http://dspace.ensta.edu.dz/jspui/handle/123456789/152 Mon, 30 Mar 2026 04:09:03 GMT 2026-03-30T04:09:03Z http://dspace.ensta.edu.dz/jspui/handle/123456789/408 Title: State of the Art on Metamaterials and their Different Domain of Application Authors: SAHNOUN, Belkacem; BOUDIAR, Toufik (Directeur de thèse) Abstract: Metamaterials are a class of artificially engineered structures designed to manipulate electromagnetic, acoustic, and elastic waves in ways not possible with conventional materials. Their unique properties, such as the negative refractive index, arise not from their chemical composition but from their carefully designed subwavelength architecture. This capability has unlocked a vast spectrum of applications across diverse domains, including next-generation telecommunications, super-resolution imaging, acoustic cloaking, targeted medical therapies, and efficient energy harvesting. Recent research trends are advancing the field towards tunable and reconfigurable designs, practical two-dimensional metasurfaces, and the exploration of topological metamaterials for robust waveguiding, largely enabled by advances in additive manufacturing. However, challenges remain in fabrication precision, energy loss, and scalability for mass production. This review provides a state-of-the-art overview of metamaterials, covering their fundamental principles, classifications, and transformative applications, while also discussing current challenges and future perspectives toward their integration into real-world systems and commercial devices. Description: Mémoire de fin d’étude du Master: Systèmes de Télécommunications et Réseaux: Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 Wed, 01 Jan 2025 00:00:00 GMT http://dspace.ensta.edu.dz/jspui/handle/123456789/408 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/407 Title: Design of a wide band low noise amplifier Authors: AKHZEROUN, Aimen; BOUCHACHI, Islem (Directeur de thèse) Abstract: The article you are about to read holds the proposition of a wideband low noise amplifier operating in the microwave frequency band starting from 0.5GHz ending at 5GHz, the article includes all steps of LNA design from DC biasing, to wideband matching. It also includes simulation results of each step of the design process. The introduced LNA is a two stage device, the first stage is a pre-amplifier with a low noise figure, the second one is a high gain amplifier, that is to reach a topology with a high gain and low noise figure across the whole operating band simultaneously. The simulation results are then compared to datasheets of LNA devices to decide the performance level of the proposed device. Description: Mémoire de fin d’étude du Master: Systèmes de Télécommunications et Réseaux: Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 Wed, 01 Jan 2025 00:00:00 GMT http://dspace.ensta.edu.dz/jspui/handle/123456789/407 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/406 Title: A State of The Art: AI-Enabled Intrusion Detection Systems Authors: AIT MIMOUNE, Yasmine; REBAHI, Khadidja; LAKHDARI, Kheira (Directeur de thèse) Abstract: As digital networks expand at an unprecedented rate, alongside with the progression of cyber-attacks which present significant challenges to traditional security measures. Although intrusion detection systems (IDSs) have long served as the primary line of defense, they often struggle to keep pace with novel ,zero-day, and unknown threats. Recent advancements in Artificial Intelligence— particularly through ML and and DL approaches—By learning from evolving attack patterns, AI-powered IDS can dynamically adapt, offering faster and more precise threat detection. This paper provides a state of the art review of AI-enabled IDS approaches by examining architectures, detection techniques, and performance metrics across a range of benchmark datasets.Our comparative analysis highlights the superior accuracy of deep learning approaches on modern datasets, while also examining the impact of dataset quality, detection of rare attacks, and model efficiency.It also analysis demonstrates that while AI-driven methods markedly enhance detection accuracy and reduce false alarm rates, persistent challenges remain, especially in reliably classifying rare and novel attack types due to imbalanced datasets and computational constraints.This study offers valuable insights for future advancements toward robust, real-world intrusion detection systems Description: Mémoire de fin d’étude du Master: Systèmes de Télécommunications et Réseaux: Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 Wed, 01 Jan 2025 00:00:00 GMT http://dspace.ensta.edu.dz/jspui/handle/123456789/406 2025-01-01T00:00:00Z http://dspace.ensta.edu.dz/jspui/handle/123456789/405 Title: State of the Art of Skin Anomaly Detection Using Machine Learning Techniques Authors: BADAOUI, Aymen; BIBET, Idris; LAKHDARI, Kheira (Directeur de thèse) Abstract: Skin cancer is one of the most common and deadly forms of cancer, for which early detection remains critical. In this paper, we provide a comprehensive review and technical comparison of artificial intelligence (AI) techniques for skin lesion analysis.We discuss a wide variety of conventional machine learning and latest deep learning techniques, with particular focus on pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, EfficientNet, and MobileNet. An in-depth comparative analysis is provided in the form of classification accuracy, model complexity, training time, and inference speed. Preprocessing techniques—like hair removal, image normalization, artifact removal, and data augmentation techniques like geometric and color transformations—are examined for their effectiveness in enhancing robustness, particularly on real-world datasets like HAM10000, ISIC, and PH2. In addition, the paper addresses the feasibility of executing these models on embedded hardware platforms (Raspberry Pi, Jetson Nano, Coral Edge TPU) by analyzing their TensorFlow Lite and ONNX support and the impact of model compression techniques (quantization, pruning, distillation). The final cross-comparison identifies the optimal combination of dataset, model architecture, and hardware platform for creating an inexpensive, interpretable, and real-time skin lesion detection system. The paper concludes with the key takeaways for prototyping an embedded diagnostic system for low-resource environments. Description: Mémoire de fin d’étude du Master: Systèmes de Télécommunications et Réseaux: Alger: Ecole Nationale Supérieure des Technologie Avancées: 2025 Wed, 01 Jan 2025 00:00:00 GMT http://dspace.ensta.edu.dz/jspui/handle/123456789/405 2025-01-01T00:00:00Z