particular individual. Taking into account the convergence occurring between new conceptions of learning and new mobile technologies, the main objective of this paper is to evaluate novel pedagogical aspects of mobile learning and to propose appropriate wireless distributed infrastructures, which may support users on the move operating in informal situations considering the ubiquitous nature of learning, its constructive and social activity and its dynamic context providing people with knowledge and skills they need to succeed in a rapidly changing world.

@inproceedings{
 title = {A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction},
 type = {inproceedings},
 year = {2012},
 pages = {1-6},
 websites = {http://ieeexplore.ieee.org/document/6220084/},
 month = {5},
 publisher = {IEEE},
 id = {925c7822-9dd6-36d0-894d-bd6533e5de05},
 created = {2022-08-29T17:42:58.080Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:58.080Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.},
 bibtype = {inproceedings},
 author = {Carrera-Leon, Obed and Ramirez, Juan Manuel and Alarcon-Aquino, Vicente and Baker, Mary and D'Croz-Baron, David and Gomez-Gil, Pilar},
 doi = {10.1109/WEA.2012.6220084},
 booktitle = {2012 Workshop on Engineering Applications}
}

A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.

@article{
 title = {Path Restoration Schemes for MPLS Networks},
 type = {article},
 year = {2011},
 keywords = {Asynchronous transfer mode,IP networks,Multiprotocol label switching,Object oriented modeling,Routing protocols,Switches},
 pages = {22-28},
 volume = {30},
 websites = {http://ieeexplore.ieee.org/document/5733963/},
 month = {3},
 id = {4145faa9-5b74-313b-89bd-d84aca01415b},
 created = {2022-08-29T17:42:58.706Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:58.706Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {It is well known that the Internet is based on a connectionless, unreliable service, which implies no delivery guarantee. It is also recognized that Internet growth has taken an exponential and unstoppable course; at the same time there has been an increasing demand for new and more sophisticated services. Therefore, the technology has had to undergo fundamental changes with respect to the usual practices developed in the mid-1990s. © 2006 IEEE.},
 bibtype = {article},
 author = {Alarcon-Aquino, V and Minero-Munoz, Marcelino},
 doi = {10.1109/MPOT.2011.940647},
 journal = {IEEE Potentials},
 number = {2}
}

It is well known that the Internet is based on a connectionless, unreliable service, which implies no delivery guarantee. It is also recognized that Internet growth has taken an exponential and unstoppable course; at the same time there has been an increasing demand for new and more sophisticated services. Therefore, the technology has had to undergo fundamental changes with respect to the usual practices developed in the mid-1990s. © 2006 IEEE.

@inproceedings{
 title = {Comparing Real-Time Calculus with the existing analytical approaches for the performance evaluation of network interfaces},
 type = {inproceedings},
 year = {2011},
 pages = {119-124},
 websites = {http://ieeexplore.ieee.org/document/5749347/},
 month = {2},
 publisher = {IEEE},
 id = {10d72cd6-558b-39f1-844e-a9d1a1b377a5},
 created = {2022-08-29T17:42:59.313Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:52:44.483Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we compare an analytical framework based on Real-Time Calculus with the existing analytical approaches commonly used for the performance evaluation of network interfaces such as probabilistic queuing models, parallel computation model, and protocol offload models (LAWS and EMO). In particular, we focus on the capabilities of these alternatives that can be employed for the performance evaluation of the NIC's buffer requirements in a network node. © 2011 IEEE.},
 bibtype = {inproceedings},
 author = {Garay, Godofredo R. and Ortega, Julio and Alarcon-Aquino, Vicente},
 doi = {10.1109/CONIELECOMP.2011.5749347},
 booktitle = {CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers}
}

In this paper we compare an analytical framework based on Real-Time Calculus with the existing analytical approaches commonly used for the performance evaluation of network interfaces such as probabilistic queuing models, parallel computation model, and protocol offload models (LAWS and EMO). In particular, we focus on the capabilities of these alternatives that can be employed for the performance evaluation of the NIC's buffer requirements in a network node. © 2011 IEEE.

@inbook{
 type = {inbook},
 year = {2011},
 keywords = {3D image matching,non-rigid deformation estimation,wavelet},
 pages = {146-154},
 volume = {6718 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-21587-2_16},
 id = {8f355948-8559-3b8d-ad4b-5058f8d155e3},
 created = {2022-08-29T17:43:00.052Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:00.052Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a novel approach for registration of 3D images based on optimal free-form rigid transformation. A proposal consists in semiautomatic image segmentation reconstructing 3D object surfaces in medical images. The proposed extraction technique employs gradients in sequences of 3D medical images to attract a deformable surface model by using imaging planes that correspond to multiple locations of feature points in space, instead of detecting contours on each imaging plane in isolation. Feature points are used as a reference before and after a deformation. An issue concerning this relation is difficult and deserves attention to develop a methodology to find the optimal number of points that gives the best estimates and does not sacrifice computational speed. After generating a representation for each of two 3D objects, we find the best similarity transformation that represents the object deformation between them. The proposed approach has been tested using different imaging modalities by morphing data from Histology sections to match MRI of carotid artery. © 2011 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Rosas-Romero, Roberto and Starostenko, Oleg and Rodríguez-Asomoza, Jorge and Alarcon-Aquino, Vicente},
 doi = {10.1007/978-3-642-21587-2_16},
 chapter = {Multi-modal 3D Image Registration Based on Estimation of Non-rigid Deformation},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

This paper presents a novel approach for registration of 3D images based on optimal free-form rigid transformation. A proposal consists in semiautomatic image segmentation reconstructing 3D object surfaces in medical images. The proposed extraction technique employs gradients in sequences of 3D medical images to attract a deformable surface model by using imaging planes that correspond to multiple locations of feature points in space, instead of detecting contours on each imaging plane in isolation. Feature points are used as a reference before and after a deformation. An issue concerning this relation is difficult and deserves attention to develop a methodology to find the optimal number of points that gives the best estimates and does not sacrifice computational speed. After generating a representation for each of two 3D objects, we find the best similarity transformation that represents the object deformation between them. The proposed approach has been tested using different imaging modalities by morphing data from Histology sections to match MRI of carotid artery. © 2011 Springer-Verlag Berlin Heidelberg.

@inbook{
 type = {inbook},
 year = {2011},
 keywords = {Compression,Fovea,ROI,SPIHT,Wavelet Transforms},
 pages = {240-249},
 volume = {6718 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-21587-2_26},
 id = {de416c58-329a-33ef-82b3-933b1e95d8ff},
 created = {2022-08-29T17:43:00.655Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:00.655Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Region of interest (ROI) based compression can be applied to real-time video transmission in medical or surveillance applications where certain areas are needed to retain better quality than the rest of the image. The use of a fovea combined with ROI for image compression can help to improve the perception of quality and preserve different levels of detail around the ROI. In this paper, a fovea-ROI compression approach is proposed based on the Set Partitioning In Hierarchical Tree (SPIHT) algorithm. Simulation results show that the proposed approach presents better details in objects inside the defined ROI than the standard SPIHT algorithm. © 2011 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Galan-Hernandez, J. C. and Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J. M.},
 doi = {10.1007/978-3-642-21587-2_26},
 chapter = {Foveated ROI Compression with Hierarchical Trees for Real-Time Video Transmission},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

Region of interest (ROI) based compression can be applied to real-time video transmission in medical or surveillance applications where certain areas are needed to retain better quality than the rest of the image. The use of a fovea combined with ROI for image compression can help to improve the perception of quality and preserve different levels of detail around the ROI. In this paper, a fovea-ROI compression approach is proposed based on the Set Partitioning In Hierarchical Tree (SPIHT) algorithm. Simulation results show that the proposed approach presents better details in objects inside the defined ROI than the standard SPIHT algorithm. © 2011 Springer-Verlag Berlin Heidelberg.

@article{
 title = {Biometric Cryptosystem based on Keystroke Dynamics and K-medoids},
 type = {article},
 year = {2011},
 keywords = {Biometrics,Cryptography,K-medoids,Keystroke dynamics,Minkowski distance},
 pages = {385},
 volume = {57},
 websites = {http://www.jr.ietejournals.org/text.asp?2011/57/4/385/86341},
 id = {004d83a5-1314-3567-b226-84e3a8133e99},
 created = {2022-08-29T17:43:01.255Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:01.255Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {An approach for a biometric cryptosystem based on keystroke dynamics and the k-medoids algorithm is proposed. The stages that comprise the approach are training enrollment and user verification. The proposed approach is able to verify the identity of individuals offline avoiding the use of a centralized database. The approach as reported in this paper may be implemented in stand-alone terminals or embedded in password-based systems to increase the security. The performance of the proposed approach is assessed using 20 samples of keystroke dynamics from 20 different users. Simulation results show a false acceptance rate of 2.89% and a false rejection rate of 3.35%. The cryptographic key released by the proposed architecture may be used in several potential applications such as user login, file encryption or even portable authentication to gain access to virtual private networks. 2011 by the IETE.},
 bibtype = {article},
 author = {Alarcon-Aquino, Vicente and Ramirez-Cortes, JuanManuel and Starostenko, Oleg and Garcia-Baleon, HectorAugusto and Gomez-Gil, Pilar},
 doi = {10.4103/0377-2063.86341},
 journal = {IETE Journal of Research},
 number = {4}
}

An approach for a biometric cryptosystem based on keystroke dynamics and the k-medoids algorithm is proposed. The stages that comprise the approach are training enrollment and user verification. The proposed approach is able to verify the identity of individuals offline avoiding the use of a centralized database. The approach as reported in this paper may be implemented in stand-alone terminals or embedded in password-based systems to increase the security. The performance of the proposed approach is assessed using 20 samples of keystroke dynamics from 20 different users. Simulation results show a false acceptance rate of 2.89% and a false rejection rate of 3.35%. The cryptographic key released by the proposed architecture may be used in several potential applications such as user login, file encryption or even portable authentication to gain access to virtual private networks. 2011 by the IETE.

@inbook{
 type = {inbook},
 year = {2011},
 pages = {353-365},
 volume = {103 LNEE},
 websites = {http://link.springer.com/10.1007/978-1-4614-0373-9_27},
 id = {0bf1e82b-ce7e-3db2-ac96-92c29b90d75b},
 created = {2022-08-29T17:43:01.867Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:01.867Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented. © 2011 Springer Science+Business Media, LLC.},
 bibtype = {inbook},
 author = {Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente and Rosas-Cholula, Gerardo and Gomez-Gil, Pilar and Escamilla-Ambrosio, Jorge},
 doi = {10.1007/978-1-4614-0373-9_27},
 chapter = {Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated Eeg Signals},
 title = {Lecture Notes in Electrical Engineering}
}

An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented. © 2011 Springer Science+Business Media, LLC.

@article{
 title = {A Biometric System Based on Neural Networks and SVM Using Morphological Feature Extraction from Hand-Shape Images},
 type = {article},
 year = {2011},
 keywords = {biometry,hand-shape,identification,pattern spectrum,verification},
 pages = {225-240},
 volume = {22},
 websites = {https://informatica.vu.lt/doi/10.15388/Informatica.2011.324},
 month = {1},
 day = {1},
 id = {2f66bb6b-9774-3952-a5c3-5323bac172f4},
 created = {2022-08-29T17:43:02.475Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:02.475Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a hand-shape biometric system based on a novel feature extraction methodology using the morphological pattern spectrum or pecstrum. Identification experiments were carried out using the obtained feature vectors as an input to some recognition systems using neural networks and support vector machine (SVM) techniques, obtaining in average an identification of 98.5%. The verification case was analyzed through an Euclidean distance classifier, obtaining the acceptance rate (FAR) and false rejection rate (FRR) of the system for some K-fold cross validation experiments. In average, an Equal Error Rate of 2.85% was obtained. The invariance to rotation and position properties of the pecstrum allow the system to avoid a fixed hand position using pegs, as is the case in other reported systems. The results indicate that the pattern spectrum represents a good alternative of feature extraction for biometric applications. © 2011 Vilnius University.},
 bibtype = {article},
 author = {Ramirez-Cortes, Juan-Manuel and Gomez-Gil, Pilar and Alarcon-Aquino, Vicente and Baez-Lopez, David and Enriquez-Caldera, Rogerio},
 doi = {10.15388/Informatica.2011.324},
 journal = {Informatica},
 number = {2}
}

This paper presents a hand-shape biometric system based on a novel feature extraction methodology using the morphological pattern spectrum or pecstrum. Identification experiments were carried out using the obtained feature vectors as an input to some recognition systems using neural networks and support vector machine (SVM) techniques, obtaining in average an identification of 98.5%. The verification case was analyzed through an Euclidean distance classifier, obtaining the acceptance rate (FAR) and false rejection rate (FRR) of the system for some K-fold cross validation experiments. In average, an Equal Error Rate of 2.85% was obtained. The invariance to rotation and position properties of the pecstrum allow the system to avoid a fixed hand position using pegs, as is the case in other reported systems. The results indicate that the pattern spectrum represents a good alternative of feature extraction for biometric applications. © 2011 Vilnius University.

@article{
 title = {A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series},
 type = {article},
 year = {2011},
 pages = {215-233},
 volume = {33},
 websites = {http://link.springer.com/10.1007/s11063-011-9174-0},
 month = {6},
 day = {8},
 id = {e26c9012-5ad8-3a0e-a782-efebde326ff3},
 created = {2022-08-29T17:43:03.102Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:57:45.887Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster when the model is built using data generated by highly dynamic or chaotic systems. This paper presents a topology and training scheme for a novel artificial neural network, named “Hybrid-connected Complex Neural Network” (HCNN), which is able to capture the dynamics embedded in chaotic time series and to predict long horizons of such series. HCNN is composed of small recurrent neural networks, inserted in a structure made of feed-forward and recurrent connections and trained in several stages using the algorithm back-propagation through time (BPTT). In experiments using a Mackey-Glass time series and an electrocardiogram (ECG) as training signals, HCNN was able to output stable chaotic signals, oscillating for periods as long as four times the size of the training signals. The largest local Lyapunov Exponent (LE) of predicted signals was positive (an evidence of chaos), and similar to the LE calculated over the training signals. The magnitudes of peaks in the ECG signal were not accurately predicted, but the predicted signal was similar to the ECG in the rest of its structure.},
 bibtype = {article},
 author = {Gómez-Gil, Pilar and Ramírez-Cortes, Juan Manuel and Pomares Hernández, Saúl E. and Alarcon-Aquino, Vicente},
 doi = {10.1007/s11063-011-9174-0},
 journal = {Neural Processing Letters},
 number = {3}
}

The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster when the model is built using data generated by highly dynamic or chaotic systems. This paper presents a topology and training scheme for a novel artificial neural network, named “Hybrid-connected Complex Neural Network” (HCNN), which is able to capture the dynamics embedded in chaotic time series and to predict long horizons of such series. HCNN is composed of small recurrent neural networks, inserted in a structure made of feed-forward and recurrent connections and trained in several stages using the algorithm back-propagation through time (BPTT). In experiments using a Mackey-Glass time series and an electrocardiogram (ECG) as training signals, HCNN was able to output stable chaotic signals, oscillating for periods as long as four times the size of the training signals. The largest local Lyapunov Exponent (LE) of predicted signals was positive (an evidence of chaos), and similar to the LE calculated over the training signals. The magnitudes of peaks in the ECG signal were not accurately predicted, but the predicted signal was similar to the ECG in the rest of its structure.

@inproceedings{
 title = {On the development of a simple EEG-based mouse using Empirical Mode Decomposition and DWT: A BCI application},
 type = {inproceedings},
 year = {2011},
 pages = {70-73},
 websites = {https://www-elec.inaoep.mx/~jmram/cvjmr/On the development-IGS-2011.pdf},
 id = {5e2f6119-cf14-3c46-851e-dd00f4871254},
 created = {2022-08-29T17:43:03.775Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:03.775Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents an on-going project on the development of a simple cursor control emulating the typical operations of a computer-mouse, using Electrooculography signals (EOG) obtained indirectly through a commercial 16-electrodes wireless headset originally used to acquired EEG signals. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user’s blinking with an adequate detection procedure based on spectral analysis. Empirical Mode Decomposition (EMD) technique is explored as a simple and quick computational tool, yet effective, aimed to the pulse detection in a noisy signal, as well as a validation method to distinguish between natural blinking and blinks for control. EMD is compared with a spectral analysis based on the Discrete Wavelet Transform (DWT). The experimental setup, some obtained results, and a comparison among the two used spectral analysis, are presented.},
 bibtype = {inproceedings},
 author = {Rosas-Cholula, G and Ramirez-Cortes, J M and Escamilla-Ambrosio, J and Alarcon-Aquino, V},
 booktitle = {15th IGS Conference}
}

This paper presents an on-going project on the development of a simple cursor control emulating the typical operations of a computer-mouse, using Electrooculography signals (EOG) obtained indirectly through a commercial 16-electrodes wireless headset originally used to acquired EEG signals. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user’s blinking with an adequate detection procedure based on spectral analysis. Empirical Mode Decomposition (EMD) technique is explored as a simple and quick computational tool, yet effective, aimed to the pulse detection in a noisy signal, as well as a validation method to distinguish between natural blinking and blinks for control. EMD is compared with a spectral analysis based on the Discrete Wavelet Transform (DWT). The experimental setup, some obtained results, and a comparison among the two used spectral analysis, are presented.

@article{
 title = {A Fuzzy Reasoning Model for Recognition of Facial Expressions},
 type = {article},
 year = {2011},
 pages = {163-180},
 volume = {15},
 websites = {http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462011000400004},
 id = {726059e3-6bf7-39a3-9454-905ab90f162c},
 created = {2022-08-29T17:43:04.387Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:04.387Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non–prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG–4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs – FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn–Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.},
 bibtype = {article},
 author = {Starostenko, Oleg and Contreras, Renan and Alarcon-Aquino, V and Pulido, Leticia Flores and Asomoza, Jorge Rodriguez and Sergiyenko, Oleg and Tyrsa, Vira},
 journal = {Computación y Sistemas},
 number = {2}
}

In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non–prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG–4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs – FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn–Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.

@inproceedings{
 title = {Fovea Window for Wavelet-based Compression},
 type = {inproceedings},
 year = {2011},
 keywords = {Data compression,Discrete cosine transforms,Discrete wavelet transforms,Image processing},
 pages = {661-672},
 volume = {152 LNEE},
 websites = {http://link.springer.com/10.1007/978-1-4614-3535-8_55},
 publisher = {Springer},
 id = {bf0fa016-5f7e-3a10-941f-edf6223d81e3},
 created = {2022-08-29T17:43:05.035Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:05.035Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead while keeping high visual quality. Such algorithm leads into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly used the discrete cosine transform (DCT). In this paper, a fovea window for wavelet-based compression is proposed. The proposed window allows isolate a fovea region over an image. A comparative analysis has been performed showing different error and compression rates between the proposed fovea window for wavelet-based and the DCT-based compression algorithms. Simulation results show that with foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI. © 2013 Springer Science+Business Media.},
 bibtype = {inproceedings},
 author = {Galan-Hernandez, J. C. and Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J. M.},
 doi = {10.1007/978-1-4614-3535-8_55},
 booktitle = {Lecture Notes in Electrical Engineering}
}

Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead while keeping high visual quality. Such algorithm leads into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly used the discrete cosine transform (DCT). In this paper, a fovea window for wavelet-based compression is proposed. The proposed window allows isolate a fovea region over an image. A comparative analysis has been performed showing different error and compression rates between the proposed fovea window for wavelet-based and the DCT-based compression algorithms. Simulation results show that with foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI. © 2013 Springer Science+Business Media.

@article{
 title = {A biometric system based on neural networks and svm using morphological feature extraction from hand-shape images},
 type = {article},
 year = {2011},
 keywords = {biometry,hand-shape,identification,pattern spectrum,verification},
 volume = {22},
 id = {c83474fd-c914-3951-9da6-752390b95848},
 created = {2022-08-29T17:43:05.758Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:05.758Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {This paper presents a hand-shape biometric system based on a novel feature extraction methodology using the morphological pattern spectrum or pecstrum. Identification experiments were carried out using the obtained feature vectors as an input to some recognition systems using neural networks and support vector machine (SVM) techniques, obtaining in average an identification of 98.5%. The verification case was analyzed through an Euclidean distance classifier, obtaining the acceptance rate (FAR) and false rejection rate (FRR) of the system for some K-fold cross validation experiments. In average, an Equal Error Rate of 2.85% was obtained. The invariance to rotation and position properties of the pecstrum allow the system to avoid a fixed hand position using pegs, as is the case in other reported systems. The results indicate that the pattern spectrum represents a good alternative of feature extraction for biometric applications. © 2011 Vilnius University.},
 bibtype = {article},
 author = {Ramirez-Cortes, J.-M. and Gomez-Gil, P. and Alarcon-Aquino, V. and Baez-Lopez, D. and Enriquez-Caldera, R.},
 journal = {Informatica},
 number = {2}
}

This paper presents a hand-shape biometric system based on a novel feature extraction methodology using the morphological pattern spectrum or pecstrum. Identification experiments were carried out using the obtained feature vectors as an input to some recognition systems using neural networks and support vector machine (SVM) techniques, obtaining in average an identification of 98.5%. The verification case was analyzed through an Euclidean distance classifier, obtaining the acceptance rate (FAR) and false rejection rate (FRR) of the system for some K-fold cross validation experiments. In average, an Equal Error Rate of 2.85% was obtained. The invariance to rotation and position properties of the pecstrum allow the system to avoid a fixed hand position using pegs, as is the case in other reported systems. The results indicate that the pattern spectrum represents a good alternative of feature extraction for biometric applications. © 2011 Vilnius University.

@inproceedings{
 title = {Shape indexing and semantic image retrieval based on ontological descriptions},
 type = {inproceedings},
 year = {2011},
 volume = {719},
 id = {08f4a84c-b46c-3a2e-a5d4-6474559861eb},
 created = {2022-08-29T17:43:06.447Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:06.447Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {This paper presents some hybrid approaches for visual information retrieval that combine image low-level feature analysis with semantic descriptors of image content. The aim of this proposal is to improve retrieval process by reducing nonsense results to user query. In the proposed approach user may submit textual queries, which are converted to image characteristics providing in this way searching, indexing, interpretation, and retrieval. In the case of visual query, both an image and sketch may be used. Approaches for image interpretation and retrieval are applied to color filtering, shape indexing and semantic. In order to assess the proposed approaches, some systems for image retrieval have been designed. The simplest system uses color region arrangement and neural network or wavelet based classifiers. Then this system has been improved using shape analysis with its indexing by ontological descriptions. For shape matching two proposed approaches are used such as star field or two-segment turning functions, which are invariant to spatial deformation of objects in image. The ontological annotations of objects in image provide machine-understandable semantics. The evolution of the proposed approaches and improvement of retrieval process are described in this paper. Four designed systems are assessed: RetNew, IRWC, Butterfly, and IRONS tested on standard COIL-100 and CE-Shape-1 image collections. The obtained results will allow to develop novel methods for solving efficient image retrieval processes.},
 bibtype = {inproceedings},
 author = {Starostenko, O. and Flores-Pulido, L. and Rosas, R. and Alarcon-Aquino, V. and Sergiyenko, O. and Tyrsa, V.},
 booktitle = {CEUR Workshop Proceedings}
}

This paper presents some hybrid approaches for visual information retrieval that combine image low-level feature analysis with semantic descriptors of image content. The aim of this proposal is to improve retrieval process by reducing nonsense results to user query. In the proposed approach user may submit textual queries, which are converted to image characteristics providing in this way searching, indexing, interpretation, and retrieval. In the case of visual query, both an image and sketch may be used. Approaches for image interpretation and retrieval are applied to color filtering, shape indexing and semantic. In order to assess the proposed approaches, some systems for image retrieval have been designed. The simplest system uses color region arrangement and neural network or wavelet based classifiers. Then this system has been improved using shape analysis with its indexing by ontological descriptions. For shape matching two proposed approaches are used such as star field or two-segment turning functions, which are invariant to spatial deformation of objects in image. The ontological annotations of objects in image provide machine-understandable semantics. The evolution of the proposed approaches and improvement of retrieval process are described in this paper. Four designed systems are assessed: RetNew, IRWC, Butterfly, and IRONS tested on standard COIL-100 and CE-Shape-1 image collections. The obtained results will allow to develop novel methods for solving efficient image retrieval processes.

@article{
 title = {Computational approaches to support image-based language learning within mobile environment},
 type = {article},
 year = {2010},
 keywords = {PoPS,computer-based learning assistance,image processing,mobile language learning,networking technologies,portable personal spaces},
 pages = {150},
 volume = {4},
 websites = {http://www.inderscience.com/link.php?id=32634},
 id = {e025819e-b03a-393d-b2a0-c0313a4e3d77},
 created = {2022-08-29T17:43:07.044Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:55:25.573Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The exploration of emerging data exchange technologies and design of image-based language learning (IBLL) applications are presented in this paper. For integrating, the mobile devices to learning process the generic interfaces have been created for portable personal spaces (PoPS) providing mobile access to multimedia documents based on XML technologies. The IBLL implies image processing, recognition and retrieval, thereby some algorithms have been proposed for learning assistant applications used by mobile devices. Furthermore, for multimedia data exchange in wireless environment the compression of visual information based on wavelet transforms and several thresholding techniques are supported. The proposed approaches can suggest ways of studying and organising resources which provide long-term guidance on developing skills and support experiential learning. They have been tested for selecting the best ones with the highest processing speed and recognition grade for interpretation of Japanese kanji or Mayan glyphs on mobile devices with limited resources and restricted networking capabilities. © 2010 Inderscience Enterprises Ltd.},
 bibtype = {article},
 author = {Starostenko, Oleg and Alarcon-Aquino, Vicente and Morales, Humberto Lobato and Sergiyenko, Oleg},
 doi = {10.1504/IJMLO.2010.032634},
 journal = {International Journal of Mobile Learning and Organisation},
 number = {2}
}

The exploration of emerging data exchange technologies and design of image-based language learning (IBLL) applications are presented in this paper. For integrating, the mobile devices to learning process the generic interfaces have been created for portable personal spaces (PoPS) providing mobile access to multimedia documents based on XML technologies. The IBLL implies image processing, recognition and retrieval, thereby some algorithms have been proposed for learning assistant applications used by mobile devices. Furthermore, for multimedia data exchange in wireless environment the compression of visual information based on wavelet transforms and several thresholding techniques are supported. The proposed approaches can suggest ways of studying and organising resources which provide long-term guidance on developing skills and support experiential learning. They have been tested for selecting the best ones with the highest processing speed and recognition grade for interpretation of Japanese kanji or Mayan glyphs on mobile devices with limited resources and restricted networking capabilities. © 2010 Inderscience Enterprises Ltd.

@inproceedings{
 title = {Wavelet-Based Foveated Compression Algorithm for Real-Time Video Processing},
 type = {inproceedings},
 year = {2010},
 keywords = {Compression,Discrete cosine transform,Foveation,Real-time video processing,Wavelets},
 pages = {405-410},
 websites = {http://ieeexplore.ieee.org/document/5692371/},
 month = {9},
 publisher = {IEEE},
 id = {e60a02cc-5900-3091-ba9a-b645731392ae},
 created = {2022-08-29T17:43:07.674Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:07.674Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. However, each wavelet can yields into different results on compression ratio. Classic foveation filters also applies cutoff frequencies over the region of interest (ROI). A comparative analysis has then been performed showing the different error rate for different cutoff frequency windows that preserves the ROI coefficients intact against windows that apply cutoffs over the ROI. Simulation results show that with wavelet-based foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Galan-Hernandez, J.C. and Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J.M.},
 doi = {10.1109/CERMA.2010.52},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. However, each wavelet can yields into different results on compression ratio. Classic foveation filters also applies cutoff frequencies over the region of interest (ROI). A comparative analysis has then been performed showing the different error rate for different cutoff frequency windows that preserves the ROI coefficients intact against windows that apply cutoffs over the ROI. Simulation results show that with wavelet-based foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI. © 2010 IEEE.

@inbook{
 type = {inbook},
 year = {2010},
 keywords = {Emotion recognition,facial features,knowledge-based framework,rules-based fuzzy classifier},
 pages = {11-21},
 volume = {6256 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-15992-3_2},
 id = {0009e2fc-2dfb-33c7-8672-ce98a6123194},
 created = {2022-08-29T17:43:08.298Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:08.298Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a fuzzy reasoning system that can measure and recognize the intensity of basic or non-prototypical facial expressions. The system inputs are the encoded facial deformations described either in terms of Ekmańs Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard. The proposed fuzzy system uses a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows the modeling of facial features obtained from geometric parameters coded by AUs - FAPs and also the definition of rules required for classification of measured expressions. This paper also presents the designed framework for fuzzyfication of input variables for fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantićs MMI face databases. The proposed system has been tested in order to evaluate its capability for detection, classifying, and interpretation of facial expressions. © 2010 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Contreras, Renan and Starostenko, Oleg and Alarcon-Aquino, Vicente and Flores-Pulido, Leticia},
 doi = {10.1007/978-3-642-15992-3_2},
 chapter = {Facial Feature Model for Emotion Recognition Using Fuzzy Reasoning},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

In this paper we present a fuzzy reasoning system that can measure and recognize the intensity of basic or non-prototypical facial expressions. The system inputs are the encoded facial deformations described either in terms of Ekmańs Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard. The proposed fuzzy system uses a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows the modeling of facial features obtained from geometric parameters coded by AUs - FAPs and also the definition of rules required for classification of measured expressions. This paper also presents the designed framework for fuzzyfication of input variables for fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantićs MMI face databases. The proposed system has been tested in order to evaluate its capability for detection, classifying, and interpretation of facial expressions. © 2010 Springer-Verlag Berlin Heidelberg.

@inproceedings{
 title = {Intrusion detection and classification of attacks in high-level network protocols using Recurrent Neural Networks},
 type = {inproceedings},
 year = {2010},
 pages = {129-134},
 websites = {https://www.researchgate.net/publication/221231694_Intrusion_Detection_and_Classification_of_Attacks_in_High-Level_Network_Protocols_Using_Recurrent_Neural_Networks},
 id = {3f66b5e1-c82c-3c99-ad7b-d0f7d8212f9e},
 created = {2022-08-29T17:43:08.911Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:08.911Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents an application-based model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocol-based malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Three different neural network-based systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feed-forward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding. © 2010 Springer Science+Business Media B.V.},
 bibtype = {inproceedings},
 author = {Alarcon-Aquino, Vicente and Oropeza-Clavel, Carlos A. and Rodriguez-Asomoza, Jorge and Starostenko, Oleg and Rosas-Romero, Roberto},
 doi = {10.1007/978-90-481-3662-9-21},
 booktitle = {Novel Algorithms and Techniques in Telecommunications and Networking}
}

This paper presents an application-based model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocol-based malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Three different neural network-based systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feed-forward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding. © 2010 Springer Science+Business Media B.V.

@inproceedings{
 title = {An extension of least squares methods for smoothing oscillation of motion predicting function},
 type = {inproceedings},
 year = {2010},
 keywords = {Image processing,Interpolating polynomial oscillation and stabiliza,Least squares model,Motion prediction},
 pages = {285-290},
 id = {9513c5e6-ffbc-335b-ba0a-3a4a6098ff34},
 created = {2022-08-29T17:43:09.510Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:09.510Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {A novel hybrid technique for detection and predicting the motion of objects in video stream is presented in this paper. The novelty consists in extension of Savitzky-Golay smoothing filter applying difference approach for tracing object mass center with or without acceleration in noised images. The proposed adaptation of least squares methods for smoothing the fast varying values of motion predicting function permits to avoid the oscillation of that function with the same degree of used polynomial. The better results are obtained when the time of motion interpolation is divided into subintervals, and the function is represented by different polynomials over each subinterval. Therefore, in proposed hybrid technique the spatial clusters with objects in motion are detected by the image difference operator and behavior of those clusters is analyzed using their mass centers in consecutive frames. Then the predicted location of object is computed using modified algorithm of weighted least squares model. That provides the tracing possible routes which now are invariant to oscillation of predicting polynomials and noise presented in images. For irregular motion frequently occurred in dynamic scenes, the compensation and stabilization technique is also proposed in this paper. On base of several simulated kinematics experiments the efficiency of proposed technique is analyzed and evaluated. © Springer Science+Business Media B.V. 2010.},
 bibtype = {inproceedings},
 author = {Starostenko, O. and Tello-Martínez, J. T. and Alarcon-Aquino, V. and Rodriguez-Asomoza, J. and Rosas-Romero, R.},
 doi = {10.1007/978-90-481-9112-3-48},
 booktitle = {Innovations in Computing Sciences and Software Engineering}
}

A novel hybrid technique for detection and predicting the motion of objects in video stream is presented in this paper. The novelty consists in extension of Savitzky-Golay smoothing filter applying difference approach for tracing object mass center with or without acceleration in noised images. The proposed adaptation of least squares methods for smoothing the fast varying values of motion predicting function permits to avoid the oscillation of that function with the same degree of used polynomial. The better results are obtained when the time of motion interpolation is divided into subintervals, and the function is represented by different polynomials over each subinterval. Therefore, in proposed hybrid technique the spatial clusters with objects in motion are detected by the image difference operator and behavior of those clusters is analyzed using their mass centers in consecutive frames. Then the predicted location of object is computed using modified algorithm of weighted least squares model. That provides the tracing possible routes which now are invariant to oscillation of predicting polynomials and noise presented in images. For irregular motion frequently occurred in dynamic scenes, the compensation and stabilization technique is also proposed in this paper. On base of several simulated kinematics experiments the efficiency of proposed technique is analyzed and evaluated. © Springer Science+Business Media B.V. 2010.

@article{
 title = {Initialisation and training procedures for wavelet networks applied to chaotic time series},
 type = {article},
 year = {2010},
 keywords = {Approximation theory,Chaotic time series,Multi-resolution analysis,Wavelet networks,Wavelets},
 pages = {15-23},
 volume = {18},
 websites = {https://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1499},
 id = {93489541-84de-364b-af3f-4067341bf504},
 created = {2022-08-29T17:43:10.140Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:10.140Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Wavelet networks are a class of neural network that take advantage of good localization properties of multi-resolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of back-propagation algorithm is used for its learning. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets and some of the most powerful and used wavelets do not satisfy this property. In this paper we report an algorithm for initialising and training wavelet networks applied to the approximation of chaotic time series. The proposed algorithm which has its foundations on correlation analysis of signals allows the use of different types of wavelets, namely, Daubechies, Coiflets, and Symmlets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet network. © 2010 CRL Publishing Ltd.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J. M. and Gomez-Gil, P. and Garcia-Treviño, E. S.},
 journal = {Engineering Intelligent Systems},
 number = {1}
}

Wavelet networks are a class of neural network that take advantage of good localization properties of multi-resolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of back-propagation algorithm is used for its learning. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets and some of the most powerful and used wavelets do not satisfy this property. In this paper we report an algorithm for initialising and training wavelet networks applied to the approximation of chaotic time series. The proposed algorithm which has its foundations on correlation analysis of signals allows the use of different types of wavelets, namely, Daubechies, Coiflets, and Symmlets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet network. © 2010 CRL Publishing Ltd.

@inproceedings{
 title = {On Signal P-300 Detection for BCI Applications Based on Wavelet Analysis and ICA Preprocessing},
 type = {inproceedings},
 year = {2010},
 keywords = {BCI,DWT,ICA,P300},
 pages = {360-365},
 websites = {http://ieeexplore.ieee.org/document/5692363/},
 month = {9},
 publisher = {IEEE},
 id = {e8c818d8-71a3-35e7-aeb5-068ddf69f88a},
 created = {2022-08-29T17:43:10.744Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:10.744Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner-Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Rosas-Cholula, Gerardo and Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente and Martinez-Carballido, Jorge and Gomez-Gil, Pilar},
 doi = {10.1109/CERMA.2010.48},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner-Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection. © 2010 IEEE.

@inbook{
 type = {inbook},
 year = {2010},
 pages = {19-35},
 volume = {312},
 websites = {http://link.springer.com/10.1007/978-3-642-15111-8_2},
 id = {0aecd0ce-4755-34a5-a38b-bf977c411940},
 created = {2022-08-29T17:43:11.352Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:11.352Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present the morphological operator pecstrum, or pattern spectrum, as a feature extractor of discriminating characteristics in microscopic leukocytes images for classification purposes. Pecstrum provides an excellent quantitative analysis to model the morphological evolution of nuclei in blood white cells, or leukocytes. According to their maturity stage, leukocytes have been classified by medical experts in six categories, from myeloblast to polymorphonuclear corresponding to the youngest and oldest extremes, respectively. A feature vector based on the pattern spectrum, normalized area, and nucleus - cytoplasm area ratio, was tested using a multilayer perceptron neural network trained by backpropagation, and a Support Vector Machine algorithm. Results from Euclidean distance and k-nearest neighbor classifiers are also reported as reference for comparison purposes. A recognition rate of 87% was obtained in the best case, using 36 patterns for training and 18 for testing, with a three-fold validation scheme. Additional experiments exploring larger databases are currently in progress. © 2010 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Ramirez-Cortes, Juan Manuel and Gomez-Gil, Pilar and Alarcon-Aquino, Vicente and Gonzalez-Bernal, Jesus and Garcia-Pedrero, Angel},
 doi = {10.1007/978-3-642-15111-8_2},
 chapter = {Neural Networks and SVM-Based Classification of Leukocytes Using the Morphological Pattern Spectrum},
 title = {Studies in Computational Intelligence}
}

In this paper we present the morphological operator pecstrum, or pattern spectrum, as a feature extractor of discriminating characteristics in microscopic leukocytes images for classification purposes. Pecstrum provides an excellent quantitative analysis to model the morphological evolution of nuclei in blood white cells, or leukocytes. According to their maturity stage, leukocytes have been classified by medical experts in six categories, from myeloblast to polymorphonuclear corresponding to the youngest and oldest extremes, respectively. A feature vector based on the pattern spectrum, normalized area, and nucleus - cytoplasm area ratio, was tested using a multilayer perceptron neural network trained by backpropagation, and a Support Vector Machine algorithm. Results from Euclidean distance and k-nearest neighbor classifiers are also reported as reference for comparison purposes. A recognition rate of 87% was obtained in the best case, using 36 patterns for training and 18 for testing, with a three-fold validation scheme. Additional experiments exploring larger databases are currently in progress. © 2010 Springer-Verlag Berlin Heidelberg.

@inproceedings{
 title = {Wavelet-based smoke detection in outdoor video sequences},
 type = {inproceedings},
 year = {2010},
 pages = {383-387},
 websites = {http://ieeexplore.ieee.org/document/5548865/},
 month = {8},
 publisher = {IEEE},
 id = {f0de1c3c-239d-3590-ad2d-1c7ed88d5606},
 created = {2022-08-29T17:43:11.951Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:11.951Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Gonzalez-Gonzalez, R. and Alarcon-Aquino, V. and Rosas-Romero, R. and Starostenko, O. and Rodriguez-Asomoza, J. and Ramirez-Cortes, J. M.},
 doi = {10.1109/MWSCAS.2010.5548865},
 booktitle = {2010 53rd IEEE International Midwest Symposium on Circuits and Systems}
}

In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application. © 2010 IEEE.

@inproceedings{
 title = {Shape Indexing and Semantic Image Retrieval Based on Ontological Descriptions},
 type = {inproceedings},
 year = {2010},
 pages = {58-73},
 volume = {1},
 websites = {https://www.researchgate.net/publication/230710742_Shape_Indexing_and_Semantic_Image_Retrieval_Based_on_Ontological_Descriptions},
 id = {7c932e52-030b-39b4-b02a-5b3be342404d},
 created = {2022-08-29T17:43:12.601Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:51.197Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents some hybrid approaches for visual information retrieval that combine image low-level feature analysis with semantic descriptors of image content. The aim of this proposal is to improve retrieval process by reducing nonsense results to user query. In the proposed approach user may submit textual queries, which are converted to image characteristics providing in this way searching, indexing, interpretation, and retrieval. In the case of visual query, both an image and sketch may be used. Approaches for image interpretation and retrieval are applied to color filtering, shape indexing and semantic. In order to assess the proposed approaches, some systems for image retrieval have been designed. The simplest system uses color region arrangement and neural network or wavelet based classifiers. Then this system has been improved using shape analysis with its indexing by ontological descriptions. For shape matching two proposed approaches are used such as star field or two-segment turning functions, which are invariant to spatial deformation of objects in image. The ontological annotations of objects in image provide machine-understandable semantics. The evolution of the proposed approaches and improvement of retrieval process are described in this paper. Four designed systems are assessed: RetNew, IRWC, Butterfly, and IRONS tested on standard COIL-100 and CE-Shape-1 image collections. The obtained results will allow to develop novel methods for solving efficient image retrieval processes.},
 bibtype = {inproceedings},
 author = {Starostenko, Oleg and Flores-Pulido, Leticia and Rosas-Romero, R and Alarcon-Aquino, Vicente and Sergiyenko, Oleg and Tyrsa, Vera},
 booktitle = {AIAR2010: Proceedings of the 1st Automatic Image Annotation and RetrievalWorkshop 2010.}
}

This paper presents some hybrid approaches for visual information retrieval that combine image low-level feature analysis with semantic descriptors of image content. The aim of this proposal is to improve retrieval process by reducing nonsense results to user query. In the proposed approach user may submit textual queries, which are converted to image characteristics providing in this way searching, indexing, interpretation, and retrieval. In the case of visual query, both an image and sketch may be used. Approaches for image interpretation and retrieval are applied to color filtering, shape indexing and semantic. In order to assess the proposed approaches, some systems for image retrieval have been designed. The simplest system uses color region arrangement and neural network or wavelet based classifiers. Then this system has been improved using shape analysis with its indexing by ontological descriptions. For shape matching two proposed approaches are used such as star field or two-segment turning functions, which are invariant to spatial deformation of objects in image. The ontological annotations of objects in image provide machine-understandable semantics. The evolution of the proposed approaches and improvement of retrieval process are described in this paper. Four designed systems are assessed: RetNew, IRWC, Butterfly, and IRONS tested on standard COIL-100 and CE-Shape-1 image collections. The obtained results will allow to develop novel methods for solving efficient image retrieval processes.

@inproceedings{
 title = {Special Image Post-Processing For Recognition of Compound Structures},
 type = {inproceedings},
 year = {2010},
 publisher = {Sociedad Mexicana de Materiales A. C.},
 id = {099922ab-bef6-31d4-99a0-2860a1500f12},
 created = {2022-08-29T17:43:13.224Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:13.224Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Necessity of reconstruction of compound structures appears in different applications. As the rule, a tomography is used for this aim. However, obtained result usually contains sufficient visible noise. To eliminate such noise a post-processing is used, which consists in application of special image processing techniques and transforms. In this paper, we consider two types of special image postprocessing. The fist type is based on Recursive Spline Smoothing method [1], which is realized as fast and stable algorithm and MATLAB software adapted to image reconstruction. If recuperating structure has piecewise constant characteristics with known values, then the algorithm includes also the projection of the pre-reconstructed data to the known set of values with respect to the absolute or relative criterion. The second type is based on dual-tree complex wavelet transform (DT-CWT), proposed in [2]. The approach follows four basic procedures such as, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. Both of proposed approaches are tested on numerical examples, which demonstrate their good quality and acceptable range of errors. Acknowledgments. This research is sponsored by Mexican National Council of Science and Technology, CONACyT, Project #109417 [1] A. I. Grebennikov Spline Approximation Method and Its Applications, MAX Press, Russia, 2004 (in English). [2] V. Alarcon-Aquino, O. Starostenko, et al. Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform, Journal Engineering Intelligent Systems, #1, 2009, pp.49-63. S3-},
 bibtype = {inproceedings},
 author = {O. Starostenko A. Grebennikov, V Alarcon-Aquino},
 booktitle = {SYMPOSIUM 3 STRUCTURAL AND CHEMICAL CHARACTERIZATION OF METALS ALLOYS AND COMPOUNDS}
}

Necessity of reconstruction of compound structures appears in different applications. As the rule, a tomography is used for this aim. However, obtained result usually contains sufficient visible noise. To eliminate such noise a post-processing is used, which consists in application of special image processing techniques and transforms. In this paper, we consider two types of special image postprocessing. The fist type is based on Recursive Spline Smoothing method [1], which is realized as fast and stable algorithm and MATLAB software adapted to image reconstruction. If recuperating structure has piecewise constant characteristics with known values, then the algorithm includes also the projection of the pre-reconstructed data to the known set of values with respect to the absolute or relative criterion. The second type is based on dual-tree complex wavelet transform (DT-CWT), proposed in [2]. The approach follows four basic procedures such as, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. Both of proposed approaches are tested on numerical examples, which demonstrate their good quality and acceptable range of errors. Acknowledgments. This research is sponsored by Mexican National Council of Science and Technology, CONACyT, Project #109417 [1] A. I. Grebennikov Spline Approximation Method and Its Applications, MAX Press, Russia, 2004 (in English). [2] V. Alarcon-Aquino, O. Starostenko, et al. Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform, Journal Engineering Intelligent Systems, #1, 2009, pp.49-63. S3-

@inbook{
 type = {inbook},
 year = {2010},
 pages = {431-436},
 websites = {http://link.springer.com/10.1007/978-90-481-3662-9_74},
 publisher = {Springer Netherlands},
 city = {Dordrecht},
 id = {c1f851ae-89f3-390d-b4fb-1530514966c7},
 created = {2022-08-29T17:43:13.815Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:13.815Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CHAP},
 private_publication = {false},
 abstract = {Multi-Protocol Label Switching (MPLS) is an alternative to integrate Internet Protocol (IP) routing and switching technologies because it provides end-to-end Quality of Service (QoS), guarantees traffic engineering, and support Virtual Private Networks (VPNs). However, MPLS must use path restoration schemes to guarantee the delivery of packets through a network. Considering that MPLS is a label switching technology, it uses a method for label distribution based on signaling protocols like LDP (Label Distribution Protocol) among others. In this paper we present the simulation of some LDP messages and assess the performance of three path restoration schemes (Haskin, Makam, and Simple Dynamic) in an MPLS network using OMNET++. The simulation results show that the Simple Dynamic scheme presents a reduced arrival time when sending a message from the source to a destination, when compared to those times obtained to Haskin and Makam schemes.},
 bibtype = {inbook},
 author = {Minero-Muñoz, Marcelino and Alarcon-Aquino, Vicente and García-Fierro, Jose Galdino and Rosas-Romero, Roberto and Rodriguez-Asomoza, Jorge and Starostenko, Oleg},
 doi = {10.1007/978-90-481-3662-9_74},
 chapter = {Performance Evaluation of MPLS Path Restoration Schemes using OMNET++},
 title = {Novel Algorithms and Techniques in Telecommunications and Networking}
}

Multi-Protocol Label Switching (MPLS) is an alternative to integrate Internet Protocol (IP) routing and switching technologies because it provides end-to-end Quality of Service (QoS), guarantees traffic engineering, and support Virtual Private Networks (VPNs). However, MPLS must use path restoration schemes to guarantee the delivery of packets through a network. Considering that MPLS is a label switching technology, it uses a method for label distribution based on signaling protocols like LDP (Label Distribution Protocol) among others. In this paper we present the simulation of some LDP messages and assess the performance of three path restoration schemes (Haskin, Makam, and Simple Dynamic) in an MPLS network using OMNET++. The simulation results show that the Simple Dynamic scheme presents a reduced arrival time when sending a message from the source to a destination, when compared to those times obtained to Haskin and Makam schemes.

@inproceedings{
 title = {P-300 rhythm detection using anfis algorithm and wavelet feature extraction in eeg signals},
 type = {inproceedings},
 year = {2010},
 volume = {1},
 websites = {http://www.iaeng.org/publication/WCECS2010/WCECS2010_pp619-623.pdf},
 publisher = {Proceedings of the 2010 Word Congress on Engineering and Computer Science(WCECS 2010)},
 id = {8a43f79a-c4d1-39b5-aed5-1564aca48c51},
 created = {2022-08-29T17:43:14.447Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:14.447Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpectedevents. In this work, an experiment on the detection of a P-300 rhythmfor potential applications on brain computer interfaces (BCI)using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected usingthe discrete wavelet transform (DWT)applied on the preprocessed signal as afeature extractor, and further enteredto the ANFIS system. Experimental results are presented.},
 bibtype = {inproceedings},
 author = {Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente and Rosas-Cholula, Gerardo and Gomez-Gil, Pilar and Escamilla-Ambrosio, Jorge},
 booktitle = {Proceedings of the 2010 Word Congress on Engineering and Computer Science(WCECS 2010)}
}

P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpectedevents. In this work, an experiment on the detection of a P-300 rhythmfor potential applications on brain computer interfaces (BCI)using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected usingthe discrete wavelet transform (DWT)applied on the preprocessed signal as afeature extractor, and further enteredto the ANFIS system. Experimental results are presented.

@inproceedings{
 title = {Modeling Motion Prediction Techniques: Linear and Quadratic Splines vs Circular-Queue Vector Approach},
 type = {inproceedings},
 year = {2010},
 pages = {366-371},
 websites = {http://ieeexplore.ieee.org/document/5692364/},
 month = {9},
 publisher = {IEEE},
 id = {b5e2e2be-86e7-3aae-8914-7ff8c1e86b9b},
 created = {2022-08-29T17:43:15.047Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:15.047Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {The modeling and performance analysis of two novel approaches for motion prediction of objects in a limited sequence of images is presented in this paper. Particularly, these proposed approaches are based on linear or quadratic splines and on circular-queue vector correspondence. They use two basic motion detection techniques also proposed by authors that provide object tracing by its representation by envelope or by mass center grid. The performance and efficiency of the proposed motion prediction approaches are evaluated applying several simulated kinematics experiments either with constant velocity and accelerated motion, or with parabolic and circular motion.},
 bibtype = {inproceedings},
 author = {Tello-Martinez, Jose Tomas and Starostenko, Oleg and Alarcon-Aquino, Vicente and Hernandez, Wilmar},
 doi = {10.1109/CERMA.2010.49},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

The modeling and performance analysis of two novel approaches for motion prediction of objects in a limited sequence of images is presented in this paper. Particularly, these proposed approaches are based on linear or quadratic splines and on circular-queue vector correspondence. They use two basic motion detection techniques also proposed by authors that provide object tracing by its representation by envelope or by mass center grid. The performance and efficiency of the proposed motion prediction approaches are evaluated applying several simulated kinematics experiments either with constant velocity and accelerated motion, or with parabolic and circular motion.

@inproceedings{
 title = {fMRI activated-voxel detection based on ICA decomposition and wavelet analysis},
 type = {inproceedings},
 year = {2010},
 websites = {http://www.cic.ipn.mx/~pescamilla/papers/Morales-Floresetal2010.pdf},
 id = {64a9a473-a9a8-3f2f-9bd9-9ad617e2ae5a},
 created = {2022-08-29T17:43:15.680Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:15.680Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Functional magnetic resonance imaging (fMRI) is a technique for investigating activity in the brain in response to some applied stimulus during a mental process. In this paper, detection of activated voxels in a two-class block-design fMRI experiment is described. A methodology based on blind source separation using Independent Component Analysis (ICA), applied on wavelet decomposition of voxel time signals is shown to provide an adequate and robust data separation. Results obtained from simulated as well as experimental fMRI data obtained from the public repository of the fMRI Data Center, are presented.},
 bibtype = {inproceedings},
 author = {Morales-Flores, E and Ramirez-Cortes, J M and Escamilla-Ambrosio, P J and Alarcon-Aquino, V},
 booktitle = {ROPEC 2010}
}

Functional magnetic resonance imaging (fMRI) is a technique for investigating activity in the brain in response to some applied stimulus during a mental process. In this paper, detection of activated voxels in a two-class block-design fMRI experiment is described. A methodology based on blind source separation using Independent Component Analysis (ICA), applied on wavelet decomposition of voxel time signals is shown to provide an adequate and robust data separation. Results obtained from simulated as well as experimental fMRI data obtained from the public repository of the fMRI Data Center, are presented.

@article{
 title = {DWT Foveation-Based Multiresolution Compression Algorithm},
 type = {article},
 year = {2010},
 pages = {197-206},
 volume = {50},
 id = {9c0313e4-4158-39c8-a97b-71edca7a6cf4},
 created = {2022-08-29T17:43:16.279Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Discrete Wavelet Transform (DWT) foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. Such algorithms lead into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly used discrete cosine transform (DCT). An analysis has then been performed showing different error and compression rates for the DWT-based and the DCT-based foveated compression algorithms. Simulation results show that with foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI.},
 bibtype = {article},
 author = {Galan-Hernandez, J C and Alarcon-Aquino, V and Starostenko, O and Ramirez-Cortes, J M},
 journal = {Research in Computing Science}
}

Discrete Wavelet Transform (DWT) foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. Such algorithms lead into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly used discrete cosine transform (DCT). An analysis has then been performed showing different error and compression rates for the DWT-based and the DCT-based foveated compression algorithms. Simulation results show that with foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI.

@inproceedings{
 title = {A Framework for Designing Image-Based Language Learning Applications Within Mobile Environment},
 type = {inproceedings},
 year = {2010},
 pages = {6004-6013},
 websites = {https://library.iated.org/view/STAROSTENKO2010AFR},
 publisher = {IATED},
 id = {53d2dae2-d5d8-3615-91a6-b85de4f9f790},
 created = {2022-08-29T17:43:16.901Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:16.901Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {The exploration of emerging data exchange technologies and design of IBLL (image-based language learning) applications are presented in this paper. For integrating the mobile devices to learning process the generic interfaces have been created for portable personal spaces providing mobile access to multimedia documents based on XML technologies. The IBLL implies image processing, recognition and retrieval, thereby some algorithms have been proposed for learning assistant applications. The proposed framework can suggest ways of studying and organizing resources, provide long-term guidance on developing skills, and support experiential learning. It has been tested evaluating processing speed and recognition grade for interpretation and learning Japanese kanji on mobile devices. The computational approaches implemented in designed system can suggest ways of studying and organizing resources remembering ideas and events within portable personal spaces. The system can act as a learning assistant in performing tasks or solving problems by suggesting solutions and recommendations for IBLL.},
 bibtype = {inproceedings},
 author = {Starostenko, O and Alarcon-Aquino, V and Flores-Pulido, L and Rodriguez-Asomoza, J},
 booktitle = {EDULEARN10 Proceedings}
}

The exploration of emerging data exchange technologies and design of IBLL (image-based language learning) applications are presented in this paper. For integrating the mobile devices to learning process the generic interfaces have been created for portable personal spaces providing mobile access to multimedia documents based on XML technologies. The IBLL implies image processing, recognition and retrieval, thereby some algorithms have been proposed for learning assistant applications. The proposed framework can suggest ways of studying and organizing resources, provide long-term guidance on developing skills, and support experiential learning. It has been tested evaluating processing speed and recognition grade for interpretation and learning Japanese kanji on mobile devices. The computational approaches implemented in designed system can suggest ways of studying and organizing resources remembering ideas and events within portable personal spaces. The system can act as a learning assistant in performing tasks or solving problems by suggesting solutions and recommendations for IBLL.

@inproceedings{
 title = {Comparing Simulation Alternatives for High-Level Abstraction Modeling of NIC's Buffer Requirements in a Network Node},
 type = {inproceedings},
 year = {2010},
 pages = {68-73},
 websites = {http://ieeexplore.ieee.org/document/5692314/},
 month = {9},
 publisher = {IEEE},
 id = {7abead26-49d5-3573-a44a-e1d4e4508bcb},
 created = {2022-08-29T17:43:18.263Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:52:51.635Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {In this paper we compare nine simulation alternatives which can be used for modeling and analysis the hardware components and processing tasks involved in processing a packet flow entering in a network node. In particular, we focus on the capabilities of these alternatives that can be employed for validating an analytical model based on Real-Time Calculus for the performance evaluation of the NIC's buffer requirements at high-level abstraction.},
 bibtype = {inproceedings},
 author = {Garay, Godofredo R and Leon, M. and Aguilar, Rolando and Alarcon-Aquino, Vicente},
 doi = {10.1109/CERMA.2010.94},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

In this paper we compare nine simulation alternatives which can be used for modeling and analysis the hardware components and processing tasks involved in processing a packet flow entering in a network node. In particular, we focus on the capabilities of these alternatives that can be employed for validating an analytical model based on Real-Time Calculus for the performance evaluation of the NIC's buffer requirements at high-level abstraction.

@inproceedings{
 title = {An FPGA-based architecture for linear and morphological image filtering},
 type = {inproceedings},
 year = {2010},
 pages = {90-95},
 websites = {http://ieeexplore.ieee.org/document/5440788/},
 month = {2},
 publisher = {IEEE},
 id = {2f6d3325-0623-3dae-9902-1b28d2546dbc},
 created = {2022-08-29T17:43:18.888Z},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
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 source_type = {CONF},
 private_publication = {false},
 abstract = {Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suited to video image processing applications. The unique architecture of the FPGA has allowed the technology to be used in many applications encompassing all aspects of video image processing. Among those algorithms, linear filtering based on a 2D convolution, and non-linear 2D morphological filters, represent a basic set of image operations for a number of applications. In this work, an implementation of linear and morphological image filtering using a FPGA NexysII, Xilinx, Spartan 3E, with educational purposes, is presented. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station. The FPGA-based system is accessed through a Matlab graphical user interface, which handles the communication setup. A comparison between results obtained from MATLAB simulations and the described FPGA-based implementation is presented.},
 bibtype = {inproceedings},
 author = {Ramirez, Juan Manuel and Flores, Emmanuel Morales and Martinez-Carballido, Jorge and Enriquez, Rogerio and Alarcon-Aquino, Vicente and Baez-Lopez, David},
 doi = {10.1109/CONIELECOMP.2010.5440788},
 booktitle = {2010 20th International Conference on Electronics Communications and Computers (CONIELECOMP)}
}

Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suited to video image processing applications. The unique architecture of the FPGA has allowed the technology to be used in many applications encompassing all aspects of video image processing. Among those algorithms, linear filtering based on a 2D convolution, and non-linear 2D morphological filters, represent a basic set of image operations for a number of applications. In this work, an implementation of linear and morphological image filtering using a FPGA NexysII, Xilinx, Spartan 3E, with educational purposes, is presented. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station. The FPGA-based system is accessed through a Matlab graphical user interface, which handles the communication setup. A comparison between results obtained from MATLAB simulations and the described FPGA-based implementation is presented.

@inproceedings{
 title = {A Power-Line Communication Modem Based on OFDM},
 type = {inproceedings},
 year = {2009},
 keywords = {Coupling circuit,FPGA,OFDM,Power-line communications,VHDL},
 pages = {208-213},
 websites = {http://ieeexplore.ieee.org/document/5163919/},
 month = {2},
 publisher = {IEEE},
 id = {a778d65e-9cf2-3c4e-a5cd-e6df4fb629c4},
 created = {2022-08-29T17:43:19.485Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:19.485Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present the design and implementation of a PLC (Power-Line Communication) Modem based on Orthogonal Frequency Division Multiplexing (OFDM). The PLC device implements OFDM in both transmitter and receiver using VHDL programming. The OFDM processor is synthesized in a Field Programmable Gate Array (FPGA) that acts as a Core Processor in the PLC Modem. Furthermore, the prototype includes the design and implementation of the stages needed to inject the signal into the power-line and to recover the same signal in other point of the PLC network. An analysis of several noise sources show that the PLC Modem performance produced a bit error percentage of 4.46% in the worst case, while 0.89% in the best case for the transmission/reception tested. © 2009 IEEE.},
 bibtype = {inproceedings},
 author = {Garcia-Baleon, H.A. and Alarcon-Aquino, V.},
 doi = {10.1109/CONIELECOMP.2009.30},
 booktitle = {2009 International Conference on Electrical, Communications, and Computers}
}

In this paper, we present the design and implementation of a PLC (Power-Line Communication) Modem based on Orthogonal Frequency Division Multiplexing (OFDM). The PLC device implements OFDM in both transmitter and receiver using VHDL programming. The OFDM processor is synthesized in a Field Programmable Gate Array (FPGA) that acts as a Core Processor in the PLC Modem. Furthermore, the prototype includes the design and implementation of the stages needed to inject the signal into the power-line and to recover the same signal in other point of the PLC network. An analysis of several noise sources show that the PLC Modem performance produced a bit error percentage of 4.46% in the worst case, while 0.89% in the best case for the transmission/reception tested. © 2009 IEEE.

@inproceedings{
 title = {Cryptographic Key Generation from Biometric Data Using Wavelets},
 type = {inproceedings},
 year = {2009},
 keywords = {Biometrics,Cryptography,Wavelet transform},
 pages = {15-20},
 websites = {http://ieeexplore.ieee.org/document/5342020/},
 month = {9},
 publisher = {IEEE},
 id = {c6529fd6-2be9-32e5-bb72-9e378ecd039c},
 created = {2022-08-29T17:43:20.142Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:20.142Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present an approach for biometric key generation using wavelets and electrocardiogram (ECG) signals. The stages that comprise the approach are one time enrollment and key derivation. This work is based on the uniqueness and quasi-stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different information is generated and then stored in a token for authentication purposes. Also, this approach implements an error-correction layer using the Hadamard code. The performance of the proposed approach is assessed using ECG signals from MIT-BIH database. Simulation results show a false acceptance rate (FAR) of 4.60% and a false rejection rate (FRR) of 7.90%. The random biometric key released by the proposed approach can be used in several encryption algorithms. © 2009 IEEE.},
 bibtype = {inproceedings},
 author = {Garcia-Baleon, H. A. and Alarcon-Aquino, V.},
 doi = {10.1109/CERMA.2009.16},
 booktitle = {2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA)}
}

In this paper we present an approach for biometric key generation using wavelets and electrocardiogram (ECG) signals. The stages that comprise the approach are one time enrollment and key derivation. This work is based on the uniqueness and quasi-stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different information is generated and then stored in a token for authentication purposes. Also, this approach implements an error-correction layer using the Hadamard code. The performance of the proposed approach is assessed using ECG signals from MIT-BIH database. Simulation results show a false acceptance rate (FAR) of 4.60% and a false rejection rate (FRR) of 7.90%. The random biometric key released by the proposed approach can be used in several encryption algorithms. © 2009 IEEE.

@inbook{
 type = {inbook},
 year = {2009},
 keywords = {Biometrics,Cryptography,K-medoids,Keystroke dynamics},
 pages = {85-94},
 volume = {5856 LNCS},
 websites = {http://link.springer.com/10.1007/978-3-642-10268-4_10},
 id = {6f3d8227-0466-3145-a1c3-feb617a4c644},
 created = {2022-08-29T17:43:20.758Z},
 file_attached = {false},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we report an approach for cryptographic key generation based on keystroke dynamics and the k-medoids algorithm. The stages that comprise the approach are training-enrollment and user verification. The proposed approach is able to verify the identity of individuals off-line avoiding the use of a centralized database. The performance of the proposed approach is assessed using 20 samples of keystroke dynamics from 20 different users. Simulation results show a false acceptance rate (FAR) of 5.26% and a false rejection rate (FRR) of 10%. The cryptographic key released by the proposed approach may be used in several encryption algorithms. © 2009 Springer-Verlag Berlin Heidelberg.},
 bibtype = {inbook},
 author = {Garcia-Baleon, H. A. and Alarcon-Aquino, V. and Starostenko, O.},
 doi = {10.1007/978-3-642-10268-4_10},
 chapter = {K-Medoids-Based Random Biometric Pattern for Cryptographic Key Generation},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

In this paper we report an approach for cryptographic key generation based on keystroke dynamics and the k-medoids algorithm. The stages that comprise the approach are training-enrollment and user verification. The proposed approach is able to verify the identity of individuals off-line avoiding the use of a centralized database. The performance of the proposed approach is assessed using 20 samples of keystroke dynamics from 20 different users. Simulation results show a false acceptance rate (FAR) of 5.26% and a false rejection rate (FRR) of 10%. The cryptographic key released by the proposed approach may be used in several encryption algorithms. © 2009 Springer-Verlag Berlin Heidelberg.

@inproceedings{
 title = {A wavelet-based 128-bit key generator using electrocardiogram signals},
 type = {inproceedings},
 year = {2009},
 pages = {644-647},
 websites = {http://ieeexplore.ieee.org/document/5236010/},
 month = {8},
 publisher = {IEEE},
 id = {fdde3254-786e-3ea7-b243-b655856543af},
 created = {2022-08-29T17:43:21.346Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present a wavelet-based 128-bit key generator using electrocardiogram (ECG) signals. The key generator comprises two independent stages, namely, enrollment and verification-generation. In the latter, an algorithm for determining the keys is also proposed. This work is based on the uniqueness and quasi- stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different keys are released to different individuals. The performance of the proposed key generator is assessed using ECG signals from MIT-BIH database. Simulation results show a false accept rate (FAR) of 22.3% and a false reject rate (FRR) of 18.1%. The 128-bit key released by the generator proposed in this work can be used in several encryption algorithms. © 2009 IEEE.},
 bibtype = {inproceedings},
 author = {Garcia-Baleon, H. A. and Alarcon-Aquino, V. and Starostenko, O.},
 doi = {10.1109/MWSCAS.2009.5236010},
 booktitle = {2009 52nd IEEE International Midwest Symposium on Circuits and Systems}
}

In this paper, we present a wavelet-based 128-bit key generator using electrocardiogram (ECG) signals. The key generator comprises two independent stages, namely, enrollment and verification-generation. In the latter, an algorithm for determining the keys is also proposed. This work is based on the uniqueness and quasi- stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different keys are released to different individuals. The performance of the proposed key generator is assessed using ECG signals from MIT-BIH database. Simulation results show a false accept rate (FAR) of 22.3% and a false reject rate (FRR) of 18.1%. The 128-bit key released by the generator proposed in this work can be used in several encryption algorithms. © 2009 IEEE.

@inproceedings{
 title = {Modelling data segmentation for image retrieval systems},
 type = {inproceedings},
 year = {2009},
 pages = {201-208},
 volume = {533},
 id = {98d364d4-7824-32bb-8f8f-3124ad4c0b98},
 created = {2022-08-29T17:43:21.929Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:21.929Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The analysis of a large amount of data with complex structures is a challenging task in engineering and scientific applications. The data segmentation task usually involves either probabilistic or statistical approaches, however, global minima disadvantage is still present in each mentioned approaches. A combination of these two approaches is based on subspace arrangements avoiding global minima in classification methods. In this paper we propose a new approach for a generalized principal component analysis algorithm (GPCA) improving knowledge representation in images. The linear algebra concepts achieve an abstraction of data sets whose items as images, documents or stellar spectra could be handled providing a knowledge description for image classification process of involved data. We describe a solution to optimization GPCA function using Gutmann Algorithm for segmentation data sets.},
 bibtype = {inproceedings},
 author = {Flores-Pulido, Leticia and Starostenko, Oleg and Rodrguez-Gomez, Gustavo and Alarcon-Aquino, Vicente},
 booktitle = {CEUR Workshop Proceedings}
}

The analysis of a large amount of data with complex structures is a challenging task in engineering and scientific applications. The data segmentation task usually involves either probabilistic or statistical approaches, however, global minima disadvantage is still present in each mentioned approaches. A combination of these two approaches is based on subspace arrangements avoiding global minima in classification methods. In this paper we propose a new approach for a generalized principal component analysis algorithm (GPCA) improving knowledge representation in images. The linear algebra concepts achieve an abstraction of data sets whose items as images, documents or stellar spectra could be handled providing a knowledge description for image classification process of involved data. We describe a solution to optimization GPCA function using Gutmann Algorithm for segmentation data sets.

@inproceedings{
 title = {Bimodal Biometric System for Cryptographic Key Generation Using Wavelet Transforms},
 type = {inproceedings},
 year = {2009},
 keywords = {Biometrics,Cryptography,Wavelet transform},
 pages = {185-196},
 websites = {http://ieeexplore.ieee.org/document/5452561/},
 publisher = {IEEE},
 id = {ee3b0e37-bab9-38bd-a573-fa5a4e3dc3bb},
 created = {2022-08-29T17:43:22.533Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:22.533Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a bimodal biometric system for cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric samples, and a token. The stages that comprise the architecture are one time enrollment and key derivation. The system architecture is able to verify the identity of individuals off-line avoiding the use of a centralized database for storing the biometric information. The system also implements an error-correction layer using the Hadamard code. The performance of the system is assessed using ECG signals from the MIT-BIH database and speech signals from a speech database created for testing purposes. Simulation results report a false acceptance rate (FAR) of 1.27% and a false rejection rate (FRR) of 10.62% for the system. The random cryptographic key released by the system may be used in several encryption algorithms. © 2009 IEEE.},
 bibtype = {inproceedings},
 author = {Garcia-Baleon, H. A. and Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cruz, J. F.},
 doi = {10.1109/ENC.2009.25},
 booktitle = {2009 Mexican International Conference on Computer Science}
}

In this paper we present a bimodal biometric system for cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric samples, and a token. The stages that comprise the architecture are one time enrollment and key derivation. The system architecture is able to verify the identity of individuals off-line avoiding the use of a centralized database for storing the biometric information. The system also implements an error-correction layer using the Hadamard code. The performance of the system is assessed using ECG signals from the MIT-BIH database and speech signals from a speech database created for testing purposes. Simulation results report a false acceptance rate (FAR) of 1.27% and a false rejection rate (FRR) of 10.62% for the system. The random cryptographic key released by the system may be used in several encryption algorithms. © 2009 IEEE.

@inproceedings{
 title = {CBIR for image-based language learning within mobile environment},
 type = {inproceedings},
 year = {2009},
 pages = {734-738},
 websites = {http://ieeexplore.ieee.org/document/5235990/},
 month = {8},
 publisher = {IEEE},
 id = {d77e4043-2d77-3e53-a495-f73df1330423},
 created = {2022-08-29T17:43:23.126Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:23.126Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents the analysis of emerging data exchange technologies used for integration of mobile devices to image-based learning process of second language. Particularly, the design of portable personal spaces providing mobile access to multimedia documents based on XML technologies and creation of generic interfaces for learning environments have been carried out. For image-based languages learning that implies the image processing, recognition, and retrieval Two Segment Turning function has been proposed and analyzed for possible adoption in applications assisted by mobile devices. The evaluation of designed prototype of image-based language learning system for interpretation of Japanese kanji and Mayan glyphs on mobile devices is discussed in this paper. Additionally the performance of proposed approaches used in content-based image retrieval is evaluated for their possible integration within mobile learning environments. © 2009 IEEE.},
 bibtype = {inproceedings},
 author = {Starostenko, O. and Contreras Gomez, R. and Alarcon-Aquino, V. and Sergiyenko, O.},
 doi = {10.1109/MWSCAS.2009.5235990},
 booktitle = {2009 52nd IEEE International Midwest Symposium on Circuits and Systems}
}

This paper presents the analysis of emerging data exchange technologies used for integration of mobile devices to image-based learning process of second language. Particularly, the design of portable personal spaces providing mobile access to multimedia documents based on XML technologies and creation of generic interfaces for learning environments have been carried out. For image-based languages learning that implies the image processing, recognition, and retrieval Two Segment Turning function has been proposed and analyzed for possible adoption in applications assisted by mobile devices. The evaluation of designed prototype of image-based language learning system for interpretation of Japanese kanji and Mayan glyphs on mobile devices is discussed in this paper. Additionally the performance of proposed approaches used in content-based image retrieval is evaluated for their possible integration within mobile learning environments. © 2009 IEEE.

@article{
 title = {Algorithmic Error Correction of Impedance Measuring Sensors},
 type = {article},
 year = {2009},
 keywords = {C-v,Error correction,G-v characteristic meter,Impedance measuring sensor},
 pages = {10341-10355},
 volume = {9},
 websites = {http://www.mdpi.com/1424-8220/9/12/10341},
 month = {12},
 day = {21},
 id = {1213141d-f3ae-352d-b147-27d3bfd2441e},
 created = {2022-08-29T17:43:23.807Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:23.807Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper describes novel design concepts and some advanced techniques proposed for increasing the accuracy of low cost impedance measuring devices without reduction of operational speed. The proposed structural method for algorithmic error correction and iterating correction method provide linearization of transfer functions of the measuring sensor and signal conditioning converter, which contribute the principal additive and relative measurement errors. Some measuring systems have been implemented in order to estimate in practice the performance of the proposed methods. Particularly, a measuring system for analysis of C-V, G-V characteristics has been designed and constructed. It has been tested during technological process control of charge-coupled device CCD manufacturing. The obtained results are discussed in order to define a reasonable range of applied methods, their utility, and performance. © 2009 by the authors.},
 bibtype = {article},
 author = {Starostenko, Oleg and Alarcon-Aquino, Vicente and Hernandez, Wilmar and Sergiyenko, Oleg and Tyrsa, Vira},
 doi = {10.3390/s91210341},
 journal = {Sensors},
 number = {12}
}

This paper describes novel design concepts and some advanced techniques proposed for increasing the accuracy of low cost impedance measuring devices without reduction of operational speed. The proposed structural method for algorithmic error correction and iterating correction method provide linearization of transfer functions of the measuring sensor and signal conditioning converter, which contribute the principal additive and relative measurement errors. Some measuring systems have been implemented in order to estimate in practice the performance of the proposed methods. Particularly, a measuring system for analysis of C-V, G-V characteristics has been designed and constructed. It has been tested during technological process control of charge-coupled device CCD manufacturing. The obtained results are discussed in order to define a reasonable range of applied methods, their utility, and performance. © 2009 by the authors.

@article{
 title = {Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform},
 type = {article},
 year = {2009},
 keywords = {Breast cancer,Dual-tree complex wavelet transforms,MIAS database,Mammography,Microcalcifications,Wavelets},
 pages = {49-63},
 volume = {17},
 websites = {https://www.researchgate.net/publication/230710714_Detection_of_microcalcifications_in_digital_mammograms_using_the_dual-tree_complex_wavelet_transform_2009},
 id = {6a8f0842-9a17-3bce-929b-0782dd258501},
 created = {2022-08-29T17:43:24.447Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:24.447Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we propose an approach to detect microcalcifications in digital mammograms using the dual-tree complex wavelet transform (DT-CWT). The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. Recently, the DT-CWT has shown a good performance in applications that involve image processing due to more data phase information, shift invariance, and directionality than other wavelet transforms. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. In order to maximise the detection a morphological conversion is then proposed and applied to the high frequencies subbands of the wavelet transformation. This procedure is applied to a set of digital mammograms from the mammography image analysis society (MIAS) database. Experimental results show that the proposed denoising algorithm and morphological transformation in combination with the DT-CWT procedure performs better than the stationary and discrete wavelet transforms and the top-hat filtering. The approach reported in this paper seems to be meaningful to aid in the results on mammogram interpretation and to get an earlier and opportune diagnostic for breast cancer. © 2009 CRL Publishing Ltd.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Starostenko, O. and Ramirez-Cortes, J. M. and Rosas-Romero, R. and Rodriguez-Asomoza, J. and Paz-Luna, O. J. and Vazquez-Muñoz, K.},
 journal = {Engineering Intelligent Systems},
 number = {1}
}

In this paper we propose an approach to detect microcalcifications in digital mammograms using the dual-tree complex wavelet transform (DT-CWT). The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. Recently, the DT-CWT has shown a good performance in applications that involve image processing due to more data phase information, shift invariance, and directionality than other wavelet transforms. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. In order to maximise the detection a morphological conversion is then proposed and applied to the high frequencies subbands of the wavelet transformation. This procedure is applied to a set of digital mammograms from the mammography image analysis society (MIAS) database. Experimental results show that the proposed denoising algorithm and morphological transformation in combination with the DT-CWT procedure performs better than the stationary and discrete wavelet transforms and the top-hat filtering. The approach reported in this paper seems to be meaningful to aid in the results on mammogram interpretation and to get an earlier and opportune diagnostic for breast cancer. © 2009 CRL Publishing Ltd.

@article{
 title = {Reconfigurable path restoration schemes for MPLS networks},
 type = {article},
 year = {2009},
 pages = {29-38},
 volume = {8},
 websites = {https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/258},
 id = {7c7153f8-9759-3c5d-811e-8142d133aa73},
 created = {2022-08-29T17:43:25.059Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:25.059Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Multi-Protocol Label Switching (MPLS) is an alternative to integrate the traditional Internet Protocol (IP) routing and switching technologies because it provides end-to-end Quality of Service (QoS), guarantees Traffic Engineering, and support Virtual Private Networks (VPNs). However, MPLS must use path restoration schemes to guarantee the delivery of packets through a network. In this paper we present three reconfigurable architectures for the implementation of path restoration schemes, namely, Haskin, Makam, and Simple Dynamic. These schemes are implemented using an entity-based model that provides the advantage of reusability of entities, thus reducing the overall resource utilisation. The results show that Haskin and Makam schemes present similar resource utilisation. On the other hand, the simple dynamic scheme uses a similar entity-based model that provides a slight decrease in percentage utilisation when compared to those obtained for the two aforementioned schemes.},
 bibtype = {article},
 author = {Minero-Muñoz, Marcelino and Alarcon-Aquino, Vicente},
 journal = {INFOCOMP},
 number = {2}
}

Multi-Protocol Label Switching (MPLS) is an alternative to integrate the traditional Internet Protocol (IP) routing and switching technologies because it provides end-to-end Quality of Service (QoS), guarantees Traffic Engineering, and support Virtual Private Networks (VPNs). However, MPLS must use path restoration schemes to guarantee the delivery of packets through a network. In this paper we present three reconfigurable architectures for the implementation of path restoration schemes, namely, Haskin, Makam, and Simple Dynamic. These schemes are implemented using an entity-based model that provides the advantage of reusability of entities, thus reducing the overall resource utilisation. The results show that Haskin and Makam schemes present similar resource utilisation. On the other hand, the simple dynamic scheme uses a similar entity-based model that provides a slight decrease in percentage utilisation when compared to those obtained for the two aforementioned schemes.

@article{
 title = {Change detection in time series using the maximal overlap discrete wavelet transform},
 type = {article},
 year = {2009},
 pages = {145-152},
 volume = {39},
 websites = {http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932009000200009},
 id = {61be75aa-8ada-37c7-bf72-4d0a00932929},
 created = {2022-08-29T17:43:25.686Z},
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 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {The problem of change detection of time series with abrupt and smooth changes in the spectral characteristics is addressed. We first review the main characteristics of the discrete wavelet transform and the maximal overlap discrete wavelet transform. An algorithm for sequential change detection in time series is then reported based on the maximal overlap discrete wavelet transform and Bayesian analysis. The wavelet-based algorithm checks the wavelet coefficients across resolution levels and locates smooth and abrupt changes in the spectral characteristics in the given time series by using the wavelet coefficients at these levels. Simulation results demonstrate the good detection properties of the proposed algorithm when compared with previous reported algorithms, and also indicate that the quadratic spline and least-asymmetric wavelets have less amount of shift in position after wavelet decomposition and therefore an alignment of events to be detected in a multi-resolution analysis with respect to the original time series is obtained.},
 bibtype = {article},
 author = {Alarcon-Aquino, V and Barria, J A},
 journal = {Latin American applied research},
 number = {2}
}

The problem of change detection of time series with abrupt and smooth changes in the spectral characteristics is addressed. We first review the main characteristics of the discrete wavelet transform and the maximal overlap discrete wavelet transform. An algorithm for sequential change detection in time series is then reported based on the maximal overlap discrete wavelet transform and Bayesian analysis. The wavelet-based algorithm checks the wavelet coefficients across resolution levels and locates smooth and abrupt changes in the spectral characteristics in the given time series by using the wavelet coefficients at these levels. Simulation results demonstrate the good detection properties of the proposed algorithm when compared with previous reported algorithms, and also indicate that the quadratic spline and least-asymmetric wavelets have less amount of shift in position after wavelet decomposition and therefore an alignment of events to be detected in a multi-resolution analysis with respect to the original time series is obtained.

@inproceedings{
 title = {FPGA Implementation of the Morphological Operator Pecstrum for Real Time Image Recognition Applications},
 type = {inproceedings},
 year = {2009},
 websites = {https://www-elec.inaoep.mx/~jmram/cvjmr/FPGA implementation of the morpho2009.pdf},
 id = {7c1d24be-cf7e-3a71-8c1d-4b2ee64df2d1},
 created = {2022-08-29T17:43:26.295Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:52.061Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Morphological image processing is a nonlinear theory and technique to quantitatively describe shape‐oriented operations in a digital image. The morphological operators are described by combinations of a basic set of numerical manipulations between an image A and a small object B, called a structuring element, which can be seen as a probe that scans the image and modifies it according to some specified rule. An important morphological operator is the pattern spectrum or pecstrum, defined through the morphological operations erosion and dilation with the same structuring element applied sequentially. This operator decomposes the target image in morphological components according to the shape and size of the structuring element, providing a quantitative analysis of the morphological content of the image. Although it presents excellent properties as a shape extractor, with invariance to translation and rotation, pecstrum has not been extensively used because it might be computationally intensive in some applications, however, the available current hardware resources overcome this disadvantage. In this work, an implementation of the pecstrum operator using a FPGA NexysII, Xilinx, Spartan 3E, is presented. The system is currently under test in some projects based on real time image recognition, such as biometric and cytology applications.},
 bibtype = {inproceedings},
 author = {Cortes, Juan Manuel Ramirez and Carballido, Jorge Martinez and Alarcon-Aquino, V and Moreno, Miguel Angel and Cedeno, Emmanuel Morales Flores},
 booktitle = {SOMI XXIV}
}

Morphological image processing is a nonlinear theory and technique to quantitatively describe shape‐oriented operations in a digital image. The morphological operators are described by combinations of a basic set of numerical manipulations between an image A and a small object B, called a structuring element, which can be seen as a probe that scans the image and modifies it according to some specified rule. An important morphological operator is the pattern spectrum or pecstrum, defined through the morphological operations erosion and dilation with the same structuring element applied sequentially. This operator decomposes the target image in morphological components according to the shape and size of the structuring element, providing a quantitative analysis of the morphological content of the image. Although it presents excellent properties as a shape extractor, with invariance to translation and rotation, pecstrum has not been extensively used because it might be computationally intensive in some applications, however, the available current hardware resources overcome this disadvantage. In this work, an implementation of the pecstrum operator using a FPGA NexysII, Xilinx, Spartan 3E, is presented. The system is currently under test in some projects based on real time image recognition, such as biometric and cytology applications.

@article{
 title = {A Knowledge-based Framework for Analysis of Facial Expressions Using FACS and MPEG-4 Standards},
 type = {article},
 year = {2009},
 pages = {251-256},
 volume = {15},
 websites = {http://www.scielo.org.mx/pdf/cys/v15n2/v15n2a4.pdf},
 publisher = {Computación y Sistemas},
 id = {0bdf230c-9ae3-31a5-87a0-2d01f6c69d91},
 created = {2022-08-29T17:43:27.516Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:27.516Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non-prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs - FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.},
 bibtype = {article},
 author = {Contreras, R and Starostenko, O and Alarcon-Aquino, V},
 journal = {Computación y Sistemas},
 number = {2}
}

In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non-prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs - FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.

@inproceedings{
 title = {Mammographic image analysis for breast cancer detection using complex wavelet transforms and morphological operators},
 type = {inproceedings},
 year = {2009},
 keywords = {Breast cancer,Dual-tree complex wavelet transforms,Mammography,Microcalcifications,Wavelets},
 websites = {https://www.scitepress.org/papers/2009/22364/22364.pdf},
 id = {47fb8f93-f88d-3a1c-942a-f7cc70b0f916},
 created = {2022-08-29T17:43:28.152Z},
 accessed = {2024-05-28},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T22:30:09.761Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {This paper presents an approach for early diagnostic of Breast Cancer using the dual-tree complex wavelet transform (DT-CWT), which detect micro-calcifications in digital mammograms. The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. In order to maximize the detection a morphological conversion is proposed and applied to the high-frequencies subbands of the wavelet transformation. This procedure is applied to a set of digital mammograms from the Mammography Image Analysis Society (MIAS) database. Experimental results show that the proposed denoising algorithm and morphological transformation in combination with the DT-CWT procedure performs better than previous reported approaches.},
 bibtype = {inproceedings},
 author = {Alarcon-Aquino, V. and Starostenko, O. and Rosas-Romero, R. and Rodriguez-Asomoza, J. and Paz-Luna, O.J. and Vazquez-Muñoz, K. and Flores-Pulido, L.},
 booktitle = {SIGMAP 2009 - International Conference on Signal Processing and Multimedia Applications, Proceedings}
}

This paper presents an approach for early diagnostic of Breast Cancer using the dual-tree complex wavelet transform (DT-CWT), which detect micro-calcifications in digital mammograms. The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. In order to maximize the detection a morphological conversion is proposed and applied to the high-frequencies subbands of the wavelet transformation. This procedure is applied to a set of digital mammograms from the Mammography Image Analysis Society (MIAS) database. Experimental results show that the proposed denoising algorithm and morphological transformation in combination with the DT-CWT procedure performs better than previous reported approaches.

@inproceedings{
 title = {Advanced methods for algorithmic corrections of errors in immitance measurement},
 type = {inproceedings},
 year = {2008},
 pages = {1-5},
 websites = {http://ieeexplore.ieee.org/document/4542658/},
 month = {4},
 publisher = {IEEE},
 id = {dfbba9cb-c9a3-3d24-9199-6ba62b43549c},
 created = {2022-08-29T17:43:29.525Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:29.525Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents an analysis of some design concepts and development of advanced techniques for incrementing speed and accuracy of CLR meters of low cost. Some measuring systems have been designed in order to estimate performance of novel approaches, such as a structural method of error correction and iterating correction method of errors in immitance measurement. The expressions of functional conversion (transfer functions) and block diagrams of designed equipment are presented and discussed for these algorithmic methods. Using the best iterating correction method the E7-13a LCR meter has been designed and tested for estimation of its accuracy and speed during measurement of capacitance, inductance, resistance, and conductance. The obtained results are evaluated to define efficiency of proposed methods, their utility and performance. These results encourage more researches in this open problem of immitance measurement. ©2008 IEEE.},
 bibtype = {inproceedings},
 author = {Starostenko, Oleg and Rodriguez-Asomoza, Jorge and Alarcon-Aquino, Vicente},
 doi = {10.1109/ICCDCS.2008.4542658},
 booktitle = {2008 7th International Caribbean Conference on Devices, Circuits and Systems}
}

This paper presents an analysis of some design concepts and development of advanced techniques for incrementing speed and accuracy of CLR meters of low cost. Some measuring systems have been designed in order to estimate performance of novel approaches, such as a structural method of error correction and iterating correction method of errors in immitance measurement. The expressions of functional conversion (transfer functions) and block diagrams of designed equipment are presented and discussed for these algorithmic methods. Using the best iterating correction method the E7-13a LCR meter has been designed and tested for estimation of its accuracy and speed during measurement of capacitance, inductance, resistance, and conductance. The obtained results are evaluated to define efficiency of proposed methods, their utility and performance. These results encourage more researches in this open problem of immitance measurement. ©2008 IEEE.

@article{
 title = {Parameter Adjustment of Active Models for Contour Detection with Application in AFM Images},
 type = {article},
 year = {2008},
 pages = {1247-1262},
 volume = {6},
 websites = {http://www.revista-nanociencia.ece.buap.mx/6nr2/5Rosas y Oleg.doc.pdf},
 id = {f79757a8-2613-349f-a63c-52eba675ff8e},
 created = {2022-08-29T17:43:30.141Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:30.141Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {In the area of digital image processing, active models or snakes are mainly used for detection of object contours such as those in AFM images (Atomic Force Microscope), with certain characteristics defined by the user using prior knowledge. This information is utilized for object segmentation, object tracking, object recognition and other tasks. In this report an analysis of the main characteristics of three parametric active models (Kass, Cohen and Xu) is done. A comparative table is shown to help the user to define which could be the best model according to the application. Finally an experimental design is used to adjust the parameters of the models to guarantee a desired out put. Due to the fact that under some environments some active contour models can be recognized as being the most suitable for application, the relation among the three most referenced parametric active contour models and the selection of parameter values for each model is required for complex applications. Parameter selection is a general problem that is continuously commented in the references of this paper, and that is why a new alternative was developed to find the best parameters by means of experimentation},
 bibtype = {article},
 author = {Hwang, J N and Torres-Tello, M A and Rosas-Romero, R and Starostenko, O and Alarcon-Aquino, V and Rodriguez-Asomoza, J},
 journal = {Journal Nanociencia et Moletronica},
 number = {2}
}

In the area of digital image processing, active models or snakes are mainly used for detection of object contours such as those in AFM images (Atomic Force Microscope), with certain characteristics defined by the user using prior knowledge. This information is utilized for object segmentation, object tracking, object recognition and other tasks. In this report an analysis of the main characteristics of three parametric active models (Kass, Cohen and Xu) is done. A comparative table is shown to help the user to define which could be the best model according to the application. Finally an experimental design is used to adjust the parameters of the models to guarantee a desired out put. Due to the fact that under some environments some active contour models can be recognized as being the most suitable for application, the relation among the three most referenced parametric active contour models and the selection of parameter values for each model is required for complex applications. Parameter selection is a general problem that is continuously commented in the references of this paper, and that is why a new alternative was developed to find the best parameters by means of experimentation

@article{
 title = {Design and Implementation of a Security Layer for RFID Systems},
 type = {article},
 year = {2008},
 pages = {69-82},
 volume = {6},
 websites = {http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232008000200001},
 id = {d5423b2a-dded-3af8-b074-52634a2f00c5},
 created = {2022-08-29T17:43:30.747Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:30.747Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {RFID (Radio Frequency Identification) is a technology whose employment will certainly grow in the following years. It is therefore necessary to consider the security issues that come out from the implementation of that type of systems. In this paper we present an approach to solve the security problems in RFID systems by designing a naive security layer based on authentication and encryption algorithms. The authentication mechanism is the mutual authentication based on a three-way handshaking model, which authenticates both the reader and the tag in the communication protocol. The cipher algorithm based on a symmetric-key cryptosystem is RC4 implemented in a proposed modification to the existing WEP protocol to make it more secure in terms of message privacy. The proposed approach is implemented using VHDL in FPGAs communicated through RF transceivers. The results show that the security layer is simple enough to be implemented in a low-price RFID tag.},
 bibtype = {article},
 author = {Alarcon-Aquino, V and Dominguez-Jimenez, M and Ohms, C},
 journal = {Journal of applied research and technology},
 number = {2}
}

RFID (Radio Frequency Identification) is a technology whose employment will certainly grow in the following years. It is therefore necessary to consider the security issues that come out from the implementation of that type of systems. In this paper we present an approach to solve the security problems in RFID systems by designing a naive security layer based on authentication and encryption algorithms. The authentication mechanism is the mutual authentication based on a three-way handshaking model, which authenticates both the reader and the tag in the communication protocol. The cipher algorithm based on a symmetric-key cryptosystem is RC4 implemented in a proposed modification to the existing WEP protocol to make it more secure in terms of message privacy. The proposed approach is implemented using VHDL in FPGAs communicated through RF transceivers. The results show that the security layer is simple enough to be implemented in a low-price RFID tag.

@inproceedings{
 title = {An Approach to Lossy Image Compression Using 1-D Wavelet Transforms},
 type = {inproceedings},
 year = {2007},
 websites = {https://www.researchgate.net/publication/228496377_AN_APPROACH_TO_LOSSY_IMAGE_COMPRESSION_USING_1-D_WAVELET_TRANSFORMS},
 id = {d03a265e-8fe4-3017-ad42-cbb939725194},
 created = {2022-08-29T17:43:31.361Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:52.478Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {In this paper, an approach to lossy image compression using 1-D wavelet transforms is proposed. The analyzed image is divided in little sub-images and each one is decomposed in vectors following a fractal Hilbert curve. A Wavelet Transform is thus applied to each vector and high frequency components are suppressed. The Huffman coding algorithm is then applied in order to reduce image weight. The 64-point vector transforms used in this work allow lower computational time rather than conventional 8x8-matrix transforms. Two different percentages of high frequency coefficients in one and two level wavelet transform are suppressed achieving good compression ratios and SNR. Exploiting the properties of human visual system, simulation results show that image distortion is less appreciated in image sizes 512 by 512 pixels and greater. Keywords – Discrete Wavelet Transform (DWT), Hilbert curve, coefficient suppression, Huffman coding.},
 bibtype = {inproceedings},
 author = {Lobato-Morales, Humberto and Paz-Luna, Otto J and Alarcon-Aquino, Vicente},
 booktitle = {ELECTRO}
}

In this paper, an approach to lossy image compression using 1-D wavelet transforms is proposed. The analyzed image is divided in little sub-images and each one is decomposed in vectors following a fractal Hilbert curve. A Wavelet Transform is thus applied to each vector and high frequency components are suppressed. The Huffman coding algorithm is then applied in order to reduce image weight. The 64-point vector transforms used in this work allow lower computational time rather than conventional 8x8-matrix transforms. Two different percentages of high frequency coefficients in one and two level wavelet transform are suppressed achieving good compression ratios and SNR. Exploiting the properties of human visual system, simulation results show that image distortion is less appreciated in image sizes 512 by 512 pixels and greater. Keywords – Discrete Wavelet Transform (DWT), Hilbert curve, coefficient suppression, Huffman coding.

@inproceedings{
 title = {Wavelet-Networks for Prediction of Ozone Levels in Puebla City Mexico},
 type = {inproceedings},
 year = {2007},
 pages = {17-17},
 websites = {http://ieeexplore.ieee.org/document/4127257/},
 month = {2},
 publisher = {IEEE},
 id = {6ad9fc92-55d9-3170-9dcd-baba6b38ddc1},
 created = {2022-08-29T17:43:31.963Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:31.963Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Wavelet-networks are inspired by both the feed forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. In this paper a novel approach, based on a wavelet neural network structure with correlation-based initialisation and training algorithm, is introduced in order to face with the problem of pollutant estimation in a metropolitan area. In particular a short-term prediction of the maximum ozone pollutant value has been performed. Ozone gas is considered one of the most common and damaging air contaminants. The results reported in this work show clearly that wavelet networks have good prediction properties and seriously represent a novel alternative to the traditional ozone forecasting methods.},
 bibtype = {inproceedings},
 author = {Garcia-Trevino, E.S. and Alarcon-Aquino, V. and Herrera-Garcia, M.A.},
 doi = {10.1109/CONIELECOMP.2007.39},
 booktitle = {17th International Conference on Electronics, Communications and Computers (CONIELECOMP'07)}
}

Wavelet-networks are inspired by both the feed forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. In this paper a novel approach, based on a wavelet neural network structure with correlation-based initialisation and training algorithm, is introduced in order to face with the problem of pollutant estimation in a metropolitan area. In particular a short-term prediction of the maximum ozone pollutant value has been performed. Ozone gas is considered one of the most common and damaging air contaminants. The results reported in this work show clearly that wavelet networks have good prediction properties and seriously represent a novel alternative to the traditional ozone forecasting methods.

@article{
 title = {Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction},
 type = {article},
 year = {2006},
 keywords = {Finite-impulse-response (FIR) neural networks,Multiresolution learning,Network traffic prediction,Wavelet transforms,Wavelets},
 pages = {208-220},
 volume = {36},
 websites = {https://spiral.imperial.ac.uk/handle/10044/1/772},
 month = {3},
 id = {f63a7c0a-585e-3517-b50f-78ef8b5df2e4},
 created = {2022-08-29T17:43:32.567Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:32.567Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translation-invariant property of the MODWT allows aligment of events in a multiresolution analysis with respect to the original time series and, therefore, preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm. © 2006 IEEE.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Barria, J.A.},
 doi = {10.1109/TSMCC.2004.843217},
 journal = {IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)},
 number = {2}
}

In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translation-invariant property of the MODWT allows aligment of events in a multiresolution analysis with respect to the original time series and, therefore, preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm. © 2006 IEEE.

@inproceedings{
 title = {Satellite-Indoor Mobile Communications Path Propagation Losses},
 type = {inproceedings},
 year = {2006},
 pages = {4-4},
 volume = {2006},
 websites = {http://ieeexplore.ieee.org/document/1604700/},
 publisher = {IEEE},
 id = {17138c2f-de07-335e-96e1-ed510af419b7},
 created = {2022-08-29T17:43:33.187Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:33.187Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {A software that calculates satellite indoor loss path for three different systems of mobile satellite communications is presented. The systems are GLOBALSTAR, IRIDIUM and a third option for a GENERAL system in which the user have the possibility of change some parameters of the calculations. The user can choose from 5 different indoor propagation models. The software was developed using Visual Basic. © 2006 IEEE.},
 bibtype = {inproceedings},
 author = {Campos, D. and Guerrero-Ojeda, Luis-G. and Alarcon-Aquino, V. and Baez-Lopez, D.},
 doi = {10.1109/CONIELECOMP.2006.51},
 booktitle = {16th International Conference on Electronics, Communications and Computers (CONIELECOMP'06)}
}

A software that calculates satellite indoor loss path for three different systems of mobile satellite communications is presented. The systems are GLOBALSTAR, IRIDIUM and a third option for a GENERAL system in which the user have the possibility of change some parameters of the calculations. The user can choose from 5 different indoor propagation models. The software was developed using Visual Basic. © 2006 IEEE.

@inproceedings{
 title = {A Hierarchical Approach for Modelling an MPLS Network Using VHDL},
 type = {inproceedings},
 year = {2006},
 pages = {29-29},
 websites = {http://ieeexplore.ieee.org/document/1604725/},
 publisher = {IEEE},
 id = {68be539a-0310-3b49-8817-1ec6cbbf84ec},
 created = {2022-08-29T17:43:33.814Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:33.814Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents a hierarchical approach for modelling an MPLS (Multi-Protocol Label Switching) network using VHDL (Very high-speed integrated circuits Hardware Description Language). The MPLS technology is used because it offers a better performance and flexibility than IP routing. Six MPLS switches are modelled and simulated as follows. A label is assigned to the IP packet header by the first switch, which indicates the route that the frame must follow in the MPLS network. This label is then used by the MPLS middle switches for its management inside the network. Four middle switches change the label depending on the destination of the packet. The final switch removes the label of the IP packet header and the frame continues its path outside the MPLS network. Simulation results show that this approach allows the simulation of large networks consisting of even 256 MPLS switches.},
 bibtype = {inproceedings},
 author = {Minero-Munoz, Marcelino and Alarcon-Aquino, V},
 doi = {10.1109/CONIELECOMP.2006.7},
 booktitle = {16th International Conference on Electronics, Communications and Computers (CONIELECOMP'06)}
}

This paper presents a hierarchical approach for modelling an MPLS (Multi-Protocol Label Switching) network using VHDL (Very high-speed integrated circuits Hardware Description Language). The MPLS technology is used because it offers a better performance and flexibility than IP routing. Six MPLS switches are modelled and simulated as follows. A label is assigned to the IP packet header by the first switch, which indicates the route that the frame must follow in the MPLS network. This label is then used by the MPLS middle switches for its management inside the network. Four middle switches change the label depending on the destination of the packet. The final switch removes the label of the IP packet header and the frame continues its path outside the MPLS network. Simulation results show that this approach allows the simulation of large networks consisting of even 256 MPLS switches.

@inproceedings{
 title = {Single-Step Prediction of Chaotic Time Series Using Wavelet-Networks},
 type = {inproceedings},
 year = {2006},
 pages = {243-248},
 volume = {1},
 websites = {https://ieeexplore.ieee.org/document/4019745/},
 month = {9},
 publisher = {IEEE},
 id = {14561ed4-6d36-3340-a41e-5b6965fa855a},
 created = {2022-08-29T17:43:34.998Z},
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 read = {false},
 starred = {false},
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 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents a wavelet neural-network for chaotic time series prediction. Wavelet-networks are inspired by both the feed-forward neural network and the theory underlying wavelet decompositions. Wavelet-networks are a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feedforward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet-networks have better prediction properties than its similar back-propagation networks.},
 bibtype = {inproceedings},
 author = {Garcia-trevino, E. S. and Alarcon-Aquino, V.},
 doi = {10.1109/CERMA.2006.86},
 booktitle = {Electronics, Robotics and Automotive Mechanics Conference (CERMA'06)}
}

This paper presents a wavelet neural-network for chaotic time series prediction. Wavelet-networks are inspired by both the feed-forward neural network and the theory underlying wavelet decompositions. Wavelet-networks are a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feedforward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet-networks have better prediction properties than its similar back-propagation networks.

@inproceedings{
 title = {Chaotic Time Series Approximation Using Iterative Wavelet-Networks},
 type = {inproceedings},
 year = {2006},
 pages = {19-19},
 websites = {http://ieeexplore.ieee.org/document/1604715/},
 publisher = {IEEE},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks.. This kind of network uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feed-forward networks trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than its similar backpropagation networks.},
 bibtype = {inproceedings},
 author = {Garcia-Trevino, E.S. and Alarcon-Aquino, V.},
 doi = {10.1109/CONIELECOMP.2006.21},
 booktitle = {16th International Conference on Electronics, Communications and Computers (CONIELECOMP'06)}
}

This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks.. This kind of network uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feed-forward networks trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than its similar backpropagation networks.

@book{
 title = {Introduccion a Redes MPLS},
 type = {book},
 year = {2006},
 pages = {180},
 websites = {http://www.e-libro.net/libros/libro.aspx?idlibro=2043},
 publisher = {El Cid Editor},
 id = {6a5427ed-8fca-38c5-86b4-2bc5167a8ca0},
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 confirmed = {true},
 hidden = {false},
 source_type = {BOOK},
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 abstract = {Investigadores mexicanos expertos en redes presentan este riguroso trabajo que ofrece el estado del arte de la tecnología MPLS y procesos de simulación, concebido y diseñado, además, para servir como libro de texto para los estudiantes de ingeniería y de áreas afines, así como para ingenieros, científicos y profesionales interesados en el tema.},
 bibtype = {book},
 author = {Alarcon-Aquino, V and Martinez-Suarez, J C}
}

Investigadores mexicanos expertos en redes presentan este riguroso trabajo que ofrece el estado del arte de la tecnología MPLS y procesos de simulación, concebido y diseñado, además, para servir como libro de texto para los estudiantes de ingeniería y de áreas afines, así como para ingenieros, científicos y profesionales interesados en el tema.

@inproceedings{
 title = {Improving Wavelet-Networks Performance with a New Correlation-based Initialisation Method and Training Algorithm},
 type = {inproceedings},
 year = {2006},
 pages = {11-17},
 websites = {http://ieeexplore.ieee.org/document/4023781/},
 month = {11},
 publisher = {IEEE},
 id = {c3b2e021-1143-3edb-888d-3a27a3f12e3c},
 created = {2022-08-29T17:43:36.776Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:36.776Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Wavelet-networks are inspired by both the feedforward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets, but unfortunately, most of powerful and used wavelets do not satisfy this property. This paper presents a new initialisation procedure and a new training algorithm for wavelet neural-networks that improve its performance allowing the use of different kind of wavelets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet-network.},
 bibtype = {inproceedings},
 author = {Garcia-trevino, Edgar and Alarcon-Aquino, Vicente and Ramirez-cruz, Jose},
 doi = {10.1109/CIC.2006.41},
 booktitle = {2006 15th International Conference on Computing}
}

Wavelet-networks are inspired by both the feedforward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets, but unfortunately, most of powerful and used wavelets do not satisfy this property. This paper presents a new initialisation procedure and a new training algorithm for wavelet neural-networks that improve its performance allowing the use of different kind of wavelets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet-network.

@inproceedings{
 title = {Instance Selection and Feature Weighting Using Evolutionary Algorithms},
 type = {inproceedings},
 year = {2006},
 pages = {73-79},
 websites = {http://ieeexplore.ieee.org/document/4023791/},
 month = {11},
 publisher = {IEEE},
 id = {9d25b946-cb70-358e-972c-8796b7984187},
 created = {2022-08-29T17:43:37.369Z},
 file_attached = {false},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
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 abstract = {Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a genetic algorithm (GA) and evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier.},
 bibtype = {inproceedings},
 author = {Ramirez-Cruz, Jose-Federico and Fuentes, Olac and Alarcon-Aquino, Vicente and Garcia-Banuelos, Luciano},
 doi = {10.1109/CIC.2006.42},
 booktitle = {2006 15th International Conference on Computing}
}

Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a genetic algorithm (GA) and evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier.

@inbook{
 type = {inbook},
 year = {2006},
 pages = {187-196},
 websites = {http://link.springer.com/10.1007/1-84628-352-3_19},
 publisher = {Springer London},
 city = {London},
 id = {21616319-e7a6-37b4-b87d-9fe84ce46451},
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 abstract = {In this paper, we present an approach for detecting and classifying attacks in computer networks by using neural networks. Specifically, a design of an intruder detection system is presented to protect the hypertext transfer protocol (HTTP). We propose the use of an application-based model using neural networks to model properly non-linear data. The benefit of this perspective is to work directly on the causes of an attack, which are determined directly by the commands used in the protected application. The intruder detection system is designed by defining three different neural networks, which include two multi-layer feed-forward networks and the Elman recurrent network. The results reported in this paper show that the Elman recurrent network achieved a performance around ninety percent of good detection, which demonstrates the reliability of the designed system to detect and classify attacks in high-level network protocols.},
 bibtype = {inbook},
 author = {Alarcon-Aquino, V. and Mejia-Sanchez, J. A. and Rosas-Romero, R. and Ramirez-Cruz, J. F.},
 doi = {10.1007/1-84628-352-3_19},
 chapter = {Detecting and Classifying Attacks in Computer Networks Using Feed-Forward and Elman Neural Networks},
 title = {EC2ND 2005}
}

In this paper, we present an approach for detecting and classifying attacks in computer networks by using neural networks. Specifically, a design of an intruder detection system is presented to protect the hypertext transfer protocol (HTTP). We propose the use of an application-based model using neural networks to model properly non-linear data. The benefit of this perspective is to work directly on the causes of an attack, which are determined directly by the commands used in the protected application. The intruder detection system is designed by defining three different neural networks, which include two multi-layer feed-forward networks and the Elman recurrent network. The results reported in this paper show that the Elman recurrent network achieved a performance around ninety percent of good detection, which demonstrates the reliability of the designed system to detect and classify attacks in high-level network protocols.

@inproceedings{
 title = {Proceedings 10th IEEE International Power Electronics Congress, CIEP 2006: Preface},
 type = {inproceedings},
 year = {2006},
 id = {d6e4684b-a102-3bc7-badf-411e5f16e8fd},
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 bibtype = {inproceedings},
 author = {Alarcon-Aquino, V.},
 doi = {10.1109/CIEP.2006.312140},
 booktitle = {International Power Electronics Congress - CIEP}
}

@inproceedings{
 title = {Learning and Approximation of Chaotic Time Series Using Wavelet-Networks},
 type = {inproceedings},
 year = {2005},
 pages = {182-188},
 volume = {2005},
 websites = {http://ieeexplore.ieee.org/document/1592217/},
 publisher = {IEEE},
 id = {82f1544b-1870-3807-96b7-b72fa6cf8797},
 created = {2022-08-29T17:43:39.255Z},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet networks are a class of neural network that take advantage of good localization and approximation properties of multiresolution analysis. These networks use wavelets as activation functions in the hidden layer and a hierarchical method is used for learning. Comparisons are made between a wavelet network, tested with two different wavelets, and the typical feed-forward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than back-propagation networks. © 2005 IEEE.},
 bibtype = {inproceedings},
 author = {Alarcon-Aquino, V. and Garcia-Trevino, E.S. and Rosas-Romero, R. and Ramirez-Cruz, J.F},
 doi = {10.1109/ENC.2005.27},
 booktitle = {Sixth Mexican International Conference on Computer Science (ENC'05)}
}

This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet networks are a class of neural network that take advantage of good localization and approximation properties of multiresolution analysis. These networks use wavelets as activation functions in the hidden layer and a hierarchical method is used for learning. Comparisons are made between a wavelet network, tested with two different wavelets, and the typical feed-forward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than back-propagation networks. © 2005 IEEE.

@inproceedings{
 title = {Estimation of stable all-pass transfer functions for delay equalization based on least-squares minimization and wavelet transform},
 type = {inproceedings},
 year = {2005},
 keywords = {All-pass filter,Delay equalization,Phase response,Stability,Wavelet transform},
 pages = {1835-1838 Vol. 2},
 volume = {2005},
 websites = {http://ieeexplore.ieee.org/document/1594480/},
 publisher = {IEEE},
 id = {d9917bb8-dd73-3dc7-96fc-0d922cebb506},
 created = {2022-08-29T17:43:40.320Z},
 file_attached = {false},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present a generalized optimal stable all-pass digital filter design algorithm that supports (1) arbitrary phase response specifications, (2) a corrective system designed to make the phase delay of a magnitude-filter substantially constant over a desired frequency range, (3) estimation of the delay δ of the best performed equalizer, (4) stability, and (5) wavelet-based non-uniform sampling of the phase response of the magnitude-filter whose delay is to be equalized. © 2005 IEEE.},
 bibtype = {inproceedings},
 author = {Rosas-Romero, R. and Rodrfguez-Asomoza, J. and Alarcon-Aquino, V. and Perez-Loyola, A.},
 doi = {10.1109/MWSCAS.2005.1594480},
 booktitle = {48th Midwest Symposium on Circuits and Systems, 2005.}
}

In this paper, we present a generalized optimal stable all-pass digital filter design algorithm that supports (1) arbitrary phase response specifications, (2) a corrective system designed to make the phase delay of a magnitude-filter substantially constant over a desired frequency range, (3) estimation of the delay δ of the best performed equalizer, (4) stability, and (5) wavelet-based non-uniform sampling of the phase response of the magnitude-filter whose delay is to be equalized. © 2005 IEEE.

@inproceedings{
 title = {A Comparative Simulation Study of Wavelet Based Denoising Algorithms},
 type = {inproceedings},
 year = {2005},
 pages = {125-130},
 volume = {2005},
 websites = {http://ieeexplore.ieee.org/document/1488547/},
 publisher = {IEEE},
 id = {78a62eb9-5f30-3a48-bc9d-4d2fb43e22c1},
 created = {2022-08-29T17:43:41.173Z},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper we present a comparative simulation study of three denoising algorithms using wavelets. The denoising algorithms (i. e., universal threshold, minimax threshold and rigorous SURE threshold) have been used to remove white Gaussian noise from synthetic and real signals. The analysis is done by applying soft and hard thresholds to signals with different sample sizes. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the rigorous SURE algorithm with a hard threshold has a better performance than other algorithms in synthetic signals. On the other hand, the universal threshold algorithm with a soft threshold shows the best performance in real signals when using the Daubechies wavelet with 5 vanishing moments. © 2005 IEEE.},
 bibtype = {inproceedings},
 author = {Rosas-Orea, M.C.E. and Hernandez-Diaz, M. and Alarcon-Aquino, V. and Guerrero-Ojeda, L.G.},
 doi = {10.1109/CONIEL.2005.6},
 booktitle = {15th International Conference on Electronics, Communications and Computers (CONIELECOMP'05)}
}

In this paper we present a comparative simulation study of three denoising algorithms using wavelets. The denoising algorithms (i. e., universal threshold, minimax threshold and rigorous SURE threshold) have been used to remove white Gaussian noise from synthetic and real signals. The analysis is done by applying soft and hard thresholds to signals with different sample sizes. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the rigorous SURE algorithm with a hard threshold has a better performance than other algorithms in synthetic signals. On the other hand, the universal threshold algorithm with a soft threshold shows the best performance in real signals when using the Daubechies wavelet with 5 vanishing moments. © 2005 IEEE.

@article{
 title = {Análisis de Tráfico Auto-similar en Redes de Comunicaciones Usando Onditas (Wavelets)},
 type = {article},
 year = {2005},
 pages = {61-66},
 volume = {16},
 websites = {http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-07642005000200010&lng=en&nrm=iso&tlng=en},
 id = {9783e08c-cb67-3695-8bec-62a97d7bb0ba},
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 source_type = {JOUR},
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 abstract = {This paper presents an analysis of self-similar (fractal) traffic in communication networks using wavelets. The aim of this work is to show the efficiency of the wavelet-based method for the analysis of self-similar processes, which posses similar statistical features over a long range of time scales. A comparative analysis of the estimated Hurst parameter using Daubechies wavelets in Ethernet and Internet traffic, are presented. The results reported in this paper show that the behavior of Ethernet traffic is asymptotically self-similar, while the Internet traffic shows behavior approximating short-range dependence. These results indicate that traffic models based on a self-similar nature are more suitable for modeling Ethernet traffic, while Poisson models may be used to model Internet traffic.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Guerrero-Ojeda, L.G. and Rodríguez-Asomoza, J. and Rosas-Romero, R.},
 doi = {10.4067/S0718-07642005000200010},
 journal = {Información tecnológica},
 number = {2}
}

This paper presents an analysis of self-similar (fractal) traffic in communication networks using wavelets. The aim of this work is to show the efficiency of the wavelet-based method for the analysis of self-similar processes, which posses similar statistical features over a long range of time scales. A comparative analysis of the estimated Hurst parameter using Daubechies wavelets in Ethernet and Internet traffic, are presented. The results reported in this paper show that the behavior of Ethernet traffic is asymptotically self-similar, while the Internet traffic shows behavior approximating short-range dependence. These results indicate that traffic models based on a self-similar nature are more suitable for modeling Ethernet traffic, while Poisson models may be used to model Internet traffic.

@inproceedings{
 title = {Simulation of an MPLS Network Using VHDL},
 type = {inproceedings},
 year = {2005},
 id = {d3d46220-44ae-3364-9b14-e61da65383c7},
 created = {2022-08-29T17:43:42.368Z},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:52.904Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {This paper presents a simulation of an MPLS (Multi-Protocol Label Switching) network using VHDL (Very high speed integrated circuits Hardware Description Language). Three MPLS switches are modelled and simulated as follows. The first switch assigns a label to the IP packet header, which will be used by the MPLS switches for its management inside the network. The middle switch changes the label depending on the destination of the packet. The final switch removes the label to the IP packet header and continues its path outside the MPLS network. The MPLS network is simulated using VHDL because of the simplicity and universality of its code. Simulation results show that network switching in MPLS networks may reduce congestion problems in IP networks.},
 bibtype = {inproceedings},
 author = {Minero-Munoz, M and Morales-Hernandez, O E and Perez-Loyola, A A and Vazquez-Munoz, K and Alarcon-Aquino, V},
 booktitle = {5th WSEAS Int. Conf. on Information Science, Communications and Applications (ISCA 2005)}
}

This paper presents a simulation of an MPLS (Multi-Protocol Label Switching) network using VHDL (Very high speed integrated circuits Hardware Description Language). Three MPLS switches are modelled and simulated as follows. The first switch assigns a label to the IP packet header, which will be used by the MPLS switches for its management inside the network. The middle switch changes the label depending on the destination of the packet. The final switch removes the label to the IP packet header and continues its path outside the MPLS network. The MPLS network is simulated using VHDL because of the simplicity and universality of its code. Simulation results show that network switching in MPLS networks may reduce congestion problems in IP networks.

@inproceedings{
 title = {Simulator of WCDMA Procedures for Educational Purposes},
 type = {inproceedings},
 year = {2005},
 pages = {16-20},
 websites = {http://ieeexplore.ieee.org/document/1488528/},
 publisher = {IEEE},
 id = {99bc7579-da72-33dc-b8f4-7fce0ccf3e05},
 created = {2022-08-29T17:43:43.068Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:52:06.746Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Patron, D.F. and Ojeda, L.G.G. and Lopez, D.B. and Alarcon-Aquino, V.},
 doi = {10.1109/CONIEL.2005.64},
 booktitle = {15th International Conference on Electronics, Communications and Computers (CONIELECOMP'05)}
}

@article{
 title = {Wavelet-Network based on L1-Norm minimisation for learning chaotic time series},
 type = {article},
 year = {2005},
 pages = {211-221},
 volume = {3},
 websites = {http://www.scielo.org.mx/pdf/jart/v3n3/v3n3a5.pdf},
 id = {51d3fa9a-2387-39f9-a7ae-ac089f80e13c},
 created = {2022-08-29T17:43:43.712Z},
 accessed = {2021-10-23},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:43.712Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series. The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in the hidden layer of the wavelet-network. We propose using the L1-norm, as opposed to the L2-norm, due to the well-known fact that the L1-norm is superior to the L2-norm criterion when the signal has heavy tailed distributions or outliers. A comparison of the proposed approach with previous reported schemes using a time series benchmark is presented. Simulation results show that the proposed wavelet-network based on the L1-norm performs better than the standard back-propagation network and the wavelet-network based on the traditional L2-norm when applied to synthetic data.},
 bibtype = {article},
 author = {Alarcon-Aquino, V and Garcia-Treviño, E S and Rosas-Romero, R and Ramirez-Cruz, J F and Guerrero-Ojeda, L G and Rodriguez-Asomoza, J},
 journal = {Journal of Applied Research and Technology},
 number = {03}
}

This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series. The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in the hidden layer of the wavelet-network. We propose using the L1-norm, as opposed to the L2-norm, due to the well-known fact that the L1-norm is superior to the L2-norm criterion when the signal has heavy tailed distributions or outliers. A comparison of the proposed approach with previous reported schemes using a time series benchmark is presented. Simulation results show that the proposed wavelet-network based on the L1-norm performs better than the standard back-propagation network and the wavelet-network based on the traditional L2-norm when applied to synthetic data.

@article{
 title = {Analysis of self-similar traffic in communication networks using wavelets},
 type = {article},
 year = {2005},
 keywords = {Discrete wavelet transform,Hurst parameter,Self-similar processes,Wavelets},
 volume = {16},
 id = {a67261d3-ca3e-32f8-a6ef-dbb2355f21d0},
 created = {2022-08-29T17:43:44.333Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:58:22.970Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {This paper presents an analysis of self-similar (fractal) traffic in communication networks using wavelets. The aim of this work is to show the efficiency of the wavelet-based method for the analysis of self-similar processes, which posses similar statistical features over a long range of time scales. A comparative analysis of the estimated Hurst parameter using Daubechies wavelets in Ethernet and Internet traffic, are presented. The results reported in this paper show that the behavior of Ethernet traffic is asymptotically self-similar, while the Internet traffic shows behavior approximating short-range dependence. These results indicate that traffic models based on a self-similar nature are more suitable for modeling Ethernet traffic, while Poisson models may be used to model Internet traffic.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Guerrero-Ojeda, L.G. and Rodríguez-Asomoza, J. and Rosas-Romero, R.},
 journal = {Informacion Tecnologica},
 number = {2}
}

This paper presents an analysis of self-similar (fractal) traffic in communication networks using wavelets. The aim of this work is to show the efficiency of the wavelet-based method for the analysis of self-similar processes, which posses similar statistical features over a long range of time scales. A comparative analysis of the estimated Hurst parameter using Daubechies wavelets in Ethernet and Internet traffic, are presented. The results reported in this paper show that the behavior of Ethernet traffic is asymptotically self-similar, while the Internet traffic shows behavior approximating short-range dependence. These results indicate that traffic models based on a self-similar nature are more suitable for modeling Ethernet traffic, while Poisson models may be used to model Internet traffic.

@inproceedings{
 title = {Multi-modal medical image registration based on non-rigid transformations and feature point extraction by using wavelets},
 type = {inproceedings},
 year = {2004},
 keywords = {IEEE Keywords},
 pages = {763-766},
 volume = {1},
 websites = {http://ieeexplore.ieee.org/document/1351158/},
 publisher = {IEEE},
 id = {392a05c4-814f-3b45-b5f3-5474b4e053c3},
 created = {2022-08-29T17:43:44.942Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:51:54.688Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In order to correctly match two sets of images from different modalities, our method applies a non-rigid transformation to one set to get as close as possible to the other. This requires the estimation of the optimal similarity transformation between the two sets of images. Estimation of the non-rigid deformation between two sets of images is referred to as the deformation estimation between the pair of three-dimensional objects extracted from both sets. We present a new methodology for image registration by first extracting objects from the sets of images by reconstructing the object surfaces where this extraction supports semi-automatic segmentation of sets of 3-D medical images and then finding the best similarity transformation based on matching the two extracted 3-D surfaces by minimizing the differences between them. Our approach is not only based on the matching of two sets of surface points, but also incorporates the matching of two sets of feature points, and we have shown that deformation estimates based on simultaneous matching of surfaces and features are more accurate than those based on surface matching alone. This is especially true when the deformation involves physically realistic cases, such as those in human organs. Our technique uses Free-Form Deformation Models and applies the Wavelet Transform to extract feature points in the 3D space. Feature point extraction also provides a means to compute the error in our estimates. We have applied our method to register sequences of MRI images to histology images of the carotid artery.},
 bibtype = {inproceedings},
 author = {Rosas-Romero, R. and Rodriguez-Asomoza, J. and Alarcon-Aquino, V. and Baez-Lopez, D.},
 doi = {10.1109/IMTC.2004.1351158},
 booktitle = {Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)}
}

In order to correctly match two sets of images from different modalities, our method applies a non-rigid transformation to one set to get as close as possible to the other. This requires the estimation of the optimal similarity transformation between the two sets of images. Estimation of the non-rigid deformation between two sets of images is referred to as the deformation estimation between the pair of three-dimensional objects extracted from both sets. We present a new methodology for image registration by first extracting objects from the sets of images by reconstructing the object surfaces where this extraction supports semi-automatic segmentation of sets of 3-D medical images and then finding the best similarity transformation based on matching the two extracted 3-D surfaces by minimizing the differences between them. Our approach is not only based on the matching of two sets of surface points, but also incorporates the matching of two sets of feature points, and we have shown that deformation estimates based on simultaneous matching of surfaces and features are more accurate than those based on surface matching alone. This is especially true when the deformation involves physically realistic cases, such as those in human organs. Our technique uses Free-Form Deformation Models and applies the Wavelet Transform to extract feature points in the 3D space. Feature point extraction also provides a means to compute the error in our estimates. We have applied our method to register sequences of MRI images to histology images of the carotid artery.

@article{
 title = {MPLS/IP analysis and simulation for the implementation of path restoration schemes.},
 type = {article},
 year = {2004},
 pages = {1911-1916},
 volume = {3},
 websites = {https://dl.acm.org/doi/abs/10.5555/1374195.1374232},
 id = {32a77894-5641-3c18-a55b-b0da5e03585b},
 created = {2022-08-29T17:43:45.569Z},
 accessed = {2021-10-23},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:53.390Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Traditionally, IP (Internet Protocol) has been routed over ATM (Asynchronous Transfer Mode) via Virtual Circuits (VCs) or Multi-Protocol over ATM (MPOA). These methods proved to have a complicated operation mode, reason why the necessity for a simpler method was felt. All these necessities can be supported by the Multi-Protocol Label Switching (MPLS) since it integrates the most important characteristics of layers 2 and 3 of the OSI model. This paper explains and analyzes the concepts of MPLS, as well as performing a simulation and comparison of path restoration schemes using Network Simulator (NS). The results show the advantages of working over a connection-oriented technology (MPLS) by supporting a more efficient rerouting scheme for link failures.},
 bibtype = {article},
 author = {Alarcon-Aquino, V and Takahashi-Iturriaga, Y L and Martinez-Suarez, J C and Guerrero-Ojeda, L G},
 journal = {WSEAS Transactions on Computers},
 number = {6}
}

Traditionally, IP (Internet Protocol) has been routed over ATM (Asynchronous Transfer Mode) via Virtual Circuits (VCs) or Multi-Protocol over ATM (MPOA). These methods proved to have a complicated operation mode, reason why the necessity for a simpler method was felt. All these necessities can be supported by the Multi-Protocol Label Switching (MPLS) since it integrates the most important characteristics of layers 2 and 3 of the OSI model. This paper explains and analyzes the concepts of MPLS, as well as performing a simulation and comparison of path restoration schemes using Network Simulator (NS). The results show the advantages of working over a connection-oriented technology (MPLS) by supporting a more efficient rerouting scheme for link failures.

@inproceedings{
 title = {A multiservice architecture for dynamic bandwidth allocation and traffic engineering applications},
 type = {inproceedings},
 year = {2004},
 pages = {138-143},
 websites = {http://ieeexplore.ieee.org/document/1269562/},
 publisher = {IEEE},
 id = {64af564c-2b0d-3200-ae37-a35aef41fe3f},
 created = {2022-08-29T17:43:46.157Z},
 accessed = {2021-10-23},
 file_attached = {true},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:51:45.276Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {The paper analyzes bandwidth control techniques in order to propose an architecture to improve bandwidth usage. A network architecture employs several techniques in order to manage, predict and handle traffic issues due to Ethernet's best effort delivery system. Bandwidth allocation, traffic engineering and a resulting post analysis architecture are presented in that order. Ethernet's resources are dissected in order to study each component separately and present a traffic-effective architecture at each hierarchical level. The analysis indicates that, by improving each topology hierarchical level, network performance is increased globally.},
 bibtype = {inproceedings},
 author = {Takahashi-Iturriaga, Y.L. and Martinez, J. and Alarcon-Aquino, V.},
 doi = {10.1109/ICECC.2004.1269562},
 booktitle = {14th International Conference on Electronics, Communications and Computers, 2004. CONIELECOMP 2004.}
}

The paper analyzes bandwidth control techniques in order to propose an architecture to improve bandwidth usage. A network architecture employs several techniques in order to manage, predict and handle traffic issues due to Ethernet's best effort delivery system. Bandwidth allocation, traffic engineering and a resulting post analysis architecture are presented in that order. Ethernet's resources are dissected in order to study each component separately and present a traffic-effective architecture at each hierarchical level. The analysis indicates that, by improving each topology hierarchical level, network performance is increased globally.

@inproceedings{
 title = {Adaptive Sequential Segmentation of Non-stationary Signals Using Wavelet Transforms},
 type = {inproceedings},
 year = {2003},
 city = {Buenos Aires},
 id = {a4e27321-cf81-3f45-8851-67d1c2add8da},
 created = {2022-08-29T17:43:46.777Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T17:59:07.673Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {The problem of adaptive segmentation of time series with abrupt and smooth changes in the spectral characteristics is addressed. In this paper, an algorithm for adaptive sequential segmentation of time series is proposed based on the maximal overlap discrete wavelet transform and Bayesian analysis. The proposed algorithm is evaluated using synthetic data. The results demonstrate the good detection properties of the proposed algorithm when compared with previous reported algorithms.},
 bibtype = {inproceedings},
 author = {Alarcon-Aquino, V. and Guerrero, L.G. and Takahashi, Y},
 booktitle = {INMAT}
}

The problem of adaptive segmentation of time series with abrupt and smooth changes in the spectral characteristics is addressed. In this paper, an algorithm for adaptive sequential segmentation of time series is proposed based on the maximal overlap discrete wavelet transform and Bayesian analysis. The proposed algorithm is evaluated using synthetic data. The results demonstrate the good detection properties of the proposed algorithm when compared with previous reported algorithms.

@phdthesis{
 title = {Anomaly detection and prediction in communication networks using wavelet transforms},
 type = {phdthesis},
 year = {2003},
 pages = {1-233},
 websites = {https://spiral.imperial.ac.uk/handle/10044/1/11475},
 publisher = {Imperial College London, UK},
 city = {London},
 institution = {Imperial College London},
 department = {Electrical and Electronic Engineering},
 id = {92c40961-3160-36e6-8fb2-c4ce1cd111a1},
 created = {2022-08-29T17:43:47.402Z},
 accessed = {2021-10-23},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2024-05-28T18:20:28.883Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {THES},
 user_context = {PhD Thesis},
 private_publication = {false},
 abstract = {It is important for service providers to monitor their systems in order to detect network anomalies and performance degradations in advance of network/service disruptions. In this regard, several anomaly detection schemes have already been proposed in the literature. These schemes are in most cases based on parametric models and thresholding techniques. The underlying aim of this thesis is to develop models and algorithms based on wavelet transforms for analysing the statistical behaviour of network metrics in order to detect and predict network anomalies and performance degradations in communication networks. The original contributions of this research can be classified into three categories. Firstly, a novel wavelet-based algorithm is proposed for detecting network anomalies in communication networks. The wavelet-based algorithm is then used to detect events in different network metrics of a Dial Internet Protocol service and corporate Proxy servers. A sensor fusion scheme, which combines local decisions made from dispersed wavelet-based sensors, is also investigated. This sensor fusion scheme incorporates the spatial dependencies among the monitored network metrics and hence reduces the number of false alarms generated by each network metric. Secondly, a novel learning algorithm is proposed for time series prediction based on finite impulse response (FIR) neural networks and the multiresolution analysis framework of wavelet theory. A gradient descent method is used to adapt the gain of the non-linear functions in FIR networks at each level of resolution. The multiresolution learning algorithm is compared with previously reported algorithms using a benchmark time series. The algorithm is also applied to network traffic prediction in an Ethernet environment. The results show that the generalisation ability of the FIR network is improved by the multiresolution learning algorithm. Finally, a method is proposed for predicting and monitoring communication network metrics. The proposed method learns to predict the normal behaviour of the monitored network metric and together with an online decision-making algorithm detects and classify deviations from the normal operation region. The proposed method is used to predict and monitor events in corporate Proxy servers and Local/Wide area network traces. Experimental results show that the proposed method is able to identify moderate and severe abnormal network behaviours in advance of reported network faults and thereby providing a useful method for proactively managing communication networks.},
 bibtype = {phdthesis},
 author = {Alarcon-Aquino, Vicente}
}

It is important for service providers to monitor their systems in order to detect network anomalies and performance degradations in advance of network/service disruptions. In this regard, several anomaly detection schemes have already been proposed in the literature. These schemes are in most cases based on parametric models and thresholding techniques. The underlying aim of this thesis is to develop models and algorithms based on wavelet transforms for analysing the statistical behaviour of network metrics in order to detect and predict network anomalies and performance degradations in communication networks. The original contributions of this research can be classified into three categories. Firstly, a novel wavelet-based algorithm is proposed for detecting network anomalies in communication networks. The wavelet-based algorithm is then used to detect events in different network metrics of a Dial Internet Protocol service and corporate Proxy servers. A sensor fusion scheme, which combines local decisions made from dispersed wavelet-based sensors, is also investigated. This sensor fusion scheme incorporates the spatial dependencies among the monitored network metrics and hence reduces the number of false alarms generated by each network metric. Secondly, a novel learning algorithm is proposed for time series prediction based on finite impulse response (FIR) neural networks and the multiresolution analysis framework of wavelet theory. A gradient descent method is used to adapt the gain of the non-linear functions in FIR networks at each level of resolution. The multiresolution learning algorithm is compared with previously reported algorithms using a benchmark time series. The algorithm is also applied to network traffic prediction in an Ethernet environment. The results show that the generalisation ability of the FIR network is improved by the multiresolution learning algorithm. Finally, a method is proposed for predicting and monitoring communication network metrics. The proposed method learns to predict the normal behaviour of the monitored network metric and together with an online decision-making algorithm detects and classify deviations from the normal operation region. The proposed method is used to predict and monitor events in corporate Proxy servers and Local/Wide area network traces. Experimental results show that the proposed method is able to identify moderate and severe abnormal network behaviours in advance of reported network faults and thereby providing a useful method for proactively managing communication networks.

@inproceedings{
 title = {Multi-Sensor Fusion System Using Wavelet Based Detection Algorithm Applied to Network Monitoring},
 type = {inproceedings},
 year = {2002},
 pages = {361-364},
 websites = {https://www.ee.ucl.ac.uk/lcs/previous/LCS2002/LCS056.pdf},
 publisher = {London Communications Symposium},
 city = {London},
 id = {ff1f03a2-f98e-3cb0-abf8-245f04cf396f},
 created = {2022-08-29T17:43:48.010Z},
 accessed = {2021-10-23},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:48.010Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {A multi-sensor fusion system using wavelet based detection algorithm is proposed for network anomaly detection. The proposed approach is applied to monitor events indifferent network metrics of a Dial Internet Protocol service. The results show that the approach is able to identify the presence of abnormal behaviours in advance of reported networkanomalies, and reduce the number of false alarms generated by each network metric.},
 bibtype = {inproceedings},
 author = {Alarcon-Aquino, V and Barria, J A},
 booktitle = {London Communications Symposium}
}

A multi-sensor fusion system using wavelet based detection algorithm is proposed for network anomaly detection. The proposed approach is applied to monitor events indifferent network metrics of a Dial Internet Protocol service. The results show that the approach is able to identify the presence of abnormal behaviours in advance of reported networkanomalies, and reduce the number of false alarms generated by each network metric.

@article{
 title = {Anomaly detection in communication networks using wavelets},
 type = {article},
 year = {2001},
 pages = {355},
 volume = {148},
 websites = {http://www2.ee.ic.ac.uk/publications/p1711.pdf},
 id = {8ad8a17b-5bd5-34ed-9c0f-829705a45ee0},
 created = {2022-08-29T17:43:48.627Z},
 accessed = {2021-10-23},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:48.627Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {An algorithm is proposed for network anomaly detection based on the undecimated discrete wavelet transform and Bayesian analysis. The proposed algorithm checks the wavelet coefficients across resolution levels, and locates smooth and abrupt changes in variance and frequency in the given time series, by using the wavelet coefficients ar these levels. The unknown variance of the wavelet coefficients is considered as a stochastic nuisance parameter. Marginalisation is then used to remove this nuisance parameter by using three different priors: flat, Jeffreys' and the inverse Wishart distribution (scalar case). The different versions of the proposed algorithm are evaluated using synthetic data, and compared with autoregressive models and thresholding techniques. The proposed algorithm is applied to monitor events in a Dial Internet Protocol service. The results show that the proposed algorithm is able to identify the presence of abnormal network behaviours in advance of reported network anomalies.},
 bibtype = {article},
 author = {Alarcon-Aquino, V. and Barria, J.A.},
 doi = {10.1049/ip-com:20010659},
 journal = {IEE Proceedings - Communications},
 number = {6}
}

An algorithm is proposed for network anomaly detection based on the undecimated discrete wavelet transform and Bayesian analysis. The proposed algorithm checks the wavelet coefficients across resolution levels, and locates smooth and abrupt changes in variance and frequency in the given time series, by using the wavelet coefficients ar these levels. The unknown variance of the wavelet coefficients is considered as a stochastic nuisance parameter. Marginalisation is then used to remove this nuisance parameter by using three different priors: flat, Jeffreys' and the inverse Wishart distribution (scalar case). The different versions of the proposed algorithm are evaluated using synthetic data, and compared with autoregressive models and thresholding techniques. The proposed algorithm is applied to monitor events in a Dial Internet Protocol service. The results show that the proposed algorithm is able to identify the presence of abnormal network behaviours in advance of reported network anomalies.