Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics

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Kurtosis Based Techniques for Signal Extraction

Authors
Yongjian Zhao, Manlan Hao
Corresponding Author
Yongjian Zhao
Available Online August 2016.
DOI
10.2991/emcpe-16.2016.137How to use a DOI?
Keywords
Independence, Separation, Mixture, Source, Kurtosis
Abstract

As a practical measure of non-Gaussianity, kurtosis generally represents the preferred technique for signal extraction. Here a learning rule is presented associated with kurtosis. It can recover one source signal owning the maximum absolute value of kurtosis among all original sources. Computer simulations on biomedical signals demonstrate its efficiency.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics
Series
Advances in Engineering Research
Publication Date
August 2016
ISBN
10.2991/emcpe-16.2016.137
ISSN
2352-5401
DOI
10.2991/emcpe-16.2016.137How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Yongjian Zhao
AU  - Manlan Hao
PY  - 2016/08
DA  - 2016/08
TI  - Kurtosis Based Techniques for Signal Extraction
BT  - Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics
PB  - Atlantis Press
SP  - 496
EP  - 499
SN  - 2352-5401
UR  - https://doi.org/10.2991/emcpe-16.2016.137
DO  - 10.2991/emcpe-16.2016.137
ID  - Zhao2016/08
ER  -