Étude et caractérisation d’un canal de propagation pour les réseaux VANET

Dans les réseaux VANET, la communication se fait entre les véhicules (V2V) du réseau grâce à un système sans-fil sans infrastructure fixe. Cependant, elle ne peut être faite qu’à l’intérieur d’une distance correspondant à la portée radio qui est généralement de petite taille. Il est donc nécessaire d’avoir recours aux techniques du multi-saut afin de véhiculer l’information. Un grand nombre de travaux sur les réseaux VANET utilisent des modèles de propagation simples, tels que le modèle en espace libre (freespace) ou le modèle de Friis. Toutefois, ces deux modèles demeurent loin de la réalité lorsqu’ils sont appliqués à la prédiction des communications inter-véhiculaires. Les résultats prédits par ces deux modèles sont souvent inutilisables dans cette situation et par conséquent, une modélisation plus réaliste du canal est devenue un enjeu crucial en ce qui concerne les réseaux VANET. La présente étude se penche sur l’échange de données dans un réseau VANET. Celle-ci se déroule dans un environnement minier où interviennent divers paramètres. Il est donc nécessaire de prendre en compte l’impact du canal de propagation propre à cet environnement. Cette étude se déroule en deux temps. La première étape consiste à étudier le canal de propagation dans le cas statique. Pour cela, un analyseur de réseau a été utilisé. Ce dernier réalise les mesures dans le domaine fréquentiel en effectuant un balayage de fréquences sur toute la largeur de bande choisie. Ces mesures sont ensuite transformées dans le domaine temporel utilisant la transformée de Fourier. Avec cette technique, nous avons réussi à couvrir des distances allant jusqu’à 130 mètres. La deuxième étape vise l’étude du canal dans le cas dynamique. Pour cela, nous avons placé un analyseur de spectre dans un véhicule qui bouge le long de la galerie en prenant des mesures correspondant aux différentes vitesses du véhicule

Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning

A fully autonomous car does not yet exist. But the vehicles have continued to gain in range in recent years. The main reason? The dazzling progress made in artificial intelligence, in particular by specific algorithms, known as machine learning. These example-based machine learning methods are used in particular for recognizing objects in photos. The algorithms developed for the detection and identification must respond robustly to the various disturbances observed and take into account the variability in the signs’ appearance. Variations in illumination generate changes in apparent color, shadows, reflections, or backlighting. Besides, geometric distortions or rotations may appear depending on the viewing angle and the panels’ scale. Their appearance may also vary depending on their state of wear and possible dirt, damage. In this work, to improve the accuracy of detection and classification of sign road partially covered by snow, we use the Fast Region-based Convolutional Network method (Fast R-CNN) model. To train the detection model, we collect an image dataset composed of multi-class of road signs. Our model can simultaneously multi-class of a road sign in nearly real-time

Empirical radio channel characterization at 5.9 GHz for vehicle-to-infrastructure communication

The uses of a vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications in the mining industry are expected to open significant opportunities for collecting and exchanging data. However, besides enabling the service of these technologies, radio channel propagation should be investigated. In this paper, we present extensive channel measurement and characterization at the 5.9 GHz dedicated short-range communications (DSRC) frequency band. The measurements were performed in a real underground mine gallery. The primary purpose of this study is to characterize the large-scale and small-scale fading. We provide results for the root-mean-square (RMS) delay spread and Kurtosis of the received power for both stationary and moving car scenarios. We conclude the paper by identifying the under-researched aspects of the vehicular propagation and channel modeling in underground mines

In underground vehicular radio channel characterization

Vehicular communication is characterized by a dynamic environment and high mobility. In this paper we present a shadow fading model targeting system simulations based on real measurements performed in underground gallery. As first results, we present in this paper the delay spread statistics for each investigated environment. We also study the large-scale, small-scale fading and extract some time channel parameters such as root-mean-square (RMS) delay for a realistic underground propagation environment at 5.9 GHz. Since there are so far few published results for these confined environments, the results obtained can be useful for the deployment of vehicular-to-vehicular (V2V) and vehicular-to-infrastructure (V2I) communication systems inside underground mines galleries

Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning

A fully autonomous car does not yet exist. But the vehicles have continued to gain in range in recent years. The main reason? The dazzling progress made in artificial intelligence, in particular by specific algorithms, known as machine learning. These example-based machine learning methods are used in particular for recognizing objects in photos. The algorithms developed for the detection and identification must respond robustly to the various disturbances observed and take into account the variability in the signs’ appearance. Variations in illumination generate changes in apparent color, shadows, reflections, or backlighting. Besides, geometric distortions or rotations may appear depending on the viewing angle and the panels’ scale. Their appearance may also vary depending on their state of wear and possible dirt, damage. In this work, to improve the accuracy of detection and classification of sign road partially covered by snow, we use the Fast Region-based Convolutional Network method (Fast R-CNN) model. To train the detection model, we collect an image dataset composed of multi-class of road signs. Our model can simultaneously multi-class of a road sign in nearly real-time

Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study

Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time