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dc.contributor.authorYin, X.en
dc.contributor.authorNg, B.en
dc.contributor.authorFischer, B.en
dc.contributor.authorFerguson, B.en
dc.contributor.authorAbbott, D.en
dc.identifier.citationIEEE Sensors Journal, 2007; 7(11-12):1597-1608en
dc.descriptionCopyright © 2007 IEEE. All Rights Reserved.en
dc.description.abstractIn 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.en
dc.description.statementofresponsibilityXiaoxia Yin, Brian W.-H. Ng, Bernd M. Fischer, Bradley Ferguson, and Derek Abbotten
dc.publisherThe Institute of Electrical and Electronic Engineers Inc.en
dc.titleSupport vector machine applications in terahertz pulsed signals feature setsen
dc.typeJournal articleen
pubs.library.collectionElectrical and Electronic Engineering publicationsen
dc.identifier.orcidNg, B. [0000-0002-8316-4996]en
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]en
Appears in Collections:Electrical and Electronic Engineering publications

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