Add to ORKGThe wavelet transform and related techniques are used to analyze singular and fractal signals. The normalized wavelet scalogram is introduced to detect singularities including jumps, cusps and other sharply changing points. The wavelet auto-covariance is applied to estimate the self-similarity exponent for statistical self-affine signals
Serving as a powerful tool for extracting localized variations in non-stationary signals, applicatio...
This paper presents a statistical measure for the identification of the presence, the location, and ...
The geomagnetic activity of the Dst index is analysed using wavelet transforms and it is shown that ...
Abstract-Most of a signal information is often carried by irregular structures and transient phenome...
The idea of wavelet singularity detection (WSD) can be traced back to the work of Jaffard. He showed...
The multifractal formalism for singular measures is revisited using the wavelet transform. For Berno...
Physicists and mathematicians are intensely studying fractal sets of fractal curves. Mandelbrot advo...
International audienceSingularities induced by oscillating behavior are analyzed using the wavelet t...
The self-similarity property of some kind of fractals is sudied by using Harmonic Wavelets. The scal...
Abstract—In the research of singular signal detection, the selection of wavelet function is the firs...
Introduction A self-similar process is loosely defined as a stochastic process which generates a sa...
Theoretical self-similar processes have been an essential tool for modeling a wide range of real-wor...
In this paper, we introduce some extensions of popular wavelet transforms and their applications to ...
In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian) or...
Here we detect singularities with generalized quadrature processing using the recently developed Her...
Serving as a powerful tool for extracting localized variations in non-stationary signals, applicatio...
This paper presents a statistical measure for the identification of the presence, the location, and ...
The geomagnetic activity of the Dst index is analysed using wavelet transforms and it is shown that ...
Abstract-Most of a signal information is often carried by irregular structures and transient phenome...
The idea of wavelet singularity detection (WSD) can be traced back to the work of Jaffard. He showed...
The multifractal formalism for singular measures is revisited using the wavelet transform. For Berno...
Physicists and mathematicians are intensely studying fractal sets of fractal curves. Mandelbrot advo...
International audienceSingularities induced by oscillating behavior are analyzed using the wavelet t...
The self-similarity property of some kind of fractals is sudied by using Harmonic Wavelets. The scal...
Abstract—In the research of singular signal detection, the selection of wavelet function is the firs...
Introduction A self-similar process is loosely defined as a stochastic process which generates a sa...
Theoretical self-similar processes have been an essential tool for modeling a wide range of real-wor...
In this paper, we introduce some extensions of popular wavelet transforms and their applications to ...
In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian) or...
Here we detect singularities with generalized quadrature processing using the recently developed Her...
Serving as a powerful tool for extracting localized variations in non-stationary signals, applicatio...
This paper presents a statistical measure for the identification of the presence, the location, and ...
The geomagnetic activity of the Dst index is analysed using wavelet transforms and it is shown that ...