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|Scopus||Web of Science®||Altmetric|
|Title:||Enhanced T-ray signal classification using wavelet preprocessing|
|Citation:||Medical & Biological Engineering & Computing, 2007; 45(6):611-616|
|Publisher:||Peter Peregrinus Ltd|
|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|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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