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International audience; Parkinsonism refers to Parkinsons disease (PD) and Atyp-ical parkinsonian syndromes (APS). Speech disorder is a common and early symptom in Parkinsonism which makes speech analysis a very important research area for the purpose of early diagnosis. Most of research have however focused on discrimination between PD and healthy controls. Such research does not take into account the fact that PD and APS syndromes are very similar in early disease stages. The main problem that has to be addressed first is then differential diagnosis: discrimination between PD and APS and within APS. This paper is a continuation of an earlier pioneer work in differential diagnosis where we mostly address the machine learning problem due to the small amount of training data. We show that classical linear and generalized linear models can provide interpretable and robust classifiers in term of accuracy and generalization ability.

International audience; Atrial fibrillation (AF) is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS), during episodes of chronic AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function) method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e. for times (>= 0.5 s) longer than the mean interbeat (10^{-1}s). We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative timescale structure underlying multifractal scaling. The electric energy dynamics looks like a " multifractal white noise " with quadratic (log-normal) multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS). A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall which is innervated by the ANS. In a companion paper (II. Modeling), we propose a mathematical model of a denervated heart where the kinetics of gap junction conductance alone induces a desynchronization of the myocardial excitable cells, accounting for the multifractal spectra found experimentally in the left atrial posterior wall area.

International audience; Filamentary star formation Herschel observations revealed the ubiquitous presence of interstellar filaments and their importance in star formation (e.g. [1],[2],[3]). It is proposed that they form by dissipation of kinetic energy in large scale magneto-hydrodynamic flows through low-velocity magnetised shocks (C-shocks) and then fragment in prestellar cores after reaching a density threshold of ∼ 16 M /pc [4],[5]. There are indications that these filaments accrete mass through inflow of matter from the sides along the magnetic field (e.g. [6],[7]). However, direct evidence of such shocks is vital to confirm this scenario.

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International audience; The long-term success of a dental implant is related to the properties of the bone-implant interface. It is important to follow the evolution of bone remodeling phenomena around the implant. To date, there is no satisfactory method for tracking physiological and mechanical properties of this area, and it is difficult for clinicians to qualitatively and quantitatively assess the stability of a dental implant. In this context, methods based on ultrasound wave propagation were already successfully used by our group, in the qualitative and quantitative evaluation of primary and secondary stability of dental implants. In this study we perform numerical simulations, using the finite element method, of wave propagation in a dental implant inserted into bone. To simplify the calculations, an axisymmetric geometry is considered. Given the importance of monitoring of peri-prosthetic area, particular attention is given to the boundary conditions between the implant and the bone. The numerical results are compared with those from experimental tests. These results, numerical and experimental, are then analysed with signal processing tools based on multifractal methods. Analysis of the first results shows that these methods are potentially efficient in this case because they can explore and exploit the multi-scale structure of the signal.

International audience; Background: The animal models (in vitro or in vivo) provide an excel-lent tool to study heart diseases, among them the arrhythmia remains one of the most active research subjects. It can be induced or treated by drugs, electrical stimulation, hypothermia etc.Problems: However, the inducing or treating effects in cardiac culture often happened long after the initial applications or in some relatively short time windows. So, it is necessary to capture and classify rapidly the signal change. Human-assisted monitoring is time-consuming and less efficient. An automatic classification method for real-time use would be useful and necessary. Methods: Since electrocardiological signals are features by repetitive or similar patterns reflecting the intrinsic information about the pa-tient (or culture), analyzing these patterns could help not only to monitor the status's change but also to evaluate/explore the physiol-ogic control mechanisms. Methods based on complexity analysis are of considerable interest in this case. Aims: Compare different complexity analysis methods in order to find the most appropriate ones to discriminate the normal cardiac signals from arrhythmic ones acquired from a cardiac cell culture in vitro. The selected features are then used by a SVM classifier.Results: Among the six complexity analysis methods, Time Lagging (TLag) method allowed obtaining the best discrimination index (nor-mal vs. arrhythmic, p-value, 9e-23). The proposed Modified Hurst Exponent (HExM) showed better performance than original Hurst Exponent with well-improved p-value (from 0.019 to 2e-9). The Ap-proximate Entropy (ApEn), Sample Entropy (SampEn) and Detrended Fluctuation Analysis gave good discrimination ratio but with larger p-values (at order 10^{-3}). Combination of TLag, HExM and ApEn can provide a more robust classifier and allow monitoring and classifying in an automatic way the electrical activities' changes in the cardiac cultures.

International audience; The neurological damage after cardiac arrest (CA) constitutes a big challenge of hospital discharge. The therapeutic hypothermia (34°C-32°C) has shown its benefit to reduce cerebral oxygen demand and improve neurological outcomes after the cardiac arrest. However, it can have many adverse effects, among them the cardiac arrhythmia generation represents an important part (up to 34%, according different clinical studies). Monolayer cardiac culture is prepared with cardiomyocytes from new-born rat directly on the multi-electrodes array, which allows acquiring the extracellular potential of the culture. The temperature range is 37°C - 30°C - 37°C, representing the cooling and rewarming process in the therapeutic hypothermia. Both experiments showed that at 35°C, the acquired signals are characterized by period-doubling phenomenon, compared to signals at any other temperatures. Spiral waves, commonly considered as a sign of cardiac arrhythmia, are observed in the reconstructed activation map. With an approach from nonlinear dynamics, phase space reconstruction, it is shown that at 35°C, the trajectories of these signals formed a bifurcation, even trifurcation. Another transit point is found between 30°C - 33°C, which agreed with other clinical studies that induced hypothermia after cardiac arrest should not be below 32°C. The therapeutic hypothermia after cardiac arrest can be represented by a Pitchfork bifurcation, which could explain the different ratio of arrhythmia among the adverse effects after this therapy. This nonlinear dynamics suggests that a variable speed of cooling / rewarming, especially when passing 35°C, would help to decrease the ratio of post-hypothermia arrhythmia and then improve the hospital output.

International audience; Derived from the pioneer ionic Hodgkin-Huxley model and due to its simplicity and richness from a point view of nonlinear dynamics, the FitzHugh-Nagumo model has been one of the most successful neuron / cardiac cell model. It exists many variations of the original FHN model. Though these FHN type models help to enrich the dynamics of the FHN model. The parameters used in these models are often in biased conditions. The related results would be questionable. So, in this study, the aim is to find the parameter thresholds for one of the commonly used FHN model in order to pride a better simulation environment. The results showed at first that inappropriate time step and integration tolerance in numerical solution of FHN model can give some biased results which would make some publications questionable. Then the thresholds of parameters $\alpha$, $\gamma$ and $\varepsilon$ are presented. $\alpha$ controls the global dynamics of FHN. $\alpha > 0$, the cell is in refractory mode; $\alpha < 0$, the cell is excitable. $\varepsilon$ controls the main morphology of the action potential generated and has a relation with the period ($\mathrm{P} = 3.065 \times \varepsilon^{-0.8275}+4.397$). To show oscillations of relaxation with FHN, $\varepsilon$ should be smaller than $0.0085$. $\gamma$ influences barely AP, it showed linear relationship with the period and duration of action potential. Globally, when $|\alpha| \geq 0.1$, $\varepsilon < 0.0085$, there is no definite threshold for $\gamma$, but smaller values are recommended.

International audience; Cardiac arrhythmias are very common pathologies which can be treated either by medications, invasive ablation procedures or device implantations. In order to improve theses treatments, clinical and experimental models are used to test new drugs. In this context, in vitro cultures of cardiac cells represent valuable models to study the mechanism of the arrhythmias at the cellular level. In this paper, we investigate the stability and robustness of an experimental model in normal and under external stimulation conditions. Phase space reconstructions of attractors in normal and arrhythmic cases are performed after characterizing the nonlinearity of the model, computing the embedding dimension and the time lag. Our results show that the electrical stimulation modifies slightly the embedding dimension. The parameter time lag t increases when arrhythmia happens. The shape of attractors remains globally similar, although more disturbed in case of arrhythmia than in normal conditions. The correlation dimension qualifies the attractors as strange.

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International audience; To study the cardiac arrhythmia, an in vitro experimental model and Multielectrodes Array (MEA) are used. This platform serves as an intermediary of the electrical activities of cardiac cells and the signal processing / dynamics analysis. Through it the extracellular potential of cardiac cells is acquired, allowing a real-time monitoring / analyzing. Since MEA has 60 electrodes / channels dispatched in a rectangular region, it allows real-time monitoring and signal acquisition on multiple sites. The in vitro experimental model (cardiomyocytes cultures from new-born rats' heart) is directly prepared on the MEA. This carefully prepared culture has similar parameters as cell of human's heart. In order to discriminate the cardiac arrhythmia, complexity analysis methods (Approximate Entropy, ApEn and Sample Entropy, SampEn) are used especially taking into account noise. The results showed that, in case of arrhythmia, the ApEn and SampEn are reduced to about 50\% of the original entropies. Both parameters could be used as factors to discriminate arrhythmia. Moreover, from a point of view of biophysics this decrease 50\% of Entropy coincides with the bifurcation (periods, attractors etc.) in case of arrhythmia which have been reported previously. It supports once more the hypothesis that in case of cardiac arrhythmia, the heart entered into chaos which helps to better understand the mechanism of atrial fibrillation.

International audience; The in vitro cultures of cardiac cells represent valuable models to study the mechanism of the arrhythmias at the cellular level. But the dynamics of these experimental models cannot be characterized precisely, as they include a lot of parameters that depend on experimental conditions. This paper is devoted to the investigation of the dynamics of an in vitro model using a phase space reconstruction. Our model, based on the heart cells of new born rats, generates electrical field potentials acquired using a microelectrode technology, which are analyzed in normal and under external stimulation conditions. Phase space reconstructions of electrical field potential signals in normal and arrhythmic cases are performed after characterizing the nonlinearity of the model, computing the embedding dimension and the time lag. A non-parametric statistical test (Kruskal-Wallis test) shows that the time lag $\tau$ could be used as an indicator to detect arrhythmia, while the embedding dimension is not significantly different between the normal and the arrhythmia cases. The phase space reconstructions highlight attractors, whose dimension reveals that they are strange, depicting a deterministic dynamics of chaotic nature in our in vitro model.

While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson’s disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.

International audience; Introduction: In case of resuscitation after CA, the brain suffers the ischemia and the inflammation from reperfusion. To days, the only therapy available is the mild therapeutic hypothermia (MTH) : put the patient under 34°C-32°C during 12-24 hours. MTH has been shown to increase the hospital survival rate, but it has many adverse effects, among which the cardiac arrhythmia generation represents an important part (up to 34%). Cardiac culture in vitro provides a better spatial resolution than study in vivo, which could bring some insights of the mechanism of post-hypothermia arrhythmia generation. Method: Monolayer cardiac culture is prepared with cardiomyocytes from new-born rat (1-4 days) directly on multi-electrodes array at 37°C. The experiments consist of culture cooling (37°C-30°C) and re-warming (30°C-37°C). The acquired signals are then analyzed with method phase-space reconstruction. Results: Decreasing temperature disturbed and extended the extracellular potential periods. The signals in 35°C-33°C became regular. At 35°C, period-doubling phenomena (1D) and spiral waves (2D) are observed, which can be interpreted as a transit point from normal state to chaos state. In the reconstructed phase-space, doubling-trajectories and even tripling-trajectories are observed. It implied that the general dynamics of MTH could be interpreted as a pitchfork bifurcation. These results at cellular level agreed with other clinical studies on MTH. Conclusion: Results in this study suggested that a variable speed would help to reduce the rate of post-hypothermia arrhythmia.

International audience; Introduction: One of the important challenges after cardiac arrest (CA) is the neurological damage of the brain. In case of resuscitation after CA, the brain suffers the ischemia and the inflammation from reperfusion. To days, the only therapy available is the mild therapeutic hypothermia (MTH) : put the patient under 34°C-32°C during 12-24 hours. Even though that MTH has been shown to increase the hospital survival rate, it has many adverse effects, among which the cardiac arrhythmia generation represents an important part (up to 34%). Cardiac culture in vitro provides a better spatial resolution (to cellular level) than study in vivo, which could bring some insights of the mechanism of post-hypothermia arrhythmia generation. Method: Monolayer cardiac culture is prepared with cardiomyocytes from new-born rat (1-4 days) directly on the MEA at 37°C. The experiments consist of culture cooling (37°C-30°C) and re-warming (30°C-37°C). The acquired signals are then analyzed with detrended fluctuation analysis, phase space reconstruction, principle component analysis. Results: At 35°C, Spiral waves are observed in the reconstructed activation map which is commonly considered as a sign of cardiac arrhythmia. Between 35°C-33°C, the signals became regular. However, another transit point is found between 30°C-33°C. At 35°C, period-doubling phenomena are observed, which can be translated as a transit point from normal state to chaos state, from the point view of nonlinear dynamics. From the reconstructed phase space, doubling-trajectories and rare tripling-trajectories are found. It implies that the general dynamics of MTH could be presented as a pitchfork bifurcation, which could explain exactly the doubling-trajectories and tripling-trajectories phenomena. These results at cellular level agreed with other clinical studies on MTH. Conclusion: The general hypothermia therapy uses constant cooling or re-warming. Results in this study showed that a variable speed would help to reduce the rate of post-hypothermia arrhythmia.

nouvelle version avec l'article complet; International audience; The neurological damage after cardiac arrest (CA) constitutes a big challenge of hospital discharge since years. The therapeutic hypothermia therapy (34°C-32°C) has shown its benefit to reduce cerebral oxygen demand and improve neurological outcomes after the cardiac arrest. Despite the fact that induced hypothermia after CA has been shown to increase the hospital survival rate, it can have many adverse effects, among which the cardiac arrhythmia generation represents an important part (up to 34%, according different clinical studies). Compared to studies in vivo, cardiac culture in vitro provides a better spatial resolution at cellular level, which could bring some insights of the mechanism of post-hypothermia arrhythmia (PHA) generation

International audience; Cardiac arrhythmias are one of the most important death causes in the world. Compared to the numerical models, the experimental ones provide a more realistic tool to study the mechanisms of cardiac arrhythmias. The in vitro culture of cardiac cells developed on the Multi-Electrodes Array (MEA) constitutes a suitable model in this context. The extracellular field potential (EFP) acquired from the MEA can be used to measure the electrophysiological parameters of action potential. In this article, the stability of this experimental model is investigated using the phase space reconstruction in normal and in arrhythmia conditions. The results show that the embedding dimension of signal EFP changed slightly in both cases (normal conditions and arrhythmia). The parameter time lag $\tau$ in the normal conditions is lower than in the arrhythmia. The shape of attractors remains similar but disturbed in case of arrhythmia compared to the normal conditions.

International audience; It is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.

International audience; In this letter, we propose a speckle removal denois-ing algorithm for synthetic aperture radar (SAR) images. The approach is based on the concept of extracting informative feature (based on the concept of multifractal decomposition of signals) from a speckle-induced SAR image and then estimating a noise-free image from the gradients restricted to those features. The experimental results show that the proposed technique not only improves the visual quality of the SAR images but also effectively preserves their texture. Comparison with the classical and state-of-the-art denoising techniques shows the advantages of the proposed scheme, both visually and quantitatively.

In the framework of the acquisition chain and devices built by i2S, the objective of this PhD is to provide efficient algorithms able to merge different acquired images corresponding to slight spatial translational displacements to get a wider super-resolved image. Consequently, this PhD takes place within the general subject of super-resolution. In this thesis, super-resolution is performed using different acquisitions. We propose the use of sensor displacement within the cameras of I2S. We propose a scheme to achieve an image with up to two times higher resolution using this technique. Furthermore, we also propose an additional image deconvolution algorithm that helps to improve the image quality further and to address any degradation problem that may occur through the super-resolution scheme. Our image deconvolution algorithm is based on variable splitting and takes advantage of the proximal operator and Fourier transform. We also proposed the use of new potential functions that can be used as prior information in image inverse problems for the first time used in image processing. Experimental results show promising capabilities of the proposed algorithm. The algorithm is successfully implemented within various cameras and devices of I2S. The practical experiments on real-world data prove the effectiveness and flexibility of our image-deconvolution. Experiments were conducted on Herschel observation maps, and promising results were obtained on such imaging data. In the last part of the thesis, the idea of plug-and-play priors for image denoising and deconvolution is presented. This thesis proposes the implementation of plug-and play-priors in an alternating minimisation scheme. The early result has shown potential to be adequate for image denoising/deconvolution application.; Dans le cadre de la chaîne d’acquisition et des dispositifs construits par I2S, l’objectif de cette thèse est de fournir des algorithmes efficaces capables de fusionner différentes images acquises correspondant à de légers déplacements de translation spatiale pour obtenir une image super-résolue plus large. Par conséquent, cette thèse s’inscrit dans le cadre général de la super-résolution. Dans cette thèse, la super-résolution est réalisée à l’aide de différentes acquisitions. Nous proposons l’utilisation du capteur de déplacement au sein des caméras d’I2S. Nous proposons un schéma pour obtenir une image avec une résolution jusqu’à deux fois plus élevée en utilisant cette technique. En outre, nous proposons également un algorithme de déconvolution d’image supplémentaire qui aide à améliorer davantage la qualité de l’image et à résoudre les problèmes de dégradation pouvant survenir via le schéma de super-résolution. Notre algorithme de déconvolution d’image est basé sur le fractionnement variable et tire parti de l’opérateur proximal et de la transformée de Fourier. Nous introduisons également de nouvelles fonctions potentielles qui peuvent être utilisées comme information préalable dans les problèmes inverses qui sont utilisés pour la premiére fois en traitement d’image. Les résultats expérimentaux montrent des capacités prometteuses de l’algorithme proposé. L’algorithme est mis en oeuvre avec succès dans diverses caméras et appareils d’I2S. Les expériences pratiques sur des données du monde réel prouvent l’efficacité et la flexibilité de notre méthode de déconvolution. Des expériences ont été menées sur des cartes d’observation de Herschel, et des résultats prometteurs ont été obtenus sur de telles données d’imagerie. Dans la dernière partie de la thèse, l’idée de priors plug-and-play pour le débruitage et la déconvolution des images est présentée. Cette thése proposel’implémentation de priors plug-and-play dans un schéma de minimisation alternée. Le premier résultat a montré un potentiel adéquat pour l’application de débruitage / déconvolution d’image.

International audience; Acoustic realisation of the working vowel space has been widely studied in Parkinson's disease (PD). However, it has never been studied in atypical parkinsonian disorders (APD). The latter are neurodegenerative diseases which share similar clinical features with PD, rendering the differential diagnosis very challenging in early disease stages. This paper presents the first contribution in vowel space analysis in APD, by comparing corner vowel realisation in PD and the parkinsonian variant of Multiple System Atrophy (MSA-P). Our study has the particularity of focusing exclusively on early stage PD and MSA-P patients, as our main purpose was early differential diagnosis between these two diseases. We analysed the corner vowels, extracted from a spoken sentence, using traditional vowel space metrics. We found no statistical difference between the PD group and healthy controls (HC) while MSA-P exhibited significant differences with the PD and HC groups. We also found that some metrics conveyed complementary discriminative information. Consequently, we argue that restriction in the acoustic realisation of corner vowels cannot be a viable early marker of PD, as hypothesised by some studies, but it might be a candidate as an early hypokinetic marker of MSA-P (when the clinical target is discrimination between PD and MSA-P).

Hyperspectral images (HSI) are corrupted by a combination of Gaussian and impulse noise. Successful denoising of HSI data increases the accuracy of high-level vision operations like classification, target tracking and land-cover problem. On the one hand, the traditional approach of handling the denoising problem using maximum a posteriori (MAP) criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result. On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training. A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results. In this paper, we design the training paradigm emphasizing the role of loss functions in neural network; similar to as observed in model-based optimization methods. Further, Bayesian motivated loss functions also act as priors to constrain the solution space to the types of noise observed in hyperspectral image acquisition process. As a result, loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance. Extensive analysis and experimental results on synthetically corrupted and real hyperspectral datasets suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings. Classification results over the real dataset and associated metrics like kappa coefficient and overall accuracy used as the task-based evaluators further support our hypothesis made in the proposed methodology.

Speech disorder is an early and prominent manifestation of neurological disorders. Therefore, the breakdown of speech disorders and detecting underlying pathophysiology have invaluable importance to clinical practice. Speech disorder is commonly attributed to aging onset, however, the pattern is mostly distinct for neurogenic voice. Parkinsonism is one of the neurological disorder that refers to idiopathic Parkinson’s Disease (PD) and Atypical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Differential diagnosis of latter disease groups is remains an challenging task due to similar symptoms at the early stages, while early diagnostic certainty is essential for the patient because of the diverging prognosis. Indeed, despite recent efforts, no validated objective speech marker is currently available to guide the clinician for the differential diagnosis. This thesis thus aims to design and define the speech markers that would provide deep insight into speech disorders caused by neurological diseases, and followed by differential diagnosis.Analysis of speech disorder demands at least a speech database by which pattern of speech abnormalities can be assessed. Speech database consisting PD and MSA-P neurological diseases is not available in French language. Thus development of a speech database (Voice4PD-MSA) from PD and MSA-P groups was one of the target of this thesis. While developing Voice4PD-MSA database, we explored CzechData database comprises of speech samples in Czech language for differential diagnosis.The automatic algorithm always in demand to quantify perceptual and visual observation to capture particular speech disorders. Clinically interpretable speech components are considered to capture speech abnormalities in respiration, vowel production, articulator movements, and prosody by objective methods from sustained vowel, word-initial consonants, diadochokinetic (DDK) tasks, and continuous speech. Imprecise vowel comprises deficits in vocal folds opening and closing, involuntary movements of articulator, hypernasality, tremor, and changes in vowel space area are observed to be important for differential diagnosis of MSA-P and PD patients. In imprecise obstruents, devoicing in voiced obstruents and burst in fricatives (anti-spirantization) are identified as distinctive speech markers for MSA-P. In addition, speech indexes related to the subsystem of speech production and dysarthria yield encouraging differentiation and disease specificity in disease groups. Given small amount of data, two-dimensional speech features are designed such that one of the disease group predominates in one speech dimension and consequently discriminate disease groups with good classification score.Early differential diagnosis was another critical objective of the current investigation. The present study observed some encouraging indications about early differential diagnosis exploring the trend of speech markers w.r.t. clinical signs. Thus we aspire that the presented methodology in this thesis would serve as a potential diagnosis tool in clinical practice and further inspire to develop automatic methods to investigate speech disorders in parkinsonism.; Les troubles de la parole sont une manifestation précoce et importante des troubles neurologiques. Par conséquent, la décomposition des troubles de la parole et la détection de la pathophysiologie sous-jacente ont une importance inestimable pour la pratique clinique. Les troubles de la parole sont généralement attribués au vieillissement, mais le modèle est surtout distinct pour la voix neurogène. Le parkinsonisme est l'un des troubles neurologiques qui fait référence à la maladie de Parkinson idiopathique (MPI) et aux syndromes parkinsoniens atypiques (APS), tels que la paralysie supranucléaire progressive (PSP) et l'atrophie multisystémique (AMS). Le diagnostic différentiel de ces derniers groupes de maladies reste une tâche difficile en raison de la similitude des symptômes aux premiers stades, alors que la certitude diagnostique précoce est essentielle pour le patient en raison des divergences de pronostic. En effet, malgré des efforts récents, aucun marqueur objectif validé de la parole n'est actuellement disponible pour guider le clinicien dans son diagnostic différentiel. Cette thèse a donc pour but de concevoir et de définir les marqueurs de la parole qui permettraient de mieux comprendre les troubles de la parole causés par des maladies neurologiques, puis de poser un diagnostic différentiel. L'analyse des troubles de la parole nécessite au moins une base de données vocales permettant d'évaluer le modèle des anomalies de la parole. La base de données vocales des maladies neurologiques PD et MSA-P n'est pas disponible en langue française. Ainsi, le développement d'une base de données vocales (Voice4PD-MSA) pour les groupes PD et AMS-P était l'un des objectifs de cette thèse. Tout en développant la base de données Voice4PD-MSA, nous avons exploré la base de données CzechData qui comprend des échantillons de parole en langue tchèque pour le diagnostic différentiel.L'algorithme automatique est toujours demandé pour quantifier l'observation perceptive et visuelle afin de capturer des troubles particuliers de la parole. Les composantes de la parole cliniquement interprétables sont considérées comme des anomalies de la respiration, de la production de voyelles, des mouvements de l'articulateur et de la prosodie par des méthodes objectives à partir de voyelles soutenues, de consonnes initiales de mots, de tâches diadochocinétiques (DDK) et de la parole continue. Les voyelles imprécises comprennent les déficits d'ouverture et de fermeture des plis vocaux, les mouvements involontaires de l'articulateur, l'hypernasalité, les tremblements et les modifications de la zone de l'espace vocal sont observés comme étant importants pour le diagnostic différentiel des patients atteints de AMS-P et de MPI. Dans les obstructions imprécises, le dévoicing dans les obstructions voisées et l'éclatement dans les fricatives (anti-spirantisation) sont identifiés comme des marqueurs vocaux distinctifs pour le AMS-P. En outre, les indices vocaux liés au sous-système de production de la parole et à la dysarthrie permettent une différenciation encourageante et une spécificité de la maladie dans les groupes de maladies. Compte tenu de la faible quantité de données, les caractéristiques vocales bidimensionnelles sont conçues de manière à ce que l'un des groupes de maladies prédomine dans l'une des dimensions vocales, ce qui permet de distinguer les groupes de maladies avec un bon score de classification. Le diagnostic différentiel précoce était un autre objectif essentiel de la présente étude. La présente étude a observé des indications encourageantes sur le diagnostic différentiel précoce en explorant la tendance des marqueurs de la parole par rapport aux signes cliniques. Nous espérons donc que la méthodologie présentée dans cette thèse servira d'outil de diagnostic potentiel dans la pratique clinique et inspirera le développement de méthodes automatiques pour étudier les troubles de la parole dans le parkinsonisme.

International audience; A fast image deconvolution algorithm is used to demonstrate the resolution enhancement of video rate camera acquired Terahertz images. Our algorithm is based on variable splitting technique with the use of a family of sparsity inducing regularizers for the first time in an image deconvolution application, it is also suitable for practical applications in industry with computationally constrained conditions. The results of the proposed process provide substantial enhancement on the quality and resolution of THz images.

International audience; Parkinson's disease (PD) and the parkinsonian variant of Multiple System Atrophy (MSA-P) are two neurodegenerative diseases which share similar clinical features, particularly in early disease stages. The differential diagnosis can be thus very challenging. Dysarthria is known to be a frequent and early clinical feature of PD and MSA. It can be thus used as a vehicle to provide a vocal biomarker which could help in the differential diagnosis. In particular, distortion of consonants is known to be a frequent impairment in these diseases. The aim of this study is to investigate distinctive patterns in the distortion of voiced obstruents (plosives and fricatives). It is the first study which attempts to examine such distortions in the French language for the purpose of the differential diagnosis between PD and MSA-P (and among the very few studies if we consider all languages). We carry out a perceptual and objective analysis of voiced obstruents extracted from isolated pseudo-words initials. We first show that devoicing is a significant impairment which predominates in MSA-P. We then show that voice onset time (VOT) of voiced plosives (prevoicing duration) can be a complementary feature to improve the accuracy in discrimination between PD and MSA-P.

Synthèse de l'activité scientifique en vue de l'obtention d'une Habilitation à Diriger des Recherches (HDR). Habilitation soutenue le 2021/06/09, Université de Bordeaux.

International audience; Observations of the interstellar medium (ISM) show a complex density and velocity structure, which is in part attributed to turbulence. Consequently, the multifractal formalism should be applied to observation maps of the ISM in order to characterize its turbulent and multiplicative cascade properties. However, the multifractal formalism, even in its more advanced and recent canonical versions, requires a large number of realizations of the system, which usually cannot be obtained in astronomy. We present a self-contained introduction to the multifractal formalism in a "microcanonical" version, which allows us, for the first time, to compute precise turbulence characteristic parameters from a single observational map without the need for averages in a grand ensemble of statistical observables (e.g., a temporal sequence of images). We compute the singularity exponents and the singularity spectrum for both observations and magnetohydrodynamic simulations, which include key parameters to describe turbulence in the ISM. For the observations we focus on the 250 µm Herschel map of the Musca filament. Scaling properties are investigated using spatial 2D structure functions, and we apply a two-point log-correlation magnitude analysis over various lines of the spatial observation, which is known to be directly related to the existence of a multiplicative cascade under precise conditions. It reveals a clear signature of a multiplicative cascade in Musca with an inertial range from 0.05 to 0.65 pc. We show that the proposed microcanonical approach provides singularity spectra that are truly scale invariant, as required to validate any method used to analyze multifractality. The obtained singularity spectrum of Musca, which is sufficiently precise for the first time, is clearly not as symmetric as usually observed in log-normal behavior. We claim that the singularity spectrum of the ISM toward Musca features a more log-Poisson shape. Since log-Poisson behavior is claimed to exist when dissipation is stronger for rare events in turbulent flows, in contrast to more homogeneous (in volume and time) dissipation events, we suggest that this deviation from log-normality could trace enhanced dissipation in rare events at small scales, which may explain, or is at least consistent with, the dominant filamentary structure in Musca. Moreover, we find that subregions in Musca tend to show different multifractal properties: While a few regions can be described by a log-normal model, other regions have singularity spectra better fitted by a log-Poisson model. This strongly suggests that different types of dynamics exist inside the Musca cloud. We note that this deviation from log-normality and these differences between subregions appear only after reducing noise features, using a sparse edge-aware algorithm, which have the tendency to "log-normalize" an observational map. Implications for the star formation process are discussed. Our study establishes fundamental tools that will be applied to other galactic clouds and simulations in forthcoming studies.

26 pages, 26 figures, 1 table, 4 appendixes, Observations of the interstellar medium (ISM) show a complex density and velocity structure, which is in part attributed to turbulence. Consequently, the multifractal formalism should be applied to observation maps of the ISM in order to characterize its turbulent and multiplicative cascade properties. However, the multifractal formalism, even in its more advanced and recent canonical versions, requires a large number of realizations of the system, which usually cannot be obtained in astronomy. We present a self-contained introduction to the multifractal formalism in a “microcanonical” version, which allows us, for the first time, to compute precise turbulence characteristic parameters from a single observational map without the need for averages in a grand ensemble of statistical observables (e.g., a temporal sequence of images). We compute the singularity exponents and the singularity spectrum for both observations and magnetohydrodynamic simulations, which include key parameters to describe turbulence in the ISM. For the observations we focus on the 250 μm Herschel map of the Musca filament. Scaling properties are investigated using spatial 2D structure functions, and we apply a two-point log-correlation magnitude analysis over various lines of the spatial observation, which is known to be directly related to the existence of a multiplicative cascade under precise conditions. It reveals a clear signature of a multiplicative cascade in Musca with an inertial range from 0.05–0.65 pc. We show that the proposed microcanonical approach provides singularity spectra that are truly scale invariant, as required to validate any method used to analyze multifractality. The obtained singularity spectrum of Musca, which is sufficiently precise for the first time, is clearly not as symmetric as usually observed in log-normal behavior. We claim that the singularity spectrum of the ISM toward Musca features a more log-Poisson shape. Since log-Poisson behavior is claimed to exist when dissipation is stronger for rare events in turbulent flows, in contrast to more homogeneous (in volume and time) dissipation events, we suggest that this deviation from log-normality could trace enhanced dissipation in rare events at small scales, which may explain, or is at least consistent with, the dominant filamentary structure in Musca. Moreover, we find that subregions in Musca tend to show different multifractal properties: While a few regions can be described by a log-normal model, other regions have singularity spectra better fitted by a log-Poisson model. This strongly suggests that different types of dynamics exist inside the Musca cloud. We note that this deviation from log-normality and these differences between subregions appear only after reducing noise features, using a sparse edge-aware algorithm, which have the tendency to “log-normalize” an observational map. Implications for the star formation process are discussed. Our study establishes fundamental tools that will be applied to other galactic clouds and simulations in forthcoming studies, This work is supported by the GENESIS project (GENeration and Evolution of Structure in the ISm), via the french ANR and the german DFG through grant numbers ANR-16-CE92-0035-01 and DFG1591/2-1. N. Schneider acknowledges support by the German Deutsche Forschungsgemeinschaft, DFG project number SFB 956. L. Bonne acknowledges support by the Région Nouvelle-Aquitaine, France., With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

The Bay of Biscay and the English Channel, in the North-eastern Atlantic, are considered as a natural laboratory to explore the coastal dynamics at different spatial and temporal scales. In those regions, the coastal circulation is constrained by a complex topography (e.g. varying width of the continental shelf, canyons), river runoffs, strong tides and a seasonally contrasted wind-driven circulation. Based on different numerical model experiments (from 400m to 4km spatial resolution, from 40 to 100 sigma vertical layers using 3D primitive equation ocean models), different features of the Bay of Biscay and English Channel circulation are assessed and explored. Both spatial (submesoscale and mesoscale) and temporal (from hourly to monthly) scales are considered. Modelled spatial scales, with a specific focus on the variability of fine scale features (e.g. fronts, filaments, eddies), are compared with remotely sensed observations (i.e. Sea Surface Temperature). Different methodologies as singularity and Lyapunov exponents allow describing fine scales features and are applied on both modelled and observed datasets. For temporal scales, in situ high frequency surface temperature measurements from coastal moorings (from COAST-HF observing network) provide a reference for the temporal variability to be modelled. Exploring differences in the temporal scales (from an Empirical Mode Decomposition) advises on the efficiency of our coastal modelling approach. This result overview in the Bay of Biscay and the English Channel aims illustrating the input of coastal modelling activities in understanding multi-scale interactions (spatial and temporal).

We describe here experiments on the mechanics of hydrogel particle packings from the Behringer lab, performed between 2012 and 2015. These experiments quantify the evolution of all contact forces inside soft particle packings exposed to compression, shear, and the intrusion of a large intruder. The experimental set-ups and processes are presented and the data are concomitantly published in a repository., Comment: accepted for Granular Matter

Context. Dense molecular filaments are ubiquituous in the interstellar medium, yet their internal physical conditions and formation mechanism remain debated. Aims. We study the kinematics and physical conditions in the Musca filament and the Chamaeleon-Musca complex to constrain the physics of filament formation. Methods. We produced CO(2-1) isotopologue maps with the APEX telescope that cut through the Musca filament. We further study a NANTEN2 $^{12}$CO(1-0) map of the Musca cloud and the HI emission of the Chamaeleon-Musca complex. Results. The Musca cloud contains multiple velocity components. Radiative transfer modelling of the CO emission indicates that the Musca filament consists of a cold ($\sim$10 K), dense (n$_{H_2}\sim$10$^4$ cm$^{-3}$) crest, which is best described with a cylindrical geometry. Connected to the crest, a separate gas component at T$\sim$15 K and n$_{H_2}\sim$10$^3$ cm$^{-3}$ is found, the so-called strands. The filament crest has a transverse velocity gradient that is linked to the kinematics of the nearby ambient cloud. Studying the large scale kinematics, we find coherence of the asymmetric kinematics from the 50 pc HI cloud down to the Musca filament. We also report a strong [C$^{18}$O]/[$^{13}$CO] abundance drop by an order of magnitude from the filament crest to the strands over a distance $, Comment: 24 pages, 26 figures

International audience; Analysis and study of coastal upwelling using sea surface temperature (SST) satellite images is a common procedure because of its coast effectiveness (economic, time, frequency, and manpower). Developing on the Ekman theory, we propose a robust method to identify the upwelling regions along the northwest African margin. The proposed method comes to overcome the issues encountered in a recent method devoted for the same purpose and for the same upwelling system. Afterward, we show how our method can serve as a framework to study and monitor the spatio-temporal variability of the upwelling phenomenon in the studied region.

We merge computational mechanics' definition of causal states (predictively-equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely-applicable method that infers causal structure directly from observations of a system's behaviors whether they are over discrete or continuous events or time. A structural representation -- a finite- or infinite-state kernel $\epsilon$-machine -- is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker-Plank equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably-infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally-driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high dimensional data., Comment: 23 pages, 11 figures, 64 citations; https://team.inria.fr/comcausa/continuous-causal-states/

International audience; An efficient unsupervised method is developed for automatic segmentation of the area covered by upwelling waters in the coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. The proposed approach first uses the two popular unsupervised clustering techniques, k-means and fuzzy c-means (FCM), to provide different possible classifications to each SST image. Then several cluster validity indices are combined in order to determine the optimal number of clusters, followed by a cluster fusion scheme, which merges consecutive clusters to produce a first segmentation of upwelling area. The region-growing algorithm is then used to filter noisy residuals and to extract the final upwelling region. The performance of our algorithm is compared to a popular algorithm used to detect upwelling regions and is validated by an oceanographer over a database of 92 SST images covering each week of the years 2006 and 2007. The results show that our proposed method outperforms the latter algorithm, in terms of segmentation accuracy and computational efficiency.

International audience; The frontal activity in coastal regions remains a research field where a large number of open questions needs to be addressed to quantify the potential impact of these processes on dependent systems (e.g.biogeochemical activity). Spatial and seasonal distributions of Sea Surface Temperature (SST) fronts (∼1-100 km) in the vicinity of main French rivers, Gironde and Loire, are explored over the continental shelf North of 45°N in the Bay of Biscay. A highresolution (1km spatial and daily temporal resolutions) dataset of 11 years (2003 to 2013) remotely sensed SST by MODIS sensor onboard Aqua and Terra satellites has been investigated and compared with coastal numerical model experiments. The detection and characterization fronts with fluctuating amplitudes is achieved through the Singularity Analysis (i.e. the process of calculating the degree of regularity or irregularity of a function at each point in a domain). Seasonality of frontal activity in the Bay of Biscay is then described based on the long-term satellite SST archive and coastal operational model simulations. The identifiedhot spots of higher frontal occurrences correspond on one hand to previously observed features (e.g. tidal fronts) but also reveal new features. These are investigated to identify fine-scale dynamical drivers. In winter, density fronts are prominent in a coastal strip where freshwater influence is important. In spring, this strip diminishes as plumes detach from the coast, while tidal fronts become apparent in other regions. In summer, tidal fronts in Ushant region and internal wave activity along the shelf break dominate. In autumn, coastal density fronts due to freshwater inputs reappear as these inputs increase, and reduced stratification causes a weakening of the Ushant and shelf break fronts.Additional information and an effort to dynamically interpret these fronts, based on a systematic investigation of the whole seasonal cycle and including modeling insights from coastal operational oceanography, complement the description of frontal activity in the Bay of Biscay.

Studying physical processes of an upwelling system is essential to understand its present variability and its past and future changes. This thesis presents an interdisciplinary study of the coastal upwelling system from different satellite acquired data, with the main focus placed on the North West African (NWA) upwelling system. This interdisciplinary study covers (1) the problem of the automatic identification and extraction of the upwelling phenomenon from biological and physical satellite observations. (2) A statistical study of the spatio-temporal variation of the NWA upwelling throughout its extension and different upwelling indices. (3) A Study of the nonlinear relationships between the surface mixing and biological activity in the upwelling regions. (4) Lagrangian studies of coherent eddies; their physical properties and automatic identification. (5) The study of transport made by Lagrangian eddies off the NWA Upwelling and their impact on the open ocean. [...]; L’étude des processus physiques d’un système d’upwelling est essentielle pour comprendre sa variabilité actuelle et ses changements passés et futurs. Cette thèse présente une étude interdisciplinaire du système d’upwelling côtier à partir de différentes données acquises par satellite, l’accent étant mis principalement sur le système d’upwelling d’Afrique du Nord-Ouest (NWA). Cette étude interdisciplinaire aborde (1) le problème de l’identification et de l’extraction automatiques du phénomène d’upwelling à partir d’observations satellitaires biologiques et physiques. (2) Une étude statistique de la variation spatio-temporelle de l’upwelling de la NWA tout au long de son extension et de ses différents indices d’upwelling. (3) Une étude des relations non linéaires entre le mélange de surface et l’activité biologique dans les régions d’upwelling. (4) études lagrangiennes de tourbillons cohérents; leurs propriétés physiques et identification automatique. (5) L’étude des transports effectués par les tourbillons lagrangiens de la NWA Upwelling et leur impact sur l’océan.

Studying physical processes of an upwelling system is essential to understand its present variability and its past and future changes. This thesis presents an interdisciplinary study of the coastal upwelling system from different satellite acquired data, with the main focus placed on the North West African (NWA) upwelling system. This interdisciplinary study covers (1) the problem of the automatic identification and extraction of the upwelling phenomenon from biological and physical satellite observations. (2) A statistical study of the spatio-temporal variation of the NWA upwelling throughout its extension and different upwelling indices. (3) A Study of the nonlinear relationships between the surface mixing and biological activity in the upwelling regions. (4) Lagrangian studies of coherent eddies; their physical properties and automatic identification. (5) The study of transport made by Lagrangian eddies off the NWA Upwelling and their impact on the open ocean. [...]; L’étude des processus physiques d’un système d’upwelling est essentielle pour comprendre sa variabilité actuelle et ses changements passés et futurs. Cette thèse présente une étude interdisciplinaire du système d’upwelling côtier à partir de différentes données acquises par satellite, l’accent étant mis principalement sur le système d’upwelling d’Afrique du Nord-Ouest (NWA). Cette étude interdisciplinaire aborde (1) le problème de l’identification et de l’extraction automatiques du phénomène d’upwelling à partir d’observations satellitaires biologiques et physiques. (2) Une étude statistique de la variation spatio-temporelle de l’upwelling de la NWA tout au long de son extension et de ses différents indices d’upwelling. (3) Une étude des relations non linéaires entre le mélange de surface et l’activité biologique dans les régions d’upwelling. (4) études lagrangiennes de tourbillons cohérents; leurs propriétés physiques et identification automatique. (5) L’étude des transports effectués par les tourbillons lagrangiens de la NWA Upwelling et leur impact sur l’océan.

International audience; Cardiac fibrillation a ects the atria or the ventricles and, in both cases, poses major challenges to public health management. Atrial fibrillation is the predominant cause of embolic stroke while ventricular fibrillation leads to sudden cardiac arrest and death. The medical community of cardiac electro-physiologists consider cardiac fibrillation as a disease of known cause, namely a chaotic multiplication of electric pulse reentries, but of unknown trigger. Putative triggers are targeted in an e ort to solve this riddle by a combination of safe therapies including anti-arrhythmic drugs and catheter radio-frequency ablation. In two recent works (10.3389/fphys.2017.01139 and 10.3389/fphys.2019.00480), with Evgeniya Gerasimova Françoise Argoul and Alain Arneodo, we have provided evidence and modeling which clash with the accepted wisdom. We give here the main argument for the incompatibility of the random multifractal scaling, revealed in recordings of the electrical activity of fibrillating human hearts, with the reentry paradigm. In the model, pulse dynamics is akin to a random branching birth and death process due to a highly fluctuating conductance. In this respect, we sketch a calculation of the multifractal spectrum for our cardiac modeling, which underlines how the multifractal parameter is related to the variance of the abnormal capacitive electric field at the gap junction channels. Those new observations and modeling raise a number of theoretical and practical possibilities and new challenges, such as electric recording interpretation from the level of cell-to-cell coupling up to the level of neural modulations. Gray area Is atrial fibrillation characterized only by spatio-temporal irregularity of local beat-to-beat cycles? Figure 1: Left: Atrial fibrillation schematic mechanisms. Right: High density mapping of activation times.

International audience; Parkinsonism refers to Parkinson's Disease (PD) and Atyp-ical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Discrimination between PD and APS and within APS groups in early disease stages is a very challenging task. Interestingly, speech disorder is frequently an early and prominent clinical feature of both PD and APS. This renders speech/voice analysis a promising tool for the development of an objective marker to assist neurologists in their diagnosis. This paper is a continuation of a recent work on speech-based differential diagnosis within APS. We address the difficult problem of defining disease-specific speech features which is crucial in the perspective of early differential diagnosis. We investigate this problem by considering the constraint that only a small amount of training data can be available in this setting. To do so, we perform univariate statistical analysis followed by a supervised learning that forces the designed new features to be 1-dimensional. We carry out experiments using speech recordings of MSA and PSP patients. We show that linear classification models allow the definition of new scalar variables which can be considered as speech features which are specific to each disease, MSA and PSP.

The advent of Fluorescence Microscopy over the last few years have dramatically improved the problem of visualization and tracking of specific cellular objects for biological inference. But like any other imaging system, fluorescence microscopy has its own limitations. The resultant images suffer from the effect of noise due to both signal dependent and signal independent factors, thereby limiting the possibility of biological inferencing. Denoising is a class of image processing algorithms that aim to remove noise from acquired images and has gained wide attention in the field of fluorescence microscopy image restoration. In this paper, we propose an image denoising algorithm based on the concept of feature extraction through multifractal decomposition and then estimate a noise free image from the gradients restricted to these features. Experimental results over simulated and real fluorescence microscopy data prove the merit of the proposed approach, both visually and quantitatively.

International audience; In a companion paper (I. Multifractal analysis of clinical data), we used a wavelet-based multiscale analysis to reveal and quantify the multifractal intermittent nature of the cardiac impulse energy in the low frequency range 2Hz during atrial fibrillation (AF). It demarcated two distinct areas within the coronary sinus (CS) with regionally stable multifractal spectra likely corresponding to different anatomical substrates. The electrical activity also showed no sign of the kind of temporal correlations typical of cascading processes across scales, thereby indicating that the multifractal scaling is carried by variations in the large amplitude oscillations of the recorded bipolar electric potential. In the present study, to account for these observations, we explore the role of the kinetics of gap junction channels (GJCs), in dynamically creating a new kind of imbalance between depolarizing and repolarizing currents. We propose a one-dimensional (1D) spatial model of a denervated myocardium, where the coupling of cardiac cells fails to synchronize the network of cardiac cells because of abnormal transjunctional capacitive charging of GJCs. We show that this non-ohmic nonlinear conduction 1D modeling accounts quantitatively well for the "multifractal random noise" dynamics of the electrical activity experimentally recorded in the left atrial posterior wall area. We further demonstrate that the multifractal properties of the numerical impulse energy are robust to changes in the model parameters.

International audience; We propose a new approach to enforce integrability using recent advances in non-local methods. Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior. This regularization has two main advantages : 1) the low-rank prior ensures similarity between non-local gradient patches, which helps recovering high-quality clean patches from severe outliers corruption, 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works. We propose an efficient solver for the resulting optimization formulation using alternate minimization. Experiments show that the new method leads to an important improvement compared to previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together.

International audience; We study the transport properties of mesoscale eddies (i.e. vortices of 100 ∼ 200 km in diameter) over a finite time duration. While these oceanic structures are well-known to stir and mix surrounding water, they can also carry and transport water properties in a coherent manner. In this paper, we are interested in dynamic transport properties of these coherent structures, despite their chaotic environment. Here, we reveal that such vortices can be identified based a simple decomposition of their Lagrangian trajectories. We identify and extract coherent vortices as material lines along which particles' trajectories share similar polar rotations. The proposed method identifies coherent vortices and their centers in automatic manner. We illustrate our new method by identifying and extracting Lagrangian coherent vortices in different two-dimensional flows.

International audience; This paper introduces a new algorithm for denoising SAR images. It is directly applicable to Sentinel-1 GRD images, without the need for single-look complex operations. The algorithm builds on the non-local patch matching idea for statistical denoising, similar to the SAR-BM3D and NL-SAR algorithms, but introduces two corrections : 1. A non-uniform prior for the reflectance values is used for the patch matching, thus allowing a better fit to the data, and 2. Denoising is performed in singular values space, with a prior distribution of expected "clean" singular values learned and transfered from optical images. The denoised SAR images show reduced amount of speckle compared to alternative methods.

International audience; We evidence and study the differences in turbulence statistics in ocean dynamics carried by wind forcing at the air-sea interface. Surface currents at the air-sea interaction are of crucial importance because they transport heat from low to high latitudes. At first order, oceanic currents are generated by the balance of the Coriolis and pressure gradient forces (geostrophic current) and the balance of the Coriolis and the frictional forces dominated by wind stress (Ekman current) in the surface ocean layers. The study was conducted by computing statistical moments on the shapes of spectra computed within the framework of microcanonical multi-fractal formalism. Remotely sensed daily datasets derived from one year of altimetry and wind data were used in this study, allowing for the computation of two kinds of vector fields: geostrophy with and geostrophy without wind stress forcing. We explore the statistical properties of singularity spectra computed from velocity norms and vorticity data, notably in relation with kurtosis information to underline the differences in the turbulent regimes associated with both kinds of velocity fields.

International audience; Eastern Boundary Upwelling zones include some of the most productive ecosystems in the world, particularly the North-West (NW) African upwelling which presents one of the world’s major upwelling regions. It is characterized by persistent and variable Upwelling phenomenon almost all around the year. In fact, the North-West African margin is forced by the equator-ward trade winds that lead to the upward pumping of cold and nutrient-rich deep waters. The phenomenon can be identified by seasonally variable low sea surface temperature (SST) and nutrient-rich water in coastal area. At this point, the characterization of coastal Upwelling dynamic has a fundamental importance in the study of productivity enhancement and associated fish management. Based on the use of SST satellite images and Chlorophyll-a concentration, this work aim to study the spatial and temporal variation of the main physical and biological Upwelling patterns. Moreover, the upwelling dynamics in interplay between its spatio-temporal extensions and intensity is explored through a time series of 14 years of weekly thermal satellite data.

International audience; Today, both SAR and optical data are available with good spatial and temporal resolutions. The two data modalities complement each other in many applications. There are numerous approaches to process the two data modalities, separately or combined. Domain or modality specific approaches such as polarimetric decomposition techniques or reflectance based techniques cannot work with the two datasets combined together. Data fusion approaches incur information loss during the process and are highly application specific. Machine learning (ML) approaches can operate on the combined dataset but have their own advantages and disadvantages. There is a need to explore new ML based approaches to achieve higher performance. Convolutional neural networks (CNNs) are young, trending, and promising ML tools in remote sensing applications. CNNs have the capability to learn complex features exclusively from data. Data from the two modalities can thus be brought together and processed with increased performance. In this paper an attempt is made to analyze CNN capabilities to perform land cover classification using multi-sensor data. SAR data used in this study is L band fully polarimetric PALSAR 2 data with 6 meter spatial resolution. Three basic polarimetric bands, namely, HH, HV, and VV, and four derived bands (polarization signatures) are used. Six multispectral Landsat 8 bands, pan sharpened and resampled at 6 meter spatial resolution, are used as optical data. All 13 features are stacked together and fed as input data to the proposed CNN. The areas selected for study are Haridwar and Roorkee regions of northern India. This study introduces a CNN where convolution is performed both spatially and spectrally. We show how this is an advantage over performing only spatial convolution. Five land cover classes namely, urban, bare soil, water, dense vegetation, and agriculture are considered. The CNN is trained on more than 1200 ground truth class data points measured directly on the terrain. The classification shows results with good generalization. Comparison with other classifiers such as SVMs shows that the proposed approach provides better classification results in terms of generalization, although the cross-validation accuracy is on the same order. The evaluation of the generalization of the classified image is done using ground truth knowledge on selected subset areas in the study area.

International audience; Matching pursuit (MP), particularly using the Gammatones dictionary , has become a popular tool in sparse representations of speech/audio signals. The classical MP algorithm does not however take into account psychoacoustical aspects of the auditory system. Recently two algorithms, called PAMP and PMP have been introduced in order to select only perceptually relevant atoms during MP decomposition. In this paper we compare this two algorithms on few speech sentences. The results suggest that PMP, which also has the strong advantage of including an implicit stop criterion, always outperforms PAMP as well as classical MP. We then raise the question of whether the Gam-matones dictionary is the best choice when using PMP. We thus compare it to the popular Gabor and damped-Sinusoids dictionaries. The results suggest that Gammatones always outperform damped-Sinusoids, and that Gabor yield better reconstruction quality but with higher atoms rate.

International audience; We present a new framework for fast edge-aware processing of images and videos. The proposed smoothing method is based on an optimization formulation with a non-convex sparse regularization for a better smoothing behavior near strong edges. We develop mathematical tools based on first order approximation of proximal operators to accelerate the proposed method while maintaining high-quality smoothing. The first order approximation is used to estimate a solution of the proximal form in a half-quadratic solver, and also to derive a warm-start solution that can be calculated quickly when the image is loaded by the user. We extend the method to large-scale processing by estimating the smoothing operation with independent 1D convolution operations. This approach linearly scales to the size of the image and can fully take advantage of parallel processing. The method supports full color filtering and turns out to be temporally coherent for fast video processing. We demonstrate the performance of the proposed method on various applications including image smoothing, detail manipulation, HDR tone-mapping, fast edge simplification and video edge-aware processing.

International audience; This paper presents a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from the speech signal. Our approach is based on a novel multiscale method that relies on precise estimation of a multiscale parameter at each time instant in the signal domain. This parameter quantifies the degree of signal singularity at each sample from a multi-scale point of view and thus its value can be used to classify signal samples accordingly. We use this property to develop a simple algorithm for detection of GCIs and we show that for the case of clean speech, our algorithm performs almost as well as a recent state-of-the-art method. Next, by performing a comprehensive comparison in presence of 14 different types of noises, we show that our method is more accurate (particularly for very low SNRs). Our method has lower computational times compared to others and does not rely on an estimate of pitch period or any critical choice of parameters.