Fast MicroSleep and Yawning Detections to Assess Driver’s Vigilance Level

Driver hypovigilance, often caused by fatigue and/or drowsiness, receives increasing attention in the last years; especially after it became evident that hypovigilance is a one of the major factor causing traffic accidents. Monitoring and detecting driver hypovigilance could contribute significantly to improve road traffic safety. This paper proposes fast methods to identify drowsiness and fatigue using respectively microsleep and yawning detections. In this study, the proposed scheme begins by a face detection using local Successive Mean Quantization Transform (SMQT) features and split up Sparse Network of Winnows (SNoW) classifier. After performing face detection, the novel approach for eye/mouth detection, based on Circular Hough Transform (CHT), is applied on eyes and mouth extracted regions. Our proposed methods works in real-time and yield a high detection rates whether for drowsiness or fatigue detections

Performance Analysis of Synthetic Mobility Models and Mobile Ad Hoc Routing Protocols

Routing protocols heavily influenced by the node motion applied. Many performance analyses are already done with a lot of flaws. But, they do not look to all influenced constraints. Sometimes, they evaluate routing protocols without taken into consideration mobility models. They often analyze them using one routing protocol. Whereas, Simulation time employed is too short. It mainly impacts performance metrics of many mobility models. Or usually, simulation area used is small. It influences the number of packets received. In this study, we aim to summarize all these several parameters into 90 different scenarios with an average of 1350 simulated files. We will combine some well-known mobility models with the most prominent mobile Ad hoc routing protocols in order to analyze their accurate behaviors in one experimental synthesis paper. That shows results of three performance metrics combined with five mobile ad hoc routing protocols under three synthetic mobility models. All these parameters are applied to two dissimilar simulation areas, a small one with (220 m x 220 m) and a large one with (1020 m x 1020 m). Basing on one exhaustive analysis with all these details like this paper; leads to well understand the accurate behaviors of routing protocols and mobility models used. By displaying the ability of every routing protocol to deal with some topology changes, as well as to ensure network performances

Schéma multirésolution d'estimation d'un champ de disparités dense sous contrainte épipolaire pour les images bruitées

Cet article présente des algorithmes d'estimation de disparité. L'originalité de la méthode repose sur le procédé d'estimation d'une carte de disparité dense en utilisant une méthode de corrélation robuste et sur l'utilisation des Statistiques d'Ordre Supérieur (SOS). Nous avons effectué des expérimentations afin de comparer nos résultats à ceux obtenus par les méthodes qui existent dans la littérature. La carte de disparité dense obtenue par notre méthode proposée est fiable par rapport aux résultats obtenus par celle de corrélation classique. Nous obtenons également une carte de disparité reflétant des facettes planes 3D

A Kalman Filter Process for Energy Optimization in WSNs

Wireless Sensor Networks (WSNs) consist of a large number of small interconnected devices. The aim of such networks is the monitoring of some types of area. This work is done by collaboration between these devices. All of them must sense and send information to the sink. These devices are characterized by limited memory, limited computing resource and they are usually powered by an irreplaceable battery, which limits their lifetime. Therefore it is essential to design an effective and energy aware protocols in order to extend the network lifetime by reducing the energy consumption. In this article, a new communication mechanism for IEEE 802.15.4 based WSNs called "Kalman based MAC (K-MAC) protocol" is proposed. K-MAC is designed to maximize the efficiency of the energy consumption. Therefore, the network nodes lifetime will extend through a predicting filter. The objectif of this filter is to optimize the sleep interval time of nodes between consecutive wake-ups.The network node be awake only if it have to receive or to send data. In other words, there will be an adaptation between the activation of the transceivers and the node traffic load. The simulation results show that K-MAC obtains better performance in terms of energy efficiency, Packet Delivery Ratio (PDR), the whole without affecting negatively the latency

A Kalman Filter Process for Energy Optimization in WSNs

Wireless Sensor Networks (WSNs) consist of a large number of small interconnected devices. The aim of such networks is the monitoring of some types of area. This work is done by collaboration between these devices. All of them must sense and send information to the sink. These devices are characterized by limited memory, limited computing resource and they are usually powered by an irreplaceable battery, which limits their lifetime. Therefore it is essential to design an effective and energy aware protocols in order to extend the network lifetime by reducing the energy consumption. In this article, a new communication mechanism for IEEE 802.15.4 based WSNs called "Kalman based MAC (K-MAC) protocol" is proposed. K-MAC is designed to maximize the efficiency of the energy consumption. Therefore, the network nodes lifetime will extend through a predicting filter. The objectif of this filter is to optimize the sleep interval time of nodes between consecutive wake-ups.The network node be awake only if it have to receive or to send data. In other words, there will be an adaptation between the activation of the transceivers and the node traffic load. The simulation results show that K-MAC obtains better performance in terms of energy efficiency, Packet Delivery Ratio (PDR), the whole without affecting negatively the latency

A novel texture descriptor: Homogeneous Rotated Local Binary Pattern (HRLBP)

Suite à la situation actuelle relative au Covid-19, la conférence initialement planifiée pour juillet 2020 a été reporté à avril 2021. La conférence a été réalisé virtuellement par visioconférence. - Due to the current events related to Covid-19, the conference originally planned for July 2020 has been to April 7-9, 2021. The conference will be organized virtually by videoconference. Session 1: Image and Video Processing (IVP)International audienceInvariant rotation in the application of texture classification is generally beneficial due to the material: camera or auto rotation, which can affect objects captured by arbitrary angles. This letter, introduce a new, efficient rotation invariant descriptor for texture analysis appointed Homogeneous Rotated Local Binary Pattern (HRLBP). The goal of this novel method is to take more account of the intrinsic characteristics of the images in rotation changing by using the incidence of homogeneity tolerance h provide from General Adaptive Neighborhood (GAN). A significant features are generated from HRLBP by thresholding the center and each neighbor pixels with an homogeneity tolerance value which help to get more efficient and discriminating features for rotation variation further multi-scale changing owing to use a variation of the parameter of homogeneity tolerance h and radius R. The experiments are evaluated using two publicly available texture database OTC10, furthermore, HRLBP shown a high performance in classification accuracy for problem of rotation variation

Completed homogeneous LBP for remote sensing image classification

International audienceLand cover (LC) classification remains a challenging task due to the diversity of terrain and topography, limited prior knowledge, and complex data sets. These variations can lead to differences in illumination and shading, which can affect the appearance of objects in the image. The local binary pattern (LBP) model is an effective technique for capturing local texture information in an image, which can help to overcome the effects of topography diversity by analysing patterns in different image regions, even if the illumination or shading conditions are different. However, LBP alone is inadequate for characterizing high-resolution remotely sensed images with complex semantic content as it only utilizes sign information in the local region. In this paper, a new texture characterization descriptor, known as completed homogeneous LBP (CHLBP), is proposed as an improved version of homogeneous LBP (HLBP) for LC classification of remotely sensed images. The CHLBP method mainly involves the following steps: first, sign and magnitude information from the HLBP descriptor is extracted, providing an effective alternative to the HLBP complementary contrast measure. Second, for each sign and magnitude function, a new splitting factor δ is used to obtain a depth relationship between the centre and its neighbouring pixels and to enhance noise robustness. Finally, the centre pixels representing the image grey level are also considered to contain discriminative information. The performance of our descriptor is evaluated using four challenging texture databases: Outex (TC10, TC12), Geofen Image Dataset (GID), and large-scale aerial (AID). Extensive experiments were performed using four classifiers: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP), demonstrating the effectiveness and robustness of our descriptor against noise and free noise conditions in public remote sensing datasets