By Andrzej Cichocki
With reliable theoretical foundations and various power purposes, Blind sign Processing (BSP) is likely one of the most well liked rising parts in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and photo processing and offers useful and effective algorithms for blind resource separation: autonomous, valuable, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and photo Processing gives you an unparalleled number of worthy thoughts for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signs and knowledge.
- Offers a vast assurance of blind sign processing concepts and algorithms either from a theoretical and sensible aspect of view
- Presents greater than 50 basic algorithms that may be simply transformed to fit the reader's particular genuine international problems
- Provides a consultant to primary arithmetic of multi-input, multi-output and multi-sensory systems
- Includes illustrative labored examples, machine simulations, tables, specified graphs and conceptual types inside self contained chapters to aid self study
- Accompanying CD-ROM positive factors an digital, interactive model of the publication with totally colored figures and textual content. C and MATLAB common software program applications also are provided
MATLAB is a registered trademark of The MathWorks, Inc.
By supplying a close creation to BSP, in addition to featuring new effects and up to date advancements, this informative and encouraging paintings will attract researchers, postgraduate scholars, engineers and scientists operating in biomedical engineering, communications, electronics, machine technology, optimisations, finance, geophysics and neural networks.
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Extra info for Adaptive Blind Signal and Image Processing
One successful and promising application domain of blind signal processing includes those biomedical signals acquired with multi-electrode devices: Electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG) and magnetoencephalography (MEG). , removing/filtering maternal electrocardiogram signals and noise from fetal electrocardiogram signals. • Enhancement of low-level ECG components. • Separation of transplanted heart signals from residual original heart signals. • Separation of heart sounds from gastrointestinal acoustic phenomena (bowel-sounds).
Decomposition of brain sources as independent components and then localizing them in time and space. Let us consider in more detail, some exemplary promising biomedical applications. 2 Blind Separation of Electrocardiographic Signals of Fetus and Mother The mechanical action of the heart muscles is stimulated by an electrical depolarization and repolarization signals. These quasi-periodical signals project potential differences to the skin level which can be measured and visualized as functions of time using electrocardiogram (ECG).
Chapters 9-11 are partially based on joint works with Liqing Zhang and they include his crucial and important contributions. Chapters 7 and 8 are influenced by joint works with Sergio Cruces and Scott Douglas. Chapter 5 is partially based on joint works with Ruck Thawonmas, Allan Barros, Seungjin Choi and Pando Georgiev. Chapters 4 and 6 are partially based on joint works with Seungjin Choi, Adel Belouchrani, Reda Gharieb and Liqing Zhang. 6 is devoted to the total least squares problem and is based partially on joint work with John Mathews.