Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/39634
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Type: Journal article
Title: Enhanced T-ray signal classification using wavelet preprocessing
Author: Yin, X.
Kong, K.
Lim, J.
Ng, B.
Ferguson, B.
Mickan, S.
Abbott, D.
Citation: Medical & Biological Engineering & Computing, 2007; 45(6):611-616
Publisher: Peter Peregrinus Ltd
Issue Date: 2007
ISSN: 0140-0118
1741-0444
Statement of
Responsibility: 
X. X. Yin, K. M. Kong, J. W. Lim, B. W.-H. Ng, B. Ferguson, S. P. Mickan and D. Abbott
Abstract: This 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.
Keywords: Mahalanobis distance classifier; Wavelet denoising; T-rays
Description: The original publication is available at www.springerlink.com
RMID: 0020070937
DOI: 10.1007/s11517-007-0185-y
Published version: http://www.springerlink.com/content/u30728g5774135k0/
Appears in Collections:Electrical and Electronic Engineering publications

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