Abundance and species richness of lombric macrofauna in a semi-arid forest ecosystem

The importance of earthworms for soils has evolved over time. Our study was conducted in the forest of Ouled yagoub (North East Algerian).Sampling at three different altitudes resulted in a total of forty-nine individuals (49) and only three species were identified: Octodrilus complanatus, Allolobophora molleri and Aporrectodea rosea. Spread over two ecological categories. The specific richness is higher in the site of 1400 m of altitude. The Simpson index (Is) varies between 0.44 and 0.49 for the three study sites. The Shannon index fluctuates between 0.41 and 0.74. The values of the Hill index vary between 1 and 1.5 in the three Sites.Keywords: Abundance, earthworm, species richness, biodiversit

Smart Localization Using a New Sensor Association Framework for Outdoor Augmented Reality Systems

Augmented Reality (AR) aims at enhancing our the real world, by adding fictitious elements that are not perceptible naturally such as: computer-generated images, virtual objects, texts, symbols, graphics, sounds, and smells. The quality of the real/virtual registration depends mainly on the accuracy of the 3D camera pose estimation. In this paper, we present an original real-time localization system for outdoor AR which combines three heterogeneous sensors: a camera, a GPS, and an inertial sensor. The proposed system is subdivided into two modules: the main module is vision based; it estimates the user’s location using a markerless tracking method. When the visual tracking fails, the system switches automatically to the secondary localization module composed of the GPS and the inertial sensor

An Efficient Human Activity Recognition Technique Based on Deep Learning

In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition

Sensor data integration for indoor human tracking

A human tracking system based on the integration of the measurements from an inertial motion capture system and a UWB (Ultra-Wide Band) location system has been developed. On the one hand, the rotational measurements from the inertial system are used to track precisely all limbs of the body of the human. On the other hand, the translational measurements from both systems are combined by three different fusion algorithms (a Kalman filter, a particle filter and a combination of both) in order to obtain a precise global localization of the human in the environment. Several experiments have been performed to compare their accuracy and computational efficiency.This work is supported by the Spanish Ministry of Education and Science (MEC) under the research projects DPI2005-06222 and DPI2008-02647 and the grant AP2005-1458

On the Hybrid Aid-Localization for Outdoor Augmented Reality Applications

In mobile outdoor augmented reality applications, accurate localization is critical to register virtual augmentations over a real scene. Vision-based approaches provide accurate localization estimates but are still too sensitive to outdoor conditions (brightness changes, occlusions, etc.). This drawback can be overcome by adding other types of sensors. In this work, we combine a GPS and an inertial sensor with a camera to provide accurate localization. We will present the calibration process and we will discuss how to quantify the 3D localization accuracy. Experimental results on real data are presented

3D Human Tracking in a Top View Using Depth Information Recorded by the Xtion Pro-Live Camera

International audienceThis paper addresses the problem of the tracking of 3D human body pose from depth image sequences given by a Xtion Pro-Live camera. Human body poses could be estimated through model tting using dense correspondences between depth data and an articulated human model. Although, most of the time for the video surveillance, the camera is placed above the persons, all the tracking methods use the front view. Indeed the human shape is more discriminative in this view. We propose a new model to be tted to the top view in a particle lter framework for a real-time markerless tracking. The model is composed of two parts: a 2D model providing the human localization and a 3D model providing its pose. There are few wrong estimations and they are e ciently detected by a con dence measure

3D Human Tracking from Depth Cue in a Buying Behavior Analysis Context

International audienceThis paper presents a new robust and reliable marker less camera tracking system for outdoor augmented reality using only a mobile handheld camera. The proposed method is particularly efficient for partially known 3D scenes where only an incomplete 3D model of the outdoor environment is available. Indeed, the system combines an edge-based tracker with a sparse 3D reconstruction of the real-world environment to continually perform the camera tracking even if the model-based tracker fails. Experiments on real data were carried out and demonstrate the robustness of our approach to occlusions and scene changes

Synthesis of N-pyridyl azoles using a deprotometalation-iodolysis-N-arylation sequence and evaluation of their antiproliferative activity in melanoma cells

International audienc

La ressource en eaux non conventionnelles facteur de développement en zone saharienne

The AQUASIS project proposes to implement new methods of water management in Saharan oases in order to decrease the losses and to optimize the valuation of marginal waters in particular those of the uncluttered urban waste waterLa consommation en eau en milieu urbain au Sahara algérien et tunisien a atteint des niveaux démesurément élevés puisque l'on enregistre à certains endroits des consommations de 500L/jour/personne. Les eaux captées ne sont pourtant pas renouvelables et c'est ainsi que la Tunisie, par exemple, a désormais mobilisé une grande partie de la ressource en eau conventionnelle dont elle disposait. La Tunisie puise maintenant dans son capital et ses réserves diminuent inexorablement, en particulier en domaine saharien, d'autant qu'une partie de la ressource est en train de se saliniser. Malheureusement les eaux une fois utilisées, et traitées comme en Tunisie ou non traitées comme en Algérie, même s'il existe quelques stations d'épuration, sont rarement valorisées, alors qu'elles constituent un potentiel de développement, notamment agricole, intéressant. Le projet AQUASIS est destiné à proposer un système de fonctionnement durable du milieu oasien, en particulier en ce qui concerne la gestion de la ressource en eau. Il a pour objectif - d'évaluer le fonctionnement actuel des oasis tunisiennes et algériennes (étude de l'organisation des oasis, des structures agraires, des pratiques agricoles, du cycle de l'eau, de la répartition entre les différents usages, du bilan des pertes, du potentiel en eaux marginales, etc..) - de déterminer les nouvelles méthodes de gestion à mettre en œuvre pour diminuer les pertes et valoriser au maximum la ressource en eau - de tester sur périmètre expérimental des structures agraires plus adaptées - d'optimiser la valorisation des eaux marginales en particulier celles des eaux usées urbaines épurées La valorisation de ces eaux nécessitent bien entendu d'éliminer tout risque sanitaire