By Andrzej Cichocki
With stable theoretical foundations and diverse power purposes, Blind sign Processing (BSP) is without doubt one of the most well liked rising components in sign Processing. This quantity unifies and extends the theories of adaptive blind sign and snapshot processing and offers functional and effective algorithms for blind resource separation,Independent, vital, 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 choice of worthy options for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signs and data.* bargains a wide insurance of blind sign processing concepts and algorithms either from a theoretical and sensible perspective* provides greater than 50 uncomplicated algorithms that may be simply converted to fit the reader's particular actual global difficulties* presents a advisor to primary arithmetic of multi-input, multi-output and multi-sensory structures* contains illustrative labored examples, desktop simulations, tables, specific graphs and conceptual versions inside self contained chapters to help self research* Accompanying CD-ROM positive aspects an digital, interactive model of the e-book with totally colored figures and textual content. C and MATLAB(r) easy software program programs also are providedMATLAB(r) is a registered trademark of The MathWorks, Inc.By supplying an in depth advent to BSP, in addition to offering new effects and up to date advancements, this informative and encouraging paintings will entice researchers, postgraduate scholars, engineers and scientists operating in biomedical engineering,communications, electronics, machine technological know-how, optimisations, finance, geophysics and neural networks.
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Additional info for Adaptive Blind Signal and Image Processing
We study the problem from the perspective of optimal signal estimation and nonlinear adaptive systems. Our mathematical analysis and computer simulations demonstrate that such neural networks can be quite effective and useful in removing of interference and noise. In particular, it will be shown that the Amari-Hopfield recurrent neural network (see Chapter 8) can be more effective than feed-forward networks for certain noise distributions, where the data exhibit a long memory structure (temporal correlation).
3 27 Enhancement and Decomposition of EMG Signals The movement and positioning of limbs are controlled by electrical signals travelling back and forth between the central nervous system and the muscles. Electromyography is a technique of recording of the electrical signals in the muscle (muscle action potentials). Electromyographic (EMG) signals recorded by a multi-electrode system provide important information about the brain motor system and the diagnosis of neuromuscular disorders that affect the brain, spinal cord, nerves or muscles.
5 Blind Extraction of Signals There are two main approaches to solve the problem of blind separation and deconvolution. The first approach, which was mentioned briefly in previous sections, is to simultaneously separate all sources. In the second one, we extract sources sequentially in a blind fashion, one by one, rather than separating them all simultaneously. In many applications, a large number of sensors (electrodes, microphones or transducers) are available but only a very few source signals are subjects of interest.