Electronique

Nombre total de résultats : 508
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Étude et réalisation d'un systèmeembarqué pour leContrôle et l’aide à la navigation d'unquadri-rotors.

F. LAMROUSSI, M.S.KEBIECHE  (2021)
Mémoire de Master

This work consists firstly in the realization of an autopilot board for Quad-rotor basedon the ARDUINO MEGA development board. The latter must be able to acquire and processinformation from sensors: MPU6050 (The accelerometer, the gyroscope, the magnetometer)and the GPS sensors in real time to command and control the Quad-rotor. The resultsobtained by the sensors must be displayed on a graphical interface representing the groundstation. The second part is to realize and embark of a navigation aid system based onultrasonic sensors. Voir les détails

Mots clés : Quad-rotor, autopilot, Arduino MEGA, GPS, MPU6050, ground station, real time, Ultrasound

Implémentation Des Algorithmes de Traitement du Signal sur carte FPGA

S.AISSAOUI, S. MOKHTARI  (2021)
Mémoire de Master

The aim of this work is to realize an on-board system allowing to automate the control in NDT by ultrasonic. For this, we performed the transmission and reception of signals using an FPGA card that acquisition and signal processing are implemented on the same FPGA circuit. Voir les détails

Mots clés : Non-destructive testing (CND), ultrasonic signals, FPGA, Signal processing

Segmentation des images radiographiques

I. karmal, A. Henniche  (2020)
Mémoire de Master

Image segmentation is an important step in any image analysis process. The subject has already been tackled by multiple approaches. These approaches are based on various tools such as mathematical morphology, wavelet decomposition, active contours; some are based on the detection of contours and others on the identification of regions. Each of these classes of methods has its advantages and disadvantages. In this dissertation, we present the different segmentation approaches and we choose a hybrid method for segmenting chest X-ray images composed of two algorithms. One is the FCM as an initial contour for the other segmentation method LEVEL SET which is a variant of the deformable models based on the active contour method, we injecting the images resulting from this segmentation process into a convolutional neural network (CNN) in order to classify them according to the pathological state. Therefore the aim of our work is the implementation of a model for segmentation and classification of medical X-ray images to assist doctors in the detection of pulmonary pathologies. Voir les détails

Mots clés : image segmentation, chest X-ray image, Active contour, FCM, Level set, classification, CNN

Conception et réalisation d’un système de contrôle non destructif par courant de Foucault pulsé

B. GHOBCHI Younes  (2020)
Mémoire de Master

The conventional EC method is used for near-surface defects finding [5] [48] [12] [2]. On the other hand, and in order to control great depths, pulsed eddy currents are used [35] [48] [12] [2]. Through this project we aim to realize an analogue-digital PEC-NDT system, interfaced by a microcomputer. A set of experiments is carried out in order to prove the sensitivity of the designed system towards some parameters [2], where the determined mechanical properties are compared with the results obtained in order to in order to confirm some hypotheses [32]. Voir les détails

Mots clés : cnd, Instrumentation, Courants de Foucault pulsés

Conception d’une chaîne de visionembarquée sur une plateforme FPGA

L. HAKEM  (2019)
Mémoire de Master

Our work consisted of a strategic development for the design and implementation of an embeddedvision chain with the use of a smart camera. The implementation of our vision chain for the acquisition ofvideo data is essentially based on the EMBV Python1300C embedded vision kit. The implementation of areconfigurable architecture is based on the exploitation of a Xilinx Zynq 7020 FPGA SoC Voir les détails

Mots clés : embedded vision chain, EMBV Python1300C, FPGA, ZYNQ-7000

Apport des techniques de traitement de signal dans la localisation spatiale d’un défaut par ultrasons

Z.NABI, Y. MOHAMMEDI  (2019)
Mémoire de Master

Le contrôle non destructif (CND) est une étape importante dans un processus industriel. Il permet de contrôler l’intégrité des composants sans les endommager, pendant ou à la fin de leur fabrication, et en situation d’utilisation. Différents phénomènes physiques permettent ces contrôles de par leur caractère pénétrant dans les objets (ondes électromagnétique, ondes acoustiques, champs magnétique, etc.), menant à différents modes de contrôle. Nous nous intéressons dans ce travail au CND par ultrasons, modalité qui consiste à émettre des ondes acoustiques dans le matériau a inspecté. Les ondes se propagent dans le milieu, et récupérées par le capteur ultrasonore, permettent dans la mesure du possible de détecter et d’identifier les défauts contenus dans la pièce. Le même procédé peut être appliqué pour évaluer les matériaux, c’est-à-dire pour estimer des paramètres physiques propres, tels que la vitesse des ondes ou le coefficient d’atténuation. Le but de l’utilisateur est de visualiser les échos et d’en déduire une information spatiale sur l’objet inspecté. Nous nous intéressons aux discontinuités, qui présentent des transitions franches dans l’objet (surface, arrêts, défauts, etc.). L’analyse du signal peut cependant se révéler difficile a l’œil nu pour plusieurs raisons : bruit, atténuation, diffraction, superposition d’échos, etc. L’allure des échos rétrodiffusés (réfléchis) du signal ultrasonore, lors d’une opération d’inspection, donne une indication sur la forme géométrique, la taille et l’orientation des réflecteurs se trouvant sur la zone étudiée. Une estimation correcte et précise de la forme des échos ultrasonores est essentielle pour déterminer les propriétés du milieu de propagation. Des techniques de traitement du signal sont alors employées pour améliorer la résolution des signaux. Dans ce travail nous nous intéressons à la localisation spatiale d’un défaut dans une pièce métallique en appliquant des techniques de traitement de signal. Voir les détails

Mots clés : CND par ultrasons, ondes électromagnétique, ondes acoustiques, champs magnétique

Unsupervised weld defect classification in radiographic images usingmultivariate generalized Gaussian mixture model with exactcomputation of mean and shape parameters

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou  (2019)
Publication

In industry, the welding inspection is considered as a mandatory stage in the process of quality assurance/quality control. This inspection should satisfy the requirements of the standards and codes governing themanufacturing process in order to prevent unfair harm to the industrial plant in construction. For thispurpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographictesting is presented, where a succession of operations of preprocessing, image segmentation, featureextraction andfinally defects classification is carried out on radiographic images. The last operationwhich is the main contribution in this paper consists in an unsupervised classifier based on afinitemixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newlyapplied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGDbasedmixture model are estimated using the Expectation-Maximization algorithm where, exactcomputations of mean and shape parameters are originally provided. The weld defect database representfour weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by ashape geometric descriptor composed of geometric measures. An outstanding performance of theproposed mixture model, compared to the one using the multivariate Gaussian distribution, is shown,where the classification rate is improved by 3.2% for the whole database, to reach more than 96%. Theefficiency of the proposed classifier is mainly due to theflexiblefitting of the input data, thanks to theMGGD shape parameter. Voir les détails

Mots clés : Mixture model, Multivariate GGD, radiography, weld defect, classification

Video Processing and Analysisfor Endoscopy-Based InternalPipeline Inspection

Nafaa Nacereddine, Aissa Boulmerka, Nadia MHAMDA  (2019)
Publication

Because of the increasing requirements in regards to the pipeline transport regulations, the operators take care to the rigorous application of checking routines that ensure nonoccurrence of leaks and failures. In situ pipe inspection systems such as endoscopy, remains a reliable mean to diagnose possible abnormalities in the interior of a pipe such as corrosion. Through digital video processing, the acquired videos and images are analyzed and interpreted to detect the damaged and the risky pipeline areas. Thus, the objective of this work is to bring a powerful analysis tool for a rigorous pipeline inspection through the implementation of specific algorithms dedicated to this application for a precise delimitation of the defective zones and a reliable interpretation of the defect implicated, in spite of the drastic conditions inherent to the evolution of the endoscope inside the pipeline and the quality of the acquired images and videos. Voir les détails

Mots clés : video processing, endoscopy, Pipeline inspection

Asymmetric Generalized Gaussian DistributionParameters Estimation based on MaximumLikelihood, Moments and Entropy

Nafaa Nacereddine, Aicha Baya Goumeidane  (2019)
Article de conférence

In this paper, we address the problem of estimatingthe parameters of Asymmetric Generalized Gaussian Distribution(AGGD) using three estimation mehods, namely, Maximum LikelihoodEstimation (MLE), Moment Matching Estimation (MME)and Entropy Matching Estimaion (EME). For this purpose, thesemethods are applied on an unimodal histogram fitting of animage corrupted with AGGD noise. Experiments show that theeffectiveness of each method comparatively to the other onedepends on the variation range of the shape factor. Voir les détails

Mots clés : Asymmetric generalized Gaussian distribution, Parameter estimation, maximum likelihood, Moments, Entropy.

A novel correlation filter based on variational calculus

Djemel Ziou, Dayron Rizo Rodriguez, Nafaa Nacereddine, Salvatore Tabbone  (2019)
Publication

Correlation filters have been a popular technique for tackling image classification problems. The traditionalcriteria used to design correlation filters overlook some properties that can improve their discriminative power.Therefore, new criteria are proposed to design a novel correlation filter. Such criteria take advantage ofnegative samples, spatial information and the smoothness of the correlation output space. A closed formis derived from the criteria proposed using variational calculus. Moreover, it is shown that the resultingcorrelation filter is a bandpass filter. Experiments are conducted for face identification under illuminationvariation for a single training image per subject and head pose classification. The correlation filter proposeddelivers favorable scores when compared to other correlation filters and state-of-the-art approaches Voir les détails

Mots clés : Correlation filter, Variational calculus, Face identification, Illumination variation, Single training image, Pose classification