Laurent Duval

• PhD
• Project Manager at IFP Energies nouvelles

This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of chromatogram peaks as sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric mod...

The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the l1/l2 function raises some difficulties when solving the nonconvex and nonsmooth m...

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused...

This note describes the content of LUNDIsim dataset. It consists in five main meshes in GRDECL format: one common hexaedral (Corner Point Grid) mesh model, plus four meshes filled with permeability and porosity values, corresponding to distinct subsurface environments. The latter four meshes are provided with lower-resolution versions, meant for ge...

This study focuses on improving the seafloor compliance noise removal method, which relies on estimates of the compliance transfer function frequency response (the deformation of the seafloor under long-period pressure waves). We first propose a new multi-scale deviation analysis of broadband ocean bottom seismometer data to evaluate stationarity p...

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separ...

Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least S...

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separ...

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separ...

Motivation The cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics technologies has favored the generation of large-scale disparate data and the consequent demand for simu...

Background Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and effic...

Background: Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and effi...

Solventogenic clostridia have been employed in industry for more than a century, initially being used in the acetone-butanol-ethanol (ABE) fermentation process for acetone and butanol production. Interest in these bacteria has recently increased in the context of green chemistry and sustainable development.

We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a nonconvex minimization of the sum of a data fitting term and a penalization term. In contrast with most previous works which settle for approximated local solutions, we s...

Background The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in th...

We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a nonconvex minimization of the sum of a data fitting term and a penalization term. In contrast with most previous works which settle for approximated local solutions, we s...

Background The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in t...

Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in...

Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in t...

Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in t...

Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in...

Underdetermined or ill-posed inverse problems require additional information for sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristics to that matter, with numerous applications in signal restoration, image recovery, or machine learning. Since the $\ell_0$ count measure is barely tractable, many statistical o...

Underdetermined or ill-posed inverse problems require additional information for sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristics to that matter, with numerous applications in signal restoration, image recovery, or machine learning. Since the $\ell _0$ count measure is barely tractable, many statistica...

With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution grids and point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels of high-perform...

Manipulation of large data volumes is becoming a concern in the field of simulation. In reservoir simulation, grids are complex structures composed of heterogeneous objects, with a potentially large number of cells. HexaShrink is a perfectly reversible multiscale decomposition tool based on 3D wavelet transformation, dedicated to structured hexahed...

With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution grids and point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels of high-perform...

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy...

The BEADS toolbox jointly addresses the problem of simultaneous baseline correction and noise reduction, for positive and sparse signals arising in analytical chemistry (Raman, infrared, XRD, etc.), here applied to gas chromatography signals. The baseline is similar to slow-varying trends, instrumental drifts or background offset. The proposed base...

Blind deconvolution refers to deconvolution of a signal without exact knowledge of the impulse response function used in the convolution. This is usually achieved by adding appropriate assumptions on the input and/or the impulse response to restore the output. We take into account here the sparsity or parsimony of the input signal. It is generally...

Building high-fidelity system-level models of Cyber-Physical Systems (CPS) is a challenging duty. A first problem is the diversity of modeling and simulation environments used by the various involved multidisciplinary teams. Particular environments are preferred for a specific use, due to distinctive strengths (modeling language, libraries, solvers...

Les mesures physico-chimiques expérimentales sont parfois altérées par des perturbations stochastiques ou systémiques, inhérentes aux protocoles expérimentaux et aux méthodes d’acquisition. Nous nous intéressons ici à la séparation de trois ”composantes” souvent présentes dans les données analytiques : une mesure d’intérêt formée d’une combinaison...

The discovery of novel gene regulatory processes improves the understanding of cell phenotypic responses to external stimuli for many biological applications, such as medicine, environment or biotechnologies. To this purpose, transcriptomic data are generated and analyzed from DNA microarrays or more recently RNAseq experiments. They consist in gen...

A wrap-up on the recent works: -S. Saidi et al., ”Automatic Parallelization of Multi-Rate FMI-based Co-Simulation on Multi-Core ” Spring Simulation Multiconference (Apr. 2017) -A. Ben Khaled et al., ”CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex system”, Simulation (March 2017)

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy...

The growing complexity of Cyber-Physical Systems (CPS), together with increasingly available par-allelism provided by multi-core chips, fosters the parallelization of simulation. Simulation speed-ups are expected from co-simulation and parallelization based on model splitting into weak-coupled sub-models, as for instance in the framework of Functio...

We propose a 2D generalization to the $M$-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets. We particularly address (\textit{i}) the construction of the dual basis and (\textit{ii}) the resulting directional analysis. We also revisit...

Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and...

(Comprehensive) Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined an...

The growing complexity of Cyber-Physical Systems (CPS), together with increasingly available parallelism provided by multi-core chips, fosters the parallelization of simulation. Simulation speed-ups are expected from co-simulation and parallelization based on model splitting into weak-coupled sub-models, as for instance in the framework of Function...

Background Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires addit...

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires add...

This special section covers present-day algorithms for seismic signal processing. In the last decade, important efforts were made in the development of signal processing methods that exploit the multidimensionality of seismic data. A stringent requirement for multidimensional seismic data processing is the elaboration of strategies to represent sei...

This paper proposes a novel approach for echo-like multiple removal in two-dimensional seismic images. It is based on constrained adaptive filtering associated with geometric wavelets. Approximate templates of multiple reflections are assumed to be available and they are matched to multiple reflections throughout estimated finite impulse response f...

Bon nombre de mesures expérimentales sont altérées par des fluctuations stochastiques et/ou systémiques, souvent inhérentes aux protocoles expérimentaux et aux méthodes d'acquisition. Dans ce contexte, cet article présente une méthode permettant de retrouver le signal d'intérêt débruité et corrigé de sa tendance ou ligne de base. En s'appuyant sur...

The obtention of representative graphs is a key problem in an increasing number of fields, such as computer graphics, social sciences, and biology to name a few. Due to the large number of possible solutions from the available amount of data, building meaningful graphs is often challenging. Nonetheless, enforcing a priori on the graph structure, su...

This introductory paper aims at summarizing some problems and state-of-the-art techniques encountered in image processing for material analysis and design. Developing generic methods for this purpose is a complex task given the variability of the different image acquisition modalities (optical, scanning or transmission electron microscopy; surface...

Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtained through time-varying filtering, compensating inaccuracies in given approximate templates. The unde...

Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtained through time-varying filtering, compensating inaccuracies in given approximate templates. The unde...

The growing complexity of systems, together with increasing available parallelism provided by multi-core chips, calls for the parallelization of simulation. Simulation speed-ups are expected from co-simulation and parallelization based on models splitting into loosely coupled sub-systems in the framework of Functional Mockup Interface (FMI). Howeve...

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused...

The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours...

Valve trays column design for gas treatment still relies on empirical correlations developed on pilot units. The available correlations lead to large discrepancies and thus more experimental works are needed. The present hydrodynamic study was carried out on a Plexiglas 1.26 m per 0.1905 m absorption column containing four V-4 Glitsch valve trays....

La quête de représentations optimales en traitement d’images et vision par ordinateur se heurte à la variété de contenu des données bidimensionnelles. De nombreux travaux se sont cependant attelés aux tâches de séparation de zones régulières, de contours, de textures, à la recherche d’un compromis entre complexité et efficacité de représentation. L...

Coherent noise or surface waves filtering represents one of the most complex issues in land seismic data processing. Wavelet based filtering has recently begun to challenge the popular and robust frequency-wavenumber (-Ü -Ý) filter. Wavelet filters provide fine time-scale representations and non linear filtering capabilities that yield in some inst...

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...

Both random and structured perturbations affect seismic data. Their removal, to unveil meaningful geophysical information, requires additional priors. Seismic multiples are one form of structured perturbations related to wave-field bouncing. In this paper, we model these undesired signals through a time-varying filtering process accounting for inac...

La quête de représentations optimales en traitement d'images et vision par ordinateur se heurte à la variété de contenu des données bidimensionnelles. De nombreux travaux se sont cependant attelés aux tâches de séparation de zones régulières, de contours, de textures, à la recherche d'un compromis entre complexité et efficacité de représentation. L...

Multiple attenuation is one of the greatest challenges in seismic processing. Due to the high cross-correlation between primaries and multiples, attenuating the latter without distorting the former is a complicated problem. We propose here a joint multiple model-based adaptive subtraction, using single-sample unary filters' estimation in a complex...

Multiple attenuation is a crucial task in seismic data processing because multiples usually cover primaries from fundamental reflectors. Predictive multiple suppression methods remove these multiples by building an adapted model, aiming at being subtracted from the original signal. However, before the subtraction is applied, a matching filter is re...

Due to complex subsurface structure properties, seismic records often suffer from coherent noises such as multi-ples. These undesired signals may hide the signal of inter-est, thus raising difficulties in interpretation. We propose a new variational framework based on Maximum A Pos-teriori (MAP) estimation. More precisely, the problem of multiple r...

Filtrage de multiples par ondelettes. Conception d'algorithmes robustes au bruit par minimisation sous contraintes convexes dans le but de supprimer les échos.

Directional filters are commonly used tools in modern seis-mic data processing to address coherent signals, depending on their apparent slowness or slope. This operation enhances the characterization of the great variety of signals present in a seismic dataset that enables a better characterization of the subsurface structure. This paper compares t...

Summary Wavelet related techniques have been proved successful in many seismic processing applications, such as filtering or compression. While seismic data compression is not yet widely accepted, we propose a com- pression based on filter banks as a means to remove uncoherent noise from seismic data, and thus improve the SNR. Results are demonstra...

Dual-tree wavelet decompositions have recently gained much popularity, mainly due to their ability to provide an accurate directional analysis of images combined with a reduced redundancy. When the decomposition of a random process is performed -- which occurs in particular when an additive noise is corrupting the signal to be analyzed -- it is use...

Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples, and thus reduces multiple contamination in the dataset after subtraction. Due to the high cross-correlation between their waveform, the main challenge resides in attenuating mul...