The Experts below are selected from a list of 31770 Experts worldwide ranked by ideXlab platform
Peter J. Schreier - One of the best experts on this subject based on the ideXlab platform.
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Regularized Preconditioning for Krylov Subspace Equalization of OFDM Systems over Doubly Selective Channels
IEEE Wireless Communications Letters, 2013Co-Authors: Jun Tong, Peter J. SchreierAbstract:Orthogonal frequency division multiplexing (OFDM) systems over doubly selective channels may simultaneously suffer from inter-carrier interference (ICI) and imperfect channel estimation. The performance of a linear equalizer can degrade significantly if the presumed system model is mismatched with the actual system. Krylov subspace equalization, which is often combined with preconditioning, can improve the robustness against model mismatch. In this letter, we show that conventional preconditioners degrade performance when the model mismatch is serious. To address this problem, we design regularized preconditioners that cluster only the largest eigenvalues of the relevant system matrix. We demonstrate that the regularized preconditioners can effectively Reduce Complexity and at the same time preserve the robustness of Krylov subspace equalizers.
J. Flanagan - One of the best experts on this subject based on the ideXlab platform.
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Sound capture from spatial volumes: matched-filter processing of microphone arrays having randomly-distributed sensors
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1996Co-Authors: Ea Ee Jan, J. FlanaganAbstract:This report describes microphone arrays and parallel signal processing for high-quality sound capture in noisy, reverberant enclosures. The technique incorporates matched-filtering of individual sensors and parallel processing to provide spatial volume selectivity that mitigates effects of noise interference and multipath distortion. Truncated causal approximations to ideal matched-filters and arrays composed of randomly distributed transducers Reduce Complexity and improve the quality of sound capture. The method outperforms traditional delay-and-sum beamformers which provide only directional selectivity
Anthony Soong - One of the best experts on this subject based on the ideXlab platform.
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Simplified Relay Selection and Power Allocation in Cooperative Cognitive Radio Systems
IEEE Transactions on Wireless Communications, 2011Co-Authors: Liying Li, Xiangwei Zhou, Hongbing Xu, Geoffrey Ye Li, Dandan Wang, Anthony SoongAbstract:In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to Reduce Complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.
Liang Chuan - One of the best experts on this subject based on the ideXlab platform.
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Research on Hydrology Time Series Prediction Based on Grey Theory and [epsilon]-Support Vector Regression
2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, 2011Co-Authors: Zhao Cheng-ping, Liang ChuanAbstract:Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance Complexity of prediction model and Complexity of samples and raise forecasting accuracy, combined prediction model based on support vector machine and grey theory was proposed. The grey time series prediction method was used to Reduce Complexity of samples and the support vector machine regression was used to Reduce Complexity of prediction model. The incoming water time series of Minjiang River in 1937-2002 were taken as the sample to be analyzed. The results show that the combined algorithm of ε-support vector regression and grey theory has better effects in simulate of trend data and the random data in medium and long-term forecasting.
Chuan Liang - One of the best experts on this subject based on the ideXlab platform.
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Research on Hydrology Time Series Prediction Based on Grey Theory and epsilon-Support Vector Regression
2011 Second International Conference on Digital Manufacturing & Automation, 2011Co-Authors: Cheng-ping Zhao, Chuan LiangAbstract:Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance Complexity of prediction model and Complexity of samples and raise forecasting accuracy, combined prediction model based on support vector machine and grey theory was proposed. The grey time series prediction method was used to Reduce Complexity of samples and the support vector machine regression was used to Reduce Complexity of prediction model. The incoming water time series of Minjiang River in 1937-2002 were taken as the sample to be analyzed. The results show that the combined algorithm of e-support vector regression and grey theory has better effects in simulate of trend data and the random data in medium and long-term forecasting.
