Étude d'un réseau de capteur UWB pour la localisation et la communication dans un environnement minier

Le jour n'est peut-être pas très loin où une mine pourra compter sur un système de communication sans fil pour échanger des données, transmettre des informations ou localiser des travailleurs dans le cas d'une activité normale ou en cas d'urgence. Au point de vue de la sécurité, un système de communications sans fil aurait l'avantage de localiser en temps réel un travailleur ou un engin. Les travailleurs se déplacent sans cesse dans une mine. Avec une technologie sans fil permanente, on pourrait localiser les personnes de manière relativement précise. Même en cas d'éboulement, avec une technologie adaptée, il serait possible de savoir où se trouve la personne en détresse. Notre travail de recherche s'inscrit dans la perspective du développement d'un réseau de capteurs ultra large bande (UWB) pour deux applications : l'aide à la radiolocalisation et l'extension du réseau de capteurs sans fil dans la mine. Cette étude est focalisée sur trois aspects. La première partie de notre étude consiste à étudier tous les problèmes reliés à la radiolocalisation dans la mine. Vue l'importance de cette application, nous avons mis en oeuvre un réseau de capteurs en tenant compte d'un futur déploiement dans la mine. La technologie utilisée repose sur la technologie ultra large bande. Comme il n'existe pas de travaux qui traitent ce genre de problèmes, nous avons commencé notre étude par une caractérisation du canal UWB dans les mines souterraines. Pour atteindre ces objectifs, plusieurs campagnes de mesure sur site (mine expérimentale) ont été menées. Nous sommes parvenus à une modélisation du canal de propagation et à avancer des recommandations pour aider au dimensionnement d'un réseau de capteurs dans ce type d'environnement. Dans la première partie, le but est d'étudier le problème de radiolocalisation avec les réseaux de capteurs. Notre scénario proposé serait de placer des capteurs sur chaque agent (mineur, engin). On suppose que chaque noeud (agent) qui circule à travers un réseau d'ancre maillé (déjà déployé), va extraire des informations de distance (en utilisant le critère de temps d'arrivée), ensuite il va utiliser un algorithme de positionnement distribué afin de déterminer sa propre position. Lors de cette partie nous avons aussi étudié quelques estimateurs cohérents et non-cohérents du temps d'arrivée. La caractérisation de l'erreur de mesure utilisant le temps d'arrivée dans un environnement minier a été aussi évaluée. Enfin, dans la dernière partie, nous avons analysé par simulations un déploiement d'un réseau de capteurs UWB ad hoc dans la mine. Nous avons choisi d'adopter une approche théorique afin d'évaluer les performances de cette configuration. Une conception intercouche pour un routage optimal a été étudiée. Nous avons utilisé la couche physique/réseau afin de minimiser l'énergie consommée lors de l'acheminement du données

MAC Protocols for Industrial Delay-Sensitive Applications in Industry 4.0: Exploring Challenges, Protocols, and Requirements

The Industrial Internet of Things (IIoT) is expected to enable Industry 4.0 through the extensive deployment of low-power devices. However, industrial applications require, most of the time, high reliability close to 100% and low end-to-end delays. This corresponds to very challenging objectives in wireless (lossy) environments. This delay can be disastrous in time-sensitive industrial IoT deployments where immediate detection and actions impact security, safety, and machine failures. With an efficient MAC protocol, data will be provided quickly to enable the IoT to be fully effective for mission-critical applications. Efficient medium sharing is even more difficult in IIoT due to ultra-low latency, high reliability, and high quality of service (QoS) compared to best-effort for IoT. This article does not survey all existing MAC protocols for IoTs, which was already done in other works. The goal of this paper is to analyze existing MAC protocols that are more suitable for IIoT

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

Entropy-based algorithms for signal processing

Entropy, the key factor of information theory, is one of the most important research areas in computer science. Entropy coding informs us of the formal limits of today’s storage and communication infrastructure. Over the last few years, entropy has become an important trade-off measure in signal processing. Entropy measures especially have been used in image and video processing by applying sparsity and are able to help us to solve several of the issues that we are currently facing. As the daily produced data are increasing rapidly, a more effective approach to encode or compress the big data is required. In this sense, applications of entropy coding can improve image and video coding, imaging, quality assessment in agricultural products, and product inspection, by applying more effective coding approaches. In light of these and many other challenges, a Special Issue of Entropy-Based Algorithms for Signal Processing has been dedicated to address the current status, challenges, and future research priorities for the entropy of signal processing

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented

Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue

SVC device optimal location for voltage stability enhancement based on a combined particle swarm optimization-continuation power flow technique

The increased power system loading combined with the worldwide power industry deregulation requires more reliable and efficient control of the power flow and network stability. Flexible AC transmission systems (FACTS) devices give new opportunities for controlling power and enhancing the usable capacity of the existing transmission lines. This paper presents a combined application of the particle swarm optimization (PSO) and the continuation power flow (CPF) technique to determine the optimal placement of static var compensator (SVC) in order to achieve the static voltage stability margin. The PSO objective function to be maximized is the loading factor to modify the load powers. In this scope, two SVC constraints are considered: the reference voltage in the first case and the total reactance and SVC reactive power in the second case. To test the performance of the proposed method, several simulations were performed on IEEE 30-Bus test systems. The results obtained show the effectiveness of the proposed method to find the optimal placement of the static var compensator and the improvement of the voltage stability

Hybrid Deep Learning Vision-based Models for Human Object Interaction Detection by Knowledge Distillation

People hope that computers can be in constant intelligence development. Just like humans, they can ”see” the world and ”recognize” a visual event. We propose an approach based on computer vision methods to recognize Human-Object interaction(HOI). The technique stands on aggregating significant contextual features Human-Object interactions and scene recognition. We design a branch architecture consisting of the main branch for HOI detection and a supplementary branch for scene recognition. We explore the deep learning models through the knowledge distillation method and the Cross Branch Integration mechanism for encoding models into graph neural network architecture. We construct a knowledge graph to merge between high-level context information. When trained collaboratively, those models allow computing efficiency, strong context knowledge

Optimization of UHF RFID five-slotted patch tag design using PSO algorithm for biomedical sensing systems

In this paper, a new flexible wearable radio frequency identification (RFID) five-shaped slot patch tag placed on the human arm is designed for ultra-high frequency (UHF) healthcare sensing applications. The compact proposed tag consists of a patch structure provided with five shaped slot radiators and a flexible substrate, which minimize the human body’s impact on the antenna radiation performance. We have optimized our designed tag using the particle swarm optimization (PSO) method with curve fitting within MATLAB to minimize antenna parameters to achieve a good return loss and an attractive radiation performance in the operating band. The PSO-optimized tag’s performance has been examined over the specific placement in some parts of the human body, such as wrist and chest, to evaluate the tag response and enable our tag antenna conception in wearable biomedical sensing applications. Finally, we have tested the robustness of this tag by evaluating its sensitivity as a function of the antenna radiator placement over the ground plane or by shaping the ground plane substrate for the tag’s position from the human body. Our numerical results show an optimal tag size with good matching features and promising read ranges near the human body