By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrare...

This paper presents a wavelet transform (WT) and artificial neural network (ANN) based algorithm for estimating fault location on transmission lines. The algorithm is developed as a one-end frequency based technique and used both voltage and current effect resulting from remote end of the power system. Fault simulation is carry out in Alternative Transient Program (ATP). One cycle of waveform, ...

Power swing caused by various disturbances will affect distance relay behavior and may result in relay misoperation. This paper provides a new wavelet-based method for detection and classification of transmission line faults during power swing. The multi-resolution analysis based on wavelet transform (WT) has the ability to decompose the analyzed signals into different frequency bands. With the...

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and...

velet theory provides a unified framework for a number of techniques which had been developed independently for various signal processing applications. For example. multiresolution signal processing. used in computer vision; subband coding, developed for speech and image compression; and wavelet series expansions, developed in applied mathematics, have been recently recognized as different view...

introduction: one of the major problems in the development of nuclear medicine images is the presence of noise. the noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a fourier transform environment. the main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

Field seismic data can contain many sorts of noise and interferences. If the noise is stronger in order of magnitude than the signal, most techniques often applied in seismic processing will be severely affected. Denoising methods, even very robust schemes such as physical wavelet frame denoising (PWFD), are not exceptions. In this paper, we present a robust, data adaptive and fast 1D wavelet t...

State – of – the – art Automatic Speech Recognition (ASR) employs rigorous experimental evaluations on large, standard corpora from the real world. In recent years ASR and Machine Learning (ML) algorithms have had a great deal of influences on each other and feature selections can be considered as an essential task in ML. Compared with traditional basic speech feature extraction techniques, Wav...

In this paper, we develop a novel transform called semisubsampled wavelet transform (SSWT) and employ it to image watermarking. SSWT consists of two parts, one is nonsubsampled tight frame transform, the other is critically sampled wavelet transform (WT). Embedding watermark into the low-frequency sub-band of SSWT, the imperceptibility and robustness of watermark can be significantly improved c...