The Experts below are selected from a list of 50049 Experts worldwide ranked by ideXlab platform
Xingmei Zhong - One of the best experts on this subject based on the ideXlab platform.
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An Adaptive Algorithm for narrow-band interference rejection in DSSS systems
2000 IEEE International Symposium on Circuits and Systems (ISCAS), 2000Co-Authors: Guowen Song, Dapeng Yu, Xingmei ZhongAbstract:In this paper, a fast convergent nonlinear Adaptive Algorithm for narrow-band interference rejection in direct sequence spread spectrum (DSSS) systems is proposed. Simulation results show that the new Algorithm can significantly increase the convergence rate of the nonlinear Adaptive filter.
Guowen Song - One of the best experts on this subject based on the ideXlab platform.
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An Adaptive Algorithm for narrow-band interference rejection in DSSS systems
2000 IEEE International Symposium on Circuits and Systems (ISCAS), 2000Co-Authors: Guowen Song, Dapeng Yu, Xingmei ZhongAbstract:In this paper, a fast convergent nonlinear Adaptive Algorithm for narrow-band interference rejection in direct sequence spread spectrum (DSSS) systems is proposed. Simulation results show that the new Algorithm can significantly increase the convergence rate of the nonlinear Adaptive filter.
Muhammad Moinuddin - One of the best experts on this subject based on the ideXlab platform.
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A noise constrained least mean fourth (NCLMF) Adaptive Algorithm
Signal Processing, 2020Co-Authors: Azzedine Zerguine, Muhammad Moinuddin, Syed Ali Aamir ImamAbstract:The learning speed of an Adaptive Algorithm can be improved by properly constraining the cost function of the Adaptive Algorithm. In this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is proposed. The NCLMF Algorithm is obtained by constraining the cost function of the standard LMF Algorithm to the fourth-order moment of the additive noise. The NCLMF Algorithm can be seen as a variable step-size LMF Algorithm. The main aim of this work is to derive the NCLMF Adaptive Algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF Algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF Algorithm.
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Convergence and tracking analysis of a constrained least mean fourth Adaptive Algorithm
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Syed Ali Aamir Imam, Azzedine Zerguine, Muhammad MoinuddinAbstract:It is a well established fact that the addition of a constraint to an Adaptive Algorithm improves its performance properties. Consequently, in this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is developed. The NCLMF Algorithm is based on a constrained minimization problem that includes knowledge of the noise variance. Moreover, this noise constrained LMF Algorithm can be seen as a variable-step-size LMF Algorithm. The convergence analysis as well the tracking analysis of the NCLMF Adaptive Algorithm are developed using the concept of energy conservation. Finally, simulation results are presented to demonstrate the superiority of the NCLMF Algorithm over the conventional LMF Algorithm as well corroborating the theoretical findings.
Syed Ali Aamir Imam - One of the best experts on this subject based on the ideXlab platform.
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A noise constrained least mean fourth (NCLMF) Adaptive Algorithm
Signal Processing, 2020Co-Authors: Azzedine Zerguine, Muhammad Moinuddin, Syed Ali Aamir ImamAbstract:The learning speed of an Adaptive Algorithm can be improved by properly constraining the cost function of the Adaptive Algorithm. In this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is proposed. The NCLMF Algorithm is obtained by constraining the cost function of the standard LMF Algorithm to the fourth-order moment of the additive noise. The NCLMF Algorithm can be seen as a variable step-size LMF Algorithm. The main aim of this work is to derive the NCLMF Adaptive Algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF Algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF Algorithm.
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Convergence and tracking analysis of a constrained least mean fourth Adaptive Algorithm
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Syed Ali Aamir Imam, Azzedine Zerguine, Muhammad MoinuddinAbstract:It is a well established fact that the addition of a constraint to an Adaptive Algorithm improves its performance properties. Consequently, in this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is developed. The NCLMF Algorithm is based on a constrained minimization problem that includes knowledge of the noise variance. Moreover, this noise constrained LMF Algorithm can be seen as a variable-step-size LMF Algorithm. The convergence analysis as well the tracking analysis of the NCLMF Adaptive Algorithm are developed using the concept of energy conservation. Finally, simulation results are presented to demonstrate the superiority of the NCLMF Algorithm over the conventional LMF Algorithm as well corroborating the theoretical findings.
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A Noise Constrained Least Mean Fourth Adaptive Algorithm
2007 IEEE International Conference on Signal Processing and Communications, 2007Co-Authors: Syed Ali Aamir Imam, Azzedine Zerguine, Mohamed DericheAbstract:In this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is proposed. Based on the fact that in many practical applications an accurate estimate of the measurement noise variance is available, or can be easily estimated, the learning speed of the LMF Algorithm can be then increased considerably by adding a constraint to it. This noise constrained LMF Algorithm can be seen as a variable step-size LMF Algorithm. The main aim of this paper is to derive the NCLMF Adaptive Algorithm, analyze its convergence behaviour, and assess its performance in different noise environments. Moreover, the concept of energy conservation is used to carry out the rigorous steady-state analysis. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF Algorithm.
H. Kubota - One of the best experts on this subject based on the ideXlab platform.
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A new block Adaptive Algorithm using order recursive UD factorization method
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1994Co-Authors: T. Furukawa, S. Yoshimoto, H. KubotaAbstract:Adaptive Algorithm especially plays the important role in Adaptive signal processing, and much work has been proposed with various techniques until up to now. Among them, the Algorithms using orthogonal projection are well known, which can be expressed with Moore-Penrose type generalized inverse matrix, with respect to the convergence characteristics. This paper presents a new block Adaptive Algorithm in which Moore-Penrose type generalized matrix can be efficiently calculated with order-update UD factorization. The proposed Algorithm is expected to have stable property, since it includes less divisions in the procedures compared with the traditional last block Adaptive Algorithms.
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A robust Adaptive Algorithm, for block signal processing
1991. IEEE International Sympoisum on Circuits and Systems, 1991Co-Authors: T. Furukawa, H. KubotaAbstract:An analysis is made of the behavior of a block orthogonal projection Algorithm (BOPA) applied to a practical system in which the input signal is smooth-colored and additive noise is observed at the output of an unknown filter. Using this result, a block Adaptive Algorithm is presented. This Algorithm is based on singular-value decomposition and obtained by truncating several smaller singular values.