ENSEA UCP CNRS

Collegium IdF PRES-UPGO

Actualités

Post-doctorat : Méthodes pour l'Annotation Automatique des Images Patrimoniales

Post-doctorat : conception de méthodes d'indexation et de classification pour l'annotation automatique des images patrimoniales de la Bibliothèque Nationale de France (BnF). Le post-doctorat démarre le 1er janvier 2015, pour une durée de un an.

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Un robot au musée - Proposition de thèse

"Un robot au Musée. Apprentissage cognitif et conduite esthétique", proposition de thèse à pourvoir, équipe Neurocybernétique.

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Le robot Tino à la radio et à la TV

"Tino, le robot hydraulique", émission "Grand angle", France-Inter, vendredi 11 avril 2014, et sur la télé "VO-news".

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Rapport d'activités

Le rapport d'activités du laboratoire ETIS pour la période 2008-2013 est disponible sur ce site.

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Agenda

15/07/2014 (Tuesday)

Séminaire ICI : Paul Ferrand

14:30 - 16:00

"Frequency-space interference alignment in downlink cellular networks", séminaire présenté par Paul Ferrand, INRIA Lyon.

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Who are we?

ETIS, Information Processing and Systems Lab, is a joint research lab of CNRS (UMR 8051), ENSEA Cergy and University of Cergy-Pontoise.

ETIS is located in Cergy, 30kms NW from Paris.

ETIS research concerns four main domains:

  • MIDI: Multimedia Indexing and Data Integration
  • ICI: Information, Communications, Imagery
  • ASTRE: Architectures, Systems, Technologies for embedded REconfigurables units
  • NEURO: Neurocybernetics

Last publications

Hardware and software architecture facilitating the operation by the industry of dynamically adaptable heterogeneous embedded systems.

Laurent Gantel

This thesis aims to define software and hardware mechanisms helping in the management the Heterogeneous and dynamically Reconfigurable Systems-on-Chip (HRSoC). The heterogeneity is due to the presence of general processing units and reconfigurable IPs. Our objective is to provide to an application developer an abstracted view of this heterogeneity, regarding the task mapping on the available processing elements. [...]

Towards Semantic-Social Recommender Systems

Dalia Sulieman

In this thesis we propose semantic-social recommendation algorithms, that recommend an input item to users connected by a collaboration social network. These algorithms use two types of information: semantic information and social information.The semantic information is based on the semantic relevancy between users and the input item; while the social information is based on the users position and their type and quality of connections in the collaboration social network. [...]

Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers

Jérôme Fellus

This paper considers dimensionality reduction in large decentralized networks with limited node-local computing and memory resources and unreliable point-to-point connectivity (e.g peer-to-peer, sensors or ad-hoc mobile networks). We propose an asynchronous decentralized algorithm built on a Gossip consensus protocol that perform Principal Components Analysis (PCA) of data spread over such networks. [...]