Search results for " processing"
showing 10 items of 7549 documents
GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
2017
On August 14, 2017 at 10 30:43 UTC, the Advanced Virgo detector and the two Advanced LIGO detectors coherently observed a transient gravitational-wave signal produced by the coalescence of two stellar mass black holes, with a false-alarm rate of 1 in 27 000 years. The signal was observed with a three-detector network matched-filter signal-to-noise ratio of 18. The inferred masses of the initial black holes are 30.5-3.0+5.7M and 25.3-4.2+2.8M (at the 90% credible level). The luminosity distance of the source is 540-210+130 Mpc, corresponding to a redshift of z=0.11-0.04+0.03. A network of three detectors improves the sky localization of the source, reducing the area of the 90% credible regio…
Optimisation conjointe de la taille de stockage et des performances de modèles de classification pour l’authentification de visages
2017
International audience
Dark Count rate measurement in Geiger mode and simulation of a photodiode array, with CMOS 0.35 technology and transistor quenching.
2014
International audience; Some decades ago single photon detection used to be the terrain of photomultiplier tube (PMT), thanks to its characteristics of sensitivity and speed. However, PMT has several disadvantages such as low quantum efficiency, overall dimensions, and cost, making them unsuitable for compact design of integrated systems. So, the past decade has seen a dramatic increase in interest in new integrated single-photon detectors called Single-Photon Avalanche Diodes (SPAD) or Geiger-mode APD. SPAD detectors fabricated in a standard CMOS technology feature both single-photon sensitivity, and excellent timing resolution, while guarantying a high integration. SPAD are working in ava…
Automatic emission spots identification in static and dynamic imaging by research of local maxima.
2014
International audience
A Neural Network Meta-Model and its Application for Manufacturing
2015
International audience; Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an appr…
Le grand débat national, une aide pour prendre des décisions locales?
2021
The Great National Debate, decided by Emmanuel Macron at the beginning of 2019 to respond to the Yellow Vests social movement, allowed the collection of citizens’ contributions on the ecological transition via an online platform. In this article, we use the corpus constituted by these contributions to identify areas where participants are asking for the development of bicycle paths and railway facilities. For this purpose, we have created a classification model to identify contributions dealing with the theme of transportation and proposed a method for extracting patterns that reflect the contributors’ proposals. We then represented these patterns on maps, using the contributors’ postal cod…
Bridging Sensing and Decision Making in Ambient Intelligence Environments
2009
Context-aware and Ambient Intelligence environments represent one of the emerging issues in the last decade. In such intelligent environments, information is gathered to provide, on one hand, autonomic and easy to manage applications, and, on the other, secured access controlled environments. Several approaches have been defined in the literature to describe context-aware application with techniques to capture and represent information related to a specified domain. However and to the best of our knowledge, none has questioned the reliability of the techniques used to extract meaningful knowledge needed for decision making especially if the information captured is of multimedia types (image…
Incorporating depth information into few-shot semantic segmentation
2021
International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…
Système de sécurité biométrique multimodal par imagerie, dédié au contrôle d’accès
2019
Research of this thesis consists in setting up efficient and light solutions to answer the problems of securing sensitive products. Motivated by a collaboration with various stakeholders within the Nuc-Track project, the development of a biometric security system, possibly multimodal, will lead to a study on various biometric features such as the face, fingerprints and the vascular network. This thesis will focus on an algorithm and architecture matching, with the aim of minimizing the storage size of the learning models while guaranteeing optimal performances. This will allow it to be stored on a personal support, thus respecting privacy standards.
Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series
2020
L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…