arXiv subjects

Shahram Hosseini

Publications and source records attributed to Shahram Hosseini.

4 recordsLinked to original sources

arXiv2025

New methods to improve the decontamination of slitless spectra

This paper proposes four new methods to decontaminate spectra of stars and galaxies resulting from slitless spectroscopy used in many space missions such as Euclid. These methods are based on two distinct approaches and simultaneously take into account multiple dispersion directions of light. The first approach, called the local instantaneous approach, is based on an approximate linear instantaneous model. The second approach, called the local convolutive approach, is based on a more realistic convolutive model that allows simultaneous decontamination and deconvolution of spectra. For each approach, a mixing model was developed that links the observed data to the source spectra. This was done either in the spatial domain for the local instantaneous approach or in the Fourier domain for the local convolutive approach. Four methods were then developed to decontaminate these spectra from the mixtures, exploiting the direct images provided by photometers. Test results obtained using realistic, noisy, Euclid-like data confirmed the effectiveness of the proposed methods.

astro-ph.IM↗

arXiv2010

Correction to: "Blind maximum likelihood separation of a linear-quadratic mixture"

An error occurred in the computation of a gradient in our paper entitled "Blind maximum likelihood separation of a linear-quadratic mixture", presented in ICA'2004. The equations (20) in Appendix and (17) in the text were not correct. The current paper presents the correct version of these equations.

math.ST↗

arXiv2009

Effect of indirect dependencies on "A mutual information minimization approach for a class of nonlinear recurrent separating systems"

In a recent paper [4], Duarte and Jutten investigated the Blind Source Separation (BSS) problem, for the nonlinear mixing model that they introduced in that paper. They proposed to solve this problem by using information-theoretic tools, more precisely by minimizing the mutual information (MI) of the outputs of the separating structure. When applying the MI approach to BSS problems, one usually determines the analytical expressions of the derivatives of the MI with respect to the parameters of the considered separating model. In the literature, these calculations were mainly reported for linear mixtures up to now. They are more complex for nonlinear mixtures, due to dependencies between the considered quantities. Moreover, the notations commonly employed by the BSS community in such calculations may become misleading when using them for nonlinear mixtures, due to the above-mentioned dependencies. We claim that the calculations reported in [4] contain an error, because they did not take into account all these dependencies. In this document, we therefore explain this phenomenon, by showing the effect of indirect dependencies on the application of the MI approach to the mixing and separating models considered in [4]. We thus introduce a corrected expression of the gradient of the considered BSS criterion based on MI. This correct gradient may then e.g. be used to optimize the adaptive coefficients of the considered separating system by means of the well-known gradient descent algorithm. As explained hereafter, this investigation has some similarities with an analysis that we previously reported in another arXiv document [3]. However, these two investigations concern different problems (mixture and separating structure, mathematical tools: see paper).

stat.ME↗

arXiv2008

Differential fast fixed-point algorithms for underdetermined instantaneous and convolutive partial blind source separation

This paper concerns underdetermined linear instantaneous and convolutive blind source separation (BSS), i.e., the case when the number of observed mixed signals is lower than the number of sources.We propose partial BSS methods, which separate supposedly nonstationary sources of interest (while keeping residual components for the other, supposedly stationary, "noise" sources). These methods are based on the general differential BSS concept that we introduced before. In the instantaneous case, the approach proposed in this paper consists of a differential extension of the FastICA method (which does not apply to underdetermined mixtures). In the convolutive case, we extend our recent time-domain fast fixed-point C-FICA algorithm to underdetermined mixtures. Both proposed approaches thus keep the attractive features of the FastICA and C-FICA methods. Our approaches are based on differential sphering processes, followed by the optimization of the differential nonnormalized kurtosis that we introduce in this paper. Experimental tests show that these differential algorithms are much more robust to noise sources than the standard FastICA and C-FICA algorithms.

physics.data-an↗