MOHAMED Sahed

Zakarya MENOUAR , Mokrane BELFERROUM

Assessing the Performance of Mean-Level CFAR Detectors in Gamma-Distributed Radar Clutter

Abir DAIRA , Fadwa BENMEBAREK

Analysis of Some CFAR Processors for High-Resolution Radar Systems

HADJI Abdallah , HABACI Imadeddine

Etude et conception d’une antenne MIMO avec des structures EBG pour les applications mobiles modernes

Mohammed Nizar BOUNIF , Toufik BEN RAYA

Optimization of Transmission Channel in a GSM Telecommunications Network

Rania BENHAMED , Selma BOUNIF

Étude et Analyse des Détecteurs Adaptatifs CFAR de Cibles Radar Noyées dans un Milieu Non Gaussien

M’HAMDI Ali , CHABANI Mouna

Performance du détecteur GM-CFAR avec intégration non-cohérente en milieux côtiers Pareto distribués

LAROUUSI Souhil , MOUSSAOUI Walid, DAOUDI Fateh

Réalisation d’un Système d’irrigation automatique à base de carte Arduino

ABDELKEBIR Chafika , BEN TAYEB Selwa

Réalisation d’un distributeur d’eau automatique à base de carte Arduino

SAFER Manel , SAADI Amina

Serrure électronique codée à base d’une carte Arduino UNO

Bensalem Akram , Djouilem Abou Bakr, Ben Tahar Adel

Etude d’un système de détection de gaz toxiques à base d’une carte Arduino UNO Rev3

SACI Yassine , LOGMA Mohcen, DECHOUCHA Ahlem

Réalisation d’un détecteur de Gaz à base de PIC16F877A

GUENDOUZ Cheima , CHEBABHI Hala

Conception et réalisation d’un détecteur de métaux à induction pulsée

CHOUIREB Elmahdi , BARKATI Badreddine, BOUGOUTTAYA Salahedine

Réalisation d’un détecteur de gaz à base du microcontrôleur ATMega328

In this correspondence, two exact closed-form expressions for the probability of false alarm have been derived assuming a homogeneous Gamma distributed clutter. These expressions concern both the cell averaging CFAR (CA-CFAR) and the greatest of CFAR (GO-CFAR) detectors. As it will be shown, the proposed expressions are given in terms of the Gauss hypergeometric function and the second Appell function. Our expressions are then examined and validated numerically, by comparing them to their counterparts computed using numerical integrals and Monte-Carlo simulations, considering various scenarios.

Citation

ELhadi KENANE , Mohamed SAHED , Ali KHALFA , Farid Djahli, , (2023), Exact Closed-Form Pfa Expressions for CA- and GO-CFAR Detectors in Gamma-Distributed Radar Clutter, IEEE Transactions on Aerospace and Electronic Systems, Vol:59, Issue:4, pages:4674 - 4679, IEEE

This letter presents an analytical study of the performance of the maximum-likelihood gamma constant false alarm rate (MLG-CFAR) detector operating over a homogenous gamma-distributed (GM) clutter. To accomplish this task, a novel closed-form expression for the probability of false alarm and a highly accurate approximation for the probability of detection are derived. Specifically, this proposed approximation is also given in a closed form. These expressions, which can be of great interest to the radar community, are given in terms of the Gauss hypergeometric function and the Tricomi function. Besides, the proposed expressions can be easily implemented and computed using most popular scientific software packages such as MATLAB, Maple, and Mathematica. Our propositions are then tested and validated using numerical computing and Monte Carlo simulations. The obtained results demonstrate a high accuracy and significant computational advantages of the proposed expressions compared to the alternative ones. Moreover, the detection performance of the MLG-CFAR detector is also conducted assuming a homogenous clutter with a known shape parameter.

Citation

Mohamed SAHED , ELhadi KENANE , Ali KHALFA , fdjahli@yahoo.fr, , (2023), Performance Analysis of the MLG-CFAR Detector in Homogenous Gamma-Distributed Sea Clutter, IEEE Geoscience and Remote Sensing Letters, Vol:20, Issue:2023, pages:1-5, IEEE

Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.

Citation

Mohamed SAHED , , (2022), A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm, Engineering, Technology & Applied Science Research, Vol:12, Issue:6, pages:9680-9686, EOS Association

In this conference paper, the problem of channel optimization in GSM network in the city of M’sila is examined. In fact, this paper is a combination of theoretical study and practical activities which are carried out on site in collaboration with ATM Mobilis maintenance team. The main objective behind this study was made in order to enhance the quality of service of Mobilis cellular network by taking into account different parameters that come into play in the optimization process. We propose an optimization procedure by adding an extra E1 module, in order to overcome the degradation in the performance of GSM network and the bad quality of services (calls set-up problems, calls cuts, … etc). In doing that, we used the WebLCT software developed by Huawei. Obtained results show the efficiency of the proposed procedure and the quality of services is remarkably enhanced.

Citation

Mohamed SAHED , ,(2022), Transmission Channel Optimization of GSM Network in M’Sila City, Algeria,Mohamed SAHED,Unoversité de Ain Témouchent

Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.

Citation

ELhadi KENANE , Ali KHALFA , Mohamed SAHED , Farid Djahli, , (2022), A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm, Engineering, Technology & Applied Science Research, Vol:12, Issue:6, pages:9680-9686, ETASR

The Pareto distribution has been validated recently as a new statistical model for radar clutter backscattered from the sea surface for X-band high resolution maritime surveillance radar. Consequently, the derivation of the corresponding detection schemes has become of much interest, for operation under such clutter model, with the constant false alarm rate (CFAR) property. In this paper, a novel order statistic based CFAR detector operating in Pareto distributed clutter is introduced. This detector, which will be referred to us TGMR-CFAR, consists on using the trimmed geometric mean of ratios of consecutive order statistics obtained from samples of the sliding window. It is designed essentially to operate in a more realistic case, where nonhomogeneous clutter is considered. The author provides, here, a complete performance analysis for the TGMR-CFAR processor in homogenous and when interfering targets are present within the reference window. The exact expression of the false alarm probability of the proposed algorithms in a homogenous background is also presented. The detection performance of the TGMR-CFAR detector is investigated and compared with some exciting detectors by means of simulations.

Citation

Mohamed SAHED , ,(2021-08-12), Non-coherent Radar CFAR Detection Based on Ratios of Consecutive Order Statistics for Pareto Distributed Clutter,The First International Conference on Electronics, Artificial Intelligence and New Technologies (ICEAINT’2021),Oum El Bouaghi, Algeria

The interpolation method has been employed in non-integer-order moments estimators obtained in a previous work for the parameters of compound-Gaussian clutter model with inverse Gaussian texture (CGIG). Closed-form parameter estimators for the CGIG distributed clutter are derived with no special functions. These are achieved after a simple combination of three basic expressions: non-integer positive moments, non-integer negative moments and Bessel recurrence relation. Simulation results show that the derived estimators are well suited for multilook data and have a comparable accuracy as well as the numerically solved maximum-likelihood approach, but they are much simpler and faster to compute

Citation

Mohamed SAHED , AMAR Mezache , , (2018), Closed-form estimators of CGIG distributed parameters, Electronics Letters, Vol:54, Issue:2, pages:99-101, IET

In this paper, the problem to estimating the parameters of the K-distributed clutter plus noise is addressed. The existing nonasymptotic z-log-z estimators are only applicable to multiple-pulse data due to the use of the harmonic mean, which is undefined for single-pulse reverberation data. When either single- or multiple-pulse data are used, new closed-form estimators are derived in this work. They combine two interesting statistical ratios based on fractional positive- and negative-order moments so that the hypergemetric functions are absolutely eliminated. For single-pulse transmission, the proposed estimators demonstrate computational advantages compared to the existing approaches. Besides, in the multiple-pulse case, they yield about the same accuracy as the closed-form z-log-z-based estimators obtained in previous work.

Citation

Mohamed SAHED , AMAR Mezache , , (2017), Closed-form fractional-moment-based estimators for K-distributed clutter-plus-noise parameters, IEEE Transactions on Aerospace and Electronic Systems, Vol:53, Issue:4, pages:2094-2100, IEEE

In this study, closed-form expressions of the parameters estimators for Pareto distributed clutter plus noise are derived. To achieve this, the authors combine two statistical ratios based principally on non-integer positive and negative order moments. This allows absolute elimination of all related special functions contained in some existing estimators. For realistic applications, the proposed non-integer positive and negative order moments based estimators are suited to single-look and multi-look data. Simulation results show that the new estimators not only substantially reduce the computational requirements, but also yield accurate and lower variances parameters compared with the methods of moments and z log z obtained in previous works especially in the situations of heavy-tailed clutter.

Citation

Mohamed SAHED , AMAR Mezache , Faouzi Soltani, , (2017), Closed-form estimators for the Pareto clutter plus noise parameters based on non-integer positive and negative order moments, IET Radar, Sonar & Navigation, Vol:11, Issue:2, pages:359-369, IET

This study deals with the estimation problem of the inverse-Gaussian (IG)–compound-Gaussian (CG) distributed clutter parameters. Under the assumption of the absence of thermal noise, non-integer moments, log-moments and maximum-likelihood-based approaches are employed for model parameter estimation in the cases of single pulse and non-coherent integration of N pulses. The forms of the non-integer-order moments estimator and the [z log(z)] estimator are derived in terms of the Bessel and the exponential–integral functions, respectively. These formulas maintain monotonic nature over all values of the ratio between the shape parameter and the mean clutter power. For a single look data, the ML estimate combined with the mean clutter power is constructed in one dimension search for the shape parameter. By accommodating the mean square error metric, the estimation assessments are investigated via simulated and real data. Authors’ results illustrate that the derived non-integer-order moments estimator is asymptotically efficient for the IG–CG distributed clutter parameters particularly in heavy tailed clutter situations.

Citation

AMAR Mezache , Mohamed SAHED , Ahmed Bentoumi, , (2017), Parameter estimation for compound-Gaussian clutter with inverse-Gaussian texture, IET Radar, Sonar & Navigation, Vol:11, Issue:4, pages:586-596, IET

In this work, we extend the performance analysis of some single-pulse transmission signal processors that produce CFAR under the assumption of Pareto distributed clutter for more realistic approach where non coherent integrated pulses are transmitted. A logarithmic transformation approach, that enables true Gaussian CFAR processes to be translated for target detection in Pareto clutter scenario, is used. Consequently, the resulting CFAR processes depend all explicitly on the Pareto scale parameter. Thus, it will be assumed that this parameter is fully known. In the case of multi looks case, analytic expressions for the false alarm probability of the Geometric Mean (GM)-, Greatest Of (GO)- and Smallest Of (SO)-CFAR schemes are derived. Through simulated data, we evaluate the superiority of these developed CFAR detectors against the existing single-pulse CFAR detectors.

Citation

Mohamed SAHED , AMAR Mezache , ,(2015), Analysis of CFAR detection with multiple pulses transmission case in Pareto distributed clutter,2015 4th International Conference on Electrical Engineering (ICEE),Boumerdes, Algeria

In this paper, we present a novel [z log(z)]-based approach to the evaluation of estimators of the K-distributed clutter plus thermal noise parameters. In doing this, we start by deriving expressions of log-based moments of the received data, i.e., means of [log(z)] and [z log(z)], which are related to the parameters of the K plus noise distribution, the digamma, and the hypergeometric functions. Then, by accommodating a single pulse and noncoherent integration of N pulses, respectively, we first determine the new estimators in terms of log-based moments and first- and second-order moments. As the computation of these nonlinear estimates requires the use of numerical routines, we resort to the inverse of the harmonic mean of the received data to get equivalent but more interesting estimates in which expressions are independent of the confluent hypergeometric functions. Monte Carlo simulations show that the proposed estimators are more efficient than existing methods for various clutter plus noise situations.

Citation

Mohamed SAHED , AMAR Mezache , Toufik Laroussi, , (2015), A novel [z log (z)]-based closed form approach to parameter estimation of K-distributed clutter plus noise for radar detection, IEEE Transactions on Aerospace and Electronic Systems, Vol:51, Issue:1, pages:492-505, IEEE

In this study, the authors present two novel methods to estimate the parameters of the compound K-clutter in presence of additive thermal noise. Based on the parametric fitting to the tail of the clutter distribution, the first method estimates simultaneously the unknown parameters. This is achieved by comparing the experimental cumulative distribution function, drawn from the recorded data intensity, to a set of curves derived from the mathematical model. To this effect, a multidimensional unconstraint non-linear algorithm; namely the Nelder–Mead method is used to minimise the residuals between the real data and the fitted curve with unknown parameters. Considering always the presence of thermal noise and based on the neuronal approaches and fuzzy inference systems, the second method also yields an accurate estimation and guarantees an inexpensive computation of the unknown parameters when the clutter-to-noise ratio (CNR), is known a priori. To assess the obtained results, the authors illustrate the effectiveness of these new methods through Monte-Carlo simulations.

Citation

Mohamed SAHED , AMAR Mezache , Toufik Laroussi, Djamel Chikouche, , (2011), Two novel methods for estimating the compound K-clutter parameters in presence of thermal nois, IET radar, sonar & navigation, Vol:5, Issue:9, pages:934-942, IET

In this paper, we have introduced a method for estimating the parameters of the sea clutter compound K-distribution in presence of thermal noise by using the artificial neural networks (ANN) and the fuzzy neural networks (FNN) multi inputs/ single outputs (MISO) approaches. In order to accurate more the estimation, the FNN multi inputs/ multi outputs (MIMO) is also considered in this work to estimate simultaneously the shape and the scale parameters of the compound K-distribution. Using the back propagation (BP) algorithm and the genetic learning algorithm (GA), the proposed estimation procedures are trained based on the moments and the ratio of arithmetic and geometric means of the samples. However, the estimation accuracy obtained by these techniques are validated and compared for various values of the shape parameter with different sample sizes.

Citation

AMAR Mezache , Mohamed SAHED , , (2010), Parameter Estimation in K-Distributed Clutter with Noise using Nonlinear Networks, Journal of Automation & Systems Engineering, Vol:4, Issue:1, pages:1-12, JASE

In this paper, we propose an efficient approach to the estimation of the compound K-distribution parameters in presence of additive thermal noise. This is acquired by means of a multidimensional unconstrained nonlinear minimization algorithm based upon the Nelder-Mead direct search method. In doing this, we minimize the sum of squared residuals. The best fit is simply achieved by a direct comparison of the experimentally measured cumulative distribution function (CDF) of the recorded data with the set of curves derived from the model of interest. A good minimization can be reached only if the real CDF is accurately estimated. We show, particularly, that the new approach yields the best spiky clutter parameter estimation. The proposed estimator is more efficient than existing estimation methods.

Citation

AMAR Mezache , Mohamed SAHED , ,(2009), K-distribution parameters estimation based on the Nelder-Mead algorithm in presence of thermal noise,2009 International Conference on Advances in Computational Tools for Engineering Applications,Zouk Mosbeh, Lebanon

This paper investigates a new technique for estimating the shape parameter of a K-distribution based on fuzzy neural network (FNN). In order to improve the estimation accuracy with inexpensive computational requirement, the FNN estimator is used to accurate the solutions of the nonlinear equations and the inverse functions (g k (nucirc))of the Raghavanpsilas and ML/MOM (Maximum-Likelihood and Method Of Moments)methods respectively. A long this line, the estimated arithmetic and geometric means of data and the estimated function g k (nucirc) of the two estimators are combined and modeled by the FNN shape parameter estimator where an off-line optimization of their weights via genetic algorithms (GA) is considered. The simulation results are carried out to demonstrate the validity of the approach as well as the successfulness of the FNN estimator for low mean square error (MSE) of parameter estimates when compared with existing Raghavanpsilas, HOFM (Higher Order and Fractional Moments), ML/MOM and [(z)log(z)] estimators. Additionally, the FNN method yields parameter estimates with lower computational complexity which allows rapid calculation in real time implementation.

Citation

AMAR Mezache , Mohamed SAHED , ,(2008), A Method for Estimating the Parameters of the K-Distribution Using a Nonlinear Network Based on Fuzzy System and Neural Networks,2008 2nd International Conference on Signals, Circuits and Systems,Monastir, Tunisia