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\n\n \n \n \n \n \n Multiband Patch Antenna Design Using Nature-Inspired Optimization Method.\n \n \n \n\n\n \n Boursianis, A. D.; Papadopoulou, M. S.; Pierezan, J.; Mariani, V. C.; Coelho, L. S.; Sarigiannidis, P.; Koulouridis, S.; and Goudos, S. K.\n\n\n \n\n\n\n
IEEE Open Journal of Antennas and Propagation, 2: 151-162. 2021.\n
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@ARTICLE{9311660, author={Boursianis, Achilles D. and Papadopoulou, Maria S. and Pierezan, Juliano and Mariani, Viviana C. and Coelho, Leandro S. and Sarigiannidis, Panagiotis and Koulouridis, Stavros and Goudos, Sotirios K.}, journal={IEEE Open Journal of Antennas and Propagation}, title={Multiband Patch Antenna Design Using Nature-Inspired Optimization Method}, year={2021}, volume={2}, number={}, pages={151-162}, doi={10.1109/OJAP.2020.3048495}}\n\n
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\n\n \n \n \n \n \n Design of Unit Cells for Intelligent Reflection Surfaces Based on Transparent Materials.\n \n \n \n\n\n \n Chalkidis, S.; Vassos, E.; Boursianis, A. D.; Feresidis, A.; and Goudos, S. K.\n\n\n \n\n\n\n In
2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST), pages 1-4, 2021. \n
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@INPROCEEDINGS{9493335, author={Chalkidis, Savvas and Vassos, Evangelos and Boursianis, Achilles D. and Feresidis, Alexandros and Goudos, Sotirios K.}, booktitle={2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)}, title={Design of Unit Cells for Intelligent Reflection Surfaces Based on Transparent Materials}, year={2021}, volume={}, number={}, pages={1-4}, doi={10.1109/MOCAST52088.2021.9493335}}\n\n
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\n\n \n \n \n \n \n Dual-Band Frequency Selective Surface Design Using Harris Hawks Optimization.\n \n \n \n\n\n \n Boursianis, A. D.; Salucci, M.; Koulouridis, S.; Georgiadis, A.; Tentzeris, M.; and Goudos, S. K.\n\n\n \n\n\n\n In
2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST), pages 1-4, 2021. \n
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@INPROCEEDINGS{9493382, author={Boursianis, Achilles D. and Salucci, Marco and Koulouridis, Stavros and Georgiadis, Apostolos and Tentzeris, Manos and Goudos, Sotirios K.}, booktitle={2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)}, title={Dual-Band Frequency Selective Surface Design Using Harris Hawks Optimization}, year={2021}, volume={}, number={}, pages={1-4}, doi={10.1109/MOCAST52088.2021.9493382}}\n\n
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\n\n \n \n \n \n \n From Spatial Urban Site Data to Path Loss Prediction: An Ensemble Learning Approach.\n \n \n \n\n\n \n Sotiroudis, S. P.; Boursianis, A. D.; Goudos, S. K.; and Siakavara, K.\n\n\n \n\n\n\n
IEEE Transactions on Antennas and Propagation,1-1. 2021.\n
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@ARTICLE{9667264, author={Sotiroudis, Sotirios P. and Boursianis, Achilles D. and Goudos, Sotirios K. and Siakavara, Katherine}, journal={IEEE Transactions on Antennas and Propagation}, title={From Spatial Urban Site Data to Path Loss Prediction: An Ensemble Learning Approach}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TAP.2021.3138257}}\n\n
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\n\n \n \n \n \n \n \n Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications.\n \n \n \n \n\n\n \n Boursianis, A. D.; Papadopoulou, M. S.; Koulouridis, S.; Rocca, P.; Georgiadis, A.; Tentzeris, M. M.; and Goudos, S. K.\n\n\n \n\n\n\n
Sensors, 21(10). 2021.\n
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@Article{s21103460,\nAUTHOR = {Boursianis, Achilles D. and Papadopoulou, Maria S. and Koulouridis, Stavros and Rocca, Paolo and Georgiadis, Apostolos and Tentzeris, Manos M. and Goudos, Sotirios K.},\nTITLE = {Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications},\nJOURNAL = {Sensors},\nVOLUME = {21},\nYEAR = {2021},\nNUMBER = {10},\nARTICLE-NUMBER = {3460},\nURL = {https://www.mdpi.com/1424-8220/21/10/3460},\nPubMedID = {34065618},\nISSN = {1424-8220},\nABSTRACT = {A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the Moth Search Algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.},\nDOI = {10.3390/s21103460}\n}\n\n
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\n A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the Moth Search Algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.\n
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\n\n \n \n \n \n \n \n Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization.\n \n \n \n \n\n\n \n Pierezan, J.; Coelho, L. d. S.; Mariani, V. C.; Goudos, S. K.; Boursianis, A. D.; Kantartzis, N. V.; Antonopoulos, C. S.; and Nikolaidis, S.\n\n\n \n\n\n\n
Technologies, 9(2). 2021.\n
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@Article{technologies9020035,\nAUTHOR = {Pierezan, Juliano and Coelho, Leandro dos S. and Mariani, Viviana C. and Goudos, Sotirios K. and Boursianis, Achilles D. and Kantartzis, Nikolaos V. and Antonopoulos, Christos. S. and Nikolaidis, Spiridon},\nTITLE = {Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization},\nJOURNAL = {Technologies},\nVOLUME = {9},\nYEAR = {2021},\nNUMBER = {2},\nARTICLE-NUMBER = {35},\nURL = {https://www.mdpi.com/2227-7080/9/2/35},\nISSN = {2227-7080},\nABSTRACT = {Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.},\nDOI = {10.3390/technologies9020035}\n}\n\n
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\n Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.\n
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\n\n \n \n \n \n \n \n Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems.\n \n \n \n \n\n\n \n Coelho, L. d. S.; Mariani, V. C.; Goudos, S. K.; Boursianis, A. D.; Kokkinidis, K.; and Kantartzis, N. V.\n\n\n \n\n\n\n
Telecom, 2(2): 222–231. 2021.\n
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@Article{telecom2020015,\nAUTHOR = {Coelho, Leandro dos S. and Mariani, Viviana C. and Goudos, Sotirios K. and Boursianis, Achilles D. and Kokkinidis, Konstantinos and Kantartzis, Nikolaos V.},\nTITLE = {Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems},\nJOURNAL = {Telecom},\nVOLUME = {2},\nYEAR = {2021},\nNUMBER = {2},\nPAGES = {222--231},\nURL = {https://www.mdpi.com/2673-4001/2/2/15},\nISSN = {2673-4001},\nABSTRACT = {The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.},\nDOI = {10.3390/telecom2020015}\n}\n\n
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\n The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.\n
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\n\n \n \n \n \n \n \n High-Efficiency Triple-Band RF-to-DC Rectifier Primary Design for RF Energy-Harvesting Systems.\n \n \n \n \n\n\n \n Papadopoulou, M. S.; Boursianis, A. D.; Volos, C. K.; Stouboulos, I. N.; Nikolaidis, S.; and Goudos, S. K.\n\n\n \n\n\n\n
Telecom, 2(3): 271–284. 2021.\n
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\n\n \n \n Paper\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 \n \n \n \n \n \n\n \n \n \n\n\n\n
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@Article{telecom2030018,\nAUTHOR = {Papadopoulou, Maria S. and Boursianis, Achilles D. and Volos, Christos K. and Stouboulos, Ioannis N. and Nikolaidis, Spyridon and Goudos, Sotirios K.},\nTITLE = {High-Efficiency Triple-Band RF-to-DC Rectifier Primary Design for RF Energy-Harvesting Systems},\nJOURNAL = {Telecom},\nVOLUME = {2},\nYEAR = {2021},\nNUMBER = {3},\nPAGES = {271--284},\nURL = {https://www.mdpi.com/2673-4001/2/3/18},\nISSN = {2673-4001},\nABSTRACT = {Radio Frequency (RF) energy harvesting has been emerged as a potentially reliable method to replace the costly and difficult to maintain source of low-power wireless sensor networks. A plethora of dual-band rectifier designs has been proposed in the literature operating in various frequency bands. In this paper, a triple-band RF-to-DC rectifier that operates in the frequency bands of LoRaWAN, GSM-900, and WiFi 2.4 GHz is presented. The system is composed of an impedance-matching circuit, an RF-to-DC rectifier, that converts the ambient RF energy into DC voltage able to feed low-power devices, and an output load. The proposed system resonates at three different frequencies of 866 MHz, 948 MHz and 2423 MHz, which fall within the aforementioned frequency bands of interest. The feasible solution of the proposed system was based on a dual-band rectifier operating in the frequency bands of LoRaWAN and GSM-900. A series of shunt stubs was utilized in the initial design to form the feasible solution of the proposed system. The proposed triple-band rectifier was optimized using a powerful optimization algorithm, i.e., the genetic algorithm. The overall system exhibited improved characteristics compared to the initial design in terms of its resonance. Numerical results demonstrated that the overall system exhibited an efficiency of 81% with 3.23 V of the output voltage, for an input power of 0 dBm and a load of 13 kOhm.},\nDOI = {10.3390/telecom2030018}\n}\n\n
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\n Radio Frequency (RF) energy harvesting has been emerged as a potentially reliable method to replace the costly and difficult to maintain source of low-power wireless sensor networks. A plethora of dual-band rectifier designs has been proposed in the literature operating in various frequency bands. In this paper, a triple-band RF-to-DC rectifier that operates in the frequency bands of LoRaWAN, GSM-900, and WiFi 2.4 GHz is presented. The system is composed of an impedance-matching circuit, an RF-to-DC rectifier, that converts the ambient RF energy into DC voltage able to feed low-power devices, and an output load. The proposed system resonates at three different frequencies of 866 MHz, 948 MHz and 2423 MHz, which fall within the aforementioned frequency bands of interest. The feasible solution of the proposed system was based on a dual-band rectifier operating in the frequency bands of LoRaWAN and GSM-900. A series of shunt stubs was utilized in the initial design to form the feasible solution of the proposed system. The proposed triple-band rectifier was optimized using a powerful optimization algorithm, i.e., the genetic algorithm. The overall system exhibited improved characteristics compared to the initial design in terms of its resonance. Numerical results demonstrated that the overall system exhibited an efficiency of 81% with 3.23 V of the output voltage, for an input power of 0 dBm and a load of 13 kOhm.\n
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\n\n \n \n \n \n \n Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study.\n \n \n \n\n\n \n Goudos, S. K.; Boursianis, A. D.; Mohamed, A. W.; Wan, S.; Sarigiannidis, P.; Karagiannidis, G. K.; and Suganthan, P. N.\n\n\n \n\n\n\n 2021.\n
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@misc{goudos2021large,\n title={Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study}, \n author={Sotirios K. Goudos and Achilles D. Boursianis and Ali Wagdy Mohamed and Shaohua Wan and Panagiotis Sarigiannidis and George K. Karagiannidis and Ponnuthurai N. Suganthan},\n year={2021},\n eprint={2102.11275},\n archivePrefix={arXiv},\n primaryClass={cs.NE}\n}\n\n
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\n\n \n \n \n \n \n \n Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers.\n \n \n \n \n\n\n \n Boursianis, A. D.; Papadopoulou, M. S.; Salucci, M.; Polo, A.; Sarigiannidis, P.; Psannis, K.; Mirjalili, S.; Koulouridis, S.; and Goudos, S. K.\n\n\n \n\n\n\n
Applied Sciences, 11(18). 2021.\n
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@Article{app11188330,\nAUTHOR = {Boursianis, Achilles D. and Papadopoulou, Maria S. and Salucci, Marco and Polo, Alessandro and Sarigiannidis, Panagiotis and Psannis, Konstantinos and Mirjalili, Seyedali and Koulouridis, Stavros and Goudos, Sotirios K.},\nTITLE = {Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers},\nJOURNAL = {Applied Sciences},\nVOLUME = {11},\nYEAR = {2021},\nNUMBER = {18},\nARTICLE-NUMBER = {8330},\nURL = {https://www.mdpi.com/2076-3417/11/18/8330},\nISSN = {2076-3417},\nABSTRACT = {Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.},\nDOI = {10.3390/app11188330}\n}\n\n
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\n Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.\n
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\n\n \n \n \n \n \n \n Novel Design Framework for Dual-Band Frequency Selective Surfaces Using Multi-Variant Differential Evolution.\n \n \n \n \n\n\n \n Boursianis, A. D.; Papadopoulou, M. S.; Nikolaidis, S.; Sarigiannidis, P.; Psannis, K.; Georgiadis, A.; Tentzeris, M. M.; and Goudos, S. K.\n\n\n \n\n\n\n
Mathematics, 9(19). 2021.\n
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@Article{math9192381,\nAUTHOR = {Boursianis, Achilles D. and Papadopoulou, Maria S. and Nikolaidis, Spyridon and Sarigiannidis, Panagiotis and Psannis, Konstantinos and Georgiadis, Apostolos and Tentzeris, Manos M. and Goudos, Sotirios K.},\nTITLE = {Novel Design Framework for Dual-Band Frequency Selective Surfaces Using Multi-Variant Differential Evolution},\nJOURNAL = {Mathematics},\nVOLUME = {9},\nYEAR = {2021},\nNUMBER = {19},\nARTICLE-NUMBER = {2381},\nURL = {https://www.mdpi.com/2227-7390/9/19/2381},\nISSN = {2227-7390},\nABSTRACT = {Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz.},\nDOI = {10.3390/math9192381}\n}\n\n
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\n Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz.\n
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\n\n \n \n \n \n \n \n State-of-the-Art Techniques in RF Energy Harvesting Circuits.\n \n \n \n \n\n\n \n Bougas, I. D.; Papadopoulou, M. S.; Boursianis, A. D.; Kokkinidis, K.; and Goudos, S. K.\n\n\n \n\n\n\n
Telecom, 2(4): 369–389. 2021.\n
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@Article{telecom2040022,\nAUTHOR = {Bougas, Ioannis D. and Papadopoulou, Maria S. and Boursianis, Achilles D. and Kokkinidis, Konstantinos and Goudos, Sotirios K.},\nTITLE = {State-of-the-Art Techniques in RF Energy Harvesting Circuits},\nJOURNAL = {Telecom},\nVOLUME = {2},\nYEAR = {2021},\nNUMBER = {4},\nPAGES = {369--389},\nURL = {https://www.mdpi.com/2673-4001/2/4/22},\nISSN = {2673-4001},\nABSTRACT = {The exigency for continuous use of electrical devices has created greater demands for electricity along with more efficient transmission techniques. Energy from natural resources can be solar, thermal, vibration, friction, or Radio Frequencies (RF) signals. This state-of-the-art work provides a summary of RF energy harvesting techniques and can be used as a guide for the manufacture of RF energy scavenging modules. The use of Radio Frequency (RF) Energy Harvesting (EH) technique contributes to the development of autonomous energy devices and sensors. A rectenna system includes three main units: the receiving antenna, the impedance matching network, and the rectifier. We thoroughly analyze how to design a rectenna system with special emphasis given on the design of the rectifier. At the same time many works of the last 10 years are presented. This review article categorizes the used topologies depending on the type of antennas, IMNs, and rectifiers and comparatively presents their advantages and disadvantages.},\nDOI = {10.3390/telecom2040022}\n}\n\n
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\n The exigency for continuous use of electrical devices has created greater demands for electricity along with more efficient transmission techniques. Energy from natural resources can be solar, thermal, vibration, friction, or Radio Frequencies (RF) signals. This state-of-the-art work provides a summary of RF energy harvesting techniques and can be used as a guide for the manufacture of RF energy scavenging modules. The use of Radio Frequency (RF) Energy Harvesting (EH) technique contributes to the development of autonomous energy devices and sensors. A rectenna system includes three main units: the receiving antenna, the impedance matching network, and the rectifier. We thoroughly analyze how to design a rectenna system with special emphasis given on the design of the rectifier. At the same time many works of the last 10 years are presented. This review article categorizes the used topologies depending on the type of antennas, IMNs, and rectifiers and comparatively presents their advantages and disadvantages.\n
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