Internationalisation et localisation

Résumé

vignette|350x350px| Capture d'écran des logiciels TDE, principalement localisés en chinois (traditionnel). En informatique, l'internationalisation et la localisation sont des moyens d'adapter des logiciels informatiques dans différentes langues, en prenant en compte les particularités régionales et exigences techniques d'un lieu cible. L'internationalisation est le processus de conception d'une application logicielle afin de l'adapter à différentes langues et régions sans modifications techniques. La localisation est le processus d'adaptation d'un logiciel internationalisé pour une région ou une langue spécifique en traduisant le texte et en ajoutant des composants spécifiques aux paramètres régionaux. La localisation (qui est parfois effectuée plusieurs fois, pour différents paramètres régionaux) utilise l'infrastructure ou la flexibilité fournie par l'internationalisation (qui est idéalement effectuée une seule fois avant la localisation, ou en tant que partie intégrante du développe

Source officielle

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

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Publications associées (30)

Network Neutrality Inference using Network Tomography

Ovidiu Sebastian Mara

Our work studies network neutrality, a property of communication networks which means that they treat all traffic the same, regardless of application, content provider or communication protocol. This is an important problem, because sometimes users suspect their ISPs of violating network neutrality, but apply inaccurate methods to check their suspicions, reaching incorrect conclusions. Prior non-neutrality detection methods either provide only detection, but lack localization capabilities; or also perform localization, but assume perfect measurements. For the latter, we show that, in practice, the measurement process can severely impact the results. We present a method that performs both non-neutrality detection and localization, using only end-to-end measurements, and assuming an imperfect measurement process. We identify the sources of measurement error that may affect our method, we address them, and evaluate the method extensively with simulations, emulations and experiments on the Internet. We also use our method in two studies, investigating suspicions that a set of ISPs prioritize speed-test traffic, or differentiate against BitTorrent traffic; despite circumstancial evidence that they do, we obtain reliable evidence to the contrary. Finally, we present the network emulator that we built to evaluate our method, hoping that it will be a useful tool in future research. We conclude that it is feasible to detect and localize network neutrality violations based solely on end-to-end measurements, without assuming a perfect measurement process; and that it is important that reasoning about network neutrality is based on reliable evidence of network behavior.

EPFL2018

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, , ,

Recent progress in electronics has allowed the construction of affordable mobile robots. This opens many new opportunities, in particular in the context of collective robotics. However, while several algorithms in this field require global localization, this capability is not yet available in low-cost robots without external electronics. In this paper, we propose a solution to this problem, using only approximate dead-reckoning and infrared sensors measuring the grayscale intensity of a known visual pattern on the ground. Our approach builds on a recursive Bayesian filter, of which we demonstrate two implementations: a dense Markov Localization and a particle-based Monte Carlo Localization. We show that both implementations allow accurate localization on a large variety of patterns, from pseudo-random black and white matrices to grayscale images. We provide a theoretical estimate and an empirical validation of the necessary traveled distance for convergence. We demonstrate the real-time localization of a Thymio II robot. These results show that our system solves the problem of absolute localization of inexpensive robots. This provides a solid base on which to build navigation or behavioural algorithms.

SPRINGER INTERNATIONAL PUBLISHING AG2019

Chargement

SegMap: Segment-based mapping and localization using data-driven descriptors

Andrei Cramariuc, Roland Siegwart

Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.

2019

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