
Adaptive Signal Processing : Next Generation Solutions
by Tü; lay Adali (University of Maryland, Baltimore County ); Simon Haykin (McMaster University )-
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Summary
Table of Contents
Preface | |
Contributors | |
Complex-Valued Adaptive Signal Processing | |
Introduction | |
Preliminaries | |
Optimization in the Complex Domain | |
Widely Linear Adaptive Filtering | |
Nonlinear Adaptive Filtering with Multilayer Perceptrons | |
Complex Independent Component Analysis | |
Summary | |
Acknowledgment | |
Problems | |
References | |
Robust Estimation Techniques for Complex-Valued Random Vectors | |
Introduction | |
Statistical Characterization of Complex Random Vectors | |
Complex Elliptically Symmetric (CES) Distributions | |
Tools to Compare Estimators | |
Scatter and Pseudo-Scatter Matrices | |
Array Processing Examples | |
MVDR Beamformers Based on M-Estimators | |
Robust ICA | |
Conclusion | |
Problems | |
References | |
Turbo Equalization | |
Introduction | |
Context | |
Communication Chain | |
Turbo Decoder: Overview | |
Forward-Backward Algorithm | |
Simplified Algorithm: Interference Canceler | |
Capacity Analysis | |
Blind Turbo Equalization | |
Convergence | |
Multichannel and Multiuser Settings | |
Concluding Remarks | |
Problems | |
References | |
Subspace Tracking for Signal Processing | |
Introduction | |
Linear Algebra Review | |
Observation Model and Problem Statement | |
Preliminary Example: Oja's Neuron | |
Subspace Tracking | |
Eigenvectors Tracking | |
Convergence and Performance Analysis Issues | |
Illustrative Examples | |
Concluding Remarks | |
Problems | |
References | |
Particle Filtering | |
Introduction | |
Motivation for Use of Particle Filtering | |
The Basic Idea | |
The Choice of Proposal Distribution and Resampling | |
Some Particle Filtering Methods | |
Handling Constant Parameters | |
Rao-Blackwellization | |
Prediction | |
Smoothing | |
Convergence Issues | |
Computational Issues and Hardware Implementation | |
Acknowledgments | |
Exercises | |
References | |
Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems | |
Introduction | |
Back-Propagation and Support Vector Machine-Learning Algorithms: Review | |
Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation | |
The Extended Kalman Filter | |
Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms | |
Concluding Remarks | |
Problems | |
References | |
Bandwidth Extension of Telephony Speech | |
Introduction | |
Organization of the Chapter | |
Nonmodel-Based Algorithms for Bandwidth Extension | |
Basics | |
Model-Based Algorithms for Bandwidth Extension | |
Evaluation of Bandwidth Extension Algorithms | |
Conclusion | |
Problems | |
References | |
Index | |
Table of Contents provided by Publisher. All Rights Reserved. |
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