May 4, 2013

Signal Processing for Chemical Sensing: ICASSP 2013 Special session

ICASSP 2013 (International conference on acoustic, speech and signal processing) takes place at the end of May 2013 in Vancouver, Canada. The technical program is online.

Among the numerous tracks and sessions, there were a special session on Signal Processing for Chemical Sensing (Friday, May 31, 08:00 - 10:00). The chairpersons were: Leonardo T. Duarte, Laurent Duval, Christian Jutten. Here are posted the slides (all slides in a .zip file) presented at the conference and paper abstracts.

Typical chemical signals: 1D gas chromatogram
Summary of the Special Session:
This special session aims at showing some relevant problems in chemical engineering that can be addressed with classical or advanced methods of signal and image processing. It will be introduced by a tutorial paper, presented by the organizers, who will offer a large overview of issues which have been addressed in this application domain, like chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, TEP imaging, etc. For enlarging and illustrating the points of view of the tutorial, the invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed  by various methods, e.g. source separation, Bayesian or EMD, and exploiting priors like positivity, unit-concentration or sparsity. 
Typical chemical signals: 2D comprehensive gas chromatogram (GCxGC)
Motivation and rationale of the Special Session:
With the advent of more affordable, higher resolution or innovative data acquisition techniques, chemical analysis has  been using progressively advanced signal and image processing tools. This crucial need is exemplified in the Savitzky-Golay filter, which recently was thoroughly revisited  by R. W. Schafer ("What Is a Savitzky-Golay Filter?", Signal Processing Magazine, Jul. 2011). Indeed, both specialities (analytical chemistry and signal processing) share similar values of best practice in carrying out identifications and comprehensive characterizations, albethey of chemical  samples or of numerical data. Signal and image processing, for instance, often breaks down data into atoms, molecules, with specific decompositions and priors, as common in chemistry.

This special session will gather a representative sample of recent works in chemical sensing, aiming at introducing its  specific challenges to a broader signal processing audience, for the benefits of both domains.

Papers (upcoming)

Laurent Duval, Leonardo Duarte, Christian Jutten (paper)
Abstract: This tutorial paper aims at summarizing some  problems, ranging  from  analytical chemistry to novel chemical sensors, that can be addressed with classical or advanced methods of signal and image processing. We gather them under the denomination of "chemical sensing". It is meant to introduce the special session "Signal Processing for Chemical Sensing" with a large overview of issues which have been and remain to be addressed in this application domain, including chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, etc.  For enlarging and illustrating the points of view of this tutorial, the  invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed  by various methods, e.g. source separation, Bayesian, and exploiting typical chemical signal priors like positivity, linearity, unit-concentration or sparsity.
Keywords: Chemical analysis, Chemical sensors, Gas chromatography, Signal processing algorithms, Spectroscopy
Paper: An overview of signal processing issues in chemical sensing (HAL)
Slides: ICASSP-2013-Duarte-overview-signal-processing-chemical-sensing.pdf

Abstract: This paper deals with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) spectroscopy. This large scale and ill-posed inverse problem is solved by the iterative minimization of a regularized objective function allowing to encode some prior assumptions on the sought distribution. The numerical optimization of the criterion is performed using a primal-dual interior point algorithm allowing to handle the non-negativity constraint. The performances of the proposed approach are illustrated through the processing of real data from a two-dimensional NMR experiment.
Keywords: T1-T2 relaxation times, Laplace transform inversion, interior-point, primal-dual, preconditioning
Paper: Primal-DualInterior Point Optimization for a Regularized Reconstruction of NMRRelaxation Time Distributions
Slides: ICASSP-2013-Moussaoui-NMR-Primal-Dual.pdf
Abstract: We propose a sparse modal estimation approach for analyzing 2-D NMR signals. It consists in decomposing the 2-D problem into two 1-D modal estimations. Each 1-D problem is formulated in terms of simultaneous sparse approximation which is efficiently solved using the Simultaneous Orthogonal Matching Pursuit method associated with a multi-grid dictionary refinement. Then, we propose a new criterion for mode pairing which comes down to solve a sparse approximation problem involving a low dimensional dictionary. The effectiveness of the method is demonstrated on real NMR data.
Keywords: Modal retrieval, sparse approximation, multi-grid, 2-D NMR
Paper: Sparse modal estimation of 2-D NMR signals
Slides: ICASSP-2013-Brie-Sparse-Modal-2d-NMR.pdf
Abstract: New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature selection approaches, in which the subset is selected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MMNNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability.
Keywords: Active sensing, tunable sensors, multi-modal optimization, chemical mixture analysis
Paper:  Active analysis of chemical mixtures with multi-modal sparse non-negative least squares
Slides: ICASSP-2013-Gutierrez-Osuna-active-multi-modal.pdf
Abstract: In this paper, a novel gas identification approach based on the Recursive Least Squares (RLS) algorithm is proposed. We detail some adaptations of RLS to be applied to a sensor matrix of several technologies in optimal conditions. The low complexity of the algorithm and its ability to process online samples from multi-sensor make the real-time identification of volatile compounds possible. The effectiveness of this approach to early detect and recognize explosive compounds in the air has been successfully demonstrated on an experimentally obtained dataset.
Keywords: Electronic nose, Pattern recognition, Multidimensional analysis, Recursive Least Squares
Slides: ICASSP-2013-Mayoue-recursive-least-squares.pdf
Three others ICASSP 2013 papers are closely related to the topic of chemical sensing and signal processing:
Earlier post: ICASSP 2013: Special sessions   

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