A Blind Source Separation Method for Chemical Sensor Arrays based on a Second-order mixing model

International audienceIn this paper we propose a blind source separation method to process the data acquired by an array of ion-selective electrodes in order to measure the ionic activity of different ions in an aqueous solution. While this problem has already been studied in the past, the method presented differs from the ones previously analyzed by approximating the mixing function by a second-degree polynomial, and using a method based on the differential of the mutual information to adjust the parameter values. Experimental results, both with synthetic and real data, suggest that the algorithm proposed is more accurate than the other models in the literature

Theoretical Studies and Algorithms Regarding the Solution of Non-invertible Nonlinear Source Separation

International audienceIn this paper, we analyse and solve a source separation problem based on a mixing model that is nonlinear and non-invertible at the space of mixtures. The model is relevant considering it may represent the data obtained from ion-selective electrode arrays. We apply a new approach for solving the problems of local stability of the recurrent network previously used in the literature, which allows for a wider range of source concentration. In order to achieve this, we utilize a second-order recurrent network which can be shown to be locally stable for all solutions. Using this new network and the priors that chemical sources are continuous and smooth, our proposal performs better than the previous approach

Nonlinear Blind Source Separation for Chemical Sensor Arrays Based on a Polynomial Representation

International audience—In this paper we propose an extension of a blind source separation algorithm that can be used to process the data obtained by an array of ion-selective electrodes to measure the ionic activity of different ions in an aqueous solution. While the previous algorithm used a polynomial approximation of the mixing model and used mutual information as means of estimating the mixture coefficients, but it only worked for a constrained configuration of two sources with the same ionic valence. Our proposed method is able to generalize it to any number of sources and any type of ions, and is therefore able to solve the problem for any configuration. Simulations show good results for the analyzed application

Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures

International audienceIn this work, we deal with the problem of nonlinear blind source separation (BSS). We propose a new method for BSS in overdetermined linear-quadratic (LQ) mixtures. By exploiting the assumption that the sources are sparse in a transformed domain, we define a framework for canceling the nonlinear part of the mixing process. After that, separation can be conducted by linear BSS algorithms. Experiments with synthetic data are performed to assess the viability of our proposal