M. OUDIRA Houcine

Prof

Directory of teachers

Department

Departement of ELECTRONICS

Research Interests

signal processing renewable energy

Contact Info

University of M'Sila, Algeria

Email : houcine.oudira@univ-msila.dz

On the Web:

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Recent Publications

2024-12-24

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.
Citation

M. OUDIRA Houcine, (2024-12-24), "Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)", [national] Sustainability , MDPI

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2024-12-23

Comparative Analysis of Machine Learning Models for Fault Detection in Photovoltaic Systems

Fault detection (FD) in photovoltaic (PV) systems is crucial for ensuring efficient energy production, minimizing maintenance costs, and maintaining system reliability. In this study, we conducted a comprehensive evaluation of several machine learning techniques for FD in PV systems, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Random Forest (RF). The performance of these models was analyzed based on their ability to handle the dynamic and nonlinear behavior of PV systems. Results from our experiments affirm that RF outperformed the other models in terms of robustness to noisy data and overall accuracy. MLP and ANN exhibited strong capabilities in capturing complex patterns, while SVR and KNN showed promise in handling specific data structures. This study offers valuable insights into the application of machine learning techniques for fault detection in PV systems, with RF emerging as the most reliable solution for enhancing system performance and reducing downtime.
Citation

M. OUDIRA Houcine, (2024-12-23), "Comparative Analysis of Machine Learning Models for Fault Detection in Photovoltaic Systems", [international] 5th International Conference on Scientific and Academic Research on 23-24 December in 2024 a , Konya/Turkey.

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2024-10-31

Enhanced Parameters Extraction Procedure of PV Module Using Electrical Fish Optimization

Accurate and reliable fault detection procedures are crucial to ensure normal operation of photovoltaic (PV) systems. To this end, the use of trusted model is the major step and an essential tool for monitoring and supervision the system under consideration. In his paper, the unknown parameters of the one diode model (ODM) in outdoor conditions are accurately identified using an enhanced methodology. The proposed methodology combines a novel translation method to correct the I-V curves to reference conditions and analytical formulations to derive the considered parameters in any operating condition of irradiance and temperature. For determining the five unknown parameters at standard test conditions, an optimization algorithm namely the electrical fish optimization (EFO) is used. Based on the extracted parameters, the evolution of maximum power point model was modeled and simulated versus measurements of a grid connected real MPP system . The obtain results show the effectiveness of the proposed strategy.
Citation

M. OUDIRA Houcine, (2024-10-31), "Enhanced Parameters Extraction Procedure of PV Module Using Electrical Fish Optimization", [international] 2024 IEEE International Multi-Conference on Smart Systems & Green Process , Tunisia

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2024-06-21

Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection

This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy.
Keywords: PV prediction; computational modeling; regression techniques
Citation

M. OUDIRA Houcine, (2024-06-21), "Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection", [national] Energies , MDPI

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2024-01-05

Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier

Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance.
Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and
diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and
implementing a fault detection procedure using Random Forest Classifiers (RFCs).
Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current–
voltage translation method to predict the reference curve and employing the modified grey wolf optimization
(MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP)
coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two
RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier).
Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error
(RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array
output current simulations under cloudy conditions. Regarding the fault detection procedure, our results
demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing
other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP
Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).
Citation

M. OUDIRA Houcine, (2024-01-05), "Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier", [national] Energy Conversion and Management , Elsevier

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2023-12-01

Statistical Analysis of Sea-Clutter using K-Pareto, K-CGIG, and Pareto-CGIG Combination Models with Noise

In this paper, the combinations of two compound Gaussian distributions plus thermal noise for modeling measured polarimetric clutter data are proposed. The speckle components of the proposed models are formed by the exponential distribution, while the texture components are mainly modeled using three different distributions. For this purpose, the gamma, the inverse gamma, and the inverse Gaussian distributions are considered to describe these modulation components. The study involves the analysis of underlying mixture models at X-band sea clutter data, and the parameters of the combination models are estimated using the non-linear least squares curve fitting method. Compared to existing K, Pareto type II, and KK clutter plus noise distributions, experimental results show that the proposed mixture models are well matched for fitting sea reverberation data across various range resolutions.
Citation

M. OUDIRA Houcine, (2023-12-01), "Statistical Analysis of Sea-Clutter using K-Pareto, K-CGIG, and Pareto-CGIG Combination Models with Noise", [national] WSEAS TRANSACTIONS ON SIGNAL PROCESSING , WSEAS

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2023-10-28

Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production

The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is the main step for energy prediction and an important tool for monitoring and supervision the system. However, one of the problems of this technology is that as yet there are no models in the literature to directly calculate the daily dynamic maximum power of these kinds of PV systems. The development of models is an important task to support the application of this technology because it allows the prediction of the energy yield. In this paper a model based on artificial neural networks has been developed to address this important issue. The model takes into account the main important parameters that influence the electrical output of these kinds of systems which are direct irradiance, and module temperature. Comparative study with The simulated dynamic MPP model using the single diode model is presented to demonstrate the effectiveness of the considered approach. The obtained results show that the proposed model can be used for estimating the maximum power of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers with an adequate margin of error.
Citation

M. OUDIRA Houcine, (2023-10-28), "Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production", [international] 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES) , Egypt

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2022-11-26

Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm

Accurate and reliable fault detection procedures are crucial to ensure normal operation of photovoltaic (PV) systems. To this end, the use of trusted model is the major step and an essential tool for monitoring and supervision the system under consideration. In his paper a suggested procedure based on three main steps is presented. Firstly, the unknown parameters of the one diode model (ODM) are accurately identified using modified grey wolf (MGW) algorithm. Subsequently, based on the extracted parameters, the evolution of maximum power point model was modeled and simulated versus measurements of a grid connected real MPP system . Finally, the PV array is simulated to take out the MPP coordinates by using a PSIMTM/MatlabTM co-simulation, as well as an efficient fault detection process based on simple approach is implemented. The obtain results show the effectiveness of this method in detecting and diagnosing faults for real time application.
Citation

M. OUDIRA Houcine, (2022-11-26), "Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm", [international] the 2022 International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE) , M'sila University, Algeria,

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2022

Improved Decentralized SO-CFAR and GO-CFAR Detectors via Moth Flame Algorithm

Optimization of distributed constant false alarm rate (CFAR) system parameters is an essential part in radar detection applications. In this work, the moth flame algorithm (MFO) is proposed as an optimization tool to compute scale factors of distributed Greatest of-CFAR (GO- CFAR) and Smallest of-CFAR (SO-CFAR) detectors in presence of Gaussian disturbance. Local binary decisions are obtained firstly from different sensors, at the fusion center, a fusing rule is applied to obtain a global decision. Detection performances comparisons are conducted against previous works using Gray Wolf Optimization (GWO) and Biography Based Optimization (BBO) methods. Simulation results show that the proposed optimizer demonstrates a slight superiority in some cases for ensuring fixed probability of false alarm and higher detection probabilities.
Citation

M. OUDIRA Houcine, (2022), "Improved Decentralized SO-CFAR and GO-CFAR Detectors via Moth Flame Algorithm", [international] 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE) , M'sila, Algeria

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On the Performance of GLRT-LTD CFAR Processor in Correlated Pareto Clutter Under Different Estimators

Pareto type II distribution is a class of high-resolution sea-reverberation data models. Application of the GLRT-LTD (Generalized Likelihood Ratio Test Linear Threshold Detector) algorithm requires an accurate estimation of the clutter parameters. Under the assumption of correlated Pareto clutter, several estimators could be applied. In this work, we investigate the effect of the MLE (Maximum likelihood Estimation), Integer order moments, fractional-order moments, and zlog(z) estimators on the detection performance of the GLRT-LTD procedure. From simulated datasets, it is shown that approximate results are obtained by MLE and zlog(z) methods. Moreover, the zlog(z) approach is advantageous when complicated parameter estimation scenarios occur (i.e., correlation coefficient tends to one).
Citation

M. OUDIRA Houcine, (2022), "On the Performance of GLRT-LTD CFAR Processor in Correlated Pareto Clutter Under Different Estimators", [international] 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) , Sétif, Algeria

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Failure correction of linear antenna arrays with optimized element position using Grey Wolf Algorithm

The paper concerns the problem of monitoring linear
antenna arrays using grey wolf optimization method (GWO).
When an abnormal event (fault) affects an array of antenna
elements, the radiation pattern changes and significant deviation
from the desired design pattern can occur. In this paper,
reconfiguration of the amplitude and phase distribution of the
remaining working elements in a failed array is considered. This
latter can improve the side lobe levels (SLL) and also maintain
the null position. The main purpose of using the GWO technique
is its ease of implementation and a high performance
computational technique. To assess the strength of this new
scheme, several case studies involving different types of faults
were performed. Simulation results clearly have shown the
effectiveness of the proposed algorithm to monitor the failure
correction of linear antenna arrays.
Citation

M. OUDIRA Houcine, Nora Lakhlef, christoph Dumound, , (2022), "Failure correction of linear antenna arrays with optimized element position using Grey Wolf Algorithm", [national] iJIST , Special Issue on Smart Cities, Optimization and Modeling of Complex Systems

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2020

Failure Correction of Linear Antenna Array using Grey Wolf Optimization

The work concerns the problem of monitoring linear antenna arrays using a new scheme denoted as grey wolf optimization (GWO) algorithm. When a strange event (fault) affects an antenna array, the radiation diagram changes and important deviation from the preferred pattern can occur. In this work, re-adjusted of the amplitude and phase distribution of the lasting working elements in a faulty array is considered. This latter can improve the side lobe levels (SLL) and also keep the directivity. The main point of using the GWO technique is its ease of implementation and a high performance computational technique. To assess the strength of this new scheme, different types of failures as case studies were performed. Simulation results evidently have shown the efficiency of the proposed algorithm to correct the failure correction of linear antenna arrays.
Citation

M. OUDIRA Houcine, Nora Lakhlef, Christoph. Dumond, , (2020), "Failure Correction of Linear Antenna Array using Grey Wolf Optimization", [international] 6th IEEE Congress on Information Science and Technology (CiSt) , Agadir - Essaouira, Morocco

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Parameter Estimation of Rayleigh-Generalized Gamma Mixture Model

The estimation problem of three parameters Rayleigh-Generalized Gamma Mixture (R-GG) radar clutter model is addressed in this paper. Expressions of integer order moments, non-integer order moments and logarithmic moments are presented in such away the scale parameter of the R-GG probability density function (PDF) is eliminated and a two-dimensional estimators labeled HOME, NIOME and [zlog(z)] methods are obtained. Due to the presence of gamma function with fractional variables, these estimators cannot be given in closed forms. The fitness function for each estimator is given as a sum of squared errors of nonlinear equations. Using a numerical routine based upon the simplex search algorithm, the proposed methods were tested firstly on artificial data. Tail fitting of the R-GG model and the standard K-distribution (i.e., special case of the R-GG) is assessed against recorded radar data. The accuracy of the R-GG model and the proposed estimation methods is satisfactory, with the most accuracy of the [zlog(z)] method.
Citation

M. OUDIRA Houcine, (2020), "Parameter Estimation of Rayleigh-Generalized Gamma Mixture Model", [national] Instrumentation Mesure Métrologie , IIETA

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Parameter Estimation in Radar K-Clutter Plus Noise Based on Otsu’s Algorithm

In a previous work, it has been shown that the application of a modified fractional order moment (MFOM) estimator leads to the same accuracy as the [zlog(z)] method with lower computation complexity. However, undesirable estimation performances have been observed for single look data, low sample sizes and large values of the K-distribution shape parameter. Moreover, the application of positive and negative order moments estimators (PNOME) has a serious impact on the estimation accuracy of the shape parameter. To reduce this sensitivity, it is important to apply thresholding approaches in the case of a single pulse transmission. To this effect, single and double thresholding estimators are proposed in this paper and the Otsu’s algorithm is used to compute underlying thresholds. On the basis of Monte-Carlo simulation, the performances of the proposed estimators are assessed against moments and [zlog(z)] methods. Experiment examples indicate that the thresholding approaches based on the Otsu’s algorithm is more accurate with computational advantages than existing estimators.
Citation

M. OUDIRA Houcine, (2020), "Parameter Estimation in Radar K-Clutter Plus Noise Based on Otsu’s Algorithm", [national] Ingénierie des systèmes d’ information , IIETA

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Radar CFAR detection in Weibull clutter based on zlog(z) estimator

In this paper, the zlog(z) based estimator for constant false alarm
rate (CFAR) detection in Weibull clutter is proposed. This estimation
method is obtained in terms of the digamma function where the
estimates of the shape parameter are determined by the interpolation
tool. The non-integer order moments estimator (NIOME) is also
given and coincides the zlog(z) estimation results for low values of
the moment’s fractional order. Via simulated data, it is shown that
the CFAR detection performances based on the zlog(z) estimator
have almost similar results as well as the existing maximum likelihood
(ML) CFAR detector, but with low time-consuming which is
very important in real-time applications.
Citation

M. OUDIRA Houcine, (2020), "Radar CFAR detection in Weibull clutter based on zlog(z) estimator", [national] Remote Sensing Letters , Taylor and Francis

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2019

Optimization of Distributed CFAR Detection using Grey Wolf Algorithm

In this paper, decentralized constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Grey Wolf Optimization technique is investigated. For independent signals with known power, optimal thresholds of local Greatest Of-CFAR and Smallest Of-CFAR detectors are optimized simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, both the Biogeography Based Optimization and the Grey Wolf Optimization tools are used to conduct distributed CFAR detection comparisons. In terms of achieving fixed probabilities of false alarm and higher probabilities of detection, simulation results show that the new GWO scheme performs better than the BBO method described in the literature in most cases.
Citation

M. OUDIRA Houcine, amel Gouri, , (2019), "Optimization of Distributed CFAR Detection using Grey Wolf Algorithm", [national] Procedia Computer Science , Elsevier

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Effect of fractional order moments on parameter estimation of K-Clutter plus noise

Parameter estimation of radar clutter is considered as a critical task for the development of target detectors. This work covers the shape parameter estimation of K-clutter plus noise using a modified fractional order moments based approach (MFOME). Closed form of the FOME with fixed fractional order moment is derived in a previous work [11] where undesirable estimation errors are produced in some cases with single look data and low sample sizes. In order to achieve better estimation performance, the fractional order moment and the shape parameter should be optimized together. To this effect, a numerical formula of the corresponding fitness function is given and unconstrained nonlinear optimization method based on the Nelder-Mead simplex algorithm is used to compute the unknown parameters. Via simulated K-clutter plus noise data, the effect of the fractional order on the estimation accuracy is studied firstly. Then, comparisons with existing HOME, FOME and [zlog(z)] methods are conducted to illustrate the efficiency of the proposed estimator
Citation

M. OUDIRA Houcine, Taha Hocine Kerbaa, , (2019), "Effect of fractional order moments on parameter estimation of K-Clutter plus noise", [national] 6th International Conference on Image and Signal Processing and their Applications (ISPA), , Mostaganem Algeria

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Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Biogeography Based Optimization (BBO) method is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the open literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection.
Citation

M. OUDIRA Houcine, amel.gouri@univ-msila.dz, , (2019), "Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool", [national] International Journal of Information Science & Technology , https://innove.org

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Model Selection of Sea Clutter Using Cross Validation Method

This work concerns a model selection of sea radar clutter used for adaptive target detection. Three distributions without thermal noise are considered; K, Pareto type II and compound Gaussian inverse Gaussian (CG-IG) with scale and shape parameters. The model selection is carried out by comparing the experimental complementary cumulative distribution function (CCDF), drawn from the recorded data intensity, to a set of the CCDF curves derived from the underling models. To do this, the cross validation technique is used after dividing a set of data into four segments. The best distribution is selected in which the mean of the means square of errors (MSEs) between the real CCDF curve and the fitted CCDF curve is minimal. To select a suited statistical model in most cases, fitting comparisons are illustrated through Intelligent PIxel X-band radar database (IPIX). From this study, it is shown that the appropriate model is?
Citation

M. OUDIRA Houcine, taha Houcine.kerbaa@univ-msila.dz, , (2019), "Model Selection of Sea Clutter Using Cross Validation Method", [national] Procedia Computer Science , ELSEVIER

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Printed Circular Antenna Array for Reduce SLL and High Directivity Using Cuckoo Search Algorithm

This paper presents the synthesis and optimization of printed circular antenna array using the Cuckoo Search Algorithm (CSA). The CSA is a simple and effective global optimization algorithm which can be used to solve linear and non-linear problems. It has been applied to solve a wide variety of optimization problems. In our case, it is used to find the optimum weights of amplitudes and phases of complex feeding currents of a uniform printed circular antenna array. The goal to be achieved is a directional radiation pattern. To study the effect of these optimizations, a Gaussian centered at 90° is considered in our simulations. The obtained results are promising in terms of reduced Side Lobe Level (SLL) and directional factor array.
Citation

M. OUDIRA Houcine, Nora Lakhlef, Christophe Dumondc, , (2019), "Printed Circular Antenna Array for Reduce SLL and High Directivity Using Cuckoo Search Algorithm", [national] Procedia Computer Science , ScienceDirect

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Priority Management of the Handoff Requests in Mobile Cellular Networks

Due to the motion of mobile station with respect to the base station, the handover is required frequently in the communication process. In this paper, assuming that the user location and speed can be determined, we propose a suitable scheme for managing a queuing of handover requestes in wireless cellular network. The principle of the proposed method is the use of a separate queue for each transceiver in the cell (3TRX per cell) instead of using a single one and we consider that handover request to cell is queued with dynamic priority discipline; highest priority (head of the queue), least priority (joins the end of the queue). Fixed channel allocation is considered and call blocking probability (CBP), handover failure probability (HFP) are obtained as a results. In order to choose the best model which reduces significantly the handover failure probability, a comparison between the proposed model and the classical one is considered. Simulation results highlight that the newly proposed architecture can guarantee superior performance with respect to its competitor.
Citation

M. OUDIRA Houcine, (2019), "Priority Management of the Handoff Requests in Mobile Cellular Networks", [national] Procedia Computer Science , elsevier

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Optimization of Suitable Propagation Model for Mobile Communication in Different Area

In this paper, the most widely used empirical path loss models are compared to real data; the most appropriate one (COST-231) has been optimized using three different algorithms to fit measured data for mobile communication system. The performance of the adjusted Cost-231 model obtained by the proposed methods is then compared to the experimental data. The concert criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). From numerical simulations, it was noticed a significant improvement in the prediction made by the proposed algorithm with a slight superiority of Invasive weed Optimization algorithm in term of lower RMSE value in one hand and in term of convergence speed on the other hand compared to PSO and ABC algorithm
Citation

M. OUDIRA Houcine, djouane lotfi, , (2019), "Optimization of Suitable Propagation Model for Mobile Communication in Different Area", [national] International Journal of Information Science & Technology , http://www.innove.org/

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Effect of Fractional Order Moments on Parameter Estimation of K-Clutter plus Noise

Parameter estimation of radar clutter is considered as a critical task for the development of target detectors. This work covers the shape parameter estimation of K-clutter plus noise using a modified fractional order moments based approach (MFOME). Closed form of the FOME with fixed fractional order moment is derived in a previous work [11] where undesirable estimation errors are produced in some cases with single look data and low sample sizes. In order to achieve better estimation performance, the fractional order moment and the shape parameter should be optimized together. To this effect, a numerical formula of the corresponding fitness function is given and unconstrained nonlinear optimization method based on the Nelder-Mead simplex algorithm is used to compute the unknown parameters. Via simulated K-clutter plus noise data, the effect of the fractional order on the estimation accuracy is studied firstly. Then, comparisons with existing HOME, FOME and [zlog(z)] methods are conducted to illustrate the efficiency of the proposed estimator.
Citation

M. OUDIRA Houcine, (2019), "Effect of Fractional Order Moments on Parameter Estimation of K-Clutter plus Noise", [international] 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA) , Mostaganem, Algeria

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A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module

The use of an adequate model of photovoltaic module for the energy prediction is an important tool. To this end, PV modeling primarily involves the formulation of the non-linear current versus voltage (I-V) curve. This paper presents an application of the Nelder-Mead simplex search method for identifying the parameters of solar cell and photovoltaic module models. The proposed technique is used to identify the unknown model parameters, namely, the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. The extracted parameters have been tested against several static IV characteristics of the PV module collected at different operating condition. Comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness of the proposed approach. A dynamic MPP model has also been derived and simulated using the extracted parameters against MPP real dynamic measurements of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers.
Citation

M. OUDIRA Houcine, (2019), "A prediction Model Based on Nelder-Mead Algorithm for the Energy Production of PV Module", [national] International Journal of Information Science & Technology, IJIST, , ijist

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2018

Empirical Path Loss Models Optimization for Mobile Communication

In this paper, the most widely used empirical path loss models are compared to real data; the most appropriate one (COST-231) has been optimized using three different algorithms to fit measured data for mobile communication system. The performance of the adjusted Cost-231 model obtained by the proposed methods is then compared to the experimental data. The concert criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). From numerical simulations, it was noticed a significant improvement in the prediction made by the proposed algorithm with a slight superiority of PSO algorithm in term of lower RMSE value in one hand and in term of convergence speed in the other hand compared to GA and N-M method.
Citation

M. OUDIRA Houcine, lotfi djouane, , (2018), "Empirical Path Loss Models Optimization for Mobile Communication", [international] 2018 IEEE 5th International Congress on Information Science and Technology (CiSt) , Marrakech, Morocco

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