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A current and LFN method for individually assessing layer quality in III-V multi-junctions
No full textInternational audienc
Combining Electrohydrodynamics and Biomolecular Recognition: A Study of Conductivity Effects on ACEO and Antibody-Antigen Binding
No full textInternational audienceThe detection of bacteria in health and environmental applications requires highly sensitive and specific biosensors. However, mass transfer limitations can hinder the performance of surface-based biosensors, particularly at low analyte concentrations. To address this challenge, electrohydrodynamic (EHD) phenomena such as Alternating Current Electroosmosis (ACEO) and Dielectrophoresis (DEP) are used to actively enhance mass transfer, thereby increasing target accumulation near the sensor surface. The electrical conductivity of the natural media (sprinkling or irrigation water) in which the target bacteria are to be detected can vary by several orders of magnitude. It is therefore important to assess the impact of this conductivity on the behavior of EHD phenomena, as well as on the affinity of antibody-antigen interactions that guarantee specific detection. This study introduces a novel method for real-time imaging of EHD effects, enabling direct assessment of operating parameters through vortex shape analysis and particle tracking. Experimental results are complemented by numerical simulations performed with COMSOL® to evaluate the influence of electrode geometries on particle trapping efficiency. In addition to developing this visualization tool and numerical model, ELISA assays are conducted to assess antigen-antibody affinity in low-conductivity conditions required for ACEO and positive Dielectrophoresis (pDEP). These experiments assess the feasibility of a hybrid biosensor that combines active mass transport using EHD phenomena with specific detection via efficient antigen-antibody interactions
ADVANCED NONDESTRUCTIVE DETECTION OF GRINDING BURNS VIA NV-CENTER BASED MAGNETOMETRY
No full textInternational audienceInternational Workshop on Electromagnetic Nondestructive Evaluation (ENDE’25), Jul 2025, Knoxville - Tennessee, United State
Characterization of Corona and Dielectric Barrier Discharge using Pockels Effect Based Electro-Optic Probe
No full textInternational audienc
Identifying and Localizing Dynamic Affordances to Improve Interactions with Other Agents in Continuous Environments
No full textInternational audienc
Skew Braces of Finite Morley Rank
No full textWe describe skew braces of finite Morley rank. In particular, we derive sufficient and necessary conditions for the solubility and nilpotency of skew braces of finite Morley rank. Moreover, we give a characterization of skew braces of Morley rank up to 4
Automatic generation of input-aware approximate arithmetic circuits
No full textInternational audienceApproximate Computing (AxC) is systematically applied across various abstraction levels to reduce overheads and enhance the performance of applications such as image processing and machine learning. However, AxC does not typically consider the specific workload (i.e., data input) of a given application. For instance, in signal processing applications like filters, some inputs are constants (filter coefficients), which allows for an additional level of approximation by considering the specific input distribution. This method is known as "Input-Aware Approximation" (IAA) and has shown potential advantages in previous studies. Unfortunately, existing input-aware design methodologies lack scalability as they mostly depend on ad-hoc, non-automatic design approaches, limiting their applicability. In this paper, we investigate how the input-aware approximate design approach can be integrated into a systematic, generic, and automatic design flow. We employ state-of-the-art approximation and multi-objective optimization techniques to achieve input awareness. Our experimental results, focusing on classical signal processing applications like FIR filters, demonstrate that the input-aware approach can provide significant savings in both area and power consumption
Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
No full textInternational audienceSpin qubits need to operate within a very precise voltage space around charge state transitions to achieve high-fidelity gates. However, the stability diagrams that allow the identification of the desired charge states are long to acquire. Moreover, the voltage space to search for the desired charge state increases quickly with the number of qubits. Therefore, faster stability diagram acquisitions are needed to scale up a spin qubit quantum processor. Currently, most methods focus on more efficient data sampling. Our approach shows a significant speedup by combining measurement speedup and a reduction in the number of measurements needed to tune a quantum dot device. Using an autotuning algorithm based on a neural network and faster measurements by harnessing the FPGA embedded in Keysight's Quantum Engineering Toolkit (QET), the measurement time of stability diagrams has been reduced by a factor of 9.8. This led to an acceleration factor of 2.2 for the total initialization time of a SiGe quantum dot into the single-electron regime, which is limited by the Python code execution
PEPR VDBI EN ACTION: LIVRET #1
No full textCe Livret #1 est issu d’un séminaire associant l’équipe de gouvernance-animation et les Centres opérationnels (C.O.) du PEPR VDBI, qui s’est déroulé les 1er et 2 juillet 2025. Si ce séminaire avait une vocation interne, la publication sous forme de livret des échanges et travaux ayant eu lieu s’inscrit dans le contexte expérimental du programme. Expérimenter et observer pour servir la transformation attendue ? Au-delà de la production scientifique, le PEPR VDBI est ainsi un outil permettant d’envisager de nouvelles modalités de collaboration et de travail entre les acteurs intervenant sur le sujet de la ville durable et des bâtiments innovants.Dans le cadre de ses missions d’éditorialisation et de diffusion des connaissances produites, le PEPR Ville Durable et Bâtiment Innovant (VDBI) s’apprête à publier une série de livrets. Ces livrets ont pour vocation de valoriser les avancées récentes du programme, de mettre en lumière les résultats de la recherche et de faciliter le dialogue entre la communauté scientifique, les acteurs territoriaux et l’ensemble des parties prenantes engagées dans la transition vers des villes et bâtiments plus durables.Ce Livret #1 est issu d’un séminaire associant l’équipe de gouvernance-animation et les Centres opérationnels (C.O.) du PEPR, qui s’est déroulé les 1er et 2 juillet 2025. Si ce séminaire avait une vocation interne, la publication sous forme de livret des échanges et travaux ayant eu lieu s’inscrit dans le contexte expérimental du programme. Expérimenter et observer pour servir la transformation attendue ? Au-delà de la production scientifique, le PEPR VDBI est ainsi un outil permettant d’envisager de nouvelles modalités de collaboration et de travail entre les acteurs intervenant sur le sujet de la ville durable et des bâtiments innovants.Ces publications s’adressent à tous : chercheurs, praticiens et élus des collectivités, entreprises, citoyens, désireux de s’informer et d’agir à leur échelle pour renforcer l’impact des recherches et accélérer la transformation des territoires. Nous espérons que ces livrets seront, pour chacun, une ressource précieuse pour comprendre, s’inspirer et contribuer à la dynamique collective portée par le programme VDBI
A 3D Compact Shape Descriptor based on Largest Intersection and Projection Signature
No full textInternational audienceMotivated by the performance of the shape descriptor based on the Largest Intersection and Projection (LIP) signature, we propose a three-dimensional extension in link to concrete application. By projecting 3D shape onto their principal planes and analyzing the resulting profiles, we extract compact and interpretable geometric features. Descriptors obtained from these features are then used for 3D object classification.The proposed method is evaluated on both the ModelNet benchmark and a real-world dataset of tree bark defects. Results show that the descriptor effectively captures shape variations while enabling a significant reduction in feature dimensionality