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On Operational Diagnosis for Ground Stations: A Model-Based Approach
No full textInternational audienceChallenges faced in the field of operational diagnosis have grown a deal in the last decade, especially for complex, time-critical systems. In the meantime, although model-based techniques are being used widely to address the design of complex systems, but are not extended to their operation—in particular when dealing with faults: operators can benefit from the use of formal models for system monitoring and diagnostics. We thus propose a methodology to create a new type of Operations Dedicated Model (ODM) from the already existing system design models (functional and dysfunctional), with the use of the Behaviour Tree (BT) formalism. With the assumption that Safety Analysis (SA) models describe dysfunctional aspects of the system—notably via Fault Tree Analysis (FTA), we us Fault Trees (FTs) as an input for the ODM construction. We also demonstrate our proposed approach on a Satellite Ground Station example, and discuss how ODMs can improve systems’ operations
Some selected in situ characterization tools for MBE growth and their complementarity
No full textInternational audienc
Design and Control of a Robot-Arm Actuated by Pneumatic Artificial Muscles for Post-stroke Rehabilitation
No full textInternational audienceRobots play a significant role in post-stroke rehabilitation thanks to the possibility they offer to perform simple, effective and fun repetitive exercises. They are mainly driven by electric motors combined with position or force control in the robot's task space, but with high stiffness of the movements proposed to the patient. The use of an actuation mode by artificial muscles opens the way to biomimetic flexibility of the robot's movements, in interaction with the patient. We present in the article the realization of a prototype robot with two degrees of freedom, for the rehabilitation of the upper limb, driven by pneumatic artificial muscles
Enhanced Modulation for the Single-Stage Three-Phase Quad-Active-Bridge AC-DC converter
No full textBonjour, cet article présente une modulation plus simple pour une structure existante QAB.International audienc
Polarization-Shift Backscatter Identification for SWIPT-Based Battery-Free Sensor Nodes
No full textInternational audienceBattery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocoldependent security mechanisms cannot be employed, leaving BFSNs vulnerable to replay, spoofing, and other security threats. This paper explores a protocol-independent security mechanism that enhances BFSN security by exploiting the power wave for controlled backscattering. The method introduces a Manchester-encoded digital private key generated by the BFSN's low-power microcontroller and backscattered through a polarization-shifting module enabled by a fail-safe RF switch, thereby avoiding the need for a dedicated backscattering rectifier. A LoRaWAN-based BFSN integrating this add-on module was implemented to experimentally validate the approach. Results show successful extraction of the backscattered key with minimal energy overhead (approximately 95 µJ for a 3 ms identification sequence), while the original high-efficiency RF rectifier used for harvesting remains unmodified. The orthogonal polarization between the incoming and backscattered waves additionally reduces clutter and cross-jamming effects. These findings demonstrate that secure identification can be seamlessly incorporated into existing BFSNs without altering their core architecture, offering an easy-to-integrate and energy-efficient solution for improving security in SWIPT-based sensing systems
Conception et développement d'un micro-airbag à base de nano-composites réactifs. Évaluation de la technologie pour la sécurité des personnes
No full textNational audienceThis thesis is set in the context of monitoring and detecting falls among vulnerable individuals, the leading cause of trauma-related hospitalizations. Its goal is to design a personal and intelligent airbag system capable of detecting an imminent fall and deploying before ground impact, thereby significantly reducing the risk of serious injuries, particularly hip fractures.The first chapter presents the current technological challenges of protective devices, including existing wearable airbags (for motorcyclists, athletes, elderly people) and their limitations, especially the reliance on gas canisters. This leads to a research focus on fall anticipation for seniors, with the objective of designing a reliable, autonomous, real-time embedded detection system.The second chapter describes the system engineering methodology adopted (MBSE via the ARCADIA/CAPELLA method) to model the entire system: detection, pyrotechnic triggering, airbag inflation, and energy management. Special attention is given to hardware integration and component miniaturization to ensure both lightness and compactness.The third chapter focuses on system integration: electronic design, manufacturing, assembly, and functional testing. Thermal characterization and radio frequency performance testing (antenna performance) were conducted.The fourth chapter is dedicated to the development of a specific embedded AI for pre-fall detection, based on convolutional neural networks (CNNs) trained on public datasets (KFall). The selected architecture is optimized for real-time execution on a microcontroller (MCU), with minimal energy consumption and high detection accuracy.Finally, the fifth chapter explores a major development path: embedded neuromorphic AI through the implementation of spiking neural networks (SNNs) on neuromorphic circuits. This approach enables performance comparable to deep learning while drastically reducing energy consumption, paving the way for an even more efficient and sustainable embedded solution.In conclusion, this thesis proposes a complete solution, from modeling to hardware implementation, of a next-generation innovative fall protection system.Cette thèse s’inscrit dans le contexte de la surveillance et de la détection des chutes chez les personnes vulnérables, première cause d’hospitalisation traumatique. Elle vise à concevoir un système airbag personnel et intelligent, capable de détecter une chute imminente et de se déployer avant l’impact au sol, réduisant ainsi considérablement les risques de blessures graves, notamment les fractures de la hanche.Le premier chapitre présente les défis technologiques actuels des dispositifs de protection, notamment les airbags portables déjà existants (motards, sportifs, personnes âgées) et leurs limites, en particulier le recours aux bouteilles de gaz. Il en découle une problématique centrée sur l’anticipation de la chute chez les seniors, avec l’objectif de concevoir un système embarqué à détection temps réel, fiable et autonome.Le deuxième chapitre décrit la méthodologie d’ingénierie système adoptée (MBSE via la méthode ARCADIA/CAPELLA) pour modéliser l’ensemble du dispositif : détection, déclenchement pyrotechnique, gonflage de l’airbag et gestion de l’énergie. Une attention particulière est portée à l’intégration matérielle et à la miniaturisation des composants pour garantir à la fois légèreté et faible encombrement.Le troisième chapitre est consacré à l’intégration système : conception électronique, fabrication, assemblage et tests fonctionnels. Une caractérisation thermique et une caractérisation des performances radio fréquence (performances des antennes) ont été conduite.Le quatrième chapitre est dédié au développement d’une IA embarquée spécifique pour la détection de pré-chute basée sur des réseaux de neurones convolutifs (CNN) entraînés à partir de base de données publiques (KFall). L'architecture retenue est optimisée pour une exécution en temps réel sur microcontrôleur (MCU), avec une consommation énergétique minimale et une grande précision de détection.Enfin, le cinquième chapitre explore une piste d’évolution majeure, à savoir : une IA neuromorphique embarquée par l’implémentation des réseaux de neurones à pointes (SNN) dans des circuits neuromorphiques. Cette approche permet d’atteindre des performances comparables à celles du Deep Learning, tout en réduisant considérablement la consommation énergétique, ouvrant ainsi la voie à une solution embarquée encore plus performante et durable.En conclusion, cette thèse propose une solution complète, de la modélisation à l’implémentation matérielle d’un système de protection innovant de nouvelle génération contre les chutes
Rapid Quantification of Threading Dislocation Networks in Topologically Insulating Bi 1− x Sb x Thin Films via Deep‐Learning Analysis of Etch Pits
No full textInternational audienceBiSb alloys are of prime interest because of their intriguing thermoelectric properties, as well as being attractive topological insulator materials for quantum and spintronic devices. Their properties have mostly been studied by transfer of flakes or by studying bulk materials, while epitaxial integration with industrial substrates remains challenging. Mismatches in crystalline structures and lattice parameters lead to the formation of dislocations at the substrate interface, which then propagate through the heterostructure. The effects of these dislocations on the functional properties of BiSb thin films have not been explored experimentally. A wet‐etching method with dilute HCl followed by scanning electron microscopy observation is presented here, which greatly simplifies and accelerates the quantification of the surface threading dislocations density, thus allowing for correlating crystal defects to functional properties. An off‐the‐shelf instance segmentation neural network (Mask‐R‐CNN) is fine‐tuned to rapidly detect and quantify the etch pits with a recall of 97% and an accuracy of 79%. (Scanning) transmission electron microscopy observations confirm that etch pits correspond to threading dislocations, and allow visualization of the (a/3) <2‐1‐10> lattice distortion. A platform is thus presented to study the effects of the dislocations’ density on the functional properties of BiSb thin films with a simple technique
Conversion thermophotovoltaïque - Des principes aux applications
No full textInternational audienceThe production of low-carbon electricity is a necessity that calls for the development of new technologies. Thermophotovoltaic conversion involves the direct conversion of thermal energy using the photovoltaic effect. As with photovoltaic conversion using solar radiation, it recovers radiative heat from sources withtemperatures between 500 and 2,500 °C. This article presents basic principles, science and engineering, and main applications. Powered by a wide variety of primary energy sources, coupled with energy storage in thermal form at very high temperatures (1,000 to 2,500°C), and with cell efficiencies tending towards 50 %, thermophotovoltaic systems offer new options for decarbonized electricity production.La production d’électricité décarbonée est une nécessité qui appelle le développement de nombreuses technologies. La conversion thermophotovoltaïque consiste à réaliser une conversion directe d’énergie thermique par effet photovoltaïque. Cousine de la conversion photovoltaïque associée au rayonnement solaire, elle permet de récupérer de la chaleur radiative issue de sources de températures entre 500 et 2500 °C. Cet article en présente les principes élémentaires, la science et l’ingénierie, et les applications principales. Alimentés par une grande variété de sources d’énergie primaire, couplés à du stockage d’énergie sous forme thermique à très haute température (1000 à 2500 °C), et avec des rendements de cellules tendant vers 50 %, les systèmes thermophotovoltaïques offrent de nouvelles options pour une production décarbonée d’électricité
User equilibria in heterogeneous discriminatory processor sharing queues
No full textInternational audienceWe consider a strategic routing game for a two-class discriminatory processor-sharing queue with an additional cost for joining the premium class. We show that, depending on the specific parameters of the system, various equilibria can coexist, including equilibria where the queueing system is not ergodic for the equilibrium traffic split. We also investigate how the server can select the priority of the classes and the fees charged to the customers to maximise its revenue. We then investigate learning strategies that converge to particular equilibria. Finally, we study how the elasticity of the traffic demand affects the equilibrium solutions
