Partial Match Queries in Two-Dimensional Quadtrees : a Probabilistic Approach

We analyze the mean cost of the partial match queries in random two-dimensional quadtrees. The method is based on fragmentation theory. The convergence is guaranteed by a coupling argument of Markov chains, whereas the value of the limit is computed as the fixed point of an integral equation

Enhanced light emission from Carbon Nanotubes integrated in silicon micro-resonator

Single-wall carbon nanotube are considered a fascinating nanomaterial for photonic applications and are especially promising for efficient light emitter in the telecommunication wavelength range. Furthermore, their hybrid integration with silicon photonic structures makes them an ideal platform to explore the carbon nanotube instrinsic properties. Here we report on the strong photoluminescence enhancement from carbon nanotubes integrated in silicon ring resonator circuit under two pumping configurations: surface-illuminated pumping at 735 nm and collinear pumping at 1.26 {\mu}m. Extremely efficient rejection of the non-resonant photoluminescence was obtained. In the collinear approach, an emission efficiency enhancement by a factor of 26 has been demonstrated in comparison with classical pumping scheme. This demonstration pave the way for the development of integrated light source in silicon based on carbon nanotubes

Les remontées mécaniques et les technologies de l’information et de la communication

Ce travail a pour but premier de définir les meilleures pratiques liées aux technologies de l’innovation et de la communication dans le domaine des remontées mécaniques. Le second objectif est de proposer un plan d’actions à son mandant, la Société des remontées mécaniques de Nendaz et Veysonnaz, pour lui permettre de perfectionner sa communication digitale. Pour répondre à ces objectifs, les hypothèses reposent sur une revue littéraire définissant le contexte actuel et les changements auxquels sont confrontés les sociétés de remontées mécaniques, notamment en Valais. Par la suite, des entretiens avec des représentants de stations valaisannes permettent de connaître l’offre digitale mise en place dans les domaines respectifs. Ces dernières sont comparées avec d’autre stations internationales à l’aide d’un benchmarking. Finalement, un examen des meilleures pratiques renseigne des possibilités d’amélioration dans ce secteur

A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements

International audienceThe first stage in any control system is to be able to accurately estimate the system's state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems. Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error. Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, that of Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), Monte Carlo Markov Chain (MCMC), and the original Box Particle Filter (BPF). The algorithm outperforms existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty. The BRPF reduces the computational load by 73% and 90% for SIR-PF and MCMC, respectively, with similar RMSE values. This work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods.The first stage in any control system is to be able to accurately estimate the system’s state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems.Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error.Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, the Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), the Markov Chain Monte Carlo approach (MCMC), and the original Box Particle Filter (BPF). The algorithm is demonstrated to outperform existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty.The BRPF yields a computational load reduction of 73% with respect to the SIR-PF and of 90% with respect to MCMC for similar RMSE orders of magnitude. The present work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods

Affine Approximation for Direct Batch Recovery of Euclidean Motion From Sparse Data

We present a batch method for recovering Euclidian camera motion from sparse image data. The main purpose of the algorithm is to recover the motion parameters using as much of the available information and as few computational steps as possible. The algorithmthus places itself in the gap between factorisation schemes, which make use of all available information in the initial recovery step, and sequential approaches which are able to handle sparseness in the image data. Euclidian camera matrices are approximated via the affine camera model, thus making the recovery direct in the sense that no intermediate projective reconstruction is made. Using a little known closure constraint, the FA-closure, we are able to formulate the camera coefficients linearly in the entries of the affine fundamental matrices. The novelty of the presented work is twofold: Firstly the presented formulation allows for a particularly good conditioning of the estimation of the initial motion parameters but also for an unprecedented diversity in the choice of possible regularisation terms. Secondly, the new autocalibration scheme presented here is in practice guaranteed to yield a Least Squares Estimate of the calibration parameters. As a bi-product, the affine camera model is rehabilitated as a useful model for most cameras and scene configurations, e.g. wide angle lenses observing a scene at close range. Experiments on real and synthetic data demonstrate the ability to reconstruct scenes which are very problematic for previous structure from motion techniques due to local ambiguities and error accumulation

Measuring the link between class and wealth in Europe

Economists and sociologists have often adopted different approaches to measuring inequality. Drawing on a new study, Nicolas Duvoux, Adrien Papuchon and Senmiao Yang attempt to bridge this gap by analysing wealth and income distributions among occupational groups in five European countries