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By De-Shuang Huang, George William Irwin

Result of the foreign convention on clever Computing, ICIC 2006: Lecture Notes in computing device technology (LNCS), Lecture Notes in man made Intelligence (LNAI), Lecture Notes in Bioinformatics (LNBI), Lecture Notes on top of things and knowledge Sciences (LNCIS). 142 revised complete papers are prepared in topical sections: Blind resource Separation; clever Sensor Networks; clever keep an eye on and Automation; and information Fusion, wisdom Discovery, and information Mining. encompasses a particular consultation on clever and clever domestic expertise.

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Additional info for Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August, 2006

Example text

Therefore ICA as a feature extraction method has been widely used in extracting efficient speech features and reducing noise[2,3]. However ICA requires signal from at least two separate sensors. For single channel data many ICA feature extraction methods directly segment the data into data matrix[4,5] and learn ICA feature basis using noise-free training signal as a prior knowledge[6]. Direct segmentation is based on the assumption that signal is stationary within a short time. But the assumption is approximate and not strict.

References 1. , Albizuri, F. : Morphological Scale Spaces and Associative Memories: Results on Robustness and Practical Applications, J. Math. Image. , vol. 19, no. 2 (2003), 113-131. 2. Suarez-Araujo, C. : Novel Neural Network Models for Computing Homothetic in Variances: An Image Algebra Notation,” J. Math. Imaging and Vision, vol. 7, no. 1 (1997), 69-83. 3. , Systematic Theory of Neural Networks for Pattern Recognition (in Chinese), Publishing House of Electronic Industry of China, May (1996) 4.

8) A New BSS Algorithm Based on Second-Order Statistics for TITO 31 Step 4. Form a unitary matrix U according to equation (9), which is an accuracy diagonalizer of each matrix of the pair ( P, Q ) . Namely, matrix U satisfies with the form U T PU = D1 and U QU = D2 , where D1 and D2 are both diagonal matrices. T U = RQ −1 ⋅ VF (9) Proof Applying (5) and (9) to the matrix product of PU , we shall get PU = RPT RP ⋅ RQ −1VF = RPT ⋅ ( RP RQ −1 ) ⋅ VF (10) (7) and (8) are then applied to (10) PU = RPT ⋅ F ⋅ VF = RPT ⋅ U F ΣVF T ⋅ VF = RPT ⋅ U F Σ VF TVF = I , = RQT ⋅ ( RP RQ −1 )T ⋅U F Σ = RQT ⋅ F T ⋅U F Σ = RQT ⋅ VF ΣTU F T ⋅ U F Σ , U F T U F = I = RQT ⋅VF ΣT Σ = RQT VF ⋅ Σ 2 = ( RQ −1VF ) −T Σ 2 = U −T Σ 2 (11) We can find the expression U PU = Σ from (11).

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