Scientific Publications of the University of Toulouse II Le Mirail
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Multiplexed Bio‐detection on an Interferometric Optical Waveguide Assembly
No full textInternational audienceMultiplexed remote bio‐detection is demonstrated through an optical waveguide assembly coated with interferometric layers. Image conduits (IC)s, composed of 3012 individual cores, are coated with interferometric layers of tantalum pentoxide (Ta 2 O 5 ) and silica (SiO 2 ) to transform each core into a sensitive sensor. The spectral response of the IC as a function of refractive index (RI) changes is obtained and compared with the simulated one. The experimental sensitivities and resolutions of individual cores of the waveguide are assessed in remote detection mode by imaging through the optical assembly. For 75% of the cores, a sensitivity better than 510%. RIU −1 (RI Unit) is obtained, corresponding to a resolution better than 7 × 10 −4 RIU. Furthermore, the coated face of IC is functionalized with two localized arrays of hundred‐micrometer droplets containing two different oligonucleotide (ODN) probes using a polymeric 3D‐printed microcantilever. Hybridization of complementary ODN strands is detected for one of the probes, the second being a negative control. Interaction kinetics are monitored in functionalized areas by grouping several cores or on individual cores. Thus, multiplexed bio‐detection on the surface of an interferometric waveguide is demonstrated for the first time paving the way for applications in multiplex in situ biosensing, and, ultimately, in vivo endoscopic diagnosis
A Review of CNN Applications in Smart Agriculture Using Multimodal Data
No full textInternational audienceThis review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available
‘Stamp out paper mills’ — science sleuths on how to fight fake research
No full textInternational audienceA group of scientists who are experts at spotting fraudulent research outline five essential steps to combat industrialized scientific misconduct
Learning Geometric Reasoning Networks for Robot Task and Motion Planning
No full textInternational audienceTask and Motion Planning (TAMP) is a computationally challenging roboticsproblem due to the tight coupling of discrete symbolic planning and continuousgeometric planning of robot motions. In particular, planning manipulation tasksin complex 3D environments leads to a large number of costly geometric plannerqueries to verify the feasibility of considered actions and plan their motions. Toaddress this issue, we propose Geometric Reasoning Networks (GRN), a graphneural network (GNN)-based model for action and grasp feasibility prediction,designed to significantly reduce the dependency on the geometric planner. More-over, we introduce two key interpretability mechanisms: inverse kinematics (IK)feasibility prediction and grasp obstruction (GO) estimation. These modules notonly improve feasibility predictions accuracy, but also explain why certain actionsor grasps are infeasible, thus allowing a more efficient search for a feasible solu-tion. Through extensive experimental results, we show that our model outperformsstate-of-the-art methods, while maintaining generalizability to more complex en-vironments, diverse object shapes, multi-robot settings, and real-world robots
Penser la photographie du film
No full textInternational audienceConsacré à la photographie du film, à la manière dont celle-ci est pensée, construite, cet ouvrage invite à réfléchir au travail des directeurs et directrices de la photographie, engagés au premier titre dans la création de l’image des films, mais aussi à l’implication d’autres corps de métiers dans ce processus, depuis l’écriture des scénarios jusqu’à la salle de projection, en passant par le décor, le maquillage ou l’étalonnage. Des entretiens inédits avec des professionnels se mêlent aux études de cas particuliers en vue d’éclairer l’espace de compétences partagées que constitue la photographie de toute œuvre cinématographique. Le présent ouvrage démontre à quel point cette notion peut recouvrir des réalités diverses selon les contextes et invite à repenser sa définition lorsqu’on déplace le regard vers l’animation ou les nouvelles technologies. Peut-on, par exemple, encore évoquer la photo d’un film quand il n’y a plus de « photo » à proprement parler et que l’image est générée par ordinateur
E-compagnion:Decomposition methods for the beam-layout optimization problem
No full textIn this article, we study an optimization problem related to the design of a telecommunication satellite, namely the beam-layout optimization problem in which the goal is to define the beams emitted by the antennas of a geostationary satellite to cover regions on Earth. Four key features of the application tackled are that (1) the regions to cover are defined as polygons, (2) there is a very large number of candidate beams, (3) the selected beams need to be colored (actually, allocated to an antenna reflector) and two close beams cannot have the same color, and (4) the candidate beams have heterogeneous sizes and the sizes of the selected beams must be minimized for efficiency reasons. We provide a complexity analysis, showing that the problem is NP-hard. To solve this challenging problem, we introduce two decomposition methods based on column generation and logic-based Benders decomposition, to go beyond the existing heuristic approaches. The experimental results show that these decomposition methods provide high-quality solutions within limited computational times, and that the logic-based Benders decomposition approach finds the optimal solution for many instances.Dans cet article, nous étudions un problème d'optimisation lié à la conception d'un satellite de télécommunications, à savoir le problème d'optimisation de la disposition des faisceaux dans lequel l'objectif est de définir les faisceaux émis par les antennes d'un satellite géostationnaire pour couvrir des régions sur Terre. Quatre caractéristiques clés de l'application traitée sont que (1) les régions à couvrir sont définies comme des polygones, (2) il y a un très grand nombre de candidats faisceaux, (3) les faisceaux sélectionnés doivent être colorés (en réalité, alloués à un réflecteur d'antenne) et deux faisceaux rapprochés ne peuvent pas avoir la même couleur, et (4) les candidats faisceaux ont des tailles hétérogènes et la taille des faisceaux sélectionnés doit être minimisée pour des raisons d'efficacité. Nous fournissons une analyse de complexité, ce qui montre que le problème est NP-difficile. Pour résoudre ce problème difficile, nous introduisons deux méthodes de décomposition basées sur la génération de colonnes et la décomposition de Benders basée sur la logique, afin d'aller au-delà des approches héuristiques existantes. Les résultats expérimentaux montrent que ces méthodes de décomposition fournissent des solutions de haute qualité dans des temps de calcul limités et que l'approche de décomposition de Benders basée sur la logique trouve la solution optimale pour de nombreux échantillons
Comparison of different feedback controllers on an airfoil benchmark
No full textInternational audienceThe present paper proposes a comparison of three well-established controllers: a robust proportionalintegral-derivative (PID) controller (Conord and Peaucelle, 2021), a model-free control (Fliess and Join, 2013, 2022) and an adaptive sliding-mode control based on the super-twisting algorithm (Shtessel et al., 2023). The benchmark considered is an airfoil section equipped with trailing edge jets, load sensors and a perturbation system. The objective is to track the lift command under external wind perturbations. The outcome of this work is the comparison of performances for three control laws that are suitable when little knowledge is known from the physics. This study quantifies performance not only in terms of load control, but also in the needed implementation effort
Totally Real Points in the Mandelbrot Set
No full textThe {\em postcritically finite} parameters in the Mandelbrot set are special algebraic integers for which has finite orbit under the polynomial . Recently, Noytaptim and Petsche proved that the only totally real parameters that are postcritically finite are , and . In this note, we study another distinguished algebraic subset of , the {\em parabolic} parameters. A parameter is parabolic provided that possesses a parabolic cycle. We show that the only totally real parameters that are parabolic are , , and
