Communications Technology Web page - John Wiley & Sons, Ltd.

	Adaptive Blind Signal and Image Processing Learning Algorithms and Applications
	Andrzej Cichocki, Brain Science Institute, Japan & Shun-Ichi Amari, Warsaw University of Technology, Poland
	0471 60791 6 March 2002 Hardback 528pp

With solid theoretical foundations and numerous potential applications, Blind Signal Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind Signal and Image Processing delivers an unprecedented collection of useful techniques for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signals and data. Offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view Presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems Provides a guide to fundamental mathematics of multi-input, multi-output and multi-sensory systems Includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study Accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLABŽ user-friendly software packages are also provided MATLABŽ is a registered trademark of The MathWorks, Inc. By providing a detailed introduction to BSP, as well as presenting new results and recent developments, this informative and inspiring work will appeal to researchers, postgraduate students, engineers and scientists working in biomedical engineering, communications, electronics, computer science, optimisations, finance, geophysics and neural networks. Contents: Preface 1. Introduction to Blind Signal Processing: Problems and Applications Problem formulations - Overview Potential Applications of Blind and Semi-Blind Signal Processing 2. Solving a System of Linear Equations and Related Problems Formulation of the Problem for Systems of Linear Equations Least-Squares Problems Least Absolute Deviation (L1-norm) Solution of Systems of Linear Equations Total Least-Squares and Data Least-Squares Problems Minimum Fuel Problem and Sparse Signal Representation 3. Principal/Minor Component Analysis and Related Problems Introduction Basic Properties of PCA Extraction of Principal Components Basic Cost Functions and Adaptive Algorithms for PCA Robust PCA Adaptive Learning Algorithms for MCA Unified Parallel Algorithms for PCA/MCA and PSA/MSA SVD in Relation to PCA and Matrix Subspaces Multistage PCA for BSS 4. Blind Decorrelation and Second Order Statistics for Blind Indentification Cancellation of Correlation Spatial Decorrelation - Whitening Transforms SOS Blind Identification Based on EVD Improved Blind Identification Algorithms Based on Multistage SVD/EVD Joint Diagonalization - Robust SOBI and JADE Algorithms 5. Sequential Blind Signal Extraction Introduction and Problem Formulation Learning Algorithms Based on Kurtosis as Cost Function On Line Algorithms for Blind Signal Extraction of Temporally Correlated Sources Batch Algorithms for Blind Extraction of Temporally Correlated Sources Statistical Approach to Sequential Extraction of Independent Sources Statistical Approach to Temporally Correlated Sources On-line Sequential Extraction of Convolved and Mixed Sources Computer Simulation: Illustrative Examples 6. Natural Gradient Approach to Independent Component Analysis Basic Natural Gradient Algorithms Generalizations of Basic Natural Gradient Algorithm NG Algorithms for Blind Extraction Natural Gradient Algorithms for Overcomplete Case Generalized Gaussian Distribution Model Natural Gradient Algorithms for Non-stationary Sources 7. Locally Adaptive Algorithsm for ICA and their Implementations Modified Jutten-Hé rault Algorithms for Blind Separation of Sources Iterative Matrix Inversion Approach to Derivation of Family of Robust ICA Algorithms Computer Simulation Experiments 8. Robust Techniques for BSS and ICA with Noisy Data Introduction Bias Removal Techniques for Prewhitening and ICA Algorithms Blind Separation of Signals Buried in Additive Convolutive Reference Noise Cumulants Based ICA Adaptive Algorithms Robust Extraction of Arbitrary Group of Source Signals Recurrent Neural Network Approach for Noise Cancellation 9. Multichannel Blind Deconvolution - Natural Gradient Approach SIMO Convolutive Models and Learning Algorithms for Estimation of Source Signal Multichannel Blind Deconvolution with Constraitns Imposed on FIR Filters General Models for Multi Input Multi Output Blind Deconvolution Relationships between BSS/ICA and MBD Natural Gradient Algorithms with Nonholonomic Constraints Blind Deconvolution of Non-minimum Phase System Using Filter Decomposition Approach Computer Simulations Experiments 10. Estimating Functions and Superefficiency for Blind Separation and Deconvolution Estimating Functions for Standard ICA Estimating Functions in Noisy Case Estimating Functions for Temporally Correlated Source Signals Semiparametric Models for Multichannel Blind Deconvolution Estimating Functions for MBD 11. Linear Blind Filtering and Separation Using State-Space Approach Formulation of the Problems Derivation of Basic Learning Algorithms Estimation of Matrices [A,B] by Information Back-propagation State Estimator - The Kalman Filter Two-stage Separation Algorithm 12. Nonlinear State Space Models - Semi-Blind Signal Processing General Formulation of the Problem Supervising - Unsupervising Approach References Appendices Index

Contents: