this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

The wavelet transform has become the most interesting new algorithm for still image compression. Yet there are many parameters within a wavelet analysis and synthesis which govern the quality of a decoded image. In this paper, we discuss different decomposition strategies of a two–dimensional signal and their implications for the decoded image: a pool of gray–scale images has been wavelet–trans...

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

In this paper a novel algorithm based on Discrete Wavelet Transform (DWT) approach has been applied to synthesize the sounds produced by a few traditional Indian musical instruments, viz. flute, shehnai and sitar. In this algorithm, the level of decomposition of wavelets is varied till the error norm between the original signal and that generated through DWT is below a desired level. It is obse...

Abstract Because the characteristic of wavelet transform is multi-resolution, their unique advantage of the data model is multi-scale analysis. Then, it’s widely used the Wavelet-based multi-scale sensor data fusion technology in many fields. There are two problems in operating process, which are the choice of wavelet base and the choice of decomposition level. The wavelet decomposition level i...

A wide variety of scientiic settings have to do with indirect noisy measurements. We are interesting in some object f(t) but the data is accessible only about some transform (Kf)(t), where K is some linear operator, and (Kf)(t) is in addition corrupted by noise. Recovering f(t) from indirect observations, one faces a Statistical Linear Inverse Problem (SLIP). The usual linear methods for SLIPs,...

In the mid-1980's, wavelet theory was developed in applied mathematics [1, 2, 3]. Soon, subband coding [4], which has been a very active research area for image and video compression, was identi ed as wavelet's discrete cousin. Furthermore, a fundamental insight into the structure of subband lters was developed from wavelet theory that led to a more productive approach to designing the lters [1...

Wavelet transform is an effective method of image fusion. But the decomposition level is an important factor which affects the image fusion effect and computing complexity. By using some evaluation methods of image fusion, such as entropy, standard deviation, mutual information, and quality measure methods was proposed by Wang & Bovik and Xing. Evaluation of image fusion was implemented with di...

The main objective of this paper is to present a wavelet-based procedure to characterize principle features of a special class of motions called pulse-like ground motions. Initially, continues wavelet transform (CWT) which has been known as a powerful technique both in earthquake engineering and seismology field is applied easily in automated detecting of strong pulse of earthquakes. In this pr...