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A mesh is a barrier made of connected strands of metal, fiber, or other flexible or ductile materials. A mesh is similar to a web or a net in that it has many attached or woven strands. Types

A plastic mesh may be extruded, oriented, expanded, woven or tubular. It can be made from polypropylene, polyethylene, nylon, PVC or PTFE.
A metal mesh may be woven, knitted, welded, expanded, sintered, photo-chemically etched or electroformed (screen filter) from steel or other metals.
In clothing, mesh is loosely woven or knitted fabric that has many closely spaced holes. Knitted mesh is frequently used for modern sports jerseys and other clothing like hosiery and lingerie
A mesh skin graft is a skin patch that has been cut systematically to create a mesh. Meshing of skin grafts provides coverage of a greater surface area at the recipient site, and also allows for the egress of serous or sanguinous fluid. However, it results in a rather pebbled appearance upon healing that

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Résumé

A mesh is a barrier made of connected strands of metal, fiber, or other flexible or ductile materials. A mesh is similar to a web or a net in that it has many attached or woven strands. Types

A plastic mesh may be extruded, oriented, expanded, woven or tubular. It can be made from polypropylene, polyethylene, nylon, PVC or PTFE.
A metal mesh may be woven, knitted, welded, expanded, sintered, photo-chemically etched or electroformed (screen filter) from steel or other metals.
In clothing, mesh is loosely woven or knitted fabric that has many closely spaced holes. Knitted mesh is frequently used for modern sports jerseys and other clothing like hosiery and lingerie
A mesh skin graft is a skin patch that has been cut systematically to create a mesh. Meshing of skin grafts provides coverage of a greater surface area at the recipient site, and also allows for the egress of serous or sanguinous fluid. However, it results in a rather pebbled appearance upon healing that

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Un vêtement est un article d’habillement servant à couvrir une partie du corps humain. Il est le plus souvent en tissu mais les matériaux utilisés pour sa fabrication tendent à se diversifier au fil

vignette|La Fileuse, William Bouguereau, . vignette|Détail d'un objet tissé. Un textile est un matériau susceptible d'être tissé ou tricoté. Initialement, il désigne donc un matériau qui peut se divi

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CIVIL-510: Quantitative imaging for engineers

First 2 courses are Tuesday 16-19h!This course will arm students with knowledge of different imaging techniques for practical measurements in many different fields of civil engineering. Modalities will range from satellite and drone imaging all the way down to x-ray microscopy with practical session

MSE-101(a): Materials:from chemistry to properties

Ce cours permet l'acquisition des notions essentielles relatives à la structure de la matière, aux équilibres et à la réactivité chimique en liaison avec les propriétés mécaniques, thermiques, électriques et magnétiques des matériaux.

EE-105: Electrical engineering science and technology (Spring)

Ce cours propose une introduction à l'électrotechnique. Les lois fondamentales de l'électricité et différents composants d'un circuit électrique linéaire seront étudiés. L'analyse élémentaire des circuits en régime continu et sinusoidal sera aussi couverte.

Rate-distortion optimal mesh simplification for communications

Laurent Balmelli

The thesis studies the optimization of a specific type of computer graphic representation: polygon-based, textured models. More precisely, we focus on meshes having 4-8 connectivity. We study a progressive and adaptive representation for textured 4-8 meshes suitable for transmission. Our results are valid for 4-8 meshes built from matrices of amplitudes, or given as approximations of a subdivision surface. In the latter case, the models can have arbitrary topology. In order to clarify our goals, we first describe a transmission system for computer graphics models (Chapter 1). Then, we review approximation techniques (Chapter 2) and study the computational properties of 4-8 meshes (Chapter 3). We provide an efficient method to store and access our dataset (Chapter 4). We address the problem of 4-8 mesh simplification and give an efficient θ(n log n) algorithm to compute progressive and adaptive representations of 4-8 meshes using global error (Chapter 5). We study the joint optimization of mesh and texture (Chapter 6). Finally, we conclude and give future research directions (Chapter 7).

EPFL2002

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Methodology for 3-D Substrate Network Extraction for SPICE Simulation of Parasitic Currents in Smart Power ICs

Pietro Buccella, Maher Kayal, Jean-Michel Sallese, Camillo Stefanucci

A 3-D simulation of substrate currents is crucial to analyze parasitic coupling effects due to minority carrier injection in smart power ICs. In this paper, a substrate parasitic extraction methodology is introduced by dividing the IC layout into elementary elements to solve the continuity equation for minority carriers in the volume based on the finite-difference method. A substrate parasitic network is derived from the mesh generated through the existing mixed-signal design flow. The induced substrate model is included in circuit simulators such as SPICE to predict the effects of substrate couplings during the design phase. Furthermore, this analysis enables optimization of layout with minimal parasitic effects. By linking the substrate model to the active components, the couplings between the integrated circuit with the substrate parasitic currents can be analyzed during circuit simulations. Simulations and measurements on an high voltage driver reveal consistent results and therefore confirm the validity of the method. Therefore, the approach developed herein is effective to predict parasitic couplings due the injection of minority carriers.

Ieee-Inst Electrical Electronics Engineers Inc2016

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How to teach neural networks to mesh: Application on 2-D simplicial contours

François Avellan, Pascal Clausen, Alexis Papagiannopoulos

A machine learning meshing scheme for the generation of 2-D simplicial meshes is proposed based on the predictions of neural networks. The data extracted from meshed contours are utilized to train neural networks which are used to approximate the number of vertices to be inserted inside the contour cavity, their location, and connectivity. The accuracy of the scheme is evaluated by comparing the quality of the mesh generated by the neural networks with that generated by a reference mesher. Based on an element quality metric, after conducting tests on contours for a various number of edges, the results show a maximum average deviation of 15.2% on the mean quality and 27.3% on the minimum quality between the elements of the meshes generated by the scheme and the ones generated from the reference mesher; the scheme is able to produce good quality meshes that are suitable for meshing purposes. The meshing scheme is also applied to generate larger scale meshes with a recursive implementation. The findings encourage the adaption of the scheme for 3-D mesh generation.

2021

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