Génie Eléctriques
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Item Study and design of an antenna system for wireless terminals(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Zidour, Ali; Ayad, Mouloud(Directeur de thèse)To meet the escalating demand for high-speed real-time connectivity, 5G New Radio (NR) systems have transitioned to the millimeter-wave (mmWave) spectrum. Despite this spectrum offers wide bandwidth, signal propagation is limited by severe path loss and attenuation, necessitating high-gain, beamforming phased antenna arrays. This thesis details the development of mmWave phased arrays featuring robust beam-scanning capabilities, emphasizing endfire configurations that operate across multiband operation for 5G NR requirements while ensuring spherical coverage compliance with 3GPP standards. To address current design challenges, this research introduces two novel endfire antenna arrays: (1) a horizontally polarized wideband antenna array with low form factor, and (2) a shared-aperture dual-band vertically polarized antenna array. Both designs feature compact size, low complexity, and high performance. The design methodology, simulation, fabrication, and experimental validation are thoroughly discussed. Measurement results align well with simulations, demonstrating superior merits in terms of impedance bandwidth, aperture efficiency, port isolation, gain, and symmetric radiation patterns for wide beam scanning applications. Finally, it evaluates practical considerations related to PCB integration and real-world environmental interactionsItem Remaining useful life estimation of critical industrial equipment by using a data driven approaches(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Amar Bouzid, Abir; Benazzouz, Djamel(Directeur de thèse)In the face of fierce competition in the industrial sector, manufacturing technologies have evolved considerably, from simple to complex and sophisticated systems. Machining processes cover a wide range of manufacturing systems and play a crucial role in production. Monitoring the condition of these systems through an effective maintenance strategy is therefore essential for guaranteeing production reliability and quality; however, due to their complexity, focusing on monitoring critical components is generally more practical and efficient, as their degradation has a serious impact on the entire system. Maintenance practices have undergone significant evolution, giving rise to the emergence of the concept of Prognostics and Health Management (PHM), which introduces a predictive approach in Condition-Based Maintenance (CBM). Estimating components Remaining Useful Lives (RULs) is one of the most important aspects of PHM to track their degradation and predict how long they can operate before failing. However, to achieve accurate RUL estimations, it is essential to process the raw data efficiently. Sensor-derived data often contains noise, irrelevant information, and inconsistencies that can mask essential insights. Therefore, it is crucial to conduct appropriate data processing to extract the relevant features, or Health Indicators (HIs), that reflect the system's behavior. This data refinement not only improves the accuracy of predictive models but also increases their robustness, enabling manufacturers and maintenance managers to schedule replacements, minimize unplanned downtime, and extend machine life cycles. This is the background to the present study, which proposes a novel methodology for estimating the RUL. In the context of PHM data-based methods, also known as "data-driven methods," the main objective of this thesis is to design relevant health indicators HIs capable of reflecting the degradation behavior of critical components and estimating their RULs. Initially, a new time-frequency analysis technique called Empirical Wavelet Packet Decomposition (EWPD) was introduced. This method uses a new segmentation of the signal's Fourier spectrum, which is spread over several levels to improve the structural performance of conventional methods. Subsequently, a novel health indicator HI, is developed upon the basis of an innovative selection of time-domain features for each frequency band at each level. Lastly, the RULs of the monitored components are estimated using the Long Short-Term Memory (LSTM) network. The proposed methodology is implemented on a selection of CNC milling cutters from the "Prognostics Data Challenge 2010" databaseItem Acceleration of arithmetic computations in elliptic curve cryptography(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2026) Nait-Abdesselam, Fadila; Khouas, Abdelhakim(Directeur de thèse)The Elliptic Curve Digital Signature Algorithm (ECDSA) is a fundamental cryptographic mechanism for ensuring the authenticity and integrity of digital communications. A central operation in signature verification is double point multiplication (DPM), whose computational structure directly affects performance, memory consumption, and resistance to side-channel attacks (SCAs). This thesis proposes simple and uniform constant-time algorithms for DPM based on an iterative left-to-right windowing method that performs simultaneous recoding and evaluation in a single pass. This design improves efficiency, reduces memory requirements, and strengthens protection against timing and power-analysis attacks. The proposed methods are analyzed through precise analytic formulas covering speed, memory, and security, and are compared with state-of-the-art approaches over NISTrecommended curves, as well as twisted-Edwards and Montgomery models. Unlike curve-specific techniques, the proposed algorithms are field-independent and flexible, enabling practical trade-offs between speed, memory, and security. When applied to ECDSA, the algorithms reduce point additions without increasing point doublings and require minimal precomputation, resulting in significant computational savings compared to existing methodsItem Contribution to the optimization of smart grids(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Faradji, Mohamed; Layadi, Toufik Madani(Directeur de thèse)The transition toward smart grids demands sophisticated optimization techniques to enhance efficiency, stability, and demand responsiveness. This thesis contributes to smart grid optimization by leveraging advanced optimization and computational intelligence methods, including Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The study focuses on two critical areas: demand-side management (DSM) for peak load reduction and load frequency control (LFC) for grid stability. A hybrid PSO- ANN framework is proposed for DSM to predict and optimize energy consumption patterns, while ANFIS-based controllers are designed for robust LFC under dynamic load conditions. Simulation results demonstrate superior performance compared to conventional methods, with significant improvements in demand response accuracy and frequency regulation. The findings underscore the potential of AI-driven optimization in advancing smart grid resilience and operational efficiencyItem Application of medical informatics and data analysis methods for automatic medical diagnosis(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Hammachi, Radhouane; Messaoudi, Noureddine(Directeur de thèse)With the increased size and complexity of data, interest has rapidly emerged in adopting artificial intelligence (AI) and deep learning (DL) to create data-driven models for automating medical diagnosis, and for neuromuscular disorders (NMDs) in particular. Therefore, this thesis aims to address the gaps in this context. To provide clinicians with a more objective and accurate methods for assessing muscle fatigue, a convolutional neural network (CNN)-based DL model was proposed to classify simulated surface electromyography (EMG) signals into different maximum voluntary contraction levels, achieving and accuracy of 88.88%. To ensure transparency and clinicians trust, the interpretability of Multi-Layer Perceptron (MLP) and Residual Neural Network (ResNet)-based DL models that achieved 95.67% and 98.37% testing accuracies, respectively, for myopathy diagnosis, was investigated. Shapley additive explanation (SHAP) for feature-based interpretation, and Gradient-weighted class activation mapping (Grad-CAM) for visual interpretation of raw signals, were employed, providing clear insights into the decision-making process. Furthermore, given the recent emergence and proved ability of quantum machine learning to handle high-dimensional data and solve complex tasks, a study was introduced to explore its potential in myopathy diagnosis. Quantum support vector machines (QSVMs) with variational quantum circuit-based kernels were proposed, and their performance was compared with classical methods. A hybrid QSVM model trained on deep features demonstrated promising classification ability, with training and testing accuracies of 96.7% and 85.1%, respectively. The results obtained in our research shed new light on the application of medical informatics in the field of healthcare and the EMG-based NMDs diagnosis in particular, indicating promising potential for future adoption of automated medical decision-makingItem Adaptive control of drone by rejection of disturbances(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Hadid, Samira; Boushaki, Razika(Directeur de thèse)Quadrotor or Unmanned Aerial Vehicles (UAVs), a popular type, use four propellers for flight and are gaining popularity due to their versatility and ease of use. Interest in controlling UAVs has significantly increased recently. This work focuses on the control and trajectory planning challenges of quadrotors. While many studies address disturbances and faults, the inherent underactuation (four inputs controlling six degrees of freedom) makes precise control and trajectory tracking difficult, particularly in complex scenarios. The research aims to improve quadrotor control in challenging environments. The Newton-Euler method is used in this work to develop the quadrotor's dynamic model. Then, an exploration using Dyna-Q reinforcement learning for autonomous quadrotor navigation in complex environments. The algorithm allows the quadrotor to learn optimal flight paths through trial and error. In addition, this thesis presents an in-depth investigation into improving the autonomy and control capabilities of quadrotors. The focus is on developing and implementing various linear and nonlinear control strategies to regulate the behavior of quadrotor UAVs. Each control strategy is carefully adjusted and fine-tuned to achieve the desired dynamic response and stability during quadrotor flight. Following that, we provide a comparison of the designed controllers. It then focuses on comparing the performance of fractional-order PID (FOPID) and sliding mode control (SMC) for trajectory tracking, emphasizing robustness against disturbances and nonlinearities. Furthermore, the research introduces an intelligent trajectory planning system using Dyna-Q learning to enable autonomous navigation and obstacle avoidance in complex environments, enhancing quadrotor adaptability and responsiveness for various applications. Extensive simulations validate the proposed control strategies and trajectory planning. Overall, this study contributes significantly to the field of quadrotor control and autonomy, providing valuable insights and solutions for improving flight stability and enabling secure and efficient operations in a variety of real-world scenarios.Item SoC estimation for optimal ESS’ energy management(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Zermout, Abdelaziz; Belaidi, Hadjira(Directeur de thèse)Battery energy storage systems have become indispensable to modern civilization, enabling the functionality of numerous advanced technologies, including high-performance smartphones, long-range electric vehicles, and various portable electronic, tools, and backup systems. The continuous advancement of battery technology is a key driver for future innovations. A crucial component of battery systems is the Battery Management System (BMS), which monitors and optimizes various operational parameters, including the State of Charge (SoC). SoC represents the remaining useful battery capacity relative to its total capacity, however it cannot be directly measured and must be estimated through computational techniques instead. While existing estimation methods have significantly improved in terms of accuracy and reliability, they remain challenged by complexity, sensitivity to operating conditions, and dependence on dynamic load behavior. Overcoming these challenges is essential for enhancing the performance and longevity of battery systems in next-generation applications. Our contribution is a novel estimation technique that periodically stimulates the battery with a predefined current profile during charging or discharging to determine its State of Charge (SoC). Since this method is not continuous, it is combined with Coulomb counting for calibration. The results demonstrated the method's efficiency and reliability, effectively overcoming dependency on environmental conditions and dynamic load behavior. Its key advantages include independence from operating conditions and dynamic load behavior, as well as, minimal computational complexity without sacrificing accuracy achieving an error of less than 1%. This ensures high reliability and efficiency with reduced complexityItem Multivariate statistical process monitoring using kernel statistical techniques(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Kaib, Mohammed Tahar Habib; Harkat, Mohamed Faouzi(Directeur de thèse)Fault Detection and Diagnosis (FDD) is an important part of industrial plants because monitoring systems are responsible for capturing faults as soon as they occur to avoid major casualties in equipment, operators, and the environment......Item Etude et conception d'une antenne patch à bande rejetée(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Fortas, Ibrahim; Ayad, Mouloud(Directeur de thèse)Les communications sans fil connaissent une expansion rapide, nécessitant des solutions innovantes face à la hausse des besoins en débits élevés et à la saturation du spectre. La technologie ultra-large bande (ULB) se présente comme une alternative prometteuse. Dans le cadre de cette thèse, deux nouvelles antennes ULB ont été proposées : une antenne élémentaire patch à doubles ellipses alimentée par une ligne coplanaire (CPW) et une antenne microruban réseau de dipôles logarithmiquement périodique (MLPDA) alimenté par deux lignes microrubans. Etant donné que les systèmes ULB peuvent générer des interférences avec les applications existantes (WiMAX, WLAN, bande X, etc.), l'intégration de mécanismes de bandes rejetées s'avère essentielle. Pour y remédier, des cellules à métamatériaux sous forme de résonateurs en boucle ouverte ont été placées près du patch de la première antenne, tandis que des stubs rectangulaires ont été connectés à la ligne microruban de la seconde antenne. Ces techniques permettent de rejeter les bandes WLAN et Satellite DL (bande X) pour l’antenne patch, ainsi que les bandes WiMAX et WLAN pour l’antenne MLPDA. Les performances des antennes ont été analysées via des simulations sous CST Studio, puis validées par des mesures expérimentales. Les résultats obtenus montrent une bonne concordance entre simulations et expérimentations, confirmant l’efficacité des solutions proposées pour répondre aux exigences des communications sans fil modernes en termes de large bande et de rejet d’interférencesItem Interval-valued statistical approaches for process monitoring(Universite M'Hamed Bougara Boumerdès : Institut de Génie Eléctrique et Eléctronique, 2025) Louifi, Abdelhalim; Harkat, Mohamed Faouzi(Directeur de thèse)Various data-driven approaches, such as Principal Component Analysis (PCA), are widely employed for process monitoring in industrial applications, particularly for detecting abnormal events. PCA-based Fault Detection and Isolation is a well-established strategy, praised for its robust performance. However, its reliability diminishes in uncertain systems where model uncertainties signi?cantly impact e ectiveness. To address this challenge, process modeling is conducted using PCA for interval-valued data, incorporating uncertainties directly into the modeling phase. Four of the most prominent methods for interval-valued PCA are detailed, alongside an extension of conventional PCAbased statistical process monitoring to handle interval-valued data. Over the past decade, this approach has garnered substantial research attention, leading to the development of multiple interval-valued PCA models. This thesis proposes a novel approach called Interval-Valued Principal Component Analysis (IV-PCA), designed to handle uncertainties by de?ning a safe interval for data ?uctuations. The developed technique is applied to the cement rotary kiln process and the Tennessee Eastman Process, where its performance is compared against conventional PCA and four leading Interval-Valued Data PCA (IVD-PCA) methods. Through tests involving actual involuntary system faults and various sensor faults, the IV-PCA demonstrates superior performance in accurately and quickly detecting distinct faults, even in stochastic environments with unknown and uncontrolled uncertainties. The results show signi?cant reductions in false alarms and missed detections compared to the best outcomes of the studied methods