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Performance analysis of similarity measures between multichannel optical and multipolarization radar images
No full textInternational audienceThis paper investigates the problem of measuring similarity between multimodal Remote Sensing (RS) images using both area-based and feature-based structural similarity measures (SMs). For many RS platforms, optical image is multichannel and radar image is multipolarization. Thus, vector-to-vector SMs could be applied to optical-to-radar image pairs in contrast to scalar-to-scalar SMs considered in the literature. Using two real Landsat8 - SIR-C image pairs, we demonstrate that vector variants of state-of-the-art SMs outperform their scalar counterparts. This is especially evident for Normalized Correlation Coefficient (NCC), which in vector case performs as good as or better than advanced structural SMs. © 2017 IEEE
A Low-Profile Broadband 32-Slot Continuous Transverse Stub Array for Backhaul Applications in E-Band
No full textInternational audienceA high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched (S11 andlt; -13.6 dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band. © 2017 IEEE
ACCENTS: a Vision for D2D Communications within 5G Networks
No full textInternational audienceThe paper presents the studies that have been led toward the integration of device-to-device (D2D) communications within LTE networks in the frame of the project ACCENTS (Advanced Waveforms, MAC Design and Dynamic Radio Resource Allocation for Device-to-Device in SG Wireless Networks) supported by the French National Research Agency. The solutions presented in this paper cover several aspects of D2D communications; they are the result of a close cooperation between four partners: Thales Communications and Security, CNAM, TeamCast and CentraleSupelec. Physical Layer aspects have been investigated, especially concerning waveforms envisioned for D2D communications within SG networks. Also, a new MAC protocol has been designed to address the issues raised by the integration of D2D transmissions within LTE cells. The solution coming out of the study guarantees a minimum QoS for both types of users (D2D users and regular users) and aims at reusing spectrum while offloading the burden on the base station
Circle criterion-based observer design for Lipschitz and monotonic nonlinear systems - Enhanced LMI conditions and constructive discussions
No full textInternational audienceA new LMI design technique is developed to address the problem of circle criterion-based observer design for nonlinear systems. The developed technique applies to both locally Lipschitz as well as monotonic nonlinear systems, and allows for nonlinear functions in both the process dynamics and output equations. The LMI design condition obtained is less conservative than all previous results proposed in the literature for these classes of nonlinear systems. By judicious use of a modified Young’s relation, additional degrees of freedom are included in the observer design. These additional decision variables enable improvements in the feasibility of the obtained LMI. Several recent results in the literature are shown to be particular cases of the more general observer design methodology developed in this paper. Illustrative examples are given to show the effectiveness of the proposed methodology. The application of the method to slip angle estimation in automotive applications is discussed and experimental results are presented
First investigations on stoichiometric lithium niobate as piezoelectric substrate for high-temperature surface acoustic waves applications
No full textInternational audienceSurface acoustic waves (SAW) technology is very promising to achieve high-temperature wireless sensors. However, there is currently a need for piezoelectric substrates with a high electromechanical coupling coefficient (K2 > 1%), able to operate under harsh environments, especially in the intermediate temperature range (300-600°C). None of the conventional SAW substrate can face this challenge. In particular congruent lithium niobate, whose K2 can exceed 5%, shows serious limitations from 300°C, mainly related to Li vacancies. Recent studies have demonstrated the potential of stoichiometric lithium niobate (s-LN) for high-temperature bulk acoustic waves applications. In this paper, we investigate this piezoelectric material for high-temperature SAW applications. In particular, we examine carefully the potential structural and chemical changes that s-LN surface can undergo during a high-temperature exposure. Finally, SAW resonators based on s-LN substrates are in situ characterized up to 600°C
Intelligent Digital Learning: Agent-Based Recommender System
No full textInternational audienceIn the context of intelligent digital learning, we propose an agent-based recommender system that aims to help learners overcome their gaps by suggesting relevant learning resources. The main idea is to provide them with appropriate support in order to make their learning experience more effective. To this end we design an agent-based cooperative system where autonomous agents are able to update recommendation data and to improve the recommender outcome on behalf of their past experiences in the learning platform. CCS Concepts • Information systems➝Information retrieval ➝Retrieval tasks and goals ➝ Recommender systems
An explicit formula for the splitting of multiple eigenvalues for nonlinear eigenvalue problems and connections with the linearization for the delayeigenvalue problem
No full textInternational audienceWe contribute to the perturbation theory of nonlinear eigenvalue problems in three ways. First, we extend the formula for the sensitivity of a simple eigenvalue with respect to a variation of a parameter to the case of multiple nonsemisimple eigenvalues, thereby providing an explicit expression for the leading coefficients of the Puiseux series of the emanating branches of eigenvalues. Second, for a broad class of delay eigenvalue problems, the connection between the finite- dimensional nonlinear eigenvalue problem and an associated infinite-dimensional linear eigenvalue problem is emphasized in the developed perturbation theory. Finally, in contrast to existing work on analyzing multiple eigenvalues of delay systems, we develop all theory in a matrix framework, i.e., without reduction of a problem to the analysis of a scalar characteristic quasi-polynomial
Contrôle non destructif du sol et imagerie d'objets enfouis par des systèmes bi- et multi-statiques : de l’expérience à la modélisation
No full textThe work presented in this thesis deals with the resolutions of the direct and inverse problems of the ground radar (GPR). The objective is to optimize GPR’s performance and its imaging quality. A state of the art of ground radar is realized. It focused on simulation methods and imaging techniques applied in GPR. The study of the use of the discontinuous Galerkin (GD) method for the GPR simulation is first performed. Some scenarios complete of GPR are considered and the GD simulations are validated by comparing the same scenarios’ modeling with CST-MWS and the measurements. Then a study of inverse problem resolution using the Linear Sampling Method (LSM) for the GPR application is carried out. A study with synthetic data is first performed to test the reliability of the LSM. Then, the LSM is adapted for the GPR application by taking into account the radiation of antenna. Finally, a study is designed to validate the detectability of underground electrical cables junction with GPR in a real environment.Les travaux présentés dans cette thèse portent sur les résolutions des problèmes direct et inverse associés à l’étude du radar de sol (GPR). Ils s’inscrivent dans un contexte d’optimisation des performances et d’amélioration de la qualité de l’imagerie. Un état de l’art est réalisé et l’accent est mis sur les méthodes de simulation et les techniques d’imagerie appliquées dans le GPR. L’étude de l’utilisation de la méthode du Galerkin discontinue (GD) pour la simulation GPR est d’abord réalisée. Des scénarios complets de GPR sont considérés et les simulations GD sont validées par comparaison avec des données obtenues par CST-MWS et des mesures. La suite de l’étude concerne la résolution du problème inverse en utilisant le Linear Sampling Method (LSM) pour l’application GPR. Une étude avec des données synthétiques est d’abord réalisée afin de valider et tester la fiabilité du LSM. Finalement, le LSM est adapté pour des applications GPR en prenant en compte les caractéristiques du rayonnement de l’antenne ainsi que ses paramètres S. Finalement, une étude est effectuée pour prouver la détectabilité de la jonction d‘un câble électrique souterrain dans un environnement réel
Deep learning for action and gesture recognition in image sequences: a survey
No full textA reduced version of this paper appeared appeared in the Proceedings of 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), 2017International audienceInterest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunitiesand challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research