By Cichocki A., Amari Sh.-H.
With good 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 functional and effective algorithms for blind resource separation, self reliant, significant, Minor part research, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind sign and photograph Processing gives you an unparalleled choice of valuable options for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signs and knowledge.
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Additional info for Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Extraction of a single source is closely related to the problem of blind deconvolution [612, 615, 1067, 1078]. e. one by one, rather than to separate all of them simultaneously. This procedure is called the sequential blind signal extraction in contrast with the simultaneous blind signal separation (BSS). Sequential blind signal extraction can be performed by using a cascade neural network similar to the one used for the extraction of principal components. However, in contrast with PCA, the optimization criteria for BSE are different.
Moreover, the FECG will occasionally overlap the MECG and make it normally impossible to detect. Along with the MECG, extensive electromyographic (EMG) noise also interferes with the FECG and it can completely mask the FECG. 16). The recordings pick up a mixture of FECG, MECG contributions, and other interferences, such as maternal electromyogram (MEMG), power supply interference, thermal noise from the electrodes and other electronic equipment. In fact, BSP techniques can be successfully applied to efficiently solve this problem and the first results are very promising [230, 232, 883].
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. For example, in the EEG or MEG devices, we observe typically more than 64 sensor signals, but only a few source signals are interesting; the rest can be considered as interfering noise. In another example, the cocktail party problem, it is usually essential to extract the voices of specific persons rather than separate all the source signals available from a large array of microphones.
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications by Cichocki A., Amari Sh.-H.