Get Blind Source Separation: Advances in Theory, Algorithms and PDF

By Ganesh R. Naik, Wenwu Wang

ISBN-10: 3642550150

ISBN-13: 9783642550157

ISBN-10: 3642550169

ISBN-13: 9783642550164

Blind resource Separation intends to document the recent result of the efforts at the learn of Blind resource Separation (BSS). The e-book collects novel learn rules and a few education in BSS, self sufficient part research (ICA), man made intelligence and sign processing purposes. in addition, the study effects formerly scattered in lots of journals and meetings around the world are methodically edited and awarded in a unified shape. The booklet might be of curiosity to school researchers, R&D engineers and graduate scholars in computing device technological know-how and electronics who desire to research the center ideas, tools, algorithms and purposes of BSS.

Dr. Ganesh R. Naik works at college of expertise, Sydney, Australia; Dr. Wenwu Wang works at collage of Surrey, UK.

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Extra info for Blind Source Separation: Advances in Theory, Algorithms and Applications

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AP-34, no. 3, pp. 276–280, Mar 1986 45. : What Is Life ?. Cambridge University Press, Cambridge (1944) 46. : The Mathematical Theory of Communication. University of Illinois Press, Urbana and Chicago (1949) 47. : Progress in quantum algorithms. Quantum Inf. Process. 3(1–5), pp. 5–13 (2004) 48. : Source separation in post-nonlinear mixtures. IEEE Trans. Signal Process. 47(10), 2807–2820 (1999) 49. : A generic framework for blind source separation in structured nonlinear models. IEEE Trans. Signal Process.

Low performance, even for the highest considered values of the numbers K m and K s of measurements. This may especially be due to the fact that this information-based method requires a large number of observed vectors in order to accurately estimate output signal statistics, whereas we wish to restrict ourselves to a limited number of such observed vectors (100 or 1,000), in order to limit the amount of data that must be measured to apply our methods (remember that each observed vector here requires K m = 104 or 105 measurements in the mixture estimation stage).

Opt. Soc. Am. B 24(2), 172–183 (2007) 56. : Adaptive noise cancelling: principles and applications. Proc. IEEE 63(12), 1692–1716 (1975) 57. In: Proceedings of the Sixth International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), vol. LNCS 3889, pp. 926–933. Springer, Charleston, SC, USA, 5–8 Mar 2006 Chapter 2 Blind Source Separation Based on Dictionary Learning: A Singularity-Aware Approach Xiaochen Zhao, Guangyu Zhou, Wei Dai and Wenwu Wang Abstract This chapter surveys recent works in applying sparse signal processing techniques, in particular, dictionary learning algorithms to solve the blind source separation problem.

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Blind Source Separation: Advances in Theory, Algorithms and Applications by Ganesh R. Naik, Wenwu Wang

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