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...

A standout amongst the most effective strategies for bosom malignancy early discovery is mammography. Another strategy for identification and arrangement of miniaturized scale calcifications is displayed. It should be possible in four phases: in the first place, pre processing stage manages clamour expulsion, and standardized the picture. Second stage, K-Means bunching (KMC) is utilized for div...

A standout amongst the most effective strategies for bosom malignancy early discovery is mammography. Another strategy for identification and arrangement of miniaturized scale calcifications is displayed. It should be possible in four phases: in the first place, pre processing stage manages clamour expulsion, and standardized the picture. Second stage, K-Means bunching (KMC) is utilized for div...

The aim of this study is to introduce a new methodology for isolation of ectopic rhythms of ambulatory electrocardiogram (ECG) holter data via appropriate statistical analyses imposing reasonable computational burden. First, the events of the ECG signal are detected and delineated using a robust wavelet-based algorithm. Then, using Binary Neyman-Pearson Radius test, an appropriate classifie...

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...

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new comput...

we present a steganalysis scheme for lsb matching steganography based on feature vectors extracted from integer wavelet transform (iwt). in integer wavelet decomposition of an image, the coefficients will be integer, so we can calculate co-occurrence matrix of them without rounding the coefficients. before calculation of co-occurrence matrices, we clip some of the most significant bitplanes of ...

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...

Wavelet transformation is one of the most practical mathematical transformations in the field of image processing, especially image and signal processing. Depending on the nature of the multiresolution analysis, Wavelet transformation become more accessible and powerful tools. In this paper, we refer to the mathematical foundations of this transformation.   Introduction: The...