Enhanced T-ray signal classification using wavelet preprocessing

Date

2007

Authors

Yin, X.
Kong, K.
Lim, J.
Ng, B.
Ferguson, B.
Mickan, S.
Abbott, D.

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Journal article

Citation

Medical and Biological Engineering and Computing, 2007; 45(6):611-616

Statement of Responsibility

X. X. Yin, K. M. Kong, J. W. Lim, B. W.-H. Ng, B. Ferguson, S. P. Mickan and D. Abbott

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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.

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The original publication is available at www.springerlink.com

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