Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/43936
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Type: Journal article
Title: Support vector machine applications in terahertz pulsed signals feature sets
Author: Yin, X.
Ng, B.
Fischer, B.
Ferguson, B.
Abbott, D.
Citation: IEEE Sensors Journal, 2007; 7(11-12):1597-1608
Publisher: The Institute of Electrical and Electronic Engineers Inc.
Issue Date: 2007
ISSN: 1530-437X
Statement of
Responsibility: 
Xiaoxia Yin, Brian W.-H. Ng, Bernd M. Fischer, Bradley Ferguson, and Derek Abbott
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.
Description: Copyright © 2007 IEEE. All Rights Reserved.
RMID: 0020073726
DOI: 10.1109/JSEN.2007.908243
Appears in Collections:Electrical and Electronic Engineering publications

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