Please use this identifier to cite or link to this item:
https://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 and Biological Engineering and 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 |
DOI: | 10.1007/s11517-007-0185-y |
Published version: | http://www.springerlink.com/content/u30728g5774135k0/ |
Appears in Collections: | Aurora harvest 6 Electrical and Electronic Engineering publications |
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