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