The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Rohit Kumar - One of the best experts on this subject based on the ideXlab platform.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
IEEE Wireless Communications Letters, 2019Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this letter, we consider non-contiguous wideband spectrum sensing (WSS) using the sub-Nyquist sampling approach. Compared to contiguous WSS which senses the entire spectrum, non-contiguous WSS has an additional task of determining the number and location of frequency bands for digitization and sensing. Since throughput (i.e., the number of sensed vacant bands) increases while the probability of successful sensing decreases with a decrease in the sparsity of digitized bands, we develop exploration–exploitation-based Online Learning Algorithm to learn the spectrum statistics. We provide a lower bound on the number of time slots required to learn spectrum statistics after which the proposed Algorithm intelligently selects a maximum possible number of frequency bands which are more likely to be vacant and hence, it is named as throughput optimized non-contiguous WSS. Simulation and experimental results using USRP testbed validate the efficacy of the proposed approach compared to the Myopic approach which has prior knowledge of spectrum statistics.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
arXiv: Signal Processing, 2018Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for spectrum characterization and allocation in next generation heterogeneous networks. The proposed WSS consists of sub-Nyquist sampling and digital reconstruction to sense multiple non-contiguous frequency bands. Since the throughput (i.e. the number of vacant bands) increases while the probability of successful reconstruction decreases with increase in the number of sensed bands, we develop an Online Learning Algorithm to characterize and select frequency bands based on their spectrum statistics. We guarantee that the proposed Algorithm allows sensing of maximum possible number of frequency bands and hence, it is referred to as throughput optimized WSS. We also provide a lower bound on the number of time slots required to characterize spectrum statistics. Simulation and experimental results in the real radio environment show that the performance of the proposed approach converges to that of Myopic approach which has prior knowledge of spectrum statistics.
Faajeng Lin - One of the best experts on this subject based on the ideXlab platform.
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wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution
IEEE Transactions on Power Electronics, 2015Co-Authors: Yingchih Hung, Faajeng Lin, Jonqchin Hwang, Jinkuan Chang, Kaichun RuanAbstract:A wavelet fuzzy neural network using asymmetric membership function (WFNN-AMF) with improved differential evolution (IDE) Algorithm is proposed in this study to control a six-phase permanent magnet synchronous motor (PMSM) for an electric power steering (EPS) system. First, the dynamics of a steer-by-wire EPS system and a six-phase PMSM drive system are described in detail. Moreover, the WFNN-AMF controller, which combines the advantages of wavelet decomposition, fuzzy logic system, and asymmetric membership function (AMF), is developed to achieve the required control performance of the EPS system for the improvement of stability of the vehicle and the comfort of the driver. Furthermore, the Online Learning Algorithm of WFNN-AMF is derived using back-propagation method. However, degenerated or diverged responses will be resulted due to the inappropriate selection of small or large Learning rates of the WFNN-AMF. Therefore, an IDE Algorithm is proposed to Online adapt the Learning rates of WFNN-AMF. In addition, a 32-bit floating-point digital signal processor, TMS320F28335, is adopted for the implementation of the proposed intelligent controlled EPS system. Finally, the feasibility of the proposed WFNN-AMF controller with IDE for the EPS system is verified through experimental results.
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reactive power control of three phase grid connected pv system during grid faults using takagi sugeno kang probabilistic fuzzy neural network control
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Faajeng Lin, Bohui Yang, Yungruei ChangAbstract:An intelligent controller based on the Takagi–Sugeno–Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The inverter of the three-phase grid-connected PV system should provide a proper ratio of reactive power to meet the low-voltage ride through (LVRT) regulations and control the output current without exceeding the maximum current limit simultaneously during grid faults. Therefore, the proposed intelligent controller regulates the value of reactive power to a new reference value, which complies with the regulations of LVRT under grid faults. Moreover, a dual-mode operation control method of the converter and inverter of the three-phase grid-connected PV system is designed to eliminate the fluctuation of dc-link bus voltage under grid faults. Furthermore, the network structure, the Online Learning Algorithm, and the convergence analysis of the TSKPFNN-AMF are described in detail. Finally, some experimental results are illustrated to show the effectiveness of the proposed control for the three-phase grid-connected PV system.
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fault tolerant control of a six phase motor drive system using a takagi sugeno kang type fuzzy neural network with asymmetric membership function
IEEE Transactions on Power Electronics, 2013Co-Authors: Faajeng Lin, Yingchih Hung, Jonqchin Hwang, Mengting TsaiAbstract:A Takagi-Sugeno-Kang type fuzzy neural network with asymmetric membership function (TSKFNN-AMF) is proposed in this study for the fault-tolerant control of a six-phase permanent-magnet synchronous motor (PMSM) drive system. First, the dynamics of the six-phase PMSM drive system is described in detail. Then, the fault detection and operating decision method is briefly introduced. Moreover, to achieve the required control performance and to maintain the stability of a six-phase PMSM drive system under faulty condition, the TSKFNN-AMF control, which combines the advantages of a Takagi-Sugeno-Kang type fuzzy logic system and an asymmetric membership function, is developed. The network structure, Online Learning Algorithm using a delta adaptation law, and convergence analysis of the TSKFNN-AMF are described in detail. Furthermore, to enhance the control performance of the proposed intelligent fault-tolerant control, a 32-bit floating-point digital signal processor TMS320F28335 is adopted for the implementation of the proposed fault-tolerant control system. Finally, some experimental results are illustrated to show the validity of the proposed TSKFNN-AMF fault-tolerant control for the six-phase PMSM drive system.
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fpga based elman neural network control system for linear ultrasonic motor
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, 2009Co-Authors: Faajeng Lin, Yingchih HungAbstract:A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nOnlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and Online Learning Algorithm using delta adaptation law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control Algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.
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fuzzy neural network position controller for ultrasonic motor drive using push pull dc dc converter
IEE Proceedings - Control Theory and Applications, 1999Co-Authors: Faajeng Lin, Rongjong Wai, C C LeeAbstract:A fuzzy neural network (FNN) position controller is proposed to control the ultrasonic motor (USM) servo drive. The FNN controller is trained Online using the proposed delta adaptation law. Moreover, a new driving circuit for the travelling-wave type ultrasonic motor (USM), which is a push-pull DC-DC power converter and a two-phase series-resonant inverter combination, is presented. First, the network structure, the Online Learning Algorithm and the proof of convergence of the Learning Algorithm of the FNN are described. Next, the operating principles of the proposed driving circuit for the USM are described in detail. Then, the FNN position controller is implemented to control the USM drive to reduce the influence of parameter uncertainties and external disturbances. The effectiveness of the proposed driving circuit and FNN controller is demonstrated by some experimental results.
Himani Joshi - One of the best experts on this subject based on the ideXlab platform.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
IEEE Wireless Communications Letters, 2019Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this letter, we consider non-contiguous wideband spectrum sensing (WSS) using the sub-Nyquist sampling approach. Compared to contiguous WSS which senses the entire spectrum, non-contiguous WSS has an additional task of determining the number and location of frequency bands for digitization and sensing. Since throughput (i.e., the number of sensed vacant bands) increases while the probability of successful sensing decreases with a decrease in the sparsity of digitized bands, we develop exploration–exploitation-based Online Learning Algorithm to learn the spectrum statistics. We provide a lower bound on the number of time slots required to learn spectrum statistics after which the proposed Algorithm intelligently selects a maximum possible number of frequency bands which are more likely to be vacant and hence, it is named as throughput optimized non-contiguous WSS. Simulation and experimental results using USRP testbed validate the efficacy of the proposed approach compared to the Myopic approach which has prior knowledge of spectrum statistics.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
arXiv: Signal Processing, 2018Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for spectrum characterization and allocation in next generation heterogeneous networks. The proposed WSS consists of sub-Nyquist sampling and digital reconstruction to sense multiple non-contiguous frequency bands. Since the throughput (i.e. the number of vacant bands) increases while the probability of successful reconstruction decreases with increase in the number of sensed bands, we develop an Online Learning Algorithm to characterize and select frequency bands based on their spectrum statistics. We guarantee that the proposed Algorithm allows sensing of maximum possible number of frequency bands and hence, it is referred to as throughput optimized WSS. We also provide a lower bound on the number of time slots required to characterize spectrum statistics. Simulation and experimental results in the real radio environment show that the performance of the proposed approach converges to that of Myopic approach which has prior knowledge of spectrum statistics.
Anil A Kumar - One of the best experts on this subject based on the ideXlab platform.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
IEEE Wireless Communications Letters, 2019Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this letter, we consider non-contiguous wideband spectrum sensing (WSS) using the sub-Nyquist sampling approach. Compared to contiguous WSS which senses the entire spectrum, non-contiguous WSS has an additional task of determining the number and location of frequency bands for digitization and sensing. Since throughput (i.e., the number of sensed vacant bands) increases while the probability of successful sensing decreases with a decrease in the sparsity of digitized bands, we develop exploration–exploitation-based Online Learning Algorithm to learn the spectrum statistics. We provide a lower bound on the number of time slots required to learn spectrum statistics after which the proposed Algorithm intelligently selects a maximum possible number of frequency bands which are more likely to be vacant and hence, it is named as throughput optimized non-contiguous WSS. Simulation and experimental results using USRP testbed validate the efficacy of the proposed approach compared to the Myopic approach which has prior knowledge of spectrum statistics.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
arXiv: Signal Processing, 2018Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for spectrum characterization and allocation in next generation heterogeneous networks. The proposed WSS consists of sub-Nyquist sampling and digital reconstruction to sense multiple non-contiguous frequency bands. Since the throughput (i.e. the number of vacant bands) increases while the probability of successful reconstruction decreases with increase in the number of sensed bands, we develop an Online Learning Algorithm to characterize and select frequency bands based on their spectrum statistics. We guarantee that the proposed Algorithm allows sensing of maximum possible number of frequency bands and hence, it is referred to as throughput optimized WSS. We also provide a lower bound on the number of time slots required to characterize spectrum statistics. Simulation and experimental results in the real radio environment show that the performance of the proposed approach converges to that of Myopic approach which has prior knowledge of spectrum statistics.
Sumit J Darak - One of the best experts on this subject based on the ideXlab platform.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
IEEE Wireless Communications Letters, 2019Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this letter, we consider non-contiguous wideband spectrum sensing (WSS) using the sub-Nyquist sampling approach. Compared to contiguous WSS which senses the entire spectrum, non-contiguous WSS has an additional task of determining the number and location of frequency bands for digitization and sensing. Since throughput (i.e., the number of sensed vacant bands) increases while the probability of successful sensing decreases with a decrease in the sparsity of digitized bands, we develop exploration–exploitation-based Online Learning Algorithm to learn the spectrum statistics. We provide a lower bound on the number of time slots required to learn spectrum statistics after which the proposed Algorithm intelligently selects a maximum possible number of frequency bands which are more likely to be vacant and hence, it is named as throughput optimized non-contiguous WSS. Simulation and experimental results using USRP testbed validate the efficacy of the proposed approach compared to the Myopic approach which has prior knowledge of spectrum statistics.
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throughput optimized non contiguous wideband spectrum sensing via Online Learning and sub nyquist sampling
arXiv: Signal Processing, 2018Co-Authors: Himani Joshi, Sumit J Darak, Anil A Kumar, Rohit KumarAbstract:In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for spectrum characterization and allocation in next generation heterogeneous networks. The proposed WSS consists of sub-Nyquist sampling and digital reconstruction to sense multiple non-contiguous frequency bands. Since the throughput (i.e. the number of vacant bands) increases while the probability of successful reconstruction decreases with increase in the number of sensed bands, we develop an Online Learning Algorithm to characterize and select frequency bands based on their spectrum statistics. We guarantee that the proposed Algorithm allows sensing of maximum possible number of frequency bands and hence, it is referred to as throughput optimized WSS. We also provide a lower bound on the number of time slots required to characterize spectrum statistics. Simulation and experimental results in the real radio environment show that the performance of the proposed approach converges to that of Myopic approach which has prior knowledge of spectrum statistics.
