Study Of The Aerodynamic Structure Around A NACA6409 Airfoil Rotor

The multi-copters are one of the important aircrafts in the world because they are used in both military and civil application. The problem of these aircrafts is the low efficiency of its propellers. That’s why, it is necessary to study the aerodynamic structure around every rotor. In this paper, a computer simulation has been done to study the aerodynamic structure around a NACA 6409 airfoil rotor. The numerical model is based on the resolution of the Navier-Stockes equations with a standard k-ε model. Then, the finite volume discretization of these equations leads to solve it. The local characteristics of the aerodynamic structure are extracted using the software Solidworks Flow Simulation and the validation is done with comparison with anterior result of Alexandrov (2013)

Reconnaissance de postures humaines par fusion de la silhouette et de l'ombre dans l'infrarouge

Les systèmes multicaméras utilisés pour la vidéosurveillance sont complexes, lourds et coûteux. Pour la surveillance d'une pièce, serait-il possible de les remplacer par un système beaucoup plus simple utilisant une seule caméra et une ou plusieurs sources lumineuses en misant sur les ombres projetées pour obtenir de l'information 3D ? Malgré les résultats intéressants offerts par les systèmes multicaméras, la quantité d'information à traiter et leur complexité limitent grandement leur usage. Dans le même contexte, nous proposons de simplifier ces systèmes en remplaçant une caméra par une source lumineuse. En effet, une source lumineuse peut être vue comme une caméra qui génère une image d'ombre révélant l'objet qui bloque la lumière. Notre système sera composé par une seule caméra et une ou plusieurs sources lumineuses infrarouges (invisibles à l'oeil). Malgré les difficultés prévues quant à l'extraction de l'ombre et la déformation et l'occultation de l'ombre par des obstacles (murs, meubles...), les gains sont multiples en utilisant notre système. En effet, on peut éviter ainsi les problèmes de synchronisation et de calibrage de caméras et réduire le coût en remplaçant des caméras par de simples sources infrarouges. Nous proposons deux approches différentes pour automatiser la reconnaissance de postures humaines. La première approche reconstruit la forme 3D d'une personne pour faire la reconnaissance de la posture en utilisant des descripteurs de forme. La deuxième approche combine directement l'information 2D (ombre+silhouette) pour faire la reconnaissance de postures. Scientifiquement, nous cherchons à prouver que l'information offerte par une silhouette et l'ombre générée par une source lumineuse est suffisante pour permettre la reconnaissance de postures humaines élémentaires (p.ex. debout, assise, couchée, penchée, etc.). Le système proposé peut être utilisé pour la vidéosurveillance d'endroits non encombrés tels qu'un corridor dans une résidence de personnes âgées (pour la détection des chutes p. ex.) ou d'une compagnie (pour la sécurité). Son faible coût permettrait un plus grand usage de la vidéosurveillance au bénéfice de la société. Au niveau scientifique, la démonstration théorique et pratique d'un tel système est originale et offre un grand potentiel pour la vidéosurveillance.Human posture recognition (HPR) from video sequences is one of the major active research areas of computer vision. It is one step of the global process of human activity recognition (HAR) for behaviors analysis. Many HPR application systems have been developed including video surveillance, human-machine interaction, and the video retrieval. Generally, applications related to HPR can be achieved using mainly two approaches : single camera or multi-cameras. Despite the interesting performance achieved by multi-camera systems, their complexity and the huge information to be processed greatly limit their widespread use for HPR. The main goal of this thesis is to simplify the multi-camera system by replacing a camera by a light source. In fact, a light source can be seen as a virtual camera, which generates a cast shadow image representing the silhouette of the person that blocks the light. Our system will consist of a single camera and one or more infrared light sources. Despite some technical difficulties in cast shadow segmentation and cast shadow deformation because of walls and furniture, different advantages can be achieved by using our system. Indeed, we can avoid the synchronization and calibration problems of multiple cameras, reducing the cost of the system and the amount of processed data by replacing a camera by one light source. We introduce two different approaches in order to automatically recognize human postures. The first approach directly combines the person’s silhouette and cast shadow information, and uses 2D silhouette descriptor in order to extract discriminative features useful for HPR. The second approach is inspired from the shape from silhouette technique to reconstruct the visual hull of the posture using a set of cast shadow silhouettes, and extract informative features through 3D shape descriptor. Using these approaches, our goal is to prove the utility of the combination of person’s silhouette and cast shadow information for recognizing elementary human postures (stand, bend, crouch, fall,...) The proposed system can be used for video surveillance of uncluttered areas such as a corridor in a senior’s residence (for example, for the detection of falls) or in a company (for security). Its low cost may allow greater use of video surveillance for the benefit of society

Optimization of regeneration and transformation parameters in tomato and improvement of its salinity and drought tolerance

As part of our efforts to improve tomato tolerance to abiotic stress, we have undertaken this study to introduce two candidate genes encoding: a sodium antiporter and a vacuolar pyrophosphatase, previously shown to enhance drought and salt tolerance in transgenic Arabidopsis plants. First, we evaluated the potential of primary leaves from three to four week-old in vitro-grown tomato seedlings as alternative explants to cotyledons for tomato transformation. Our results demonstrated that primaryleaves are three times more efficient then cotyledons in terms of regeneration percentage, productivity, and transformation frequencies independently of the medium and genetic construct used. Second,primary leaves were used to introduce the genes of interest using Agrobacterium-mediated transformation. Many transgenic tomato plants were easily recovered. The presence of the transgenes and their expression were confirmed by PCR and RT-PCR analysis. The transformation frequencies for primary leaf explants ranged from 4 to 10% depending on the genetic construct used. The time requiredfrom inoculation of primary leaves with Agrobacterium cells to transfer of transgenic tomato plants to soil was only 2 months compared to 3 to 4 months using standard tomato transformation protocols. The transgenic tomato plants obtained in the current study were more tolerant to salinity and drought stress than their wild-type counterparts

Hepatoid adenocarcinoma of the gallbladder

Hepatoid adenocarcinoma is a rare variant of extrahepatic adenocarcinoma which behaves like hepatocellular carcinoma in morphology and functionality

Real-time recognition of suicidal behavior using an RGB-D camera

Inmates in solitary confinement may attempt to harm themselves in many ways, resulting in trivial to mortal injuries. In this context, suicide by hanging is one of the major causes of death among the incarcerated. The Rapid detection of suicide can reduce the mortality rate. Recently, several technologies have been developed to detect suicide by hanging attempts, but most of them use bulky devices, or they are greatly depending on human attention. In this paper, we propose a computer vision based system to automatically detect suicide by hanging attempts. Our method is based on modeling suicidal actions using pose and motion features, by exploiting the body joints' positions. The proposed video surveillance system analyses depth images provided by an RGB-D camera to detect the event of interest in real-time, regardeless of illumination conditions. The experimental results obtained on a realistic dataset demonstrated the high precision of our system in detecting suicide by hanging

Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images

Integrated Maintenance-Quality policies taking into account operational constraints

Ces dernières décennies ont vu une évolution remarquable du contexte économique des entreprises. Ceci est dû certainement à la compétition fortement croissante à laquelle elles sont confrontées. Cette forte concurrence a entraîné les entreprises à investiguer des voies pour améliorer leurs performances économiques tout en répondant aux mieux aux exigences de leurs clients. La clé de la réussite de la majorité des entreprises réside dans la mise en place d’une méthode de gestion économique des différentes fonctions. Les industriels et les chercheurs ont prouvé l’existence d’une forte interaction entre trois fonctions fondamentales de l’entreprise ; la maintenance, la production et la qualité. Ils ont démontrés qu’une gestion indépendante de ces trois fonctions n’est plus efficiente. Suite à ce constat, nous nous sommes intéressés dans ce projet de recherche au développement de nouvelles stratégies de maintenance intégrée à la production et à la qualité. En tenant compte du prix de vente, des coûts de retouche, de la maintenance et ceux de la production, une analyse de l’interdépendance des trois fonctions a permis de réaliser notre objectif qui consiste à maximiser les profits des entreprises. Le but de cette thèse consiste, à proposer des nouvelles approches de maintenance intégrée à la qualité en tenant compte de la dégradation progressive du système de production, son impact sur la qualité des produits finis et l’impact économique des actions de retouche. Des modèles analytiques ont été développés afin d’illustrer les stratégies proposées. L’effet de la dégradation du système de production sur la perte de qualité du produit fini et son impact économique ont été mis en équation par le biais de modèles mathématiques. Dans ce contexte, différentes politiques de maintenance (parfaite, imparfaite, semi parfaite) ont été traitées. L’existence des solutions optimales liées aux variables de décision a été démontrée analytiquement. Enfin, des résolutions numériques, basées sur des cas industriels, ont été présentées afin de valider les résultats théoriques obtenus. La robustesse des modèles analytiques développés a été prouvée par des études de sensibilitéThe last few decades have been marked by a remarkable change in the economic environment of companies. It is due certainly to the increasing competition that they face. This strong competition has led companies to investigate new ways to improve their economic performance while meeting their customers' requirements. The key to the success of the majority of companies lies in the implementation of an economic management method of various functions. Industry and researchers have demonstrated a strong interaction between the three fundamental functions of the company; maintenance, production and quality. They have demonstrated that an independent management of these three functions is no more efficient. Following this observation, we were interested in this research project in the development of new maintenance strategies integrated production and quality. Taking into account sales prices, reworking costs, maintenance costs and production costs, an analysis of the interdependence of the three functions enabled us to achieve our goal of maximizing the profits of companies. The purpose of this thesis is to propose new approaches integrated quality and maintenance taking into account the progressive degradation of the production system, its impact on the quality of output products and the economic impact of reworking actions. Analytical models have been developed to illustrate the proposed strategies. The effect of the degradation of the production system on the degradation of quality of output products and its economic impact has been modeled using mathematical models. In this context, various maintenance policies (perfect, imperfect, semi-perfect) have been studied. The existence of optimal solutions related to the decision variables has been analytically demonstrated. Finally, numerical resolutions, based on industrial cases, have been presented in order to illustrate the theoretical results obtained. The robustness of the analytical models developed has been proved by sensitivity studie