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A technique to identify some typical radio frequency interference using support vector machine
Author(s): Yuanchao Wang; Mingtao Li; Dawei Li; Jianhua Zheng
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Paper Abstract

In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200B (21 July 2017); doi: 10.1117/12.2282039
Show Author Affiliations
Yuanchao Wang, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Mingtao Li, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Dawei Li, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Jianhua Zheng, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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