Electronique
Application de l’Algorithme K-S et de l’Ondelette discrète au traitement d’images satellitaires
This memory approaches the satellite image processing which become a discipline with share today. Its importance is particularly distinguished in case of the forest fires detection, however without doubt; one often faces serious difficulties mainly due to the size of the image, especially when its matrix is treatedin hole. To overcome these constraints, anhybrid line by line treatment method is suggested. The latteris based on the algorithm of Kolmogorov-Smirnov as well as the discrete wavelet, which allows obtaining a lossycompression relative to each line of the matrix.In contrast, the algorithm of Kolmogorov-Smirnov permits to detect the stationary segments for each line. The study aims to combine these two methods in order to get a better representation as optimal as possible of the real image. Voir les détails
Mots clés : each line processing, Discrete-wavelet, KS-algorithm, lossy compression, stationary fragments
Modeling of Electromagnetic Behavior of CompositeThin Layers using Genetic Algorithm
In this paper, we present a new model using the highfrequency electromagnetic simulator for several binary mixtureswhere the load is in the lossless thin film form with a permittivityof (? = 100, 200, 300, and 400) and for various thickness values ina range of 10 µm to 250 µm with respect to the host matrix. Themodel operates in a variety of frequencies from 8.2 GHz to 12.4GHz. The effective permittivity of composites is evaluated usingNicholson Ross Weir (NRW) algorithm in a rectangularwaveguide. The implementation of NRW algorithm is conductedon various samples simulated by HFSS, in order to estimate thedielectric composite behavior. Furthermore, we employ a geneticalgorithm methodology (GA) for the filling factor optimization ofthe proposed model by Mosallaei. The obtained results show agood agreement with the theoretical models, which ensure thevalidity of our proposed model for characterizing theelectromagnetic behaviour of dielectric thin films. Voir les détails
Mots clés : Thin films, electromagnetic behaviour, dielectric mixtures, Genetic optimization, mico wave.
Complexity Reduction of UltrasoundSub-Ultra-Harmonic Modeling by an Input Modified Volterra Approach
Contrast of echographic images has been highly improved by the injection of microbubbles, due to their nonlinearbehavior. However, this contrast enhancement is limited by the nonlinear acoustic propagation in tissue. To overcome this drawback,sub and ultra-harmonic contrast imaging can be used, since only microbubbles can generate these components. Nonlinear modeling is aprimordial step in the analysis of microbubble signals for sub and ultra-harmonic imaging. Nonlinear models like Volterra model hasbeen applied in harmonic imaging to model harmonics optimally. However, it can model harmonics only. For sub and ultra-harmonicmodeling, a multiple input single output (MISO) Volterra has been proposed. The aim of this study is to propose a simpler alternativefor the modeling of sub and ultra-harmonics. We propose a modified single input single output (SMISO) Volterra model based on inputdemodulation. The model is tested using simulated and experimental signals. Results showed that sub and ultra-harmonics aremodeled. The number of kernels is reduced to its half using SMISO model compared to MISO model. The relative mean square errorbetween the simulated signal and the modeled signal with SMISO Volterra model is −15.8 dB and it is −60.7 dB for experimentalsignals. The computational time is reduced by a factor of 4 and 5 in simulated and experimental cases respectively. SMISO model canmake easier the sub and ultra-harmonics modeling. Voir les détails
Mots clés : modeling, sub-ultra-harmonics, SMISO Volterra, demodulation, microbubble.
Fast Level Set Algorithm for Extraction and Evaluation of Weld Defects in Radiographic Images
The classification and recognition of weld defects play an important rolein weld inspection. In this paper, in order to automate inspection task, we proposean aide-decision system. We believe that to obtain a satisfied defects classificationresult, it should be based on two kinds of information. The first one concerns thedefects intensity and the second one is about its shape. The vision system containsseveral steps; the most important ones are segmentation and feature computation.The segmentation is assured using a powerful implicit active contour implementedvia fast algorithm. The curve is represented implicitly via binary level set function.Weld defect features are computed from the segmentation result. We have computedseveral features; they are ranked in two categories: Geometric features (shapeinformation) and Statistic features (intensity information). Comparative study, onsynthetic image, is made to justify our choice. Encouraging results are obtained ondifferent weld radiographic images. Voir les détails
Mots clés : Code generation, State machine, Radiographic inspection, image segmentation, Level set, Region-based models, Features computation
Fast algorithm for hybrid region-based active contours optimisation
Active contours are usually based on the optimisation of energy functionals that are built to attract the curve towardsthe objects' boundaries. This study describes a hybrid region-based active contours technique that uses global means to definethe global fitting energy and local means and variances to define the local fitting energy. The optimisation of the functional isperformed by applying a sweeping-principle algorithm, which avoids solving any partial differential equation and removes theneed for any stability conditions. Furthermore, sweeping-principle algorithm is not based on the computation of derivatives,which allows using a binary level set function during the minimisation process instead of the signed distance function,consequently this removes the need for the distance regularisation term, avoiding its subtle side effects and speeding up theoptimisation process. Successful and accurate segmentation results are obtained on synthetic and real images with a significantgain in the CPU execution time when compared with the minimisation via the commonly used gradient descent method. Voir les détails
Mots clés : image segmentation
Adaptive algorithms for target tracking.
The quality of the tracking is greatly enhanced by arobust motion estimation.The objective is to develop a target tracking algorithm of amoving object, especially motion estimation of these. To realizethe estimate, we chose stochastic filtering techniques. He concernthe Kalman filter in the linear Gaussian, and sequential MonteCarlo methods in the nonlinear.Representation of state is permanently adapted according oncurrent observations, to best represent the system dynamics. Acomparison of the results given by the extended Kalman filterand particle filter is realized into simulation of the nonlinearsystems target tracking. Voir les détails
Mots clés : stochastic filtring, Kalman filter, Extended Kalman Filter, Monte Carlo Method, Particle filtre, Target tracking
Enhancement of phased array ultrasonic signal in compositematerials using TMST algorithm
In this paper, we apply a new technique for the ultrasonic phased array signal enhancement. It is based on the threshold modifiedS-transform (TMST). The signal processing algorithms generally give very satisfactory results on synthetic signals verifying theimplicit or explicit hypotheses on which they are constructed. The obtained performances on the real signals can be howeverdifferent radically. Time–frequency analysis methods are mainly used to improve the defects detection resolution. Significantperformance enhancement is confirmed when the proposed approach is tested with the simulation of the B-scan signals contain acloser delamination to the front face. The experimental results show that the TMST Algorithm can enhance the quality of imageprovided by composite materials contained delamination defect. Voir les détails
Mots clés : Ultrasonic Phased Array, composite materials, S-transform, TMST Algorithm
Acoustic emission source localization in plate-likestructure
This article proposes a geometric approach foracoustic emission source (AE) localization in plate-like structure.In order to determine the arrival time of the acoustic emissionwaves for each sensor with more precision, we have used twotechniques, the first one is based on thresholding and the secondis based on the continuous wavelet transform. Experimental testhave been carried out on steel plate shown that the continuouswavelet transform allows to be improve the accuracy of theacoustic emission source localization. Voir les détails
Mots clés : acoustic emission signal, acoustic emission source location, arrival time, threshold, continuous wavelet transform
Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method
The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very important research project. The vibration signal is usually noisy and the information about the fault in the early stage of its development can be lost. A threshold de-noising method based on Empirical Mode Decomposition (EMD) is presented in this paper. Firstly, the signal is decomposed into a number of IMFs using the EMD decomposition. Secondly the algorithm based on the energy to determine the trip point is designed for IMF selection, then, by comparing the energy of the selected IMFs with excluded IMFs, singular selected IMFs are treated with soft threshold function, and finally the de-noised signal is obtained by summing up the selected IMFs, it is proved that the best IMFs can be summed up and properly de-noised by the proposed method. The results show the effectiveness of the proposed technique in revealing the bearing fault impulses and its periodicity and amelioration the sensibility of scalar indicator for real rolling bearing vibration signals Voir les détails
Mots clés : bearing fault detection, EMD, threshold Denoising, IMF selection, Singular IMF
Quantum and conversion efficiencies optimization of superstrate CIGS thin-films solar cells using In2Se3 buffer layer
In this present contribution, AMPS-1D device simulator is employed to study the performances of superstrate SLG/TCO/p-Cu(In,Ga)Se2(CIGS)/n-ODC/n-In2Se3/Metal thin film solar cells. The impact of theTCO and Metal work functions on the cell performance has been investigated. The combination of optical transparency and electrical property for TCO front contact layer is found to yield high efficiency. The obtained results show that the TCO work function should be large enough to achieve high conversion efficiency for superstrate CIGS solar cell. Nevertheless, it is desirable for Metal back contact layer to have low work function to prevent the effect of band bending in the n-In2Se3/Metal interface. Several TCOs materials and metals have been tested respectively as a front and back contact layers for superstrate CIGS solar cells. An efficiency of 20.18%, with Voc ≈ 0.71 V, Jsc ≈ 35.36 mA/cm2 and FF ≈ 80.42%, has been achieved with ZnSn2O3-based as TCO front contact layer. In the case of SnO2:F front contact and indium back contact layers, an efficiency of 16.31%, with Voc ≈ 0.64 V, Jsc ≈ 31.4 mA/cm2 and FF ≈ 79.4%, has been obtained. The present results of simulation suggest an improvement of superstrate CIGS solar cells efficiency for feasible fabrication. Voir les détails
Mots clés : Cu(InGa)Se2 material, Superstrate solar cells, Transparent conducting oxides, Barrier height, AMPS-1D