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\n  \n 2020\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Time and frequency based sparse bounded component analysis algorithms for convolutive mixtures.\n \n \n \n \n\n\n \n Babatas, E.; and Erdogan, A. T.\n\n\n \n\n\n\n Signal Processing, 173: 107590. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"TimePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{BABATAS2020107590,\n    author = "Babatas, Eren and Erdogan, Alper T.",\n    title = "Time and frequency based sparse bounded component analysis algorithms for convolutive mixtures",\n    journal = "Signal Processing",\n    volume = "173",\n    pages = "107590",\n    year = "2020",\n    issn = "0165-1684",\n    doi = "https://doi.org/10.1016/j.sigpro.2020.107590",\n    url = "https://www.sciencedirect.com/science/article/pii/S016516842030133X",\n    keywords = "Convolutive blind source separation,Bounded component analysis,Sparse component analysis,Sparse bounded component analysis,Blind speech separation,ML",\n    abstract = "In this paper, we introduce time-domain and frequency-domain versions of a new Blind Source Separation (BSS) approach to extract bounded magnitude sparse sources from convolutive mixtures. We derive algorithms by maximization of the proposed objective functions that are defined in a completely deterministic framework, and prove that global maximums of the objective functions yield perfect separation under suitable conditions. The derived algorithms can be applied to temporal or spatially dependent sources as well as independent sources. We provide experimental results to demonstrate some benefits of the approach, also including an application on blind speech separation."\n}\n\n
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\n In this paper, we introduce time-domain and frequency-domain versions of a new Blind Source Separation (BSS) approach to extract bounded magnitude sparse sources from convolutive mixtures. We derive algorithms by maximization of the proposed objective functions that are defined in a completely deterministic framework, and prove that global maximums of the objective functions yield perfect separation under suitable conditions. The derived algorithms can be applied to temporal or spatially dependent sources as well as independent sources. We provide experimental results to demonstrate some benefits of the approach, also including an application on blind speech separation.\n
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\n \n\n \n \n \n \n \n \n Blind Bounded Source Separation Using Neural Networks with Local Learning Rules.\n \n \n \n \n\n\n \n Erdogan, A. T.; and Pehlevan, C.\n\n\n \n\n\n\n ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020.\n \n\n\n\n
\n\n\n\n \n \n \"BlindPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{Erdogan_2020,\n    author = "Erdogan, Alper T. and Pehlevan, Cengiz",\n    title = "Blind Bounded Source Separation Using Neural Networks with Local Learning Rules",\n    isbn = "9781509066315",\n    url = "http://dx.doi.org/10.1109/ICASSP40776.2020.9053114",\n    doi = "10.1109/icassp40776.2020.9053114",\n    journal = "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",\n    publisher = "IEEE",\n    year = "2020",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Machine learning-enabled multiplexed microfluidic sensors.\n \n \n \n\n\n \n Rahmani Dabbagh, S.; Rabbi, F.; Dogan, Z.; Yetisen, A.; and Tasoglu, S.\n\n\n \n\n\n\n Biomicrofluidics, 14: 61506. 12 2020.\n \n\n\n\n
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@article{article,\n    author = "Rahmani Dabbagh, Sajjad and Rabbi, Fazle and Dogan, Zafer and Yetisen, Ali and Tasoglu, Savas",\n    year = "2020",\n    month = "12",\n    pages = "61506",\n    title = "Machine learning-enabled multiplexed microfluidic sensors",\n    volume = "14",\n    journal = "Biomicrofluidics",\n    doi = "10.1063/5.0025462",\n    keywords = "ML"\n}\n\n
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\n  \n 2019\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Compressed Training Based Massive MIMO.\n \n \n \n\n\n \n Yilmaz, B. B.; and Erdogan, A. T.\n\n\n \n\n\n\n IEEE Transactions on Signal Processing, 67: 1191-1206. 2019.\n \n\n\n\n
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@article{Yilmaz2019CompressedTB,\n    author = "Yilmaz, B. B. and Erdogan, A. T.",\n    title = "Compressed Training Based Massive MIMO",\n    journal = "IEEE Transactions on Signal Processing",\n    year = "2019",\n    volume = "67",\n    pages = "1191-1206",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Online Bounded Component Analysis: A Simple Recurrent Neural Network with Local Update Rule for Unsupervised Separation of Dependent and Independent Sources.\n \n \n \n\n\n \n Simsek, B.; and Erdogan, A. T.\n\n\n \n\n\n\n 2019 53rd Asilomar Conference on Signals, Systems, and Computers,1639-1643. 2019.\n \n\n\n\n
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@article{Simsek2019OnlineBC,\n    author = "Simsek, Berfin and Erdogan, A. T.",\n    title = "Online Bounded Component Analysis: A Simple Recurrent Neural Network with Local Update Rule for Unsupervised Separation of Dependent and Independent Sources",\n    journal = "2019 53rd Asilomar Conference on Signals, Systems, and Computers",\n    year = "2019",\n    pages = "1639-1643",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Channel Estimation for Massive MIMO: A Semiblind Algorithm Exploiting QAM Structure.\n \n \n \n\n\n \n Yilmaz, B. B.; and Erdogan, A. T.\n\n\n \n\n\n\n 2019 53rd Asilomar Conference on Signals, Systems, and Computers,2077-2081. 2019.\n \n\n\n\n
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@article{Yilmaz2019ChannelEF,\n    author = "Yilmaz, B. B. and Erdogan, A. T.",\n    title = "Channel Estimation for Massive MIMO: A Semiblind Algorithm Exploiting QAM Structure",\n    journal = "2019 53rd Asilomar Conference on Signals, Systems, and Computers",\n    year = "2019",\n    pages = "2077-2081",\n    keywords = "ML"\n}\n\n
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\n  \n 2018\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n An Algorithmic Framework for Sparse Bounded Component Analysis.\n \n \n \n\n\n \n Babatas, E.; and Erdogan, A. T.\n\n\n \n\n\n\n IEEE Transactions on Signal Processing, 66: 5194-5205. 2018.\n \n\n\n\n
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@article{Babatas2018AnAF,\n    author = "Babatas, Eren and Erdogan, A. T.",\n    title = "An Algorithmic Framework for Sparse Bounded Component Analysis",\n    journal = "IEEE Transactions on Signal Processing",\n    year = "2018",\n    volume = "66",\n    pages = "5194-5205",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Sparse Bounded Component Analysis for Convolutive Mixtures.\n \n \n \n\n\n \n Babatas, E.; and Erdogan, A. T.\n\n\n \n\n\n\n 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2741-2745. 2018.\n \n\n\n\n
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@article{Babatas2018SparseBC,\n    author = "Babatas, Eren and Erdogan, Alper T.",\n    title = "Sparse Bounded Component Analysis for Convolutive Mixtures",\n    journal = "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",\n    year = "2018",\n    pages = "2741-2745",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Fast multidimensional reduction and broadcast operations on GPU for machine learning.\n \n \n \n\n\n \n Dikbayir, D.; Çoban, E. B.; Kesen, I.; Yuret, D.; and Unat, D.\n\n\n \n\n\n\n Concurrency and Computation: Practice and Experience, 30. 2018.\n \n\n\n\n
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@article{Dikbayir2018FastMR,\n    author = "Dikbayir, Doga and {\\c{C}}oban, Enis Berk and Kesen, Ilker and Yuret, Deniz and Unat, D.",\n    title = "Fast multidimensional reduction and broadcast operations on GPU for machine learning",\n    journal = "Concurrency and Computation: Practice and Experience",\n    year = "2018",\n    volume = "30",\n    keywords = "ML,NLP"\n}\n\n
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\n \n\n \n \n \n \n \n Stationary Point Characterization for a Class of BCA Algorithms.\n \n \n \n\n\n \n Inan, H. A.; Erdogan, A. T.; and Cruces, S.\n\n\n \n\n\n\n IEEE Transactions on Signal Processing, 65: 5437-5452. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{Inan2017StationaryPC,\n    author = "Inan, Huseyin A. and Erdogan, Alper T. and Cruces, S.",\n    title = "Stationary Point Characterization for a Class of BCA Algorithms",\n    journal = "IEEE Transactions on Signal Processing",\n    year = "2017",\n    volume = "65",\n    pages = "5437-5452",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Single carrier frequency domain compressed training adaptive equalization.\n \n \n \n\n\n \n Yilmaz, B. B.; and Erdogan, A. T.\n\n\n \n\n\n\n 2017 51st Asilomar Conference on Signals, Systems, and Computers,1110-1114. 2017.\n \n\n\n\n
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@article{Yilmaz2017SingleCF,\n    author = "Yilmaz, B. B. and Erdogan, A. T.",\n    title = "Single carrier frequency domain compressed training adaptive equalization",\n    journal = "2017 51st Asilomar Conference on Signals, Systems, and Computers",\n    year = "2017",\n    pages = "1110-1114",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Compressed Training Adaptive Equalization: Algorithms and Analysis.\n \n \n \n\n\n \n Yilmaz, B. B.; and Erdogan, A. T.\n\n\n \n\n\n\n IEEE Transactions on Communications, 65: 3907-3921. 2017.\n \n\n\n\n
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@article{Yilmaz2017CompressedTA,\n    author = "Yilmaz, B. B. and Erdogan, A. T.",\n    title = "Compressed Training Adaptive Equalization: Algorithms and Analysis",\n    journal = "IEEE Transactions on Communications",\n    year = "2017",\n    volume = "65",\n    pages = "3907-3921",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n Compressed Training Adaptive MIMO equalization.\n \n \n \n\n\n \n Yilmaz, B. B.; and Erdogan, A. T.\n\n\n \n\n\n\n 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC),1-6. 2016.\n \n\n\n\n
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@article{Yilmaz2016CompressedTA,\n    author = "Yilmaz, B. B. and Erdogan, Alper T.",\n    title = "Compressed Training Adaptive MIMO equalization",\n    journal = "2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)",\n    year = "2016",\n    pages = "1-6",\n    keywords = "ML"\n}\n\n
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\n \n\n \n \n \n \n \n \n Learning Morphological Disambiguation Rules for Turkish.\n \n \n \n \n\n\n \n Yuret, D.; and Türe, F.\n\n\n \n\n\n\n In HLT-NAACL 06, June 2006. \n \n\n\n\n
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@inproceedings{Yuret2006,\n    author = {Yuret, Deniz and T{\\"u}re, Ferhan},\n    title = "Learning Morphological Disambiguation Rules for Turkish",\n    year = "2006",\n    booktitle = "HLT-NAACL 06",\n    keywords = "ML,Morphology,ai.ku,NLP",\n    month = "June",\n    url = "https://aclweb.org/anthology/N/N06/N06-1042.pdf,/pub/hlt-naacl-06,/pub/hlt-naacl-06/morph-disamb.pdf,/pub/hlt-naacl-06/hlt06.ppt"\n}\n
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