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International audience; Industry 4.0 proposes the inclusion of IoT components in industrial contexts to have a better control on production and logistic processes. Unfortunately, growing connectivity in a formerly isolated world induces major security risks which are especially critical in an industrial context. Furthermore, the highly fragmented nature of the industrial IoT market imposes interoperability management. Interoperability and heterogeneity generally poses security challenges, exacerbating these risks. oneM2M is a promising standard that can enable interoperability and ensure communication with various tier protocols. The interface between oneM2M and tiers protocols has however to be trusted, which is undesirable. In this paper, we focus on the first phase of an approach to enable end-to-end encryption between oneM2M and Zigbee devices: secure key exchange. We fully define an exchange protocol taking place during the enrolment of a Zigbee device within oneM2M. We provide and discuss an implementation of this protocol, demonstrating its technical feasibility.

Europa is a high-priority astrobiology target due to the presence of liquid water, carbon-rich materials, and energy sources that may support complex chemistry and the emergence of biological activity. The CORALS (Characterization of Ocean Residues And Life Signatures) instrument—a transformative mass spectrometer that comprises a solid-state UV laser source, custom ion transfer optics, and a high performance Orbitrap™ mass analyzer—is capable of comprehensive analyses of planetary materials that can provide important context for the origin and evolution of potential biosignatures and geologic icy matrices on Europa. The CORALS laser source, ion inlet system, and mass analyzer constitute a highly versatile and low SWaP (Size: 11,000 cm3; Weight: 8.0 kg; and Power: 41 W peak) mass spectrometer. Here we report on the design of the CORALS engineering test unit (ETU), which will be qualified for spaceflight via random vibration testing and exposure to dry heat microbial reduction, and test results from the two pathfinding prototypes that have informed the development of the ETU. The demonstrated analytical performance of the CORALS instrument supports the wide range of science goals and planetary targets this spectrometer can access, highlighting the instrument's multidimensional strengths in the search for life signatures on Europa or elsewhere in the Solar System.

International audience; Direct and inverse modeling with neural networks of a complex front end electronics for the S-AAIR project are evaluated. While direct model outputs S-parameters based on 4 electronic inputs parameters, inverse modeling has to output 4 optimal electronic parameters with respect to 5 S-parameters and noise constraints inputs. The advantages to be expected from neural netwoks include execution speed without loss of accuracy, and the possibility of modeling a system without any prior hypothesis based on measurements only, or any other source of information. This is a crucial step for designing a fully adaptive front-end electronics, with a single electronic board capable of adapting to several different observational and instrumental constraints like signal over noise, bandpass, dynamic range, power consumption, and several other possible features. In this first study, simulated data are used to evaluate the performances of such machine learning methods. The direct model is shown to reach less than 0.1 dB RMS error with respect to the simulated data, which is sufficiently accurate to be compatible with measurements-based modeling without distorsion. The inverse model is constructed by training a network on a pre-computed optimization function. It is able to output optimal electronics parameters satisfying a set of performances constraints without errors compared to the original function. Those first tests confirm neural networks are valid tools for complex RF electronic modeling, even with modest computing resources running basic networks topologies, and thanks to the availability of powerful associated algorithmy. There is thus several perpectives for improving and extending those first results

Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to better capture the information content in long range trends. This technique has been extensively used in biomedical applications for diagnosis purposes. Although PE theory is well established and explored, there is still a lack of theoretical development for MPE. In the present paper, we expand the theory by formulating an explicit MPE model of first order Autoregressive (AR) and Moving Average (MA) processes, which are well known and used in signal modeling. We first build the autocorrelation function of coarse-grained AR and MA models, which are a prerequisite for MPE calculation. Next, we use the resulting autocorrelation functions to establish the theoretical value of MPE as a function of time scale and AR or MA parameters. The theoretical result is tested against MPE measurements from simulations. We found the MPE of the 1o order AR model to converge to the maximum entropy with increasing time scale. Nonetheless, the convergence is not always monotonic. For AR parameter values greater than the Golden Ratio, the MPE curve presents a local minimum at a time scale different than one, which implies a more regular structure than the one measured with PE. The MPE of the 1o order MA model converges rapidly to the maximum entropy with increasing time scales, regardless of the MA parameter value, which is in accordance to our expectations.

In this paper, we propose a normal based filtering method for 3D mesh denoising. For this purpose, we compute the new triangle normal vectors by using a weighted sum of the average (smoothness) and the myriad (sharpness) filters in each neighborhood. These weights, that reflect the degree of the surface sharpness, are calculated according to the statistical distribution of the angles between the normal vectors of the triangles. The histogram of the angles between surface normal vectors is accurately fitted by the well known Cauchy distribution. Here, we justify the use of the myriad filter whose estimated value represents the optimum of the location parameter of the investigated distribution. Once the whole of the mesh normal vectors are filtered, the vertices positions are updated via the most used method in mesh denoising frameworks. We test the proposed method on synthetic and real scanned objects. To evaluate the performance, we use three errors metrics that are the vertex based error, the normal based error and the Hausdorff distance. Results show the superiority of our method and its efficiency is compared with some existing methods in the literature.

International audience; Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation. In the case of keystroke dynamics, existing databases have less than 200 users. For these reasons, we aim at generating a keystroke dynamics synthetic dataset. This paper presents the generation of keystroke data from known users as a first step towards the generation of synthetic datasets, and could also be used to impersonate users' identity. Results were also found for user generation, but are not part of this paper.

The Bit Flipping algorithm is a hard decision decoding algorithm originally designed by Gallager in 1962 to decode Low Density Parity Check Codes (LDPC). It has recently proved to be much more versatile, for Moderate Parity Check Codes (MDPC) or Euclidean metric. We further demonstrate its power by proposing a noisy Euclidean version of it. This tweak allows to construct a lattice based key exchange analogous to the Ouroboros protocol for Hamming metric but with a reduction to the Short Integer Solution (SIS) problem. The very efficient decoding algorithm permits to consider smaller alphabets than for NTRU or Ring-LWE decryption algorithms. Overall we obtain a new protocol which competes with the recent NEWHOPE and Kyber proposals, and also with NTRU. The resulting scheme exploits the cyclicity of the error, and benefits from the security of the renowned SIS problem. acceptedVersion

The feasibility study of the HALESIS (High-Altitude Luminous Events Studied by Infrared Spectro-imagery) project is presented. The purpose of this experiment is to measure the atmospheric perturbation in the minutes following the occurrence of transient luminous events (TLEs) from a stratospheric balloon in the altitude range of 20-40 km. The instrumentation will include a spectro-imager embedded in a pointing gondola. Infrared signatures of a single blue jet were simulated under the assumption of local thermodynamic equilibrium (LTE), and were then compared with a panel of commercially available instrument specifications. The sensitivity of the signatures with a local perturbation of the main vibrational energy level populations of CO2, CO, NO, O3, and H2O was measured and the infrared signatures of a single blue jet taking into account non-LTE hypotheses were compared with the same panel of commercially available instrument specifications. Lastly, the feasibility of the study is discussed.

International audience; Soft soles absorb impacts and cast ground un-evenness during locomotion on rough terrains. However, they introduce passive degrees of freedom (deformations under the feet) that complexify the tasks of state estimation and overall robot stabilization. We address this problem by developing a control loop that stabilizes humanoid robots when walking with soft soles on flat and uneven terrain. Our closed-loop controller minimizes the errors on the center of mass (COM) and the zero moment point (ZMP) with an admittance control of the feet based on a simple deformation estimator. We demonstrate its effectiveness in real experiments on the HRP-4 humanoid.

Indoor localization for elderly monitoring is often performed using binary PIR sensors due to their advantages. However, elderly monitoring relies on good location resolution, and this latter requires large number of PIR sensors. To increase location resolution using few sensors, a multiplex binary structure is used, and to deal with ambiguities caused by such structure (e.g. zones sharing same code) we present a novel approach free from false alarm distribution assumptions.

International audience; In this paper, we present a control strategy that enables intuitive physical human-robot collaboration with mobile manipulators equipped with an omnidirectional base. When interacting with a human operator, intuitiveness of operation is a major concern. To this end, we propose a redundancy solution that allows the mobile base to be fixed when working locally and moves it only when the robot approaches a set of constraints. These constraints include distance to singular poses, minimum of manipulability and distance to objects and angular deviation. Experimental results with a Kuka LWR4 arm mounted on a Neobotix MPO700 mobile base validate the proposed approach.

International audience; Legislation concerning the pollutant emissions of diesel passenger cars is becoming increasingly restrictive, especially for nitrogen oxide (NOx) and particulate matter (PM). A methodology that combined physical approach and a dynamic output feedback based observer for the modelling and estimation of the pollutant emissions in diesel engines is thus proposed. A grey box control-oriented model of NOx emissions is developed; this model was designed based on a deep analysis of the crank-angle discrete combustion process of the test-bench diesel engine. This model will be used to help the understanding of the thermal NOx emission formation process. It provides the basics for the further development of the diesel engine emissions control-oriented models, but it couldn't be used to predict real-time pollutant emissions on a vehicle. The model is calibrated by experimental measurements and a complete cylinder pressure and temperature analysis. Then, to solve the vehicle integration problem (real-time calculation problem) this model and a linear model of the diesel engine were used to develop an dynamic observer to estimate the quantities of particulate matter.

International audience; This paper presents an optimal control strategy for navigation of multiple magnetic microbeads for future drug targeting applications. To transport the drugs, we use therapeutic magnetic microbeads as navigable agents controlled by magnetic gradients. The main difficulty is to control independently each therapeutic agent along a trajectory with the same magnetic gradient fields. This study proposes an optimal control methodology to control a group of different therapeutic agents at desired states. Based on a dynamic model of group of magnetic microbeads, controllability and observability conditions are formulated and simulated. Based on the proposed theoretical analysis a linear quadratic with integral action control (LQI) has been chosen to be applied to the microbeads system. Finally, an experimental investigation is carried out in millimeter-sized fluidic artery vessels to demonstrate the controllability and stability of two magnetic microbeads under different velocity and trajectory constraints with a laminar viscous fluidic environment.

In this paper, we develop Cramer-Rao Bound (CRB) expressions in multichannel diffusion MRI. We consider a multi-tensor model with a Non-central Chi (Nc-Chi) distributed noise. The CRB formulas involve integral expressions that are numerically evaluated. In a second step, we propose to simplify the CRB calculation by introducing two analytical approximate expressions for high SNR and low SNR, respectively. Moreover, we develop CRB formulas for the parameters of clinical interest such as the Fractional Anisotropy (FA) and the principal tensor directions. Finally, we exploit these CRBs to analyze the impact of controllable system parameters (e.g. b-value, number of gradient diffusion, number of acquisition coils, etc.) on the clinical parameter estimation in view of future optimal design of the acquisition protocol.

International audience; This paper describes the propulsion of the catalytic tubular microjet using Navier-Stokes equations. Especially , the thrust capability of the microjet is outstanding compared with the other microsystems, but remains only partially understood. Studies have identified the internal precursory mechanisms of the propulsion of the microjet: its inner wall catalyzes the dismutation of aqueous hydrogen peroxide, and an oxygen bubble is then formed and migrates towards its widest opening. This impulses the propulsion of the microjet towards the opposite direction. However, the precise propellant role of the liquid surrounding the jet remains misunderstood. The same goes for the inner wall of the vessel in which the jet navigates, especially in narrowed environment. This article discusses these aspects in a simplified theoretical framework. Calculations are performed by explicit computation of Navier-Stokes equations. The obtained theoretical outcome are in good agreement with experimental results in the literature.

International audience; his paper proposed the design of a novel harmonic rejection system of digital control command. The system is based on integrated active inductors with the QUBiC4Xi library which is a 0.25 mu m BiCMOS process of NXP. It is dedicated to the dense aperture array for the radio astronomy project SKA (Square Kilometre Array). The proposed system allows protecting the radio frequency (RF) signals against harmonics coming from the digital signals in [300 - 1500] MHz frequency band. In this work, the Advanced Design System (ADS) is used for the design and the simulations, and Cadence for the layout. The simulation results show that the harmonics rejection is better than 105 dB from 300 MHz with a digital clock signal of 1 MHz.

International audience; In this paper, we propose a collaborative framework that is able to solve multi-view and alternative clustering problems using some clustering ensemble and semi-supervised clustering principles. We provide a mechanism to control, via a information sharing model, different clustering algorithms to obtain consensus or alternative clustering solutions. The strong point is that our approach does not need to know which clustering algorithms to use nor their parameters

Muscle fiber conduction velocity (MFCV) is based on the time delay estimation between electromyography recording channels. In order to take into account the variability of the MFCV, we assume that the time delay between the channels varies over time. In the present paper, the Maximum - Likelihood estimation (MLE) of time varying delay for two channels of EMG signals that follow a polynomial model is derived. Monte Carlo simulations are performed at different noise levels in order to evaluate the noise impact of the estimator. The Maximum-Likelihood estimation was achieved by the Newton method. The delay with unknown model (inverse sinusoidal) was also investigated by cutting this delay into many slices. This approach gives the best results by comparison with the other ones.

International audience; The objective of this paper is to investigate the effectiveness of a Multilayer Perceptron (MLP) to discriminate subjects with and without osteoporosis using a set of five parameters characterizing the quality of the bone structure. These parameters include Age, Bone mineral content (BMC), Bone mineral density (BMD), fractal Hurst exponent (Hmean) and coocurrence texture feature (CoEn). The purpose of the study is to detect the potential usefulness of the combination of different features to increase the classification rate of 2 populations composed of osteporotic patients and control subjects. k-fold Cross Validation (CV) was used in order to assess the accuracy and reliability of the neural network validation. Compared to other methods MLP-based analysis provides an accurate and reliable platform for osteoporosis prediction. Moreover, the results show that the combination of the five features provides better performance in terms of discrimination of the subjects.

International audience; A hierarchical state bounding estimation method is presented for nonlinear dynamic systems where different sensors offer several measurements of the same state vector, each of which is subject to unknown but bounded disturbances and is equipped with a local processor. For each sampling time, the proposed algorithm proceeds in two stages. At the prediction stage, an approximating outer-bounding ellipsoid is computed for the reachable set of the nonlinear function of the state vector. At the correction stage, the algorithm works at two levels : Each local processor computes the state estimate and its outer bounding ellipsoid according to the local measurements given by the corresponding sensor. These ellipsoids are transmitted simultaneously from all local processors to the fusion center which synthesizes them to compute the global state bounding ellipsoid. Then it feeds these data back to all the local processors. This feedback allows the local processors to adjust their results by taking into account the measurements of all the other sensors.

International audience; This paper addresses the choice of the control horizon in a Nonlinear Model Predictive Control (NMPC) strat- egy for nonholonomic vehicles. The latter can be modeled by chained systems. We establish a relation between the degree of nonholonomy and the minimum length of the control horizon so as to make the full-state control possible. A necessary condition on the control horizon of NMPC is given and theoretically proved whatever the dimension of the chained system consid- ered. The theoretical results are illustrated through simulations on a (2,5) chained system, describing a car-like vehicle with one trailer. Difficult motion objectives that require a lateral displacement are considered, as well as robustness to modeling errors.

International audience; This paper deals with state feedback control of chained systems based on a Nonlinear Model Predictive Control (NMPC) strategy. Chained systems can model many common nonholonomic vehicles. We establish a relation between the degree of nonholonomy and the minimum length of the control horizon so as to make the NMPC feasible. A necessary condition on the control horizon of NMPC is given and theoretically proved whatever the dimension of the chained system considered. This relation is used to design a NMPC-based control strategy for chained systems. One of the advantages of NMPC is the capability of taking into account the constraints on state and on control variables. The theoretical results are illustrated through simulations on a (2,5) chained system, describing a car-like vehicle with one trailer. Difficult motion objectives that require a lateral displacement are considered.

International audience; In this paper we introduce new models for semi-supervised clustering problem; in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-link and cannot-link constraints) and any clustering algorithm, the proposed approach aims at learning a projection space for the dataset that satisfies not only the constraints but also the required objective of the clustering algorithm on unenhanced data. We propose a boosting framework to weight the constraints and infers successive projection spaces in such a way that algorithm performance is improved. We experiment this approach on standard UCI datasets and show the effectiveness of our algorithm.

International audience; Microinjection is the highly efficient delivery method of exogenousmaterials into cells, and it has been widely used in biomedical researchareas such as transgenics and genomics. However, this direct injectiontask is time consuming and laborious, resulting in low throughputand poor reproducibility. This paper describes a telerobotic sharedcontrol (TSC) framework for the microinjection with high manipulationefficiencies, in which a micromanipulator is controlled by the sharedmotion commands of both the human operator (direct manipulation)and the autonomous controller. To determine the optimal gains betweenthe operator and the controller, we proposed a quantitative evaluationmethod using Fitts' and steering laws. The results showed that a40%−60% weighting on the human operator produced better performancefor both speed and accuracy of task completion, and suggested thatsome level of automation or human involvement is important for microinjectiontasks.

International audience; Percentages of metal are an important physical property in meteorite research and in studies of meteorite impact craters. Physical properties of meteorites have been used to rapidly classify meteorites into main classes and groups. In this paper, we use a max Entropy method for 3D segmentation and visualization of 3D scanned images of meteorites collected in Morocco. This non-destructive analysis allows studying the mineral composition of these rocks. Results show that the percentage of metal is identical when estimated by 3D analysis compared to classical measurements (point counter). The main advantage of the 3D analysis is that the sample is not destroyed, more accurate, not expensive and faster.

International audience; This article follows a line of papers focused on defining a method to improve the realization of reliability analysis during the System Engineering process. As MBSE becomes a funda-mental concept for specifying and designing systems, our me-thod takes full advantages of this approach and try to provide tools to ease the specification stage and the integration of RAMS early in the conception process. Our method called MeDISIS is related to the use of SysML to support MBSE and RAMS activities. Currently, MeDISIS is used within an industrial project to de-sign a hypersonic aircraft which is a relevant complex and critical system. During this project, MeDISIS has been adapted to take into account technologies devoted to embed-ded systems. Furthermore, MeDISIS had to comply with the tools, used by our industrial partners during the design stage. In this work, we present the new architecture of MeDISIS, and the process added recently.

International audience; The heart rate variability (HRV) spectral parameters are classically used for studying the autonomic nervous system, as they allow the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. However, this evaluation is based on the definition of frequency bands that are found to be controversial because of possible changes in the frequency boundaries due to physiological factors or experimental conditions. We propose to overcome this difficulty by dynamically modelling the power spectrum as a two Gaussian shapes mixture. It appeared that this procedure was able to more accurately follow the exact parameters in the case of simulated data, in comparison with parameters estimation obtained with a rigid frequency cutting approach or with the ITSB algorithm. Real data results obtained on a classical stand-test are also presented and discussed.

International audience; Comprehensive and training simulators that provide realistic visual and haptic feedback during cell indentation tasks are currently investigated. Complex cell geometry inherent to biological cells and intricate mechanical properties drive the need for precise mechanical and numerical modeling to assure accurate cell deformation and force calculations. Advances in alternative finite element formulation, such as mass-tensor approach, have reached the state where they are applicable to model soft cell deformation in real time. The geometrical characteristics and the mechanical properties for different cells are determined with AFM indentation. A real-time, haptics-enabled simulator for cell centered indentation has been developed which utilizes the atomic force microscopy data (mechanical and geometrical properties of embryonic stem cells (mESC)) to accurately replicate the indentation task and predict the cell deformation during indentation in real-time. This tool can be used as a mechanical marker to characterize the biological state of the cell. The operator is able to feel in real-time the change in the stiffness during cell deformation between fixed and live cells. A comparison study with finite element simulations using a commercial software and the experimental data demonstrate the effectiveness of the proposed physically-based model.

International audience; This paper reports modeling and control of a microsized polymer aggregate of magnetic particles inside an artery, using a MRI device for supplying propulsion in order to achieve targeted chemotherapy. Non-Newtonian behavior of blood is considered, as well as wall effects and interactions, resulting in a highly nonlinear model. A backstepping approach is synthesized to ensure Lyapunov stability along a pre-planned trajectory inherited from the model, with robustness concerns.

International audience; In this article, we investigate the synchronization of robot manipulators group under coordinated and cooperative scheme. In cooperative schemes all agents were fully interconnected, such that all robots have a weight on the overall dynamics. In the coordinated schemes the leader robot or the master robot establishes the synchronized action of all the slave systems. Based on emergent consensus algorithm, the proposed controller works to position synchronization of multiple robot manipulators. The control strategy is to synchronize the angular position and the velocity of each robot in the system with respect to the common desired trajectory and the angular positions and velocities of other robots. Modeled by an undirected graph, the cooperative robots network only requires local neighbor-to-neighbor information exchange between manipulators and does not assume the existence of an explicit leader in the team. However the objective in coordinated scheme is to design interconnections and feedback controllers for the slaves, such that their positions and velocities synchronize to those of the leader robot. It is assumed that network robots have the same number of joints and any configuration made possible by one in the group can be completed by each robot in the cooperative system.

International audience; The purpose of this paper is to show what image prediction can bring to Image-Based Visual Servoing. The visual feature prediction is obtained thanks to the interaction matrix. Based on a Model Predictive Control strategy, the visual servoing task is formulated into an optimization problem. The error between the reference features and the predicted features is to be minimized over a receding prediction horizon. Numerous simulations highlight the interest of prediction, especially for difficult configurations such as large motion and rotation.

International audience; This paper proposes a new ergonomic frame to describe the attitude of a robot arm and to be used for human-machine interface in telerobotic with application to telesonography. A three part psychophysical analysis enabled us to design this new system of angles exhibiting a good decorrelation among its degrees of freedom. A decorrelation improvement of up to 83% can be noticed compare to the standard 3-1-3 Euler angles. This new frame has been exploited to conceive human-machine interface with a low cost input device such as the standard IT mouse. Psychophysical results show indisputable superiority of our new system compare to the standard Euler one for orientation tracking in teleoperation conditions.

Since about ten years, dielectric barrier discharge (DBD) was studied as electro-hydrodynamic (EHD) actuator for airflow control. A DBD surface discharge generates nonthermal plasma allowing to modify the boundary layer of airflow. The active control enables fast action on airflow. A thin flexible asymmetric DBD actuator was used in our study, each elementary DBD was made with two copper electrodes of 35 mum in thickness and 6 mm in width. Dielectric was a multilayer configuration using Kaptonreg and Mylarreg. Two lengths of electrode were used in applications mentioned below: 150 mm and 900 mm. The DBD actuator was characterized by means of electric and optical measurements: discharge currents, voltages and dissipated power of DBD actuator; spectroscopic measurements were also performed. All these measurements were done for several frequencies of power supply. For flow separation controls experiments, firstly, we performed tests on a 1 m length flat plate with an elliptic leading edge placed in an open wind tunnel. This wind tunnel has a test section of a 2 m times 0.5 m times 0.5 m (LtimesHtimesW). Several DBD actuators with 150 mm length electrodes were placed on the upper surface of the flat plate. The action of DBD actuator enables to obtain a more stable laminar boundary layer and to delay the laminar-turbulent transition. Secondly, a 1 m chord and 1.10 m span wing-like airfoil (BMVR130) was used to perform measurements. This airfoil was placed in a wind tunnel whose test section has dimensions of 5 m times 2 m times 2 m. DBD actuators with electrode length of 900 mm were installed on the extrados of profile every 30 mm from x/c = 0.02 to x/c = 0.8. However, only a few elementary DBDs (up to 4) operated simultaneously. The experiments were carried out for velocities up to 15 m/s (Re = 106) and for angles of attack ranging from 8deg to 16deg. Flow visualizations were performed with a PIV system, the drag and lift coefficients were deduced by aerodynamic balance measurements. At 10 m/s (Re = 670,000), the flow was fully reattached for the angles of attack from 8deg to 12deg. A lift increase of about 5% could be observed.

International audience; This paper analyzes the benefit and the limitations of using a particular biometric technology "namely face authentication" for banking applications. We present first the general concepts of banking. We propose a method in order to replace the PIN code authentication by using biometrics data. Biometric authentication is then detailed. A face recognition method we developed is presented revealing as itself as a biometric candidate solution. We show the benefit and limits of this approach to be used in a real industrial context.

This paper proposes a novel Local Gabor Fisher Classifier (LGFC) for face recognition. Gabor feature vector has been recognized as one of the most successful face representations, however, its dimension is too high for fast extraction and accurate classification. In LGFC, Local Feature Analysis (LFA) is exploited to select the most informative Gabor features (hereinafter as local Gabor features) optimally. The selected low-dimensional local Gabor features are then classified by Fisher Linear Discriminant (FLD) for final face identification. We demonstrate that Gabor representation is much more robust than gray-level intensity to image variation caused by the imprecision of facial feature localization. Comparative studies of different similarity measures to local fisher classifier and local Gabor fisher classifier are also performed. The experiments on two traditional face databases, ORL and Aberdeen, have shown that compared with other face recognition schemes, the proposed method can effectively reduce the dimensionality of Gabor features and greatly increase the recognition accuracy.

This paper presents the first results obtained in the R&D study initiated by the CNES at the end of 2004. Numerous French experts have been gathered to determine and to tentatively understand the mechanisms responsible for the radiation sensitivity of quartz resonators and to correlate the results of various analyses in order to reduce or to anneal their susceptibilities.

International audience; Micro-scale robots introduce great prospectiveinto many different medical applications such as targeted drugdelivery, minimally invasive surgery and localized bio-metricdiagnostics. This research presents a method for object detection and tracking system of a chain-like magnetic microsphererobots using ultrasound imaging in an in-vitro environment.The method estimates the position of the microrobot in realtime using deep learning techniques. The experiments showedthat a spherical microrobot with about 500 m in diameter canbe detected and tracked in real-time with a high accuracy indynamic environments. The results exhibit a high detection andtracking accuracy for one, two and three sphere microrobotswith the highest accuracy in detection and tracking around 95% and 93% respectively.

International audience; Intrusion detection is a security concept implemented on networks in various academic and commercial solutions. Most of them rely on sensors dedicated to local area networks or Internet. However clusters rely heavily on networks. Because of their uniformity, they are sensible to attacks: one compromised node can lead to the control of whole cluster. In order to solve such security issues, we purpose a NIDS architecture which addresses the same constraints as a cluster: efficiency, scalability and reliability. It is based on the cluster paradigm. We stress on the facts that network packets must be dispatched according to streams and analysis must be load-balanced at process level. Moreover two types of practical parallel analysis are presented, depending on the type of flows. Finally, we discuss implementations and dimensioning issues.

International audience; We present in this communication a method, which enables to fit a 3D object defined by a functional representation (FRep) to a dataset of 3D points on its surface. A parametric FRep model sketching the point-set is fitted to the point-set. The best fitted parameters of the model are obtained by using a genetic algorithm, well known for its interesting properties in non-linear optimization. The efficiency of the approach is illustrated for reverse engineering applications.

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International audience; In this paper, a robust finite memory observer has been developed for the continuous-discrete linear timevarying (CD-LTV) systems, which are modeled by the ordinary differential equations (ODEs) in the presence of process noise and are observed through discrete noisy measurements. The proposed method collects a finite number of noisy measurements in the most recent time window to obtained an analytical form of state estimation in the sense of least square. In the meantime, it has also been proved in this paper that the state estimation is unbiased and has the smallest dispersion among all the linear estimators. It is worth noting that the presented method converges within a finite time and shows no initial condition problem, which appears to be a significant advantage for real practical applications. The influence of window length has equally been demonstrated attached with a window length selection strategy. A numerical simulation example is provided at the end of this paper to verify the unbiased state estimation performance of the proposed observer via Monte Carlo simulations.

International audience; This paper intends to design a continuous-discrete unknown input finite memory observer (CD-UI-FMO) for the dynamical nonlinear systems with unknown input. The nonlinear systems under consideration are at least locally Lipschitz and represented by ordinary differential equations (ODEs) with process noise while measurements are sampled at discrete time instants with measurement noise. Both process and measurement noise configurations guarantee the robustness and stability of state estimation, as well as performances in diagnosis. By an augmented model, the proposed CD-UI-FMO is designed based on least square method together with an iterative predictionupdate scheme with the aim to simultaneously estimate system state and unknown input. The unbiased estimation property is proved in deterministic case and demonstrated via Monte Carlo simulations in stochastic case, respectively. Moreover, the presented nonlinear observer is successfully applied to an actuator fault detection and estimation for a single-link joint robotic arm incorporating with the exponentially weighted moving average (EWMA) control chart. Illustrative simulation results highlight the effectiveness of the proposed approach.

This work investigates the problem of blind channel equalization in the presence of burst noise. Modeling impulsive noise distribution as generalized-Gaussian, we derive an appropriate criterion for blind equalization. Our method exploits Wijsman’s theorem and develops a maximal-invariant test of equally-likely pulsed signals against impulsive disturbance. We optimize the test and realize an adaptive equalizer, capable of not only mitigating intersymbol interference but also robust to impulsive disturbance. Numerical simulations, obtained on a baseband digital microwave radio system for amplitude-phase shift keying signaling in additive (generalized Gaussian and symmetric-alpha stable) impulsive environment, confirm the admissibility of the proposed equalizer in terms of robustness and steady convergence.

International audience; Almost all induction motor applications suffer from unbalanced supply voltage. Under such conditions the motor operation can be seriously affected. Performance, control and monitoring are directly connected to the knowledge of the supply harmonics effects on the motor, therefore there is a real need to derive a general mathematical formula predicting the harmonics growth and to describe through an analytical analysis in steady state operation, the impact of unbalanced voltage supply on induction machine performance. In this paper an original analytical formula of harmonics components is developed to propose an accurate indicator of unbalanced supply voltage fault. The validity of this analytical study is confirmed by a comparison with simulation and experimental results. NOMENCLATURE  fundamental pulsation f frequency of the supply voltage (50HZ) sd V effective values of the supply voltages of the direct systems si V effective values of the supply voltages of inverse systems s s s R L   stator time constant r r r R L   rotor time constant r s L L M 2 1   leakage factor sa i Stator current   e  Real part of the quantity ed  Direct electromagnetic torque ei  Inverse electromagnetic torque e  pulsating electromagnetic torques.

International audience; Physics-based three-dimensional numerical simulations are becoming more predictive and have already become essential. As an example in geophysics, simulations at scale with a very fine resolution, including uncertainty quantification procedures are crucial to provide the relevant physical parameters for forward modeling of seismic wave propagation. Consequently, the diversity of HPC architectures available (heterogeneity, high core counts or depth of the memory hierarchy) leads to increasing concerns regarding the portability of applicative performances. In this paper, we discuss the implementation of the classical spectral finite-elements method using the Arm C Language Extensions for both Neon and SVE SIMD units.

An important control problem in the automotive powertrain domain is related to the relative use of the two engines of Hybrid Electric Vehicles (HEVs). Aiming to minimize energy and pollutants emissions, this online optimal control problem is highly constrained by the automotive context: very low computing resources, hardly predictable future vehicle dynamic, and need for strong algorithmic robustness. In this framework, the paper proposes two enhancements to the literature reference method. First, an analytical resolution of the optimal control problem is explained, allowing both algorithmic efficiency and simplicity. In the context of Plug-In HEVs, this method needs a battery State of Charge discharge reference, as these vehicles aim to empty their battery in a trip between two charging stations. The literature method aiming to plan this reference at the beginning of the trip fails to conserve optimality in certain heterogeneous trips cases developed in the paper. The second improvement proposed in the paper is then a new method using light cartographic information to deal with problematic cases. This method allows to attain online the optimality of an offline Dynamic Programming algorithm benefiting from a total a priori knowledge of the future powertrain operating points.

This paper presents an efficient Semi-Blind (SB) approach for demixing multi-user mixtures, in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communications system. A weighted hybrid cost function is introduced based upon the Multi-Modulus (MM) criterion, applied to unknown data, and the Least Squares (LS) criterion for pilot symbols. A separation procedure is put forward based on a block gradient descent approach, in which the step size is algebraically computed at each iteration and for each user. Besides a reduced computational complexity and an accelerated convergence, simulation results show that the proposed approach allows to mitigate the inherent ambiguity of fully-blind methods, and to withstand to the pilot contamination problem in massive MIMO systems.

978-1-7281-3983-8/19; International audience; Implementing an intuitive control law for an upper-limb exoskeleton to perform force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to calibrate electromyo-graphy (EMG) data in order to detect the intention to lift or put down a charge while wearing an upper-limb exoskeleton. Based on a low-cost EMG sensor bracelet placed around the arm (Myo armband, Thalmics Lab, Ontario), a subject-specific mapping procedure is implemented to discriminate motion intentions during lifting tasks with a 1-DoF upper-limb exoskeleton. The processing is divided into two main parts: (i) direction estimation with an artificial neural network, and (ii) a model-based intensity prediction. The mapping procedure has been tested on 7 healthy participants with a precision of 96.9 $\pm$ 3.1% for the classification and a RMS Error of 3.8 $\pm$ 0.8$N$ at the end effector. This study opens up the way for fast-deployment applications involving exoskeletons or cobots.

Biomass, one of the autonomous sectors of renewable energy, is characterized by its neutrality in CO 2 emissions during its life cycle and by a low content of nitrogen and sulfur which results in low emissions of NOx and SO 2 . This paper reports some numerical results of characteristics of biogas and syngas flames. Numerical calculations are performed using the COSILAB code for one-dimensional premixed flames. Three cases of flames are analyzed: CH 4 -air, biogas (CO 2 -CH 4 -air) and syngas (CO-H2-air and CO-H 2 -CO 2 -air). Calculations include laminar flame velocity, flame temperature and CO-NOx emissions with different parameters, such as temperature and pressure of fresh gases, equivalence ratio and gas composition. Several reaction mechanisms as GRI-Mech3.0, GRI Skeletal, San Diego, NIU GALWAY, CRECK Group, S. Davis et al. are used and compared to the results from the literature. Results show that calculations with some these mechanisms reproduce well experimental results of the literature. Results of biogas flames show that CO 2 addition induces a decrease in laminar burning velocity, an increase in CO emissions and a decrease in NOx formation. Calculations of syngas flames show that the hydrogen increase leads to an increase of laminar burning velocity and flame temperature.

In this paper, new joint eigenvalue decomposition (JEVD) methods are developed by considering generalized Givens rotations. These algorithms deal with a set of square complex matrices sharing a same eigen-structure. Several existing methods, using or not generalized Givens rotations, have treated the aforementioned problem. To improve the JEVD solutions, we developed two methods, the first one is numerically stable and efficient but relatively expensive. The second one is developed by considering some justified approximations. Simulation results are provided to highlight the effectiveness and behaviour of the proposed techniques for different scenarios.