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International audience; This paper deals with the design of sensor arrays in the context involving the localization of a few acoustic sources. Sparse approximation is known to be effective to find the source locations, but it depends on different array characteristics, such as the number of sensors and the array geometry. The present paper tackles this array design problem under the form of a sequential sensor selection procedure. The proposed method alternates between two steps. One step involves a source localization estimator, given a current set of measurement points, to obtain the estimation variance. Then, the other step selects the new point where a future measurement will maximally decrease the variance from the previous step. As such, the procedure can be applied online. Both numerical and experimental studies are conducted in an indoor nearfield configuration. Results show that the proposed approach performs better than offline state-of-the-art methods, and the presented empirical study reveals a better robustness to the model mismatches originating from the room reflections.

International audience; The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For this purpose, various methodologies were developed to ensure accurate and efficient monitoring. In this context, this paper proposes an approach for bearing faults early diagnosis based on the variational mode decomposition (VMD), used as a notch filter for dominant mode cancellation, and a machine learning approach, namely the one-dimensional convolution neural network (1D-CNN), for detection and diagnosis purposes. Specifically, the proposed approach first performs features extraction using VMD for fault detection, and then triggers to multi-scale features extraction using CNN convolution and pooling layers for classification and diagnosis. The proposed bearing faults detection and diagnosis approach is evaluated, in terms of robustness and performances, using the well-known Case Western Reserve University experimental dataset. In addition, performances are evaluated versus well- established demodulation techniques, in terms of faults detection, and machine learning strategies, in terms of faults diagnosis. The achieved results show that the proposed VMD notch filter-based 1D-CNN approach is clearly promising for bearing degradations monitoring.

International audience; he HYEP project (ANR HYEP 14-CE22-0016-01 Hyperspectral imagery for Environmental urban Planning - Mobility and Urban Systems Programme - 2014) confirmed the interest of a global approach to the urban environment by remote sensing and in particular by using hyperspectral imaging (HI). The interest of hyperspectral images lies in the range of information provided over wavelengths from 0.4 to 4 μm; they thus provide access to spectral quantities of interest and to chemical or biophysical parameters of the surface. HYEP's objective was to specify this and to propose a panel of methods and treatments taking into account the characteristics of other existing sensors in order to compare their performance. The developments carried out were applied and evaluated on hyperspectral airborne images acquired in Toulouse and Kaunas (Lithuania), also used to synthesize space systems: Sentinel-2, Hypxim/Biodiversity and Pleiades. Among the locks identified were those related to improving the spatial capabilities of the sensors and spatial scale changes, which were partially overcome by fusion and sharpening approaches, which proved to be successful. After a description of our hyperspectral data set acquired over Toulouse, an analysis is conducted on several existing and accessible spectral databases. Then, the chosen methods are presented. They include extraction, fusion and classification methods, which are then applied on our dataset synthetized at different spatial resolution to evaluate the benefits and the complementarity of hyperspectral imagery in comparison with other traditional sensors. Some specific applications are investigated of interest for urban planners: impervious soil map, vegetation species cartography and detection of solar panels. Finally, discussion and perspectives are presented.; Le projet HYEP (ANR HYEP 14-CE22-0016-01 HYperspectral imagery for Environmental urban Planning - Programme Mobilité et systèmes urbains - 2014) a permis de confirmer l’intérêt d’une approche globale du milieu urbain par télédétection et notamment en utilisant l’imagerie hyperspectrale (IH). L’intérêt des images hyperspectrales réside dans la gamme d’informations fournies sur des longueurs d’onde de 0,4 à 4 μm ; elles fournissent ainsi un accès aux grandeurs spectrales d'intérêt et aux paramètres chimiques ou biophysiques de la surface. HYEP avait pour objectif de préciser ceci et de proposer un panel de méthodes et de traitements en tenant compte des caractéristiques des autres capteurs existants afin d’en comparer les performances. Les développements réalisés ont été appliqués et évalués sur des images aéroportées hyperspectrales acquises sur Toulouse (France) et Kaunas (Lituania), utilisées également pour synthétiser des systèmes spatiaux : Sentinel-2, Hypxim/Biodiversity et Pleiades. Parmi les verrous identifiés, ceux liés à l'amélioration des capacités spatiales des capteurs et aux changements d’échelle spatiale ont été en partie levés par des approches de fusion, qui se sont avérées concluantes. Des méthodes d’extraction, de fusion ou de classification ont été appliquées à différentes résolutions spatiales afin de qualifier les gains et la complémentarité de l’IH par rapport à d’autres capteurs. Ces évaluations ont ainsi porté sur des « objets » d’intérêt pour les gestionnaires et aménageurs du milieu urbain : les surfaces imperméables, la végétation ou encore la détection de panneaux solaires.

The aim of this study was to evaluate and present an automated method for registration of magnetic resonance imaging (MRI) and computed tomography (CT) or cone beam CT (CBCT) images of the mandibular region for patients with oral squamous cell carcinoma (OSCC). Registered MRI and (CB)CT could facilitate the three-dimensional virtual planning of surgical guides employed for resection and reconstruction in patients with OSCC with mandibular invasion. MRI and (CB)CT images were collected retrospectively from 19 patients. MRI images were aligned with (CB)CT images employing a rigid registration approach (stage 1), a rigid registration approach using a mandibular mask (stage 2), and two non-rigid registration approaches (stage 3). Registration accuracy was quantified by the mean target registration error (mTRE), calculated over a set of landmarks annotated by two observers. Stage 2 achieved the best registration result, with an mTRE of 2.5 ± 0.7 mm, which was comparable to the inter- and intra-observer variabilities of landmark placement in MRI. Stage 2 was significantly better aligned compared to all approaches in stage 3. In conclusion, this study demonstrated that rigid registration with the use of a mask is an appropriate image registration method for aligning MRI and (CB)CT images of the mandibular region in patients with OSCC.

International audience; Urbanization and agricultural intensification are the main drivers of biodiversity losses through multiple stressors, especially habitat fragmentation, isolation and loss. Designing Blue and Green Infrastructure Networks (BGIN) has been recommended as a potential tool for land-use planning to increase ecosystem services while preserving biodiversity. All municipalities in France are required to perform BGIN planning. This article focuses on the Couesnon watershed (Brittany, France) and the participatory process used to define and analyze five possible pathways of future land-use and land-cover changes that included implementation of BGINs. Impacts on biodiversity were estimated by quantifying the change in landscape connectivity of woodlands, grasslands and wetlands. The effectiveness of BGIN policies was assessed by comparing current landscape connectivity (2018) to those in possible futures. Landscape connectivity referred to functional connectivity for three indicator species (Abax parallelepipedus, Maniola jurtina and Arvicola sapidus) across three landscape features: woodlands, grasslands and wetlands, respectively. Results allowed impacts of urban and agricultural land-use changes to be identified in terms of extent and quality. If BGIN policies were applied effectively to control the expansion of gray infrastructure, they would help increase the area and the quality of grassland and woodland connectivity by no more than 2%. Agricultural land-use and land-cover changes could have more impact on the extent of grassland (− 82% to +38%) and wetland (− 49% to +47%) connectivity. Current and future trends for hedgerows implied a decrease in woodland connectivity of 9.8-33.8%. Impacts on the quality of landscape connectivity is not proportional with the extent, as a decrease of the latter can have relatively more negative impacts on the former, and inversely. The study highlights that the BGIN strategy can preserve landscape connectivity effectively in urban ecosystems, where human density is higher, but can be threatened by agricultural intensification.

French-German-Swiss conference on Optimization; International audience; Recent force-fatigue mathematical models in biomechanics [7] allow to predict the muscular force response to functional electrical stimulation (FES) and leads tothe optimal control problem of maximizing the force. The stimulations are Dirac pulsesand the control parameters are the pulses amplitudes and times of application, the number of pulses is physically limited and the model leads to a sampled data control problem.The aim of this article is to present and compare two methods. The first method is adirect optimization scheme where a further refined numerical discretization is applied onthe dynamics. The second method is an indirect scheme: first-order Pontryagin typenecessary conditions are derived and used to compute the optimal sampling times.; En biomécanique les modèles mathématiques de force-fatigue de la réponsemusculaire aux impulsions électriques [7] permettent de prédire et de contrôler la réponse àun train de stimulations électriques et donc de maximiser la force produite à la fin du train.Mathématiquement les stimulations sont des impulsions de Dirac dont on peut modulerles temps d’applications et les amplitudes, le nombre d’impulsions étant physiquementlimité sur le train et le modèle conduit à un problème où le contrôle est de dimensionfini. L’objectif de ce travail est de présenter deux méthodes d’optimisation en vue deles comparer. La première méthode est dite directe et l’on utilise une discrétisationnumérique de la dynamique pour transformer le problème d’optimisation en un problèmeen dimension finie. La seconde méthode dite indirecte utilise le principe du maximumde Pontriaguine dans le contexte où le contrôle est de dimension finie et on établit desconditions nécessaires d’optimalité qui peuvent être implémentées numériquement pourcalculer les temps d’impulsions optimaux.

The additional discrete transforms from cosine (DCT) and sine (DST) families that come with the Adaptive Multiple Transform (AMT) approach is one of the major enhancements involved in the new Versatile Video Coding (VVC) standard. They have increasingly brought additional complexity compared to HEVC standard. The transform module is one of the most consuming stages in terms of time and hardware resources. This paper focuses on the optimization of the DST-VII transform. It deals with a multicriteria optimization algorithm for approximate computing that aims at the identification of the optimal approximation of the DST-VII according to several approximation measures. The resulting transform matrix has extremely low arithmetic complexity as well as close proximity to the exact DST-VII. Moreover, hardware synthesis results denote that the simplified design of DST-VII consumes only a third of the hardware resources used by the original algorithm. Experimental results obtained from joint exploration model simulations show a slight bit-rate increase while maintaining almost the same video quality. Such results confirm the effectiveness of the proposed approximate transform. The latter yields good performance in terms of computational complexity reduction and proximity to the exact transform while exhibiting a video coding performance comparable to the original algorithm.

Purpose This study aims to examine how exposure to female models in advertisements can create identity tensions in senior women and how they manage the comparison and develop different adaptation strategies to deal with these tensions. Design/methodology/approach This research is based on a qualitative approach involving 27 in-depth interviews with French women aged 60 to 79. Photo-elicitation with choice of models as reference points by respondents was used to capture comparison strategies with regard to models. Findings Interviews with senior women confirm that identity tensions due to appearance arise in the context of ageing, particularly when senior women are faced with advertising models. Three reactions of senior women to identity tensions are described, namely, avoiding comparison to protect the self, engaging in comparison despite its resulting devaluation of the self, proceeding to a positive comparison that reinforces their identity. This paper finds that comparison modalities are specific to each strategy. Research limitations/implications This research opens the way to further investigation, especially with regard to understanding social comparison mechanisms in an advertising context for senior women targets. Practical implications This paper raises awareness of the effects of senior women’s exposure to advertising on their self-perception in the context of ageing. It provides practical guidance to advertising professionals on the use of models in ads when targeting senior women and helps marketing managers in their communication strategies. Social implications This research reveals pronounced identity tensions in relation to appearance among senior women in the context of advertising exposure. By providing more diverse models, advertising representations could help to improve the identity perceptions of senior women. Originality/value Very few studies have hitherto investigated identity effects on senior female consumers of female model usage in advertising.

International audience; L’objectif de cette étude est de mieux cerner les conséquences des variations relatives du niveau marin sur la transformation des paysages littoraux et leurs implications sur les sociétés humaines, en nous intéressant aux Pays de la Loire. Une première estimation de la remontée du niveau marin relatif est proposée sur les deux derniers millénaires à partir de quarante points de référence marine issus de la bibliographie. Trois phases principales sont mises en évidence à plusieurs échelles temporelles : i) la hausse récente du niveau marin liée au réchauffement climatique actuel, évaluée à env. + 0.2 m depuis 200 ans ; ii) la phase finale de la transgression marine holocène, caractérisée par une élévation du niveau marin de + 1.8 m depuis 2 000 ans ; iii) une possible oscillation entre 1200 et 1700 AD, attribuable à une phase tempétueuse intense. Cependant, la donnée est encore lacunaire en Pays de la Loire, et la reconstitution est complexe car les côtes ligériennes sont macrotidales. Une définition de l’évolution des paysages retro-littoraux est mise à l’épreuve par l’analyse de cartes anciennes et de 35 carottes sédimentaires prélevées, pour détecter de nouveaux niveaux marins historiques. Une première étude montre de profondes mutations de l’évolution du paysage du Marais poitevin occidental au cours des derniers millénaires. En Baie de Bourgneuf, la modification du trait de côte et du paysage des marais rétro-littoraux est également évaluée à partir de données sédimentologiques, biologiques, archéologiques et historiques. Selon les cartes d’époque moderne, l’environnement des ports du Bec et du Collet correspondait à un bras de mer entourant l’ancienne île de Bouin, aujourd’hui continentalisée. Sur l’estran, une occupation humaine estimée au Néolithique moyen a récemment été exhumée à 1 km au large du trait de côte actuel. Ces nouvelles données viennent préciser la reconstitution des variations relatives du niveau marin au cours des derniers milliers d'années.

Throughout the last decades, the increasing pressure of anthropogenic (climate and land-use) changes has been a major impact on biodiversity in several regions and habitat types. These drivers have disturbed the timing of reproduction in animals and plants as well as the migration of animals resulting in changes in population size and species distribution. Our study analyses the impact of climate change in the spatial distribution of selected species, between historical period (1950-2018) and future period (2041-2070), in four case studies at a watershed level over the Atlantic region, highlighting the importance of integrating landscape trends to anticipate key biodiversity pattern responses. The results were compared to predicted future climate projections (2041-2070), based on two IPCC scenarios (RCP4.5 and RCP8.5), using a 5-model ensemble developed under the EURO-CORDEX project. Further, complementary downscaling methodologies were applied, allowing the increase of the spatial resolution from ~12 km to ~1 km in all climate variables. Land cover maps were developed using the Forecasting Landscape Scenarios Model. We assessed the impact of projected climate change and land cover development on specific vulnerable species distribution for each case study. The results showed an overall temperature increase for all case studies and both representative concentration pathways scenarios and a shift in potential habitat area of species addressed to areas upstream of the catchments. These predictions have a strong importance in defining conservation strategies of these vulnerable species, and may overall bring guidelines for the management of Atlantic landscapes in response to climate change, namely as pertinent ecological indicators under realistic future changing regional scenarios.

Energy use in buildings is increasing to provide optimal comfort for the occupants. People spend 90% of their lifetime in buildings. Therefore, indoor environment quality and comfort management have a crucial role in maintaining occupants’ health and productivity. Reducing energy consumption for optimal comfort management is important to minimize CO2 emission and global warming by the building sector. According to the literature, it is possible to control and reduce energy consumption by monitoring occupants’ behavior and estimating the number of occupants. A critical review is carried out in this paper to analyze the existing methodologies for modeling occupant behavior and prediction with respect to comfort and energy management. A comprehensive analysis is also performed on recent developments and challenges in modeling, along with recommendations and future perspectives.

International audience; In a context of vegetablization of meals, a range of plant-based substitutes is developing that imitate the taste and nutritional properties of meat products. Moreover, the motivations to reduce the consumption of meat products or to replace them by plant-based substitutes are based on several arguments such as health, environment or animal welfare. Through two studies, we explore the preferences for plant-based meat substitutes by combining the tools of sensory evaluation with those of experimental economics. Thus, subjects taste real products and express themselves about them, then they are exposed to choices related to these products (maintaining consumption or returning to their usual product). Our results show that a third of the participants appreciate the substitutes and are ready to renew their consumption.; Dans un contexte de végétalisation des repas se développe une offre de substituts végétaux imitant les propriétés gustatives et nutritionnelles des produits carnés. Par ailleurs, les motivations à réduire la consommation de produits carnés ou à les remplacer par des substituts végétaux repose sur plusieurs arguments tels que la santé, l’environnement ou le bien-être animal. A travers deux études, nous explorons les préférences pour des substituts végétaux à la viande en associant les outils de l’évaluation sensorielles à ceux de l’économie expérimentale. Ainsi les sujets dégustent des produits réels et s’expriment à leur sujet, puis ils sont soumis à des choix relatifs à ces produits (maintient de la consommation ou retour à leur produit habituel). Nos résultats montre qu’un tiers des participants apprécient les substituts et qu’ils sont prêt à en renouveler la consommation.

Purpose: To analyze the relationships between the recovery kinetics experienced by professional football players and noncontact injury. Methods: A cohort of 46 professional football players (age 24.2 [4.7] y) from the same team (French Ligue 1) was monitored each day between matches when the team played twice a week. The recovery monitoring procedure was implemented after 38 matches and included some questionnaires: duration of sleep, Hooper scale (quality of sleep, level of stress, fatigue, and muscle soreness), perceived recovery status scale, creatine kinase concentrations, a countermovement jump, and an isometric force test. Noncontact injuries were collected during these periods. Results: Noncontact injuries were associated with perceived fatigue and muscle soreness 2 days (relative risk [RR] = 1.89 and 1.48, respectively) and 3 days following the matches (RR = 2.08 and 2.08, respectively). An increase of sleep quantity during the 2 nights following a match was significantly associated with a lower RR (RR = 0.65), as well as a lower decrement score on the isometric force test on each of the 3 days after the matches (RR = 0.97, RR = 0.99, and RR = 0.97, respectively). No other association was reported for the variables sleep quality, stress, perceived recovery, creatine kinase concentrations, countermovement jump, and noncontact injuries. Conclusion: During a congested schedule, implementing a recovery monitoring protocol including questionnaires about fatigue, muscle soreness, quantity of sleep, and a physical test of isometric force could help practitioners prevent injuries.

In recent years, studying the central mechanism of itch has gained momentum. However, a proper meta-analysis has not been conducted in this domain. In this study, we tried to respond to this need. A systematic search and a meta-analysis were carried out to estimate the central mechanism of itch. The itch matrix comprises the thalamus and the parietal, secondary somatosensory, insular and cingulate cortices. We have shown that the basal ganglia (BG) play an important role in itch reduction. Finally, we explored itch processing in AD patients and observed that the itch matrix in these patients was different. In conclusion, this is the first meta-analysis on the central mechanisms of itch perception and processing. Our study demonstrated that different modalities of itch induction can produce a common pattern of activity in the brain and provided further insights into understanding the underlying nature of itch central perception.

International audience; This paper investigates the data gain brought by Multiple Input Multiple Output (MIMO) principle for Underwater Acoustic (UWA) communication. By considering a class of acoustic MIMO channel where each fading coefficient is modelled by a Rice distribution law with correlation factor depending on channel Doppler spread, we derive the achievable rate of such system that takes into account both channel estimation error and training sequence overhead. Model parameters are then estimated from an experiment campaign in MIMO shallow water channel conducted in the roadstead of Brest, France. The system achievable rate is evaluated for several MIMO architectures and channel configurations and then compared against conventional Single Input Multiple Output (SIMO) transmission. The achievable rate gain is finally put in perspective with end-to-end data rate performance of a single carrier MIMO transmission system experimented at sea. Index Terms Underwater acoustic communications, channel capacity, information rate, MIMO.

Urban regions worldwide revert to scenario-based simulations to understand and cope with uncertain land-use changes and future land-use demands. Whereas scenarios account for a variety of driving forces to simulate land change, spatial planning has received limited attention. To improve understanding of the potential contribution of planning to urban land change, we developed and simulated scenarios of development for Bucharest, addressing the local scale, and Bucharest-Ilfov Development Region, addressing the regional scale. The designed scenarios reflect (i) expected future land-use demands for living space, built-up areas, green space and agricultural areas, and (ii) statutory and strategic planning intentions extracted from four spatial plans and weighted based on expert opinion. All scenarios, alongside natural and socioeconomic driving forces, were simulated for both the local and the regional scales using the CLUMondo land-change model. Findings show that all future demands can be met under all scenarios, but that planning will make little contribution. Moreover, simulations highlight that integrating strategic planning intentions would produce a higher loss of agricultural lands than simulations with statutory planning intentions. Consequences of our findings for the role of planning in driving land change at various scales in multi-level planning systems are discussed. ISSN:2210-6707

International audience; The farming practices adopted since the end of the Second World War, based on large areas of monocultures and chemical use, have adversely affected the health of farmers and consumers and dramatically reduced farmland biodiversity. As a consequence, many studies over more than twenty years have stated that agriculture is facing three main challenges: (1) feeding the growing world population (2) with more environmentally friendly products (3) at a reasonable return for the producer. Increasing the efficacy of biocontrol could be one lever for agriculture to meet these expectations. In this study we propose implementation of a relatively under-researched system based on the management of landscape level crop diversity that would reduce demand for pesticide use and increase conservation biocontrol. The principle of manipulating crop diversity over space and time at a landscape scale is to optimize resource continuity, such as food and shelter for natural enemies to increase biocontrol services, reduce pest outbreaks and crop losses. The feasibility of such management options is discussed in relation to environmental, social and economic aspects. The operational and institutional inputs and conditions needed to make the system work are explored, as well as the potential added values of such a system for different stakeholders.

International audience; The unique ecosystems and biodiversity associated with mid-ocean ridge (MOR) hydrothermal vent systems contrast sharply with surrounding deep-sea habitats, however both may be increasingly threatened by anthropogenic activity (e.g., mining activities at massive sulphide deposits). Climate change can alter the deep-sea through increased bottom temperatures, loss of oxygen, and modifications to deep water circulation. Despite the potential of these profound impacts, the mechanisms enabling these systems and their ecosystems to persist, function and respond to oceanic, crustal, and anthropogenic forces remain poorly understood. This is due primarily to technological challenges and difficulties in accessing, observing and monitoring the deep-sea. In this context, the development of deep-sea observatories in the 2000s focused on understanding the coupling between sub-surface flow and oceanic and crustal conditions, and how they influence biological processes. Deep-sea observatories provide long-term, multidisciplinary time-series data comprising repeated observations and sampling at temporal resolutions from seconds to decades, through a combination of cabled, wireless, remotely controlled, and autonomous measurement systems. The three existing vent observatories are located on the Juan de Fuca and Mid-Atlantic Ridges (Ocean Observing Initiative, Ocean Networks Canada and the European Multidisciplinary Seafloor and water column Observatory). These observatories promote stewardship by defining effective environmental monitoring including characterizing biological and environmental baseline states, discriminating changes from natural variations versus those from anthropogenic activities, and assessing degradation, resilience and recovery after disturbance. This highlights the potential of observatories as valuable tools for environmental impact assessment (EIA) in the context of climate change and other anthropogenic activities, primarily ocean mining. This paper provides a synthesis on scientific advancements enabled by the three observatories this last decade, and recommendations to support future studies through international collaboration and coordination. The proposed recommendations include: i) establishing common global scientific questions and identification of Essential Ocean Variables (EOVs) specific to MORs, ii) guidance towards the effective use of observatories to support and inform policies that can impact society, iii) strategies for observatory infrastructure development that will help standardize sensors, data formats and capabilities, and iv) future technology needs and common sampling approaches to answer today’s most urgent and timely questions.

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org., Measurement(s) brain measurement • cognitive behavior trait Technology Type(s) electroencephalography (EEG) Factor Type(s) task • age • sex Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13560311

International audience

International audience; In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

Modular multilevel converters (MMCs) have a complex structure and a large number of submodules (SMs). If there is a fault in one of the SMs, it will affect the reliable operation of the system. Therefore, rapid fault diagnosis and accurate fault positioning are crucial to ensuring the continuous operation of the system. However, the IGBT open-circuit faults in different submodules of MMCs have similar fault features, and the traditional fault feature extraction method cannot effectively extract the key features of the fault so as to accurately locate the faulty submodules. In response to this problem, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy (WAPE) and DS evidence fusion theory. The simulation results show that WAPE has better feature extraction ability than basic permutation entropy, and the fused multiscale feature decision output has better diagnostic accuracy than the single-scale feature. Compared with traditional fault diagnosis methods, this method achieves the diagnosis of multiple fault types by collecting a single signal, which greatly reduces the number of samples and leads to higher diagnostic accuracy and faster diagnostic speed.

International audience; The contribution of this paper is twofold. First we evaluate the performance of adaptive power allocations for multiuser communications in underwater acoustic channels. The study is conducted within the theoretical framework of non-cooperative games, and we consider the use of both instantaneous and statistical Channel State Information (CSI) at the transmitter side. Second, we evaluate CSI acquisition schemes to be used for adaptive power allocations strategies. Results are obtained thanks to channels sounded at-sea at the Littoral Ocean Observatory Network (LOON) provided by the Centre for Marine Research and Experiments (CMRE), La Spezia, Italy. They show that adaptive power allocation strategies can be beneficial in some multiuser environments, even considering outdated instantaneous CSI at the transmitter side thanks to the introduction of an appropriate forgetting factor in the power allocation update algorithm.

This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integrate these characteristics to estimate the quality of stereoscopic images without reference through a convolutional neural network. Our method is based on two main tasks: the first task predicts naturalness analysis based features adapted to stereo images, while the second task predicts the quality of such images. The former, so-called auxiliary task, aims to find more robust and relevant features to improve the quality prediction. To do this, we compute naturalness-based features using a Natural Scene Statistics (NSS) model in the complex wavelet domain. It allows to capture the statistical dependency between pairs of the stereoscopic images. Experiments are conducted on the well known LIVE PHASE I and LIVE PHASE II databases. The results obtained show the relevance of our method when comparing with those of the state-of-the-art. Our code is available online on https://github.com/Bourbia-Salima/multitask-cnn-nrsiqa_2021.

The H.266/Versatile Video Coding (VVC) standard, released in July 2020, has improved the encoder performance over the previous High Efficiency Video Coding (HEVC) with a significant increase in coding complexity. Enhancements on the transform module mainly involve the introduction of the Adaptive Multiple Transform (AMT) which has led to an additional computational complexity. This paper aims at reducing the transform module complexity by approximating the AMT core. The transform approximation has to reach a low MSE, a low total error energy, a low transform distortion and a high transform efficiency. The Particle Swarm Optimization (PSO) is used to solve the optimization problem modeled as a constrained one. The proposed approximate transforms preserve a good coding efficiency compared to the exact transforms and require a less arithmetic complexity as well. The hardware architectures of both the exact and the approximate versions of the 8, and 16-point DST VII transform are designed. The exact transforms are defined using multipliers and MCM-based designs. The approximate transforms are described using additions and bit-shifting operations. All the designs are implemented in the Arria 10 FPGA device. Synthesis results have shown that the proposed approximation saves more than 75% and 63% of logic utilization when compared to multipliers and MCM-based designs, respectively. The maximum operational frequency is of 180 MHz, supporting 2K and 4K videos at 231 and 58 fps, respectively.

International audience; Seafloor heterogeneity is a key parameter structuring seamount biodiversity at multiple spatial scales. However, substrates characterization at very small (meter) scale from manual processing of optical images is time-consuming and difficult to be objectively applied on a large dataset. Consequently, quantification of several substrate types per image is usually lacking.To automize seafloor classification, we proposed to use a supervised machine learning algorithm. First, a substrates dataset with 772 annotations composed of four classes was produced (volcanic, carbonate, gravel, and soft) and splitted randomly into two parts: 90% for training, 10% for validation. A biogenic class was also extracted but set aside because of its small size towards others. Note that several annotations refinements were made to respect computer vision standards.To classify theses substrates, we used a texture specialized convolutional neural network based on wavelets [1]. For the training step we used a pretrained VGG19 network as basis and we applied a transfer learning approach to tune our network for our substrates dataset. We artificially increased the number of annotations by applying a data augmentation technique. The wavelets network was improved, and we obtained a classification rate close to 84% on validation set. Finally, due to the seafloor heterogeneity, an image segmentation process based on sliding windows was applied to extract statistics over tests images with a 224x224 patch size.This substrate classification algorithm was used to assess percent cover of the four substrate classes per image from nine towed camera surveys (i.e. 9000 images) on seven seamounts and volcanic island slopes along the Mozambique Channel. From this dataset, the variability of substrate composition along and among these seamounts was tested as structuring factors of benthic megafaunal assemblages (Hanafi-Portier et al., this meeting).

International audience; One key objective of Cyber-Physical System (CPS) simulation is to evaluate different CPS configurations regarding a certain user objective. First, simulation of CPS necessitates frameworks to handle heterogeneity of CPS components (the software and hardware system control, the behavior of the CPS itself and its physical environment). Then, to build simulators, designers use paradigms like FMI (Functional Mock-Up Interface) that proposes a data-driven generic interface facilitating the integration of heterogeneous models. However, in order to facilitate simulation configuration, an approach is required to drive modeling of parametric features and operational conditions. In this paper, we present CARES, a component-based and modeldriven approach to facilitate CPS simulation. CARES is applied to evaluate an Autonomous Underwater Vehicle (AUV) navigation function by simulation. The proposed models integrate both the principles of a generic simulation (integration of Component Based Software Engineering CBSE concepts and FMI paradigm) and domain specific aspects through a component-based architecture style. From a design model, a code generator builds the structural (Java or C++) code of the simulator. The generated code relies on a given run-time library for its execution and its structure facilitates integration of domain-specific code. The experiments show the effectiveness of the approach to build simulators for evaluation of different AUV configurations.

The Two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for the study of many modern systems applied for security and biometrics. The adoption of this algorithm, which is a compute intensive task, is limited due to its hardware design complexity. The first objective of this paper is to underline the effect of the hardware/software co-design (Hw/Sw co-design) for the reduction of the processing time and power consumption. Secondly, we propose an innovative architecture for the 2D-FFT algorithm tested on Zynq Soc, which requires less processing time and memory compared to the traditional algorithm. Three implementations (one software and two Hw/Sw co-designs) of the 2D-FFT algorithm using the Zynq SoC are presented in this paper. The first is based on ARM processor. A speedup of 29x is obtained compared to the original implementation thanks to many optimizations. The second is a Hw/Sw co-design solution of the traditional 2D-FFT algorithm introduced on a hybrid platform combining an ARM Cortex-A9 processor with an FPGA. The third is also a Hw/Sw co-design solution using our optimized 2D-FFT algorithm to reach the real-time contraints for high-resolution images (1920 × 1080). It provides a speedup of 1.13x, 3.31x and 96.21x faster than the Hw/Sw co-design implementation of the traditional RC algorithm, the pure software implementations with and without optimizations, respectively.

International audience; Recent dynamical models, based on the seminal work of V. Hill, allow to predict the muscular response to functional electrostimulation (FES), in the isometric and non-isometric cases. The physical controls are modeled as Dirac pulses and lead to a sampled-data control system, sampling corresponding to times of the stimulation, where the output is the muscular force response. Such a dynamics is suitable to compute optimized controls aiming to produce a constant force or force strengthening, but is complex for real time applications. The objective of this article is to construct a finite dimensional approximation of this response to provide fast optimizing schemes, in particular for the design of a smart electrostimulator for muscularreinforcement or rehabilitation. It is an on-going industrial project based on force-fatigue models, validated by experiments.Moreover it opens the road to application of optimal control to track a reference trajectory in the joint angular variable to produce movement in the non-isometric models.

International audience; This paper presents a study of Multiuser Multiple Input Multiple Output (MU-MIMO) as a multi-access method for underwater acoustic communications. Traditional multi-access schemes such as Time Division Multiple Access (TDMA) suffer from limited data rates due to an inefficient sharing of communication resources. In a MU-MIMO systems, each user can be viewed by the receiver as an input of a conventional MIMO channel. The receiver can then exploit the spatial diversity to decode the input streams of each users. Achievable rates of MU-MIMO with Successive Interference Cancellation (SIC) are studied in a first place, and compared with single user and TDMA theoretical performance. The theoretical rates are computed on channels sounded at-sea for a two-user configuration so as to evaluate what gains can be expected from the MU-MIMO approach. Finally, comparisons with the effective rates obtained from simulations of full modulation and demodulation stages using measured channels and the underWater AcousTic channEl Replay benchMARK (WATERMARK) are provided.

The study of temporal sequences is a main topic in different domains, especially for human mobility mining. This article defines the Fuzzy Temporal Hamming (FTH) distance between temporal sequences. This new measure generalises the Hamming distance and improves it by introducing a fuzzy time-window. This fuzzy approach tolerates temporal distortions as shifting and permutations. Moreover, the time computation of FTH is competitive with other Optimal Matching methods used for temporal sequences comparison. To validate this approach, we cluster data from a real Time-Use Survey and we compare the results obtained with other methods.

International audience; The topic of microgrids (MGs) is a fast-growing and very promising field of research in terms of energy production quality, pollution reduction and sustainable development. Moreover, MGs are, above all, designed to considerably improve the autonomy, sustainability, and reliability of future electrical distribution grid. At the same time, aspects of MGs energy management, taking into consideration distribution generation systems, energy storage devices, electric vehicles, and consumption components have been widely investigated. Besides, grid architectures including DC, AC, or hybrid power generation systems, energy dispatching problems modelling, operating modes (islanded or grid connected), MGs sizing, simulations and problems solving optimization approaches, and other aspects, have been raised as topics of great interest for both electrical and computer sciences research communities. Furthermore, the United Nations Framework Convention on Climate Change and government policies and incentives have paved the way to massive electric vehicle (EV) deployment. Hence, several research studies have been conducted to investigate the integration of EVs in national power grid and future MGs. Specifically, EV charging stations’ bi-directional power flow control and energy management have been considerably explored. These issues index challenging research topics, which are in most cases still under progress. This paper gives an overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads. It reviews the main MGs architecture, operating modes, sizing and energy management systems (EMS) and EVs integration.

International audience; The brute-force behaviour of High-Efficiency Video Coding (HEVC) is the biggest hurdle in the communication of the multimedia contents. Therefore, a novel method will be presented here to expedite the intra mode decision process of HEVC. In the first step, the feasibility of the odds-algorithm for the early intra mode decision is presented by using statistical evidence. Then, various elements of odds algorithm are analyzed and then mapped to the intra mode process (elements) of HEVC. Finally, the probability required by the odds algorithm is obtained by utilizing the correlation between the current and the neighboring blocks. The proposed algorithm accelerated the encoding process of the HEVC by 25% to 35%, while the Bjontegaard Delta Bit Rate (BD-BR) is 0.95% to 1.84%.

National audience

International audience; Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

A new method proposed in this work to optimize the power generated by a hybrid renewable energy system which consists of Wind turbine/Tidal turbine/PV module/Batteries. This system has been designed to satisfy a stand-alone area in Brittany, France, as an example of load demand. The Particle Swarm Optimization technique (PSO) was proposed and developed to minimize the cost of energy. Where the ability of this algorithm was developed to reach the best results in double speeds, at a time rate better than 80% of conventional technology time and less than 20 repetitions only. The problem is defined as an economic problem, taking into consideration the optimal sizing of the system, high reliability, planning expansion for future development, the state of charge of the battery. The total net present cost (TNPSC) is introduced as the objective function, taking into consideration the minimum fitness values in the particle swarm process. The (PSO) algorithm developed has several characteristics and advantages over other traditional techniques and algorithms. In fact, it allows to achieve the optimal solution and to reduce the overall cost with high speed and accuracy. The PSO algorithm program was developed using MATLAB software.

International audience; This paper deals with hybrid electric fuel cell-powered drones energy management while targeting hydrogen saving and power supply system efficiency improvement. In this context, a commercially available quadcopter powered by the Intelligent Energy 650W power module is adopted as a case study. Its power supply system is based on fuel cell and battery, and the power is conventionally managed using a basic rule-based strategy. To improve power management, a frequency separation rule-based approach is first proposed, and then an equivalent consumption minimization strategy is implemented for fuel economy seeking. An experimental flight test is carried out using a battery-powered hexacopter to extract a real power profile for load requirement modeling. The obtained load profile is repeated several times replicating the hovering phase to obtain a larger mission lifetime. Extensive simulation results clearly show that the proposed power management strategies enables power sources operating in their nominal area, extending their lifetimes, and inducing 3% minimization in hydrogen consumption. This optimization extends the drone endurance as much as the carried fuel amount, and it can increase the world endurance record by 21.81min. It has also an economical benefit, which consists in the operating cost gain reaching 853.2€ per fuel cell module lifecycle. In fleet missions, this gain may further be increased.

International audience; In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.

International audience; High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%.

In several particularly secure applications such as the entrance to a school, it is important to know whether the person entering is an adult or a child. In this article, we propose a human body morphology detector that distinguishes whether the person is an adult or a child. This detector could be included in a smart portal to detect whether the entry person is an adult or a child to apply a different treatment depending on the morphology. A person detector module1 is deployed to detect the presence of a person within a predefined radius. When the location of the person is detected, our system can measure the height of the person and determine if the person is an adult or a child based on its height.

Behavioral analysis in an urban environment is a complex task that requires material and human resources, due to the difficulty of interpreting the situations. This paper presents a method to improve the detection of dangerous behaviors by assisting surveillance stations. Our objective is to alert when one of these behaviors is captured by a surveillance camera. To do this, we analyze the positions and paths of the persons in a global way, through a group of parameters. These parameters are determined by an automatic image analysis algorithm such as DBSCAN computed on an NVIDIA Jetson TX2. This analysis allows to detect, through the evolution and clustering of points in each cloud, phenomena qualified as abnormal, such as dispersion and rapid clustering, as well as poaching. The data used to feed our algorithm come from simulations that allow testing new and different scenarios. The performance of our proposed method is evaluated on videos representing real case situations.

International audience; In this study, we propose a novel method to encrypt fully-phase information combining the concepts of the transport of intensity equation and spatially variant polarization encoding. The transport of intensity equation is a non-iterative and non-interferometric phase-retrieval method which recovers the phase information from defocused intensities. Spatially variant polarization encoding employs defocused intensity measurements. The proposed cryptosystem uses a two-step optical experimentation process—primarily, a simple set-up for defocused intensities recording for phase retrieval and then a set-up for encoding. Strong security, convenient intensity-based measurements, and noise-free decryption are the main features of the proposed method. The simulation results have been presented in support of the proposed idea. However, the TIE section of the cryptosystem, as of now, has been experimentally demonstrated for micro-lens.

International audience; This paper presents a new constant frequency, direct grid-connected wind-based induction generator system (IGS). The proposed system includes a six-phase cage rotor with two separate three-phase balanced stator windings and a three-phase SV-PWM inverter which is used as a STATCOM. The first stator winding is connected to the STATCOM and is used to excite the machine. The main frequency of the STATCOM is considered to be constant and equal to the main grid frequency. In the second stator winding, the frequency of the induced emf is equal to the constant frequency, so the generator output frequency is independent of the load power demand and its prime mover speed. The second stator winding is directly connected to the main grid without an intermediate back-to-back converter. In order to regulate the IGS output active and reactive power components, a sliding mode control (SMC) is designed. Assuming unbalanced three-phase voltages for the main grid, a second SMC is developed to remove the machine output’s negative sequence currents. Moreover, a conventional PI controller is used to force the average exchanging active power between the machine and STATCOM to zero. This PI controller generates the reference value of the rotor angular speed. An adjustable speed pitch angle-controlled wind turbine is used as the IGS’s prime mover. The effectiveness and capability of the proposed control scheme have been supported by the simulation results.

We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the Particle Swarm Optimization (PSO) algorithm for function optimization. Four parameters drive LCPSO—the number of agents, the inertia weight, the attraction/repulsion weight, and the inter-agent distance. Using APF (Artificial Potential Field), we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

Recent mathematical nonlinear models allow to predict the muscular response to functional electrostimulation (FES) in the isometric case or non-isometric case, based on the seminal work of V. Hill. Physical controls are Dirac pulses and this leads to a sampled-data control system, where the output is the force response. In this article, our aim is to provide a finite dimensional approximation of the force response for the non-fatigue dynamics to provide fast optimizing schemes for ongoing device, in particular the design of a smart electrostimulator for muscular reinforcement or rehabilitation or to track a reference trajectory in the non-isometric case.

Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

International audience

International audience; Maritime data processing research has long used spatio-temporal relational databases. This model suits well the requirements of off-line applications dealing with average-size and known in advance geographic data that can be represented in tabular form. This chapter explores off-line maritime data processing in such relational databases and provides a step-by-step guide to build a maritime database for investigating maritime traffic and vessel behaviour. Along the chapter, examples and exercises are proposed to build a maritime database using the data available in the open, heterogeneous, integrated dataset for maritime intelligence, surveillance, and reconnaissance that is described in [41]. The dataset exemplifies the variety of data that are nowadays available for monitoring the activities at sea, mainly the Automatic Identification System (AIS), which is openly broadcast and provides worldwide information on the maritime traffic. All the examples and the exercises refer to the syntax of the widespread relational database management system PostgreSQL and its spatial extension PostGIS, which are an established and standard-based combination for spatial data representation and querying. Along the chapter, the reader is guided to experience the spatio-temporal features offered by the database management system, including spatial and temporal data types, indexes, queries and functions, to incrementally investigate vessel behaviours and the resulting maritime traffic.

In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.