Add to ORKGIn the framework of the statistical regularization method new algorithms of solving inverse problems in applied spectroscopy in the presence of correlated fractal noises are suggested. Statistical properties of fractal noises are investigated in terms of R/S-analysis. As an example illustrating the efficiency of the algorithms suggested the smoothing problem of experimental data is considered. (C) 2000 Elsevier Science Ltd. All rights reserved. | In the framework of the statistical regularization method new algorithms of solving inverse problems in applied spectroscopy in the presence of correlated fractal noises are suggested. Statistical properties of fractal noises are investigated in terms of R/S-analysis. As an example illustrating the...
We describe a framework for solving nonlinear inverse problems in a random environment. Such problem...
When investigating fractal phenomena, the following questions are fundamental for the applied resear...
International audienceThe aim of this study was to investigate the effects of a linear filter on the...
In the framework of the statistical regularization method new algorithms of solving inverse problems...
The problem of improving the resolution of composite spectra with statistically self-similar (fracta...
General principles for solving ill-posed inverse problems in applied spectroscopy were considered an...
The paper describes current methods of treatment and interpretation of experimental data in applied ...
AbstractFractal Gaussian models have been widely used to represent the singular behavior of phenomen...
In this paper we suggest a new discrete spectroscopy for analysis of random signals and fluctuations...
Inverse problems are usually ill-posed, and therefore affected by the noise which is al-ways present...
© Published under licence by IOP Publishing Ltd. The approach based on the stochastic algorithm of p...
Many random signals with clearly expressed trends can have selfsimilar properties. In order to see t...
In this paper the authors suggest a new method of detection of possible differences between similar ...
This paper describes regularized wavelets and numerical algorithms for a regularized wavelet-analysi...
We describe a framework for solving nonlinear inverse problems in a random environment. Such problem...
When investigating fractal phenomena, the following questions are fundamental for the applied resear...
International audienceThe aim of this study was to investigate the effects of a linear filter on the...
In the framework of the statistical regularization method new algorithms of solving inverse problems...
The problem of improving the resolution of composite spectra with statistically self-similar (fracta...
General principles for solving ill-posed inverse problems in applied spectroscopy were considered an...
The paper describes current methods of treatment and interpretation of experimental data in applied ...
AbstractFractal Gaussian models have been widely used to represent the singular behavior of phenomen...
In this paper we suggest a new discrete spectroscopy for analysis of random signals and fluctuations...
Inverse problems are usually ill-posed, and therefore affected by the noise which is al-ways present...
© Published under licence by IOP Publishing Ltd. The approach based on the stochastic algorithm of p...
Many random signals with clearly expressed trends can have selfsimilar properties. In order to see t...
In this paper the authors suggest a new method of detection of possible differences between similar ...
This paper describes regularized wavelets and numerical algorithms for a regularized wavelet-analysi...
We describe a framework for solving nonlinear inverse problems in a random environment. Such problem...
When investigating fractal phenomena, the following questions are fundamental for the applied resear...
International audienceThe aim of this study was to investigate the effects of a linear filter on the...