in this paper, a fault diagnosis system based on discrete wavelet transform (dwt) and artificial neural networks (anns) is designed to diagnose different types of fault in gears. dwt is an advanced signal-processing technique for fault detection and identification. five features of wavelet transform rms, crest factor, kurtosis, standard deviation and skewness of discrete wavelet coefficients of...

Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the performance of the compression scheme. In this paper we extended the commonly used algorithms to image compression and compared its performance. For an image compression technique, we have lin...

Abrupt change detection based on the wavelet transform and threshold method is very effective in detecting the abrupt changes and hence segmenting the signals recorded during disturbances in the electrical power network. The wavelet method estimates the time-instants of the changes in the signal model parameters during the pre-fault condition, after initiation of fault, after circuit-breaker op...

In this paper we propose an extension to the work of Lischinski et al [1] on combining hierarchical radiosity method and discontinuity meshing. The extension proposed here supports higher order radiosity function at the es timation s tep itself. We have carried out this extension using M ultiWavelet basis function s. Unlike th e multi-wavelet radiosity work of Gortler et al [2] which uses reg u...

Here we present a method of constructing steerable wavelet frames in L2(R) that generalizes and unifies previous approaches, including Simoncelli’s pyramid and Riesz wavelets. The motivation for steerable wavelets is the need to more accurately account for the orientation of data. Such wavelets can be constructed by decomposing an isotropic mother wavelet into a finite collection of oriented mo...

background: electromyographic (emg) signal decomposition is the process by which an emg signal is decomposed into its constituent motor unit potential trains (mupts). a major step in emg decomposition is feature extraction in which each detected motor unit potential (mup) is represented by a feature vector. as with any other pattern recognition system, feature extraction has a significant impac...

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...

The continuous wavelet transform is a new approach to the problem of time-frequency analysis of signals such as electroencephalogram (EEG) and is a promising method for EEG analysis. However, it requires a convolution integral in the time domain, so the amount of computation is enormous. In this paper, we propose a fast wavelet transform (FWT) that the corrected basic fast algorithm (CBFA) and ...

We continue the analysis of the continuous wavelet transform on the 2-sphere, introduced in a previous paper. After a brief review of the transform, we define and discuss the notion of directional spherical wavelet, i.e., wavelets on the sphere that are sensitive to directions. Then we present a calculation method for data given on a regular spherical grid G. This technique, which uses the FFT,...

We extend the wavelet transform to the space of periodic Boehmians and discuss some of its properties. 1. Introduction. The concept of Boehmians was introduced by J. Mikusi´nski and P. Mikusi´nski [7], and the space of Boehmians with two notions of conver-gences was well established in [8]. Many integral transforms have been extended to the context of Boehmian spaces, for example, Fourier trans...