One-dimensional wavelet transforms and their application to T-ray pulsed signal identification

This study investigates the application of one dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. The Fast Fourier Transform (FFT) is used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. In this work, soft threshold wavelet shrinkage de-noising plays an important part in de-noising and reconstructing T-ray pulsed signals. In addition, Mallat's pyramid algorithm and a local modulus maxima method to reconstruct T-ray signals are investigated. Particularly the local modulus maxima method is analyzed and comparisons are made before and after reconstruction of signals. The results demonstrate that these two methods are especially effective in analyzing and reconstructing T-ray pulsed responses. Moreover, to test wavelet de-noising effectiveness, the accuracy of the classiffication is calculated and results are displayed in the form of scatter-plots. Results show that soft threshold wavelet shrinkage de-noising improves the classification accuracy and successfully generates visually pleasing scatter plots at selected three frequency components.