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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:
Adaptive and Learning Systems for Signal Processing, Communications and Control #55: Adaptive Signal Processing: Next Generation Solutionsby Tulay Adali
Synopses & Reviews
Leading experts present the latest research results in adaptive signal processing
Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension.
Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.
Book News Annotation:
Algorithms for producing results in signal processing are presented in seven chapters, addressing five themes: fundamental issues (such as optimization, efficiency, and robustness in the complex domain), turbo signal processing for equalization, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension. The book includes a novel application of the extended Kalman filter (EKF) for the creation of a neural network to solve difficult pattern recognition problems, and compares this new approach with the classic back-propagation algorithm. Chapter problems are included. Adali is professor of electrical engineering and director of the Machine Learning for Signal Processing Laboratory at the University of Maryland. Haykin directs the Cognitive Systems Laboratory at McMaster University. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
This book presents the latest research results in adaptive signal processing with an emphasis on important applications and theoretical advancements. Each chapter is self-contained, comprehensive in its coverage, and written by a leader in his or her field of specialty. A uniform style is maintained throughout the book and each chapter concludes with problems for readers to reinforce their understanding of the material presented. The book can be used as a reliable reference for researchers and practitioners or as a textbook for graduate students.
About the Author
TÜLAY ADALI, PhD, is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County. Her research interests are in statistical and adaptive signal processing, with emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications.
Simon Haykin, PhD, is Distinguished University Professor and Director of the Cognitive Systems Laboratory in the Faculty of Engineering at McMaster University. A world-renowned authority on adaptive and learning systems, Dr. Haykin has pioneered signal-processing techniques and systems for radar and communication applications, culminating in the study of cognitive dynamic systems, which has become his research passion.
Table of Contents
Chapter 1 Complex-Valued Adaptive Signal Processing.
1.3 Optimization in the Complex Domain.
1.4 Widely Linear Adaptive Filtering.
1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons.
1.6 Complex Independent Component Analysis.
Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors.
2.2 Statistical Characterization of Complex Random Vectors.
2.3 Complex Elliptically Symmetric (CES) Distributions.
2.4 Tools to Compare Estimators.
2.5 Scatter and Pseudo-Scatter Matrices.
2.6 Array Processing Examples.
2.7 MVDR Beamformers Based on M -Estimators.
2.8 Robust ICA.
Chapter 3 Turbo Equalization.
3.3 Communication Chain.
3.4 Turbo Decoder: Overview.
3.5 Forward-Backward Algorithm.
3.6 Simplified Algorithm: Interference Canceler.
3.7 Capacity Analysis.
3.8 Blind Turbo Equalization.
3.10 Multichannel and Multiuser Settings.
3.11 Concluding Remarks.
Chapter 4 Subspace Tracking for Signal Processing.
4.2 Linear Algebra Review.
4.3 Observation Model and Problem Statement.
4.4 Preliminary Example: Oja’s Neuron.
4.5 Subspace Tracking.
4.6 Eigenvectors Tracking.
4.7 Convergence and Performance Analysis Issues.
4.8 Illustrative Examples.
4.9 Concluding Remarks.
Chapter 5 Particle Filtering.
5.2 Motivation for Use of Particle Filtering.
5.3 The Basic Idea.
5.4 The Choice of Proposal Distribution and Resampling.
5.5 Some Particle Filtering Methods.
5.6 Handling Constant Parameters.
5.10 Convergence Issues.
5.11 Computational Issues and Hardware Implementation.
Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems.
6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review.
6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation.
6.4 The Extended Kalman Filter.
6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms.
6.6 Concluding Remarks.
Chapter 7 Bandwidth Extension of Telephony Speech.
7.2 Organization of the Chapter.
7.3 Nonmodel-Based Algorithms for Bandwidth Extension.
7.5 Model-Based Algorithms for Bandwidth Extension.
7.6 Evaluation of Bandwidth Extension Algorithms.
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