Summary and Info
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(r) user-friendly software packages are also provided MATLAB(r) 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.Content: Chapter 1 Introduction to Blind Signal Processing: Problems and Applications (pages 1–41): Chapter 2 Solving a System of Algebraic Equations and Related Problems (pages 43–86): Chapter 3 Principal/Minor Component Analysis and Related Problems (pages 87–128): Chapter 4 Blind Decorrelation and SOS for Robust Blind Identification (pages 129–175): Chapter 5 Sequential Blind Signal Extraction (pages 177–229): Chapter 6 Natural Gradient Approach to Independent Component Analysis (pages 231–272): Chapter 7 Locally Adaptive Algorithms for ICA and their Implementations (pages 273–303): Chapter 8 Robust Techniques for BSS and ICA with Noisy Data (pages 305–334): Chapter 9 Multichannel Blind Deconvolution: Natural Gradient Approach (pages 335–382): Chapter 10 Estimating Functions and Superefficiency for ICA and Deconvolution (pages 383–421): Chapter 11 Blind Filtering and Separation Using a State?Space Approach (pages 423–441): Chapter 12 Nonlinear State Space Models – Semi?Blind Signal Processing (pages 443–452):
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