0957-4174/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.12.027 ⇑ Corresponding author. Tel.: +91 416 2202364; fax: +91 416 2243092. E-mail addresses: [email protected] (M. Monica Subashini), sksahoo@ vit.ac.in (S.K. Sahoo). 1 Tel.: +91 416 2202467; fax: +91 416 2243092. 2 Abbreviations used: PCNN, pulse coupled neural networks; ICM, i...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

This paper presents an advanced speaker verification system using wavelet transforms. The conventional signal processing techniques assume the signal to be stationary and are ineffective in recognizing non stationary signals such as voice signals. Voice signals which are more dynamic could be analysed with far better accuracy using wavelet transform. The developed voice verification system is w...

In order to diagnose incipient fault of analog circuits effectively, an analog circuit incipient fault approach by using kernel entropy component analysis (KECA) as a preprocessor is proposed in the paper. Time responses are acquired by sampling outputs of the circuits under test. Raw features with high dimension are generated by wavelet transform. Furthermore, lower dimensional features are pr...

As the number of applications and use of wavelet transforms continues to grow, so does the number of classes and variations of wavelet transform algorithms. All of these algorithms incorporate a filter convolution in some implementation, typically, as part of an iterated filter bank. In contrast to implementations of the classical Fourier transform where there is at most a choice of sign and no...

Abstract-This paper presents the various methods for the spectral analysis of signals for the stationary as well as non-stationary signals. Due to non-stationary characteristics of the signals, it has been always a challenge to achieve time frequency distribution of such signals. Between the various techniques of signal analysis, this paper uses Fourier transform, Short time Fourier transform, ...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

Image fusion is defi ned as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and transform fusion and three levels namely pixel, feature and region level. In spatial fusion, the pixel values from the source images are direct...

Fourier Transform and its tools have been applied popularly in many engineering applications. However, the Fourier Transform cannot analyze non-stationary, non-linear and intermittent signals with simultaneously time-frequency plane. Wavelet Transform have been invented recently to cope with limitations of the Fourier Transforms. This paper presents Continuous Wavelet Transform and its recent a...