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Enhanced T-ray signal classification using wavelet preprocessing

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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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Authors and Affiliations

  1. Centre for Biomedical Engineering and School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia

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

  2. Tenix Defence Systems Pty Ltd, Technology Park, Mawson Lakes, Adelaide, SA, 5095, Australia

    B. Ferguson

Authors
  1. X. X. Yin
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  2. K. M. Kong
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  3. J. W. Lim
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  4. B. W.-H. Ng
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  5. B. Ferguson
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  6. S. P. Mickan
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  7. D. Abbott
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Corresponding author

Correspondence to D. Abbott.

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Yin, X.X., Kong, K.M., Lim, J.W. et al. Enhanced T-ray signal classification using wavelet preprocessing. Med Bio Eng Comput 45, 611–616 (2007). https://doi.org/10.1007/s11517-007-0185-y

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  • DOI: https://doi.org/10.1007/s11517-007-0185-y

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