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The climatic atmosphere in which cattle live is an essential parameter of their environment because of its critical role in their productivity. An adapted cattle building must help to mitigate the effects of climatic stress and allow the farmer to properly control the climatic atmosphere during the production cycle. The most important factors influencing the climatic atmosphere inside a cattle building are temperature, humidity, and greenhouse gas emissions. We propose a case study for a wireless sensor network model placed on a cattle farm, in which each measurement node “mote” collects environmental data (temperature, humidity, and emission gas), in order to control the building's climate, this data is stored and managed in a remote database. We will present HBase, a NoSQL database management system, based on the concept of distributed storage, a column-oriented database that provides the read/write access to data on the HADOOP HDFS file system in real-time. The storage results presented in this paper are obtained via a java code that can connect with the HBase database, in order to store the received data at every second from each node constituting the measurement system via HTTP requests.

Many libraries are available in the development world to create augmented reality applications, their functionality differs depending on the technology used to detect and track an object, points or features in a scene. In this article, we will discover ARKit, ARCore two open source libraries that display a virtual object in the real world. Their goal is to merge digital content and information with the real world. They can interact with the components of the device (camera and screen) to detect and track characteristics of the scene in order to insert virtual content. This study implements and concretizes the different functionalities available in augmented reality to enrich the real world with additional information.

Popularity is one of the key reasons for the electoral success of a politician and his party. Discovering the reasons behind a political notoriety leads inevitably to the constitution of a stable and perennial political class. The present work seeks to measure, using a mixed method, the Facebook trust ratings of a political figure in Morocco, taking advantage of the huge amounts of information that big data are overflowing with, especially data arisen from social networking services, which have recently become a promising opportunity and an ingenious tool for extracting and exploring public opinion. This research has collected and examined 6 million of posts and comments about the head of Moroccan government in 2016. This study sought to identify the key reasons behind the Facebook popularity of this politician and his party. This contribution is intended to be multidisciplinary, both useful for policymakers, data mining experts and opinion analysts to explore public trends and inclinations.

We present our 3D models reconstructed based on geometric constraints. Used constraints in 3D reconstruction are frequently inaccurate. In fact, we detect an important set of theme, which one can we choose as input of reconstruction method? So, redundancies, cycles and inaccurate 3D model present a challenge to overcome. It is therefore necessary to “beautify” constraint and to elaborate an accurate 3D model before use in reconstruction systems. We present in our work a new method of 3D model elaboration. So, we remove redundancies; cycles and we bring a safe and final 3D model with an optimal freedom degree of estimated parameters. Our reconstruction algorithm first prioritizes the constraint detection. User can add them manually or through an automatic process. But the important thing is to avoid 3D modeling step which is very challenging. To detect and eliminate redundant and inconsistent constraints use freedom degree analysis. Results from our implementation show that the method can beautify and optimize recovered 3D models correctly at acceptable speed

This paper presents a robust and important method for the self-calibration of a numerical camera using a tridimensional unknown scene. The particularity of our technique resides in the use of an arc of the circle and the localization of some of the matching points detected in the use of an ensemble of images in order to define in an automated way the intrinsic parameters, which are unknown and variable. Our approach is based on minimizing the constraints of the self-calibration of the camera with the projection of the two extremities of chord of each pair of images. It is obvious that in order to produce good results from the simulations we have made the choice of the right programming language would be of prime importance. Our main criteria were simplicity, stability and performing algorithms. The results have proven that the selection of the aforementioned criteria was successful.

In the present article, we will focus on the 3D Reconstruction of objects from flat images. The method we propose allows the realization of the 3D Reconstruction of objects from images based on estimating the Affine Projection Matrix made from different existing relationships between the three methods of Three-dimensional Reconstruction and without the passage through the calibration phase and self calibration of the cameras.

3D reconstruction from uncalibrated images has many and important applications in computer vision. Different methods exist, but they present various problems. First of all, methods which are full automatic present the problem that they require a difficult and challenging self calibration or calibration step. They need complex calculation operators. And they need more than image to achieve 3D reconstruction. Under all these complicated constraints we don't have good and reliable results as well as needed. In addition to this, we have the problem of complex used material and wasted memory and time resources. In the other hand, there are interactive methods. They are less heavy than the first ones but, they suffer from the interactivity of user during the whole modelling process. In this paper, we propose a semi-automatic 3D reconstruction approach from a single or more images with less user interaction based on freehand sketches. Basically speaking, we used bundle adjustment based on Levenberg-Marquardt optimization with the aim to estimate our 3D scene. Using extracted constraints from sketches brings us many advantages; it helps us to reduce estimated parameters number and stabilizes good quality for 3D results. We try to proof the importance of freehand sketches use in 3D reconstruction in terms of results reliability and process simplicity.

In this paper, we present a 3D reconstruction approach from uncalibrated views using geometric constraints. Basically speaking, we used bundle adjustment based on Levenberg-Marquardt optimization with the aim to estimate our 3D scene. In fact, it is different to the classic case. We integrate a pose estimation algorithm in 3D reconstruction process. As it is known, Levenberg-Marquardt algorithm presents low convergence rate 0% if initial values are wrong. The use of pose estimation previously cited can improve convergence but, it is still not satisfactory for users. So, using geometric constraints present a good solution. It brings us many advantages; it helps us to reduce estimated parameters number and stabilizes good quality for 3D results. In fact, we should recall that we use uncalibrated views, so we don't have any prior information about our 3D scene to achieve 3D reconstruction with no pertinent initial values used in Levenberg-Marquardt algorithm. In this present work, we try as much as possible through a comparative analysis to proof the importance of geometric constraints use in 3D reconstruction in terms of results reliability, process speed and convergence rate. Several data will be used in the purpose to demonstrate the efficiency of our present approach using geometric constraints.

In the present paper, we will focus on a new approach for efficient and reliable tridimensional reconstruction of objects from flat images. Our approach allows the realisation of tridimensional reconstruction without passing through the calibration and self-calibration phase of the camera, but based on the estimation of the fundamental matrix and the homography at infinity to have the projective, affine and Euclidean projection. Our method is based firstly on a very important step in the 3D reconstruction that is the detection of interest points using the Harris detector to have a sufficient number of matches distributed on the images, these matches are used to estimate the 3D points, and secondly to estimate the projection matrices that are made from different existing relationships between the three types of tridimensional reconstruction (projective reconstruction, affine reconstruction, Euclidean reconstruction). Experimental results prove that this method is practical and gives satisfying results without going through the calibration step.

We emphasise on the influence of geometric constraints in 3D reconstruction. In fact, we have used a new approach of 3D reconstruction which is based on bundle adjustment optimisation and constraints. Our purpose is to achieve 3D reconstruction in a simple, fast and efficient way without any prior information about cameras and 3D space. Constraints use brings us many advantages; it helps us to reduce estimated parameters number and stabilises good results quality. We try as much as possible through a comparative analysis to prove the importance of constraints use, in terms of results reliability, process speed and convergence rate. We use uncalibrated views, so we don't have any pertinent initial values used in Levenberg-Marquardt. On the hand, and thanks to the estimation of cameras' positions with constraints during Levenberg-Marquardt optimisation, we solve with the convergence problem satisfactorily. Several data will be used with the aim to demonstrate the efficiency of our present approach under different uses and kinds of constraints.