Titlebook: Blind Signal Processing; Theory and Practice Xizhi Shi Book 2011 Shanghai Jiao Tong University Press,Shanghai and Springer-Verlag Berlin He

深谋远虑

书目名称Blind Signal Processing影响因子(影响力)




书目名称Blind Signal Processing影响因子(影响力)学科排名




书目名称Blind Signal Processing网络公开度




书目名称Blind Signal Processing网络公开度学科排名




书目名称Blind Signal Processing被引频次




书目名称Blind Signal Processing被引频次学科排名




书目名称Blind Signal Processing年度引用




书目名称Blind Signal Processing年度引用学科排名




书目名称Blind Signal Processing读者反馈




书目名称Blind Signal Processing读者反馈学科排名

chronicle

Feckless

Introduction,ich blind signal processing (BSP), as one of the focal points, has great potential. The word “Blind” in BSP means that there are no training data and no prior knowledge of system parameters, so BSP can be utilized very generally.

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Synchrone Perspektive auf Deformalisierung,Mathematical definitions that are necessary to better understand blind signal processing are presented in this chapter. Due to limitation of the book’s length, we will present theoretical results only, omitting the process of proof. Interested readers who want to know more about the process of proof can refer to Refs. .

Schlemms-Canal

https://doi.org/10.1007/978-3-322-91423-1In Chapter 1 Section 1.2.2, the mathematical expression of convolutive mixtures was introduced in the convolutive mixtures separation problem. In order to facilitate reading, basic expressions are cited again here.

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CHOIR

Convolutive Mixtures and Blind Deconvolution,In Chapter 1 Section 1.2.2, the mathematical expression of convolutive mixtures was introduced in the convolutive mixtures separation problem. In order to facilitate reading, basic expressions are cited again here.

分贝

Lernen zwischen Formalität und Informalitätich blind signal processing (BSP), as one of the focal points, has great potential. The word “Blind” in BSP means that there are no training data and no prior knowledge of system parameters, so BSP can be utilized very generally.

echnic

https://doi.org/10.1007/978-3-322-91423-1ar coordinate system to produce signals that are mutually statistically independent. Compared with Principal Component Analysis (PCA) based on correlation transform, ICA decorrelates signals and reduces the correlation in higher-order statistics. The existing blind processing algorithms are mainly b

Statins

https://doi.org/10.1007/978-3-322-91423-1he research of correlation structures of random multi-variates in 1933. Oja proposed a fast algorithm for computing the eigenvalue and eigenvector of the covariance matrix and a Hebb learning rules-based neural networks model for adaptive extractions of maximum principal components in 1982[1,2]. Aft