Dynamics and universal scaling law in geometrically-controlled sessile drop evaporation

The evaporation of a liquid drop on a solid substrate is a remarkably common phenomenon. Yet, the complexity of the underlying mechanisms has constrained previous studies to sphericallysymmetric configurations. Here we investigate well-defined, non-spherical evaporating drops of pure liquids and binary mixtures. We deduce a universal scaling law for the evaporation rate valid for any shape and demonstrate that more curved regions lead to preferential localized depositions in particle-laden drops. Furthermore, geometry induces well-defined flow structures within the drop that change according to the driving mechanism. In the case of binary mixtures, geometry dictates the spatial segregation of the more volatile component as it is depleted. Our results suggest that the drop geometry can be exploited to prescribe the particle deposition and evaporative dynamics of pure drops and the mixing characteristics of multicomponent drops, which may be of interest to a wide range of industrial and scientific applications

Application of an improved version of JPEG 2000 based on mathematical morphplogy to médical images

International audienceThe JPEG2000 is the latest presently image compression standard presented by international standards organization, which enables compressed image at very low bitrates. At a very low bitrate, the reconstructed image provides bad visual quality. At this rate, a variety of artifacts can be distinguished, and the reconstructed image provides bad visual quality. In order to enhance the visual quality of reconstructed image, we propose a compression method based on morphological filtering. This method decomposes the image data into low and high frequency subimages. These subimages are compressed by JPEG2000 encoder by assigning different bitrates to each subimage. Through decoding by the morphological filter, proposed method, applied to still medical images provides better visual quality over direct compression of the image data

Artifact reduction in JPEG2000 compressed images at low bit-rate using mathematical morphology filtering

International audienceJPEG2000 is known as an efficient standard to encode images. However, at very low bit-rates, artifacts or distortions can be observed in decoded images. In order to improve the visual quality of decoded images and make them perceptually acceptable, we propose in this work a new preprocessing scheme. This scheme consists in preprocessing the image to be encoded using a nonlinear filtering, considered as a prior phase to JPEG 2000 compression. More specifically, the input image is decomposed into low- and high-frequency sub-images using morphological filtering. Afterward, each sub-image is compressed using JPEG2000, by assigning different bit-rates to each sub-image. To evaluate the quality of the reconstructed image, two different metrics have been used, namely (a) peak signal to noise ratio, to evaluate the visual quality of the low-frequency sub-image, and (b) structural similarity index measure, to evaluate the visual quality of the high-frequency sub-image. Based on the reconstructed images, experimental results show that, at low bit-rates, the proposed scheme provides better visual quality compared to a direct use of JPEG2000 (excluding any preprocessing)

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning",

International audienc

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning

International audienceFault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity

Simulation dynamique d’un moteur : cas du Stirling de type gamma

La modélisation et la simulation numérique

Self-Diagnosis technique for Virtual Private Networks combining Bayesian Networks and Case-Based Reasoning

International audienceFault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology

Empirical mode decomposition for online handwritten signature verification

International audienceThe handwritten signature is a biometric method used to verify a person's identity. This study lies within the scope of an online handwritten signature verification system, in which a signature is modelled by an analytical approach based on the empirical mode decomposition. The organised system is tested on the SVC2004 task1 and MYCT-100 databases. The implemented evaluation protocol shows the importance of the adopted method and allows obtaining an equal error rate of 1.83 and 2.23% for the SVC2004 task1 and the MYCT-100 databases, respectively

LTD Stirling engine with regenerator. Numerical and experimental study

In this paper, a model of a low temperature difference (LTD) Stirling engine with regenerator is presented. The equations governing the heat transfer and the compressible fluid dynamics are solved numerically as a coupled system, including the ideal gas state equation, Navier Stokes equations and energy balance. The engine cycle induces flow compression, expansion and regeneration in free volumes and through porous media. The present developed CFD model makes possible to obtain the instantaneous values of the physical parameters (pressure, temperature, velocity, density, etc.). With these obtained values, the continuous p-V cycle can be analysed which leads to the mechanical work calculation. The results of the simulation concerning an engine with regeneration is compared to those obtained in previous work by an engine without regeneration and validated with experimental data obtained under similar conditions without regeneration. The preliminary results show the important improvement due to the engine regeneration operation and the related regenerator porosity effect allowing the reduction of the pressure drop and viscous dissipation