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By Cichocki A., Amari Sh.-H.

With good theoretical foundations and diverse power functions, 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 gives functional and effective algorithms for blind resource separation, self reliant, crucial, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and photograph Processing supplies an extraordinary selection of important options for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable indications and knowledge.

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Extra info for Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Sample text

There are several definitions of ICA. In this book, depending on the problem, we use different definitions given below. 1 (Temporal ICA) The ICA of a noisy random vector x(k) ∈ IRm is obtained by finding an n × m, (with m ≥ n), a full rank separating matrix W such that the output signal vector y(k) = [y1 (k), y2 (k), . . 4) contains the estimated source components s(k) ∈ IRn that are as independent as possible, evaluated by an information-theoretic cost function such as the minimum Kullback-Leibler divergence.

The real-world sensor data often build up complex nonlinear structures, so applying ICA to global data may lead to poor results. Instead, applying ICA for all available data, we can preprocess this data by grouping it into clusters or sub-bands with specific features and then apply ICA individually to each cluster or sub-band separately. The preprocessing stage of suitable grouping or clustering of data is responsible for an overall coarse nonlinear representation of the data, while the linear ICA models of individual clusters are used for describing local features of the data.

The MUAP waveforms give information about the structural organization of the motor units [1389]. Blind signal processing techniques can be used for the enhancement of EMG signals. A more challenging problem is to apply BSS for decomposition of EMG signals into independent components and MUAPs. Such blind or semi-blind processing may enable to easily cluster MUAPs into groups of similar waveforms, providing important information about the brain motor system and for the assessment of neuromuscular pathology.

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Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications by Cichocki A., Amari Sh.-H.


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