Support vector machine applications in terahertz pulsed signals feature sets
Date
2007
Authors
Yin, X.
Ng, B.
Fischer, B.
Ferguson, B.
Abbott, D.
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Journal article
Citation
IEEE Sensors Journal, 2007; 7(11-12):1597-1608
Statement of Responsibility
Xiaoxia Yin, Brian W.-H. Ng, Bernd M. Fischer, Bradley Ferguson, and Derek Abbott
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Abstract
In the past decade, terahertz radiation (T-rays) have been extensively applied within the fields of industrial and biomedical imaging, owing to their noninvasive property. Support vector machine (SVM) learning algorithms are sufficiently powerful to detect patterns hidden inside noisy biomedical measurements. This paper introduces a frequency orientation component method to extract T-ray feature sets for the application of two- and multiclass classification using SVMs. Effective discriminations of ribonucleic acid (RNA) samples and various powdered substances are demonstrated. The development of this method has become important in T-ray chemical sensing and image processing, which results in enhanced detectability useful for many applications, such as quality control, security detection and clinic diagnosis.
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Copyright © 2007 IEEE. All Rights Reserved.