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

On dispersion of compound DMCs

Code for a compound discrete memoryless channel (DMC) is required to have small probability of error regardless of which channel in the collection perturbs the codewords. Capacity of the compound DMC has been derived classically: it equals the maximum (over input distributions) of the minimal (over channels in the collection) mutual information. In this paper the expression for the channel dispersion of the compound DMC is derived under certain regularity assumptions on the channel. Interestingly, dispersion is found to depend on a subtle interaction between the channels encoded in the geometric arrangement of the gradients of their mutual informations. It is also shown that the third-order term need not be logarithmic (unlike single-state DMCs). By a natural equivalence with compound DMC, all results (dispersion and bounds) carry over verbatim to a common message broadcast channel.National Science Foundation (U.S.) (CAREER Award CCF-12-53205)National Science Foundation (U.S.). Center for Science of Information (Grant Agreement CCF-0939370

Semi-Blind Cancellation of IQ-Imbalances

International audienceThe technical realization of modern wireless receivers yields significant interfering IQ-imbalances, which have to be compensated digitally. To cancel these IQ-imbalances, we propose an algorithm using iterative blind source separation (IBSS) as well as information about the modulation scheme used (hence the term semi-blind). The novelty of our approach lies in the fact that we match the nonlinearity involved in the IBSS algorithm to the probability density function of the source signals. Moreover, we use approximations of the ideal non-linearity to achieve low computational complexity. For severe IQ-mismatch, the algorithm leads to 0.2 dB insertion loss in an AWGN channel and with 16-QAM modulation

Extension of Score Function Difference for Frequency Domain Blind Source Separation

EUSIPCO2008: 16th European Signal Processing Conference, August 25-28, 2008, Lausanne, Switzerland.Recent theoretical developments in the field of blind source separation (BSS) introduced the score function difference (SFD) and showed its close relationship with the differential of the mutual information. These results are of great interest for developing BSS methods since the mutual information can be seen as the canonical cost function for BSS. However these results were only proposed in the real case. In this paper, we extend these results to the complex case and propose a frequency domain approach of BSS based on the complex equivalent of the SFD

A mutual information minimization approach for a class of nonlinear recurrent separating systems

International audienceIn this work, we deal with nonlinear blind source separation. Our contribution is the derivation of a learning strategy that minimizes the mutual information between the outputs of a class of nonlinear recurrent separating systems. By using the concept of the differential of the mutual information, we obtain an algorithm that does not need a precise knowledge of the source distributions, in contrast to the one obtained by a direct derivation of the minimum mutual information framework, or equally the maximum likelihood approach, for the considered model. The validity of our approach is supported by simulations

Blind signal extraction via direct mutual information minimization

MLSP2008: IEEE International Workshop on Machine Learning for Signal Processing, October 16-19, 2008, Cancun, Mexico.This paper presents a new blind signal extraction method based on mutual information. Conventional blind signal extraction methods minimize the mutual information between the extracted signal and the remaining signals indirectly by using a cost function. The proposed method directly minimizes this mutual information through a gradient descent. The derivation of the gradient exploits recent results on the differential of the mutual information and the implementation is based on kernel based density estimation. Some simulation results show the performance of the proposed approach and underline the improvement obtained by using the proposed method as a post-processing for conventional methods

Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data