Faculty of Science and Technology

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3D Mobility, Resizing and Mobile Sink Nodes in Reconfigurable Wireless Sensor Networks based on Multi-agent Architecture under Energy Harvesting Constraints
(SCITEPRESS, 2020-01) Hanene Rouainia; Hanen Grichi; Laid Kahloul; Mohamed Khalgui
This paper deals with reconfigurable wireless sensor networks (RWSNs) to be composed of a set of sensor nodes, which monitor the physical and chemical conditions of the environment. RWSNs adapt dynamically their behaviors to their environment. The main challenge in RWSN is to keep the network alive as long as possible. We apply a set of solutions for energy problems by using 3D mobility, resizing and mobile sink nodes. These solutions are based on a multi-agent architecture employing a wireless communication protocol. Moreover, we develop an application named RWSNSim that allows us to simulate an RWSN and apply the proposed solutions. The performance of the proposed approach is demonstrated through a case study. The case study consists of surveying of fire in a forest which is simulated with RWSNSim application.
A Collocation Method for Fredholm Integral Equations of the First Kind via Iterative Regularization Scheme
(Mathematical Modelling and Analysis, 2023) Bechouat Tahar
To solve the ill-posed integral equations, we use the regularized collocation method. This numerical method is a combination of the Legendre polynomials with non-stationary iterated Tikhonov regularization with fixed parameter. A theoretical justification of the proposed method under the required assumptions is detailed. Finally, numerical experiments demonstrate the efficiency of this method.
A computational study of the inclusion of β‑cyclodextrin and nicotinic acid: DFT, DFT‑D, NPA, NBO, QTAIM, and NCI‑RDG studies
(Journal of Molecular Modeling, 2020) Houdhaifa R. Belhouchet; Tahar Abbaz; Amel Bendjedou; Abdelkrim Gouasmia; Didier Villemin
Forming complexes with cyclodextrins can protect nicotinic acid (vitamin B3) from premature metabolism and enhance the solubility and stability of this drug. In this work, the formation of the inclusion complex of the neutral form of nicotinic acid and β-cyclodextrin was achieved. The complex is modeled using PM3, PM6-D4H3, and PM7, by considering two orientations of the guest: A and B, one is from wide to narrow rim, and the second is from narrow to the wide rim ,respectively. The global minima positions were re-optimized using three density function methods: MN-15, B3LYP, and PW6B95-D3 with polarized Pople basis set 6-31G(d) in gas and aqueous phase. Orientation A showed the minimum complexation energy where the carboxylic functional group of nicotinic acid is located on the primary hydroxyl rim of β-cyclodextrin and the pyridine ring is totally embedded in the cavity. To further our study on the nature of complexation and the interactions of this host–guest system, different calculations were done. The reactivity indices showed that orientation A is harder than B and more electrophilic; the charge transfer occurred from the host to the guest and was confirmed by the natural population analysis (NPA). The natural bond orbitals (NBO) reveal the delocalization of orbitals between the host and the guest, quantum theory of atoms in molecules (QTAIM) analysis, and non-covalent interaction (NCI) analysis based on a reduced density gradient (RDG) give a detailed description of the nature of interactions between the host and the guest such as the hydrogen bonding and van der Waals interaction, and confirmed the stability of the complex given by the orientation A.
A fast iterative regularizationmethod for ill-posed problems
(Springer, 2024-11-27) Bechouat Tahar
Ill-posed problems manifest in a wide range of scientific and engineering disciplines. The solutions to these problems exhibit a high degree of sensitivity to data perturbations. Regularization methods strive to alleviate the sensitivity exhibited by these solutions. This paper presents a fast iterative scheme for addressing linear ill-posed problems, similar to nonstationary iterated Tikhonov regularization. Both the a-priori and a-posteriori choice rules for regularization parameters are provided, and both rules yield error estimates that are order optimal. In comparison to the nonstationary iterated Tikhonov method, the newly introduced method significantly reduces the required number of iterations to achieve convergence based on an appropriate stopping criterion. The numerical computations provide compelling evidence regarding the efficacy of our new iterative regularization method. Furthermore, the versatility of this method extends to image restorations.
A finite element model for estimating time-dependent reliability of a corroded pipeline elbow
(Emerald, 2021-04-13) Nahal Mourad; Khelif Rabia
A modified incremental density based clustering algorithm
(IEEE, 2022) Aida Chefrour
Cluster analysis, generally known as clustering, is a technique for separating data into groups (clusters) of similar objects. Except if the system is completely retrained, traditional clustering classifiers will be unable to learn new information and knowledge (attributes, examples, or classes). Only incremental learning, which outperforms when new data objects are introduced into an existing database, can solve this problem. These evolutionary strategies are applied to dynamic databases by updating the data. We’ll choose to study the Incremental Density- Based Spatial Clustering of Applications with Noise algorithm because of its capacity to discover arbitrary clusters and identify noise. In this study, a modified version of the Incremental Density Based Clustering Algorithm using an Adaptive Median Filtering Technique was used. The difference between our previous proposed AMF-IDBSCAN and the proposed algorithm developed in this work is in the evaluation performance stage. The key idea consists of a database change in the case of introducing new data items to an existing database in order to improve performance. We conducted several experiments on benchmark and synthetic data collected from the University of California Irvine repository in terms of the Generalized Dunn Index, Davies Bouldin Index, and change of time (milliseconds) with the increment of data in the original database. Experiments with datasets of various sizes and dimensions show that the proposed algorithm enhances clustering when compared to several current incremental wellknown techniques.
A new class of nonlinear conjugate gradient coefficients for unconstrained optimization
(Asian-European Journal of Mathematics, 2022) Amina Boumediene; Tahar Bechouat; Rachid Benzine; Ghania Hadji
The nonlinear Conjugate gradient method (CGM) is a very effective way in solving largescale optimization problems. Zhang et al. proposed a new CG coefficient which is defined by BNPRPk . They proved the sufficient descent condition and the global convergence for nonconvex minimization in strong Wolfe line search. In this paper, we prove that this CG coefficient possesses sufficient descent conditions and global convergence properties under the exact line search.
A new hybrid CG method as convex combination
(Mathematical Foundations of Computing, 2023) Amina Hallal; Mohammed Belloufi; Badreddine Sellami
Conjugate gradient methods are among the most efficient methods for solving optimization models. In this paper, a newly proposed conjugate gradient method is proposed for solving optimization problems as a convex combination of the Harger-Zhan and Dai-Yaun nonlinear conjugate gradient methods, which is capable of producing the sufficient descent condition with global convergence properties under the strong Wolfe conditions. The numerical results demonstrate the efficiency of the proposed method with some benchmark problems.
A new hybrid conjugate gradient algorithm based on the Newton direction to solve unconstrained optimization problems
(Journal of Applied Mathematics and Computing, 2023-03-25) Naima Hamel, Noureddine Benrabia, Mourad Ghiat, Hamza Guebbai
In this paper, we propose a new hybrid conjugate gradient method to solve unconstrained optimization problems. This new method is defined as a convex combination of DY and DL conjugate gradient methods. The special feature is that our search direction respects Newton’s direction, but without the need to store or calculate the second derivative (the Hessian matrix), due to the use of the secant equation that allows us to remove the troublesome part required by the Newton method. Our search direction not only satisfies the descent property, but also the sufficient descent condition through the use of the strong Wolfe line search, the global convergence is proved. The numerical comparison shows the efficiency of the new algorithm, as it outperforms both the DY and DL algorithms.
A new hybrid conjugate gradient method of unconstrained optimization methods
(Asian-European Journal of Mathematics, 2022) Chenna Nasreddine; Badreddine Sellami; Belloufi Mohammed
In this paper, we present a new hybrid method to solve a nonlinear unconstrained optimization problem by using conjugate gradient, which is a convex combination of Liu–Storey (LS) conjugate gradient method and Hager–Zhang (HZ) conjugate gradient method. This method possesses the sufficient descent property with Strong Wolfe line search and the global convergence with the strong Wolfe line search. In the end of this paper, we illustrate our method by giving some numerical examples.
A new hybrid conjugate gradient method of unconstrained optimization methods
(2022) Chenna Nasreddine; Sellami Badreddine; Belloufi Mohammed
In this paper, we present a new hybrid method to solve a nonlinear unconstrained optimization problem by using conjugate gradient, which is a convex combination of Liu–Storey (LS) conjugate gradient method and Hager–Zhang (HZ) conjugate gradient method. This method possesses the sufficient descent property with Strong Wolfe line search and the global convergence with the strong Wolfe line search. In the end of this paper, we illustrate our method by giving some numerical examples.
A new optimization method based on Perry's idea through the use of the matrix power
(Journal of Applied Mathematics and Computational Mechanics, 2021-04-03) Imane Hafaidia, Noureddine Benrabia, Mourad Ghiat, Hamza Guebbai
The purpose of this paper is to present a new conjugate gradient method for solving unconstrained nonlinear optimization problems, based on Perry’s idea. An accelerated adaptive algorithm is proposed, where our search direction satisfies the sufficient descent condition. The global convergence is analyzed using the spectral analysis. The numerical results are described for a set of standard test problems, and it is shown that the performance of the proposed method is better than that of the CG-DESCENT, the mBFGS and the SPDOC
A New Simulation Tool for Sensor Networks Based on an Energy-Efficient and Fault-Tolerant Methodology
(Springer, Cham, 2023-07) Hanene Rouainia; Hanen Grichi; Laid Kahloul; Mohamed Khalgui
Recently, reconfigurable wireless sensor networks (RWSNs) have attracted a lot of attention in research and industrial communities. They became more complex and dynamic systems which led to the emergence of many challenges. The lack of energy, real-time constraints, and software and hardware failures are the most important challenges in RWSNs. Indeed, several solutions have proposed to come up with these challenges. To avoid huge costs in terms of money, time, and effort of real experimentation, networks’ simulation tools have become an essential necessity to study the impact of the proposed solutions. In this work, we propose a new energy-efficient and fault-tolerant methodology that composed of a set of solutions summarized in the use of mobile sink nodes (MSNs), application of the mobility, resizing, and test packet technique using a multi-agent architecture and an energy-efficient routing protocol. Moreover, we propose a new discrete-event simulation tool named RWSNSim designed for sensor networks (WSNs & RWSNs). We present its description, modeling, and provided services. The proposed simulation tool allows simulating sensor networks with and without application of the proposed methodology. Finally, we simulate a case study using RWSNSim in a 3D environment which proves the effectiveness of the proposed methodology and demonstrate the efficiency of the suggested simulation tool.
A novel conformable fractional approach to the Brusselator system with numerical simulation
(2024-03-12) Mohamed Lamine Merikhi · Hamza Guebbai · Noureddine Benrabia · Mohamed Moumen Bekkouche
In this study, we delve into a comprehensive analysis of the Brusselator system, combining both analytical and numerical approaches. In summary, our initial approach involves revisiting the classic Brusselator system using a conformable fractional derivative-based approach. Starting from this innovative reformulation, we obtain a nonlinear Volterra-type equation. This transformation allows us to simultaneously demonstrate the existence and uniqueness of the solution, while providing us with the necessary tools to develop an efficient numerical approximation method to solve the problem. Subsequently, we present a numerical simulation based on the Nyström method.
A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++
(Lavoisier, 2019) Aida Chefrour; Labiba Souici-Meslati; Iness Difi; Nesrine Bakkouche
Incremental learning refers to the learning of new information iteratively without having to fully retain the classifier. However, a single classifier cannot realize incremental learning if the classification problem is too complex and scalable. To solve the problem, this paper combines the incremental support vector machine (ISVM) and the incremental neural network Learn++ into a novel incremental learning algorithm called the ISVM-Learn++. The two incremental classifiers were merged by parallel combination and weighted sum combination. The proposed algorithm was tested on three datasets, namely, three databases Ionosphere, Haberman's Survival, and Blood Transfusion Service Center. The results show that the ISVM Learn ++ achieved a learning rate of 98 %, better than that of traditional incremental learning algorithms. The research findings shed new light on incremental supervised machine learning.
A quadrotor controlled in real-time using hand gestures and ROS2 multi-node communication within GAZEBO 3D environment
(2023-05-05) Hamza Djizi, Abdelaziz LAKEHAL and Zoubir ZAHZOUH
A transfer learning-basedrecommender system
(2024) AYA SAHRAOUI
Transformers have revolutionized the field of Automatic Data Processing Natural Language (NLP) producing remarkable results In this study, we propose an extension of this success by introducing A new recommendation system based on Transformers. We exploited their ability to capture complex relationships between data to provide insights precise and personalized recommendations. Using extracted data from Amazon, we conducted an in-depth experiment to evaluate the effectiveness of our recommendation model. The results demonstrated the relevance and effectiveness of our approach, highlighting the advantage of transfer learning in improving recommendation performance. This research opens new perspectives in the field of systems of recommendation by exploiting recent advances in NLP to provide more precise and relevant recommendations to users. ------------------------------------------------------------------------------ لقد أحدثت المحولات ثورة في مجال المعالجة التلقائية للبيانات اللغة الطبيعية (NLP) تحقق نتائج ملحوظة في هذه الدراسة، نقترح تمديد هذا النجاح من خلال تقديم جديد نظام التوصية على أساس المحولات. لقد استغلناهم القدرة على التقاط العلاقات المعقدة بين البيانات لتقديم رؤى توصيات دقيقة وشخصية. باستخدام البيانات المستخرجة من أمازون، أجرينا تجربة متعمقة للتقييم فعالية نموذج التوصية لدينا. وأظهرت النتائج أهمية وفعالية نهجنا، وتسليط الضوء على ميزة نقل التعلم في تحسين أداء التوصية. يفتح هذا البحث آفاقا جديدة في مجال نظم التوصية من خلال استغلال التطورات الحديثة في البرمجة اللغوية العصبية لتقديمها توصيات أكثر دقة وذات صلة للمستخدمين. ------------------------------------------------------------------------------ Les Transformers ont révolutionné le domaine du Traitement Automatique du Langage Naturel (NLP) en produisant des résultats remarquables Dans cette étude, nous proposons une extension de cette réussite en introduisant UN nouveau système de recommandation basé sur les Transformers. Nous avons exploité leur capacité à capturer les relations complexes entre les données pour proposer des recommandations précises et personnalisées. En utilisant des données extraites d'Amazon, nous avons mené une expérimentation approfondie pour évaluer l'efficacité de notre modèle de recommandation. Les résultats ont démontré la pertinence et l'efficacité de notre approche, mettant en évidence l'avantage du transfert d'apprentissage dans l'amélioration des performances de recommandation. Cette recherche ouvre de nouvelles perspectives dans le domaine des systèmes de recommandation en exploitant les avancées récentes en NLP pour fournir des recommandations plus précises et pertinentes aux utilisateurs.
A Variant of Projection-Regularization Method for ill-posed linear operator equations
(International Journal of Computational Methods, 2020-09-20) Bechouat Tahar; Boussetila Nadjib; Rebbani Faouzia
In the present paper, we report on a strategy for computing the numerical approximate solution for a class of ill-posed operator equations in Hilbert spaces: Kf = g. This approach is a combination of Tikhonov regularization method and the finite rank approximation of K. Finally, numerical results are given to show the effectiveness of this method.
accélération méthodes itératives appliquées a l'optimisation non inéaire
(2021-06-27) DELLADJI Sarra
ملخص في هذه الأطروحة التي تهدف إلى تسريع تقارب طرق التدرج المترافق الكلاسيكية، اقترحنا ثلاث خوارزميات تعمل على هذا المبدأ، إذ اعتمدنا بشكل خاص على تقنية تسريع شهيرة و هي تهجين خوارزميتين من خلال مزج محدب لمعاملاتهما المحددان لمختلف طرق التدرج المترافق. بعد إثبات تقارب الخوارزميات المقترحة وباستخدام دوال تجريبية، أثبتنا من خلال التجارب العددية أن هذه الخوارزميات أكثر كفاءة و نجاعة مقارنة بالخوارزميات المدمجة. ------------------------------ Abstract In this thesis aims to accelerate the convergence of classical conjugate gradient methods, we have proposed three algorithms based on this concept, where we specifically relied on a famous acceleration technique which is the hybridization of two algorithms, by convex combination of their coefficients that determine the different standard conjugate gradient methods. After having proven the convergence of the proposed algorithms, using experimental functions, we have shown through numerical experiments that these algorithms are more efficient and perform than the combined algorithms. ----------------- Résumé Dans cette thèse, qui vise à accélérer la convergence des méthodes classiques de gradient conjugué, nous avons proposé trois algorithmes qui fonctionnent sur ce principe, où nous nous sommes spécifiquement appuyés sur une célèbre technique d'accélération qui est l'hybridation de deux algorithmes, par combinaison convexe de leurs coefficients qui déterminent les différentes méthodes de gradient conjugué standard. Après avoir prouvé la convergence des algorithmes proposés, en utilisant des fonctions expérimentales, nous avons montré à travers des expériences numériques que ces algorithmes sont plus efficaces et performants que les algorithmes combinés.
Acquisition et Traitement de la Parole Pour les Jouets Electronique
(2024) Bachir haouam
In this work, we proposed the realization of a library of IPs (hardware and/or software) especially for the design of electronic parts of toys and electronic games. The hardware IPs, initially realized on several levels (RTL/VHDL), are simulated, synthesized and tested on FPGA platforms using Xilinx tools. The software IPs are realized using the principle of co-design around the ARM processor. FPGA circuits used only for rapid prototyping before moving on to final ASIC-type circuits. Simulation and synthesis results are displayed and discussed at each level of our work. The proposed library can be enriched with new IPs for the design of various applications. ---------------------------------------------------------------------------------------- في هذا العمل، اقترحنا إنشاء مكتبة عناوينIP (أجهزة و/أو برامج) خصيصًا لتصميم األجزاء اإللكترونية من األلعاب واأللعاب اإللكترونية، وال سيما عناوينIPالخاصة باألجهزة لمعالجة الكالم. تتم محاكاة عناوينIPلألجهزة، التي تم إنتاجها مبدئيًا على عدة مستويات (RTL/VHDL،XSG، وما إلى ذلك)، وتوليفها واختبارها على منصاتFPGAواستخدام أدوات منXilinxوأداةXSGمنMatlab-Simulink . يتم إنتاج عناوينIPالخاصة بالبرامج باستخدام مبدأ التصميم المشترك حول معالجARM . تُستخدم دوائر FPGAفقط للنماذج األولية السريعة قبل االنتقال إلى الدوائر النهائية من نوعASIC. يتم عرض نتائج المحاكاة والتوليف ومناقشتها في كل مستوى منمستويات عملنا. يمكن إثراء المكتبة المقترحة بعناوينIPجديدة لتصميم تطبيقات متنوعة ومتنوعة. ----------------------------------------------------------------------------------------- Dans ce travail, nous avons proposé la réalisation d’une bibliothèque d’IPs (matériels et/ou logiciels) spécialement pour la conception des parties électroniques des jouets et des jeux électroniques en particuliers les IPs matériels pour le traitement de la parole. Les IPs matériels, réalisés initialement selon plusieurs niveaux (RTL/VHDL, XSG, …etc.), sont simulés, synthétisés et testés sur des plateformes FPGA et en utilisant les outils de chez Xilinx et l’outil XSG de Matlab-Simulink. Les IPs logiciels sont réalisés en utilisant le principe de co-design autour du processeur ARM. Les circuits FPGA sont utilisés uniquement pour le prototypage rapide avant de passer vers les circuits finals de type ASIC. Les résultats de simulation et de synthèse sont affichés et discutés à chaque niveau de notre travail. La bibliothèque proposée peut être enrichir avec des nouveaux IPs pour la conception des applications diverses et variées.