Adaptive Blind Signal and Image Processing

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 MATLAB user-friendly software packages are also provided
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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.

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Table of contents :
Content: Introduction to Blind Signal Processing: Problems and Applications —
Problem Formulations–An Overview —
Generalized Blind Signal Processing Problem —
Instantaneous Blind Source Separation and Independent Component Analysis —
Independent Component Analysis for Noisy Data —
Multichannel Blind Deconvolution and Separation —
Blind Extraction of Signals —
Generalized Multichannel Blind Deconvolution–State Space Models —
Nonlinear State Space Models–Semi-Blind Signal Processing —
Why State Space Demixing Models? —
Potential Applications of Blind and Semi-Blind Signal Processing —
Biomedical Signal Processing —
Blind Separation of Electrocardiographic Signals of Fetus and Mother —
Enhancement and Decomposition of EMG Signals —
EEG and Data MEG Processing —
Application of ICA/BSS for Noise and Interference Cancellation in Multi-sensory Biomedical Signals —
Cocktail Party Problem —
Digital Communication Systems —
Why Blind? —
Image Restoration and Understanding —
Solving a System of Algebraic Equations and Related Problems —
Formulation of the Problem for Systems of Linear Equations —
Least-Squares Problems —
Basic Features of the Least-Squares Solution —
Weighted Least-Squares and Best Linear Unbiased Estimation —
Basic Network Structure-Least-Squares Criteria —
Iterative Parallel Algorithms for Large and Sparse Systems —
Iterative Algorithms with Non-negativity Constraints —
Robust Circuit Structure by Using the Interactively Reweighted Least-Squares Criteria —
Tikhonov Regularization and SVD.