Microsoft Visual Studio

Résumé

Microsoft Visual Studio est une suite de logiciels de développement pour Windows et mac OS conçue par Microsoft. La dernière version s'appelle Visual Studio 2022. Visual Studio est un ensemble complet d'outils de développement permettant de générer des applications web ASP.NET, des services web XML, des applications bureautiques et des applications mobiles. Visual Basic, Visual C++, Visual C# utilisent tous le même environnement de développement intégré (IDE), Visual Studio Code, qui leur permet de partager des outils et facilite la création de solutions faisant appel à plusieurs langages. Par ailleurs, ces langages permettent de mieux tirer parti des fonctionnalités du framework .NET, qui fournit un accès à des technologies clés simplifiant le développement d'applications web ASP et de services web XML grâce à Visual Web Developer. Durant sa conférence Connect() 2016, Microsoft a annoncé le portage de Visual Studio sur macOS, le système d'exploitation d'Apple. Langages support

Source officielle

https://fr.wikipedia.org/wiki/Microsoft_Visual_Studio

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Concepts associés (87)

Le .NET Framework (abr. NetFx) est un cadriciel (framework) pouvant être utilisé par un système d'exploitation Microsoft Windows et Microsoft Windows Mobile depuis la version 5 (.NET Compact Framewor

C# (C sharp en anglais britannique) est un langage de programmation orientée objet, commercialisé par Microsoft depuis 2002 et destiné à développer sur la plateforme Microsoft .NET

Visual Basic .NET est un langage de programmation à la syntaxe similaire à celle de Visual Basic 6. VB.NET permet de développer en .NET via Visual Studio, sur les systèmes d'exploitation Windows (98

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Cours associés (17)

We teach the fundamental aspects of analyzing and interpreting computer languages, including the techniques to build compilers. You will build a working compiler from an elegant functional language into the new web standard for portable binaries called WebAssembly ( https://webassembly.org/ )

The students study and apply fundamental concepts and algorithms of computer graphics for rendering, geometry synthesis, and animation. They design and implement their own interactive graphics programs.

The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-action background and describes the key programming techniques for achieving efficient VR applications

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Séances de cours associées (28)

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A Dynamically Scheduled HLS Flow in MLIR

Morten Borup Petersen

In High-Level Synthesis (HLS), we consider abstractions that span from software to hardware and target heterogeneous architectures. Therefore, managing the complexity introduced by this is key to implementing good, maintainable, and extendible HLS compilers. Traditionally, HLS flows have been built on top of software compilation infrastructure such as LLVM, with hardware aspects of the flow existing peripherally to the core of the compiler. Through this work, we aim to show that MLIR, a compiler infrastructure with a focus on domain-specific intermediate representations (IR), is a better infrastructure for HLS compilers. Using MLIR, we define HLS and hardware abstractions as first-class citizens of the compiler, simplifying analysis, transformations, and optimization. To demonstrate this, we present a C-to-RTL, dynamically scheduled HLS flow. We find that our flow generates circuits comparable to those of an equivalent LLVM-based HLS compiler. Notably, we achieve this while lacking key optimization passes typically found in HLS compilers and through the use of an experimental front-end. To this end, we show that significant improvements in the generated RTL are but low-hanging fruit, requiring engineering effort to attain. We believe that our flow is more modular and more extendible than comparable open-source HLS compilers and is thus a good candidate as a basis for future research. Apart from the core HLS flow, we provide MLIR-based tooling for C-to-RTL cosimulation and visual debugging, with the ultimate goal of building an MLIR-based HLS infrastructure that will drive innovation in the field.

2022

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Experiences with SUPER, a Database Visual Environment

1991

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WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection

Stéphane Bouquet, Tatjana Chavdarova, François Fleuret, Pascal Fua, Cijo Jose, Andrii Maksai

People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals. We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.

IEEE2018

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