Ganesh R. Naik, Wenwu Wang's Blind Source Separation: Advances in Theory, Algorithms and PDF
By Ganesh R. Naik, Wenwu Wang
Blind resource Separation intends to file the recent result of the efforts at the learn of Blind resource Separation (BSS). The e-book collects novel examine principles and a few education in BSS, self reliant part research (ICA), man made intelligence and sign processing functions. in addition, the examine effects formerly scattered in lots of journals and meetings all over the world are methodically edited and provided in a unified shape. The publication might be of curiosity to school researchers, R&D engineers and graduate scholars in desktop technology and electronics who desire to examine the middle ideas, equipment, algorithms and functions of BSS.
Dr. Ganesh R. Naik works at college of know-how, Sydney, Australia; Dr. Wenwu Wang works at college of Surrey, UK.
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Extra info for Blind Source Separation: Advances in Theory, Algorithms and Applications
The √ product √ P k · sTi ∈ Rn×1 is needed to obtain and vectorize the kth patch of size n × n taken from image S i . Denote P = [P 1 , . . , P K ] ∈ Rn×KN , where K is the number of patches taken from each image. Then the extraction of multiple sources S is defined as PS = ([P 1 , . . , P K ]) · ( sT1 , sT2 , . . , sTs ⊗ I K ) = P · (ST ⊗ I K ) ∈ Rn×Ks , where symbol ⊗ denotes the Kronecker product and I K indicates the identity matrix. The computational cost associated with converting from images to patches is low.
Information Theory: New Research, pp. 145–170. Nova Science Publishers, Hauppauge, NY (2012). : Quantum computation. : A mutual information minimization approach for a class of nonlinear recurrent separating systems. In: IEEE International Workshop on Machine Learning for Signal Processing, pp. 122–127. : Blind separation of convolutive mixtures and an application in automatic speech recognition in a noisy environment. IEEE Trans. Signal Process. : Méthodes de traitement d’antenne adaptées aux radiocommunications.
Zhao et al. imaging, and communication systems. Typically, a linear mixture model is assumed where the mixtures Z ∈ Rr×N are described as Z = AS + V. Each row of S ∈ Rs×N is a source and A ∈ Rr×s models the linear combinations of the sources. The matrix V ∈ Rr×N represents additive noise or interference introduced during mixture acquisition and transmission. Usually in the BSS problem, the only known information is the mixtures Z and the number of sources. , mathematically, one needs to solve min ∈Z − AS∈2F .
Blind Source Separation: Advances in Theory, Algorithms and Applications by Ganesh R. Naik, Wenwu Wang