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Blind extraction using fractional lower-order statistics

Yang, Zuyuan, Zhou, Guoxu and Xie, Shengli 2009, Blind extraction using fractional lower-order statistics, in ICNC 2009 : Proceedings of the 5th International Conference on Natural Computation, IEEE Xplore, Piscataway, New Jersey, pp. 569-572, doi: 10.1109/ICNC.2009.269.

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Title Blind extraction using fractional lower-order statistics
Author(s) Yang, Zuyuan
Zhou, Guoxu
Xie, Shengli
Conference name 5th International Conference on Natural Computation, ICNC 2009 (August 14, 2009)
Conference location Tianjian, China
Conference dates 14 Aug. 2009
Title of proceedings ICNC 2009 : Proceedings of the 5th International Conference on Natural Computation
Editor(s) [Unknown]
Publication date 2009
Conference series International Conference of the Natural Computation
Start page 569
End page 572
Total pages 4
Publisher IEEE Xplore
Place of publication Piscataway, New Jersey
Keyword(s) Blind extraction
Fractional lower-order statistics
Summary In traditional method to blindly extract interesting source signals sequentially, the second-order or higher-order statistics of signals are often utilized. However, for impulsive sources, both of the second-order and higher-order statistics may degenerate. Therefore, it is necessary to exploit new method for the blind extraction of impulsive sources. Based on the best compression-reconstruction principle, a novel model is proposed in this work, together with the corresponding algorithm. The proposed method can be used for blind extraction of sources which are distributed from alpha stable process. Simulations are given to illustrate availability and robustness of our algorithm.
ISBN 9780769537368
Language eng
DOI 10.1109/ICNC.2009.269
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30059216

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.