Enhanced T-ray signal classification using wavelet preprocessing

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dc.contributor.authorYin, X.
dc.contributor.authorKong, K.
dc.contributor.authorLim, J.
dc.contributor.authorNg, B.
dc.contributor.authorFerguson, B.
dc.contributor.authorMickan, S.
dc.contributor.authorAbbott, D.
dc.date.issued2007
dc.descriptionThe original publication is available at www.springerlink.com
dc.description.abstractThis study demonstrates the application of one-dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. Fast Fourier transforms (FFTs) are used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. Soft threshold wavelet shrinkage de-noising is used and plays an important role in de-noising and reconstruction of T-ray pulsed signals. An iterative algorithm is applied to obtain three optimal frequency components and to achieve preferred classification performance.
dc.description.statementofresponsibilityX. X. Yin, K. M. Kong, J. W. Lim, B. W.-H. Ng, B. Ferguson, S. P. Mickan and D. Abbott
dc.identifier.citationMedical and Biological Engineering and Computing, 2007; 45(6):611-616
dc.identifier.doi10.1007/s11517-007-0185-y
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.orcidNg, B. [0000-0002-8316-4996]
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]
dc.identifier.urihttp://hdl.handle.net/2440/39634
dc.language.isoen
dc.publisherPeter Peregrinus Ltd
dc.source.urihttp://www.springerlink.com/content/u30728g5774135k0/
dc.subjectMahalanobis distance classifier
dc.subjectWavelet denoising
dc.subjectT-rays
dc.titleEnhanced T-ray signal classification using wavelet preprocessing
dc.typeJournal article
pubs.publication-statusPublished

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