Antenne Microruban Miniature Ultra Large Bande ULB pour Imagerie Micro-onde

Ce travail consiste à concevoir une antenne répondant aux exigences des systèmes Ultra Large Bande ULB. Ces derniers sont très présents dans différentes applications, à titre non exhaustif, nous citerons l’imagerie médicale microonde, les communications sans fil, les systèmes de positionnement et systèmes Radars. Pour cela, l’antenne doit présenter de bonnes performances sur la bande de fréquence 3.1- 10.6 GHz, spectre alloué à l’ULB par la commission FCC (Federal Communications Commission). Nous proposons une antenne imprimée miniature de forme rectangulaire qui satisfait les caractéristiques ULB en termes de bande passante et de coefficient de réflexion. Cette antenne est destinée à un système de détection de tumeurs malignes par imagerie microondes. Nous exploitons certaines techniques de miniaturisation et d’élargissement de la bande passante afin de réaliser notre objectif

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research

Synthèse de réseaux plans d'antennes imprimées avec effet de couplage entre éléments rayonnants

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Mésusage et dépendance aux opioïdes de prescription : prévention, repérage et prise en charge

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Mining exceptional closed patterns in attributed graphs

International audienceGeo-located social media provide a large amount of information describing urban areas based on user descriptions and comments. Such data makes possible to identify meaningful city neighborhoods on the basis of the footprints left by a large and diverse population that uses this type of media. In this paper, we present some methods to exhibit the predominant activities and their associated urban areas to automatically describe a whole city. Based on a suitably attributed graph model, our approach identifies neighborhoods with homogeneous and exceptional characteristics. We introduce the novel problem of exceptional subgraph mining in attributed graphs and propose a complete algorithm that takes benefits from closure operators, new upper bounds and pruning properties. We also define an approach to sample the space of closed exceptional subgraphs within a given time-budget. Experiments performed on 10 real datasets are reported and demonstrate the relevancy of both approaches, and also show their limits