Enhanced T-ray signal classification using wavelet preprocessing.
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.
Yin
X X
XX
Centre for Biomedical Engineering and School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia.
Kong
K M
KM
Lim
J W
JW
Ng
B W-H
BW
Ferguson
B
B
Mickan
S P
SP
Abbott
D
D
D000465:Algorithms; D005583:Fourier Analysis; D008962:Models, Theoretical; D011840:Radiation, Nonionizing