Orange S.A.

About: Orange S.A. is a company organization based out in Paris, France. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 6735 authors who have published 9190 publications receiving 156440 citations. The organization is also known as: Orange SA & France Télécom.

Strong Purcell effect for InAs quantum boxes in three-dimensional solid-state microcavities

Abstract: A strong enhancement of the spontaneous emission rate (Purcell effect) has been observed for self-assembled InAs/GaAs quantum boxes inserted in GaAs-based pillar microcavities (/spl times/5) and microdisks (/spl times/15) using time-resolved as well as c.w. photoluminescence experiments. We show that the magnitude of the Purcell effect can be quantitatively understood by considering both the Purcell figure of merit F/sub p/ of such cavities (F/sub p//spl Gt/1) and the spatial/spectral distribution of the inhomogeneous collection of atom-like emitters. These results open the way to the development of single-photon devices such as photon-guns or photon-turnstiles, able to emit photons one-by-one in a deterministic way.

Strong-coupling regime for quantum boxes in pillar microcavities: Theory

Abstract: A study of quantum box transitions coupled to three-dimensionally confined photonic modes in pillar microcavities is presented, focusing on the conditions for achieving a vacuum-field Rabi splitting. For a single InAs quantum box the oscillator strength is a factor of ten too small for being in strong coupling. A calculation of exciton states localized to monolayer fluctuations in quantum wells leads to much larger values of the oscillator strengths. Single localized excitons embedded in state-of-the-art micropillars can be in strong-coupling regime with a vacuum-field Rabi splitting.

The FRACTAL component model and its support in Java: Experiences with Auto-adaptive and Reconfigurable Systems

Abstract: This paper presents F RACTAL, a hierarchical and reflective component model with sharing. Components in this model can be endowed with arbitrary reflective capabilities, from plain black-box objects to components that allow a fine-grained manipulation of their internal structure. The paper describes J ULIA, a Java implementation of the model, a small but efficient runtime framework, which relies on a combination of interceptors and mixins for the programming of reflective features of components. The paper presents a qualitative and quantitative evaluation of this implementation, showing that component-based programming in F RACTAL can be made very efficient. Copyright © 2006 John Wiley & Sons, Ltd.

The 2005 PASCAL visual object classes challenge

Abstract: The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

A fast and accurate face detector based on neural networks

Abstract: Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a neural network model: the constrained generative model (CGM). Generative, since the goal of the learning process is to evaluate the probability that the model has generated the input data, and constrained since some counter-examples are used to increase the quality of the estimation performed by the model. To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows us to apply this detector to a real world application: the indexing of images and videos.