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Laboratory characterisation bench for high precision astrometry
No full textInternational audienceHigh precision differential astrometry assesses the positions, distances, and motions of celestial objects in relation to the stars. The focal plane of such space telescope must be calibrated with a precision down to the level of 1e-5 pixel in order to be able to detect Earth-like planets in the close vicinity of the Sun. The presented characterization bench is designed to improve the technology readiness level for the following key points: calibration of new detectors with a high number of pixels and correcting the field distortion using stars in the field of view. The first aim of the project concentrates on the characterization of a 46 megapixels sensor from PYXALIS, to assess its typical parameters using an integrating sphere. The next objective intends to map the intra and extra pixel quantum yield of the detector with a precision of 1e-5 pixels and investigate the evolution of the pixel geometry in response to environment fluctuations. To conduct these tests, an optical bench is designed with an LCD screen and a doublet, used as a source that allows directing light to specific groups of pixels. Interferometric calibration of the detector pixel centroid position will be achieved using fibers that illuminate the detector with Young's fringes. To characterize the distortion of the detector, a diaphragm will produce adjustable optical aberrations to be corrected and therefore change the source sensor positional relationship. The final step involves the simulation of a star's field, which will be imaged on the detector to assess optical quality
Learning dynamics in ultrafast laser self-organization
No full textInternational audienceWe demonstrate that ultrafast laser irradiation dynamically reshapes metallic surfaces through a cumulative, self-adaptive process that exhibits learning-like dynamics. Each laser pulse modifies the surface morphology, and the resulting structures progressively optimize energy absorption through feedback-driven reorganization. These imprints encode not only local polarization effects but also a cooperative evolution strategy, where morphological complexity increases over time to accommodate optical constraints.This behavior emerges from the interaction between thermoconvective instabilities, near-field electromagnetic effects, and dissipative self-organization in the transient molten phase [1]. By combining experimental analysis with thermodynamic modeling and a neural solver trained to predict optical responses via Maxwell’s equations, we uncover how surface patterns evolve toward configurations that enhance light-matter coupling.The evolving surface complexity [2] reflects an intrinsic form of material adaptation, where local responses are shaped by global feedback. This framework connects ultrafast non-equilibrium physics with learning dynamics in physical systems, providing new insight into controlled energy deposition and the design of functional photonic surfaces. These results open routes for tunable absorption and the broader understanding of self-organized complexity in far-from equilibrium matter.References1. A. Nakhoul, & J.P. Colombier, Beyond the Microscale – Advances in Surface Nanopatterning by Laser-Driven Self- Organization, Laser & Photonics Reviews, 2300991 (2024).2. E. Brandao, A. Nakhoul, S. Duffner, R. Emonet, F. Garrelie, A. Habrard, F. Jacquenet, F. Pigeon, M. Sebban, & J.P. Colombier, Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns, Physical Review Letters, 130(22), 226201 (2023)
Effects of LED light channels on color discrimination in low vision
No full textInternational audiencePurpose:The study aims to develop and optimize a light spectrum using an LED panel to improve color discrimination for individuals suffering from specific visual impairments, including central vision loss, tunnel vision, and blurred vision. The purpose is also to identify a minimized set of light channels able to produce white light that can be practically replicated in consumer lighting products.Methods:A total of 57 participants were recruited for the study and divided into four groups as follows: Control Group: 10 participants, Central Vision Loss: 16 participants, Blurred Vision: 15 participants, and Tunnel Vision: 16 participants.Subjects were asked to sort 28 color samples under 13 different lighting conditions provided by an LED panel. The original Farnsworth-Munsell test score was used to evaluate the accuracy of the participants' sorting. The overall performance of the 4 groups was assessed using Welch's t-test.Results:Welch's t-tests revealed significant differences in color discrimination performance across most groups, with the exception of the blurred vision and tunnel vision groups, which showed similar results. Participants in the central vision loss group performed significantly worse than those in the blurred vision and tunnel vision groups. Additionally, the blurred vision and tunnel vision groups demonstrated comparable performance levels and had the fewest deviations from the control group. The central vision loss and tunnel vision groups displayed similar channel-specific differences compared to the control group.Conclusion:First results from the study highlight that the type of visual impairment significantly affects overall and channel-specific performance, with certain light channels showing a greater influence on color discrimination. Notably, significant performance differences across groups were predominantly observed in channels corresponding to the middle of the light spectrum. These findings highlight the varying impacts of visual impairments on color discrimination and emphasize the importance of specific light spectrum ranges in optimizing visual performance
Modeling Nanoparticle Synthesis via Laser Ablation for Advanced Applications
No full textInternational audienceLaser-assisted nanoparticle synthesis is a versatile and sustainable method for producing advanced nanomaterials with precise control over size, composition, and surface properties. We present a modeling framework using molecular dynamics and hydrodynamics to elucidate the atomistic mechanisms of nanoparticle formation during ultrashort laser ablation in various environments.Our study explores the influence of laser parameters and material properties on the formation of nanoparticles, including bi-metallic and plasmonic systems. The insights gained reveal pathways to optimize nanoparticle design for diverse applications in catalysis, energy, sensing, and beyond, advancing the development of high-performance, eco-friendly materials
Periodic laser surface texturing limits hydrogen ingress in Fe-Cr alloy
No full textInternational audienceThe increasing use of hydrogen as an energy carrier necessitates ensuring the mechanical integrity of systems exposed to hydrogenated environments. In this context, surface engineering is crucial for reducing hydrogen ingress into metallic materials. This study explores the use of ultrafast laser texturing on a Fe-Cr alloy to create hydrogen-resistant surfaces. Two distinct laser-induced periodic surface structures (LIPSS) were performed: low spatial frequency laser texturing (LSFL) and high spatial frequency laser texturing (HSFL). Hydrogen uptake was evaluated through electrochemical permeation on the textured surfaces and compared to a mirror-like (Mirror) surface. Results showed significant reduction in hydrogen subsurface concentration by 89% for LSFL and 95% for HSFL, highlighting the potential of this technology for developing hydrogen-resistant surfaces. To further elucidate the mechanisms, this study decoupled the effects of oxide layers, surface topography, and subsurface defects on hydrogen uptake. Experimental investigations using X-ray Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM) revealed that the ultra-thin oxide layer formed during laser texturing plays a pivotal role in mitigating hydrogen absorption. The impact of surface topography was investigated using Atomic Force Microscopy (AFM). It appears that high skewness and kurtosis reduce hydrogen permeation by 40% in HSFL compared to LSFL topography. These findings underscore the effectiveness of ultrafast laser texturing in controlling hydrogen uptake in Fe-Cr alloys, with potential implications for enhancing the durability and performance of industrial materials
Video-based operational modal analysis of slender mechanical structures with neutral axis estimation via barycenter calculation
No full textInternational audienceThis paper presents an innovative adaptation of Operational Modal Analysis (OMA) by employing the concept of the barycenter to analyze dynamic behaviors in mechanical systems through video signal processing. Although this method is framed as a new contribution to the field of modal analysis, it is essential to recognize that its applicability is primarily confined to slender structures. Unlike conventional OMA methods reliant on numerical simulations or accelerometer measurements, this technique presents a non-contact, non-invasive measurement approach. It simultaneously captures multiple points across a wide field of view, ensuring robust measurements. The methodology was tested on a cantilever beam exposed to white Gaussian noise, with the beam recorded at 2048 frames per second to capture its dynamic response. The high-contrast environment facilitated image processing, while Laser Doppler Vibrometry (LDV) measurements served as a reference for validating the results. The proposed video-based OMA methodology demonstrated promising accuracy in capturing the system's dynamic behaviour by providing a more accessible and efficient alternative to traditional OMA techniques. Additionally, an analysis of the robustness of results concerning lighting conditions and video noise is conducted, along with a discussion on algorithmic complexity. Refinement of image processing algorithms and a broader application of this methodology to various mechanical systems and structures are proposed as important future objectives
Multi-view 3D surface reconstruction from SAR images by inverse rendering
No full textInternational audience3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constraints on the acquisition process. These last years, progress in deep learning has significantly advanced 3D reconstruction from multiple views in optical imaging, mainly through reconstruction-by-synthesis approaches popularized by Neural Radiance Fields. In this paper, we propose a new inverse rendering method for 3D reconstruction from a few incoherent SAR views, drawing inspiration from optical approaches. First, we introduce a new simplified differentiable SAR rendering model, able to synthetize images from a Digital Surface Model (DSM) and a radar backscattering coefficients map. Then, we introduce a coarse-to-fine strategy to reconstruct the DSM and the map of backscattering coefficients of a SAR scene starting only from a few SAR views. We use a neural field, i.e. a continuous parametric model based on a Multi-Layer Perceptron, to represent the SAR scene. Finally, we present preliminary results of DSM reconstruction from synthetic SAR images produced by ONERA's physically-based EMPRISE simulator, supporting the potential of applying inverse rendering approaches to SAR data in order to efficiently exploit geometric disparities in future applications such as multi-sensor data fusion
Inverse Spin Thermal Hall Effect in Nonreciprocal Photonic Systems
No full textInternational audienceA transverse radiative heat flux induced by the gradient of spin angular momentum of photons in nonreciprocal systems is predicted. This thermal analog of the inverse spin Hall effect is analyzed in magneto-optical networks exhibiting C4 symmetry, under the action of spatially variable external magnetic fields. This finding opens new avenues for thermal management and energy conversion with nonreciprocal systems through a localized and dynamic control of the spin angular momentum of light
Multimodal Explainable Automated Diagnosis of Autistic Spectrum Disorder
No full textInternational audienceAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by symptoms affecting social interaction, communication, and behavior, with diagnosis complicated by significant individual variability and the absence of definitive biomarkers. Current artificial intelligence methods have improved diagnostic accuracy, but their reliance on subjective assessments or single-modal data, coupled with their ``black-box" nature, limits consistency and clinical applicability. Addressing current limitations, this paper introduces a multimodal ASD detection framework using deep neural networks (DNN) with explainable AI (xAI) to enhance model transparency. Our model achieves a mean 5-fold cross-validation accuracy of 98.64% (± 0.86%), surpassing existing methods and demonstrating potential for clinical dependability of ASD diagnoses. The source code is available at: https://github.com/mebenyahia/Multimodal-Explainable-Automated-Diagnosis-of-Autistic-Spectrum-Disorde