Institut de Génie Electrique et d'Electronique
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Item Design, simulation, and performance assessment of MPLS core networks(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Baouali, Chems Eddine; Benzaoui, MessaoudaWith the continuous growth of the Internet and the increasing diversity of user demands, service providers face the challenge of delivering fast, reliable, and secure communication across large-scale networks. Multi-Protocol Label Switching (MPLS) has become a cornerstone technology in modern backbone networks because it goes beyond traditional IP routing by combining flexibility, efficiency, and support for advanced services. This project explores the design and simulation of an MPLS-based core network using GNS3, focusing on how Internet Service Providers (ISPs) can manage complex customer requirements while ensuring performance and scalability. Provider Edge (PE) routers are considered as the key control points, enabling customer isolation through Virtual Routing and Forwarding (VRF), resolving IP address overlaps with external BGP (eBGP), and ensuring seamless communication across multiple domains. While, Label Distribution Protocol (LDP) is used to establish label-switched paths. The findings show that MPLS is not only a powerful forwarding technique but also a strategic enabler for ISPs to deliver advanced services with efficiency and reliability. Future work will emphasize the deeper integration of QoS and MPLS-TE, providing a more adaptive and intelligent backbone capable of meeting the ever-growing demands of next-generation networks.Item Advanced classification methods applied to ECG signals(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Merdji, Fadia; Belkadi, Mohamed AmineThis work presents an advanced automated ECG classificatio nsyste mtha taddresse scritical challenges in cardiac arrhythmia diagnosis by integrating sophisticated signal processing with state-of-the-art deep learning techniques. Beginning with a thorough examination of cardiac electrophysiology, ECG waveform characteristics, and noise artifacts, the research establishes a robust foundation for subsequent algorithmic development. The study systematically evaluates traditional machine learning methods (SVM, KNN, DNN) and modern deep learning archi- tectures, culminating in an innovative multi-stage framework featuring optimized Butterworth filtering ,hybri dFourie rwavele tfeatur eextraction ,an d aspecialize d2 DCN Ndesign .Exten-sive validation on the MIT-BIH Arrhythmia Database demonstrates exceptional performance, achieving 98.60% classificatio naccurac yan d99 %precisio nfo rcritica larrhythmia swhil ere-vealing important insights about minority class recognition challenges. The work makes three key contributions: (1) a comprehensive theoretical and methodological framework for ECG analysis, (2) significan tperformanc eimprovement sthroug hmultimoda lfeatur efusion ,an d(3) practical guidelines for clinical implementation. Future research directions focus on real-time processing optimization, attention mechanism integration, and multi-modal data fusion to en-hance diagnostic capabilities across diverse healthcare environments, representing a substantial advancement in intelligent cardiac monitoring technology with the potential for significant clinical impact.Item Implementation and evaluation of conjugate gradient-based MIMO detectionin sionna for 5G and 6G-like scenarios(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Tahir, Imene; Saoudi, Amina; Smaili, NessrineAs wireless communication systems progress toward higher spectral effi ciency and support for massive connectivity, robust detection techniques for Multiple-Input Multiple-Output systems have become increasingly important. While traditional linear detectors such as Zero-Forcing and Linear Minimum Mean Square Error are simple to implement, their performance can degrade in large-scale scenarios due to high computational cost or limited accuracy. To overcome these challenges, the Conjugate Gradient algorithm has gained attention as an effi cient iterative method for solving linear systems without requiring matrix inversion, making it highly suitable for massive MIMO detection. In this work, we implement and evaluate a CG-based linear MIMO detector using Sionna , an open- source link-level simulation library developed on TensorFlow. The system is assessed using performance metrics such as Bit Error Rate versus Energy per Bit to Noise Power Spectral Density across different modulation schemes and channel conditions. The results confirm that CG provides competitive detection performance with reduced complexity, making it a promising candidate for scalable and machine learning-oriented wireless system design.Item Performance study of hybrid OFDM/OTFS system over doubly selective channel(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Dihia, Amani; Razaoui, Feriel; Smaili, NesrineAs the demand for fast and reliable wireless communication grows particularly in high-mobility environments, traditional modulation schemes are limited by Doppler shifts. Orthogonal Frequency Division Multiplexing (OFDM) performs effectively in low mobility scenarios, but experiences performance degradation under dynamic conditions. On the other hand, Orthogonal Time Frequency Space (OTFS) modulation offers greater robustness in high mobility channels due to its operation in the Delay Doppler domain, but it introduces greater complexity and does not outperform OFDM in low mobility channels. This work proposes a hybrid system that dynamically estimates user velocity and switches between OFDM and OTFS to maintain optimal performance. MATLAB simulations evaluating the Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) show that OTFS outperforms OFDM at higher speeds, while OFDM remains more effi cient in static or low mobility settings. The adaptive solution ensures effective communication under various mobility conditions and supports the waveform fl exibility for future wirelessnetworks.Item Design and implementation of a single phase grid tied inverter(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Asfirane, Nihad; Badi, Lotfi; Bentarzi, HamidWith the rapid spread of renewable energy sources, particularly solar energy, photovoltaic (PV) systems which employ solar cells to convert solar energy into electrical power become widely used nowadays. However, PV panels generate DC electricity whereas electrical grids and household appliances operate on AC electricity. So gridtied inverters are used as an interface between PV panels and the electrical grid, ensuring effi cient power conversion and synchronization with grid parameters. This report focuses on the design and implementation of a single-phase grid-connected inverter, providing a comprehensive analysis of its control strategy, hardware design, and implementation process.The control strategy, implemented on the LAUNCHXL-F28379D development board, consists of a proportional-integral (PI) controller for the DC-link voltage regulation, a proportional-resonant (PR) controller for current control, and a phase-locked loop (PLL) to ensure precise synchronization with the grid. An LCL fi lter is used to enhance power quality and minimize harmonics in the output current and voltage, effectively reducing high-frequency switching components before connecting the inverter to the grid. The proposed system is designed to comply with grid codes, ensuring stable operation, high power injection effi ciency, and minimal dis- tortion in compliance with power quality standards. It is important to note that this work is limited to the design and control of the inverter stage only, without addressing the complete PV system.Item Automated test bench for TV motherboards(University M’Hamed Bougara of Boumerdes : Institute of Electrical Engineering and Electronics, 2025) Benterkia, Ayoub; Maache, AhmedTelevision motherboard testing is essential for ensuring quality in modern smart TVs, yet traditional manual methods suffe rfro minefficien cy,inconsisten cy, andh ighoperational costs. This report presents an automated test bench designed to validate critical functionalities including HDMI, audio, USB, and Wi-Fi. Our proposed system uses a Raspberry Pi for signal generation, an ESP32 for real-time audio analysis, and OpenCV for computer vision-based display verification .Testin gresult sshowe d a75 %reduction in testing time (from 7 to 1.8 minutes per board) and a 9-fold decrease in defect escape rates (from 9% to 0.8%) in industrial deployments at Bomare (TV manufacturer). By incorporating IR-based source switching and modular test sequences, the bench achieves approximately 95% repeatability while remaining cost-effectiv ecompare dto proprietary solutions. These results highlight the system’s potential to enhance quality control in high-volume TV manufacturing.Item Transformer-Based approach for hand gesture recognition through EMG signals(University M’Hamed Bougara of Boumerdes : Institute of Electrical Engineering and Electronics, 2025) Messalit, Rayane; Sif, Yacine; Boutellaa, ElhocineHand gesture recognition using Electromyography (EMG) signals is a crucial area of research for advancing human-computer interaction, prosthetic control, and assistive technologies. Traditional machine learning and deep learning models have demonstrated success, but they struggle in capturing long-range dependencies within the time-series nature of EMG signals. This project introduces a Transformer-based architecture for the classificatio no fhan dgesture sfro mEM Gsignals ,leveraging the self-attention mechanisms to effectivel ymode ltempora lrelationship si nEMG data, potentially improving recognition accuracy and robustness. This study utilized the Ninapro DB2 dataset, a publicly available dataset. We address the challenges of EMG signal variability, noise, and inter-subject difference sthroug h arobust pre-processing pipeline and a custom model design. Our approach incorporates a convolutional feature extractor, Time2Vec positional encoding, and a Transformer encoder to capture complex temporal dependencies. The model was implemented and trained to recognize a set of 15 distinct hand gestures. Experimental results demonstrate an average test accuracy of 82.98% and a Matthews Correlation Coefficie nt(MC C) of0.817 8,consistent lyoutperformi ngconvention almode lsincluded in the comparison. Additionally, our model demonstrates exceptional parameter efficienc y,achievi ngsuperi orperforman cewi thmodera tecomplexi ty(128,5 36 parameters) compared to other over-parameterized models evaluated, highlighting the effectivenes so fou rTransformer-base darchitectur ei nbalancin gperformanc eand parameter efficienc y.T hemod elcomputation alefficie ncy, wi thanave ragetesting time of 1.86 ms per window, highlights its suitability for real-time applications in human-machine interfaces. This research highlights the promise of Transformers in learning discriminative features from complex biosignals, paving the way for more intuitive and reliable control of external devices.Item Design and implementation of quadatic boost converter(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Abdallah, Fethallh; Denghir, Abdelmounaim; Bentarzi, HamidThis project focuses on the design, modeling, analysis, and hardware implementation of a DC-DC quadratic boost converter for discharging battery energy storage systems (BESS) in a microgrid setup. As microgrids increasingly use renewable energy sources like solar and wind, power conversion effi ciency and stable voltage regulation become vital for system stability and maximizing energy use. The quadratic boost converter provides much higher voltage gain through its two-stage energy storage process. It is well-suited to raise low battery voltage to the needed DC bus level in microgrid systems. The project includes mathematical modeling, dynamic analysis, and designing a strong control strategy to ensure stable output voltage and high energy conversion effi ciency across different load conditions. In addition to simulation, we discuss choosing key hardware components and physically building the converter. A comprehensive simulation in MATLAB/Simulink examines both transient and steady-state performance, while the hardware implementation tests the theoretical design. Simulation and experimental results confi rm the converter’s ability to maintain voltage stability and enhance the effi ciencyof energy transfer from the battery to the microgrid, leading to more effi cient and reliable microgrid operation.Item Design & implementation of an energy management system(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Chebah, Sarah; Kadri, Mohammed Sayah; Bentarzi, HamidThis project presents the design, simulation, and implementation of a energy management system (EMS) for micro-grid integrating solar photovoltaic (PV), battery storage, and grid power to ensure a stable and reliable power supply. The system monitors and controls power fl ows based on real-time conditions such as solar generation, battery state of charge (SOC), and load demand. The EMS follows a clear source priority: solar energy is used fi rst, followed by the battery, and fi nally the grid when other sources are insuffi cient. This strategy maximizes the use of renewable energy and minimizes dependence on the grid. Simulation and practical results confi rm that the system operates eff ectively in bothgrid-connected and islanded modes, offering a flexible and scalable solution for modern smart energy needs. The energy management strategy prioritizes the use of renewable sources and enables seamless switching between energy sources to ensure both reliability and efficiency. Simulation and hardware implementation results demonstrate the system’scapability to operate effectively in both grid-connected and islanded modes, providing a scalable and fl exible solution for modern smart energy systems.Item Health assessment of generator step-up power transformer based on correlation Between oil analysis and electrical tests(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Benouarab, Mohamed Amine; Bouali, Roumaissa; Bouchahdane, MohamedThis thesis presents a comprehensive health assessment methodology for power transformers, with a specifi c focus on Generator Step-Up (GSU) transformers, by integrating insulating oil analysis with electrical diagnostic tests. Given that the insulation system plays a pivotal role in transformer reliability, it is particularly vulnerable to degradation caused by thermal, electrical, and environmental stresses over time. To evaluate the condition of the transformers, a series of diagnostic techniques were employed. Oil analysis included the evaluation of water content, breakdown voltage, acidity, dissipation factor, and Dissolved Gas Analysis (DGA). Among these, DGA is particularly valuable, as it provides early indications of internal faults within the active parts of the transformer such as arcing, overheating, or insulation degradation before they evolve into severe failures. Electrical diagnostics comprised insulation resistance,dissipation factor, winding resistance, and transformer turns ratio (TTR) measurements.Four real-world case studies were analyzed to illustrate how the combination ofoil and electrical tests enhances fault detection and diagnostic accuracy. The resultsdemonstrated that integrating these complementary methods yields a more reliable andholistic assessment of transformer health. This approach facilitates early fault detection,supports predictive maintenance strategies, and ultimately contributes to prolonging theoperational life of critical power infrastructure.