Microsoft Office

Résumé

Microsoft Office est une suite bureautique propriété de la société Microsoft qui fonctionne avec les plates-formes fixes et mobiles. Elle s'installe sur ordinateur et fournit une suite de logiciels comme : Word, Excel, PowerPoint, OneNote, Outlook, Access et/ou Publisher selon les suites choisies. Microsoft Office propose une version web qui s'utilise directement en ligne depuis un navigateur web. Office existe également pour les appareils mobiles : Windows Phone, iPhone, iPad, téléphones ou tablettes Android ; on l'appelle alors selon l'appareil utilisé : Office Mobile, Office pour iPad, Office pour iPhone ou Office pour Android. En 2015, Microsoft annonce que Office est utilisé par plus de 1,2 milliard de personnes. Historique Développement Microsoft Office fait son apparition en 1989, et était au commencement une sorte de paquet comprenant des applications qui étaient vendues séparément auparavant. L’avantage de la suite sur les logiciels séparés était le mo

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Microsoft Outlook (officiellement Microsoft Office Outlook) est un gestionnaire d'informations personnelles et un client de courrier électronique propriétaire édité par Microsoft. Ce logiciel informat

Microsoft Word est un logiciel de traitement de texte publié par Microsoft. La version la plus récente est Word 2021. Sa première version a été distribuée en 1983 sous le nom de Multi-Tool Word (« Mu

Microsoft Corporation ( ) est une multinationale informatique et micro-informatique américaine, fondée en 1975 par Bill Gates et Paul Allen. Microsoft fait partie des principales capitalisations bours

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Iterative Adaptation is a novel approach to adaptation for resource-limited mobile and wireless environments that supports powerful application-specific adaptations without requiring modifications to the application's source code. Common productivity applications, such as browsers, word processors, and presentation tools, export APIs that allow external applications to control their operation. The novel premise in iterative adaptation is that these APIs are sufficient to support a wide range of adaptation policies for applications running on resource-limited devices. In addition to allowing adaptation without having to change the application's source code, this approach has a unique combination of advantages. First, it supports centralized management of resources across multiple applications. Second, it makes it possible to modify application behavior after the application has been deployed. This paper evaluates the extent to which existing APIs can be used for the purposes of adapting document-based applications to run on bandwidth-limited devices. In particular, we implement a large number of bandwidth adaptations for applications from the Microsoft Office and the OpenOffice productivity suites and for Internet Explorer. Although we find limitations in their APIs, we are able to implement many adaptation policies without much complexity and with good performance. Moreover, iterative adaptation achieves performance similar to an approach that implements adaptation by modifying the application, while requiring only a fraction of the coding effort.

2005

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Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

Ngoc Hoan Do, Quoc Viet Hung Nguyen, Le Hung Tran, Zhixian Yan

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analysis. Stops are important parts of tra- jectories, such as “working at office”, “shopping in a mall”, “waiting for the bus”. There are several methods such as velocity, clustering, density algorithms being designed to discover stops. However, existing works focus on well-defined trajectories like movement of vehicle and taxi, not working well for heterogeneous cases like diverse and sparse trajectories. On the contrary, our paper addresses three main challenges: (1) provide a robust clustering-based method to discover stops; (2) discover both shared stops and personalized stops, where shared stops are the common places where many trajectories pass and stay for a while (e.g. shopping mall), whilst personalized stops are individual places where user stays for his/her own purpose (e.g. home, office); (3) further build stop hierarchy (e.g. a big stop like EPFL campus and a small stop like an office building). We evaluate our approach with several diverse and spare real-life GPS data, compare it with other methods, and show its better data abstraction on trajectory.

2011

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Statistical Analysis of Impulse Radio Ultra-Wideband Multi User Interference based on Measurements

John Farserotu, Jean-Yves Le Boudec, Hai Zhan

Multiuser interference (MUI) statistical models for impulse radio ultra-wideband (IR-UWB) systems can be an important in providing an accurate estimate of the channel state. As such, it can have a major impact on the overall system performance. In the literature, MUI in the time domain is often approximated with Middleton class A and Gaussian mixture models. We use measurements from an IR-UWB testbed under office Line of Sight (LOS) environments to assess the validity of these models. We also analysis the statistical properties of MUI in the frequency domain. We expect these findings to form the basis for useful interference models.

IEEE2008

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