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.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
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
Conference Name
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.
School/Discipline
Dissertation Note
Provenance
Description
The original publication is available at www.springerlink.com