Download Blind Signal Processing: Theory and Practice by Xizhi Shi PDF

By Xizhi Shi

"Blind sign Processing: conception and perform" not just introduces comparable primary arithmetic, but additionally displays the various advances within the box, akin to chance density estimation-based processing algorithms, underdetermined versions, advanced worth equipment, uncertainty of order within the separation of convolutive combinations in frequency domain names, and have extraction utilizing autonomous part research (ICA). on the finish of the e-book, effects from a learn performed at Shanghai Jiao Tong college within the parts of speech sign processing, underwater indications, picture characteristic extraction, facts compression, and so forth are discussed.

This booklet could be of specific curiosity to complex undergraduate scholars, graduate scholars, collage teachers and examine scientists in similar disciplines. Xizhi Shi is a Professor at Shanghai Jiao Tong University.

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Extra info for Blind Signal Processing: Theory and Practice

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Iǹ>0, i. e. 9) can realize whitening of the mixed signal. Besides eigenvalue decomposition and singular decomposition for whitening, sometimes an on-line method is adopted to realize whitening of the mixed signal, which is called equivariant adaptive whitening of the random vector. Set T as the whitening matrix. 11) where C>TA. 12) where is the Frobeniius norm of the matrix. Obviously, if and only if Ry>I, there will be (C)>0. 13)!!

For this problem, we can use the relative entropy which takes lg x as reference to comparing these two differential entropies, thereby the relative entropy is kept as a positive value. The following section will discuss relative entropy. 2. 6)!! 7)!! Therefore we can conclude that for all random variables with unit variance, the Gaussian variable has the largest entropy. It means that entropy can be t h e m e a s u r e o f n o n - G a u s s i a n . F o r e x a m p l e , w i t h a r a n d o m variable congregate in some value, its pdf has a spiky form and the entropy of distribution is small.

Diamantaras K I, Kung S Y (1996) Principal component neural networks: Theory and applications. Wiley, New York ! hen T P, Amari S, Lin Q (1998) A uniſed algorithm for principal and [63]! C minor components extraction. Neural Networks 11(3): 385—390 [64]! F riedman J H (1987) Exploratory projection pursuit. Journal of the American Statistical Association 82(397): 249—266 [65]! Jones M C, Sibson R (1987) What is projection pursuit? Journal of the Royal Statistical Society, Series A 150(1): 1—36 [66]!

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