SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation

A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models and datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. Experimental results obtained over image recognition datasets of varied natures show the efficiency of these new methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasksComment: The project is available at https://github.com/hammoudiproject/SuperpixelGridMask

SHREC2020 track:Multi-domain protein shape retrieval challenge

Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the proteinand species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost

Surface-based protein domains retrieval methods from a SHREC2021 challenge

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval)International audienceProteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online

Réseaux pair-à-pair et simulation distribuée<br />Application à la simulation multiagent

abstractMes travaux de recherche s'intéressent à la simulation distribuée et aux possibilités offertes par le paradigme pair-à-pair. Le modèle pair-à-pair est généralement asymétrique. Il est constitué de réseaux de machines totalement hétérogènes avec des configurations matérielles et des débits de connexion très variables. Les nœuds constituant un tel réseau sont hautement dynamiques et organisés de manière totalement décentralisée.Une étude comparative entre les deux paradigmes grille de calcul et pair-à-pair est présentée. Elle révèle que la taxonomie des systèmes de l'informatique répartie est simpliste et sommaire. Nous en présentons une autre plus raffinée. Elle prend en compte de nouveaux paramètres pouvant aider l'utilisateur à faire un choix entre les grilles de calculs et le modèle pair-à-pair.Ces nouveaux paramètres sont pris en compte dans le développement de la plate-forme PHAC (P2P-based Highly Available Computing Framework) présentée dans ce mémoire. Il s'agit d'un environnement de programmation de haut niveau qui permet de construire un réseau pair-à-pair hautement disponible et adapté aux besoins de l'utilisateur. La plate-forme tient compte des spécificités des réseaux pair-à-pair telles que la volatilité et l'hétérogénéité des pairs. La robustesse du réseau est étudiée selon une voie expérimentale. Les résultats montrent que le réseau est suffisamment robuste pour distribuer des simulations de manière transparente

Réseaux pair-à-pair et simulation distribuée (application à la simulation multiagent)

ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

Simultaneous temperature sensing using distributed cascading fiber Bragg grating-based single-ended Brillouin optical time-domain analyzer

International audienceWe report a novel method of simultaneous distributed temperature sensing in optical fibers. The method is based on the cascading fiber Bragg grating (FBG) single-ended Brillouin optical time-domain analyzer (BOTDA). This proposal applies the technology of cascading FBGs to improve the signal-to-noise ratio of the single-ended BOTDA. Experimental results show achievement of a 50 km sensing range with 3 °C temperature resolution and 5 m spatial resolution

Technique de trilatération pour la localisation Indoor

International audienceDans cette communication, quelques techniques de trilatération pour la localisation sont présentés en 2D et3D. Plusieurs méthodes à base de kNN (Méthode des k plus proches voisins), et d’apprentissage automatique sont pré-sentées. De plus, une simulation est réalisée pour localiser un récepteur selon les RSSIs (Indication de la force du signalreçu) detectées par le récepteur. Dans l’avenir, pour mieux résoudre le problème de localisation en 3D, une solutiondynamique sera proposée selon une trajectoire du récepteur. La racine de l'écart quadratique moyen (RMSE) sera analy-sée pour vérifier l’exactitude de la localisation

A Survey of Vehicle Localization: Performance Analysis and Challenges

Vehicle localization plays a crucial role in ensuring the safe operation of autonomous vehicles and the development of intelligent transportation systems (ITS). However, there is insufficient effort to compare the performance and challenges of different vehicle localization algorithms. This paper aims to address this gap by analyzing the comprehensive performance of existing advanced vehicle localization techniques and discussing their challenges. Firstly, we analyze the self-localization methods based on active and passive sensors. The results show that, the light detection and ranging (LiDAR) and vision-based localization techniques can reach high accuracy. However, they have high computational complexity. Only using the inertial measurement unit (IMU), global positioning system (GPS), radar, and ultrasonic sensors may not realize localization result with high accuracy. Then, we discuss V2X-based cooperative localization methods, analyze the multi-sensor based localization techniques and compare the comprehensive performance among all methods. Although the artificial intelligence (AI) techniques can effectively enhance the efficiency of vision-based localization algorithms, the high computational complexity still should be considered. In addition, since the IMU, GPS, radar, and ultrasonic sensors have good performance in terms of the availability, reliability, scalability, and cost-effectiveness, they can be used as auxiliary sensors to achieve good comprehensive performance through data fusion techniques. Finally, we propose the challenges of different techniques and look forward to future work

Collaborative Key Exchange System Based on Chinese Remainder Theorem in Heterogeneous Wireless Sensor Networks

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Formal Verification of Wireless Sensor Key Exchange Protocol Using AVISPA

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