Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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Start date: 2000

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Now showing 1 - 10 of 869
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    Deformable Transformer-Based Object Detection for Robust Perception in Autonomous Driving
    (IEEE, 2025) Kezzal, Chahira; Benderradji, Selsabil; Benlamoudi, Azeddine; Bekhouche, Salah Eddine; Taleb, Abdel; Hadid, Abdenour
    Autonomous driving demands robust and real-time object detection to safely navigate in complex environments. While Convolutional neural network (CNN)-based detectors have been widely adopted, they face challenges such as limited receptive fields and inefficiencies in handling small or occluded objects. This paper presents a deformable Transformer based object detection framework designed to address these limitations. By leveraging deformable attention mechanisms, the model dynamically focuses on relevant spatial regions, significantly enhancing detection accuracy. Evaluated on the benchmark KITTI dataset, our proposed approach achieves an interesting mAP@50 of 96.6%, surpassing many state-of-the-art methods, at the cost of slower inference speed (7.0 FPS). The experimental results also demonstrate the framework’s superior precision and adaptability in autonomous driving scenarios. This work underscores the potential of deformable transformers to advance perception systems, balancing high accuracy with the demands of real-world applications.
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    Efficient Real-Time Multi-Class Object Tracking with YOLO11 and ByteTrack in Real-World Driving Scenes
    (IEEE, 2025) Benderradji, Selsabil; Kezzal, Chahira; Benlamoudi, Azeddine; Bekhouche, Salah Eddine; Taleb, Abdel
    Accurate and real-time multi-object tracking (MOT) is essential for autonomous driving systems to ensure safe navigation and decision making in dynamic environments. This paper introduces a tracking-by-detection pipeline that integrates YOLOv11 a high speed, high-accuracy object detector with ByteTrack, a robust data association algorithm capable of lever-aging both high and low confidence detections. The proposed framework addresses key challenges in MOT such as frequent occlusions, fluctuating lighting, and dense traffic by combining efficient detection with motion-consistent identity tracking. Evaluated on the KITTI benchmark, our method demonstrates superior performance across multiple metrics, including HOTA, AssA, and MOTA, for both cars and pedestrians. Additionally, the system achieves an average runtime of 60.4 FPS, supporting its real-time applicability. The results confirm that the proposed YOLOv11 + ByteTrack integration provides a scalable, accurate, and deployment ready solution for complex urban driving scenarios.
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    Achievable Rates of Full Duplex Cooperative Relay Selection-Based Machine Learning
    (IEEE, 2025) Belaoura, Widad; Althunibat, Saud; Mazen, Hasna; Qaraqe, Khalid; Ammuri, Rula
    Machine learning (ML) is an advanced artificial intelligence technology that addresses the ever-growing complexity in communication signal processing. In this paper, the concept of ML-based classification model to choose the best relay is investigate in a full duplex (FD) cooperative system. Specifically, a K-nearest neighbors (KNN)-based relay selection is applied to accurately predict and evaluate the achievable rate of the optimal FD relay. The core idea of the multi-class KNN is to identify the optimal relay that yields the highest achievable rate performance by utilizing a large set of offline training data derived from the channel state information (CSI), ensuring that no further training is required during system processing. The results indicate that the KNN-based FD relay selection can achieve an achievable rate comparable to the optimal exhaustive search method with lower computation complexity.
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    Valuation of Physical Layer Security Under Jamming Attacks Utilizing RIS
    (2025) Refas, Souad; Meraihi, Yassine; Ivanova, Galina; Baiche, Karim; Cherif, Amar Ramdane; Acheli, Dalila
    Vehicular visible light communication (V VLC) systems, when combined with reconfigurable intelligent surfaces (RIS), present promising opportunities for improving communication reliability and efficiency in vehicle to vehicle (V2V) environments. Nevertheless, safeguarding these systems at the physical layer remains a critical challenge, particularly given their exposure to jamming threats. In this study, we investigate the physical layer security performance of RIS assisted V2V VLC systems under jamming scenarios, employing realistic V2V VLC channel models. We develop a methodology to examine the impact of various security strategies in mitigating the adverse effects of jamming. Our analysis examines key parameters including the signal to noise ratio SNR, the secure communication rate and the count of RIS units. Simulation results confirm that the proposed security systems significantly enhance the resilience of V2V VLC networks in the presence of jamming attacks. These results offer useful perspectives for the reliable design structure and deployment of RIS based V2V VLC systems in practical vehicular communication settings.
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    Groove Gap Waveguide Crossover for Butler Matrices and Beamforming in Millimeter-Wave Satellite Antenna Systems
    (Institute of Electrical and Electronics Engineers, 2025) Alibakhshikenari, Mohammad; Parand, Peiman; Zidour, Ali; Virdee, Bal S.; Kouhalvandi, Lida; Longhi, Patrick; Saber, Takfarinas; Limiti, Ernesto
    This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40-50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20-57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks
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    Towards Blockchain-Based GDPR-Compliant spontaneous and ephemeral social network
    (Institute of Electrical and Electronics Engineers, 2025) Yahiatene, Youcef; Rachedi, Abderrezak; Riahla, Mohamed Amine
    Online Social Networks (OSNs) have rapidly integrated into our daily lives since their emergence in 2004, primarily serving as platforms for sharing personal information. This paper introduces a novel category of social networks: Spontaneous and Ephemeral Social Networks (SESNs). Unlike traditional OSNs, SESNs are event-centric, facilitating real-time connections and content sharing among participants within specific contexts. The main objective of SESNs is to improve the production, sharing, and consumption of digital content among network members. SESNs operate on a distributed peer-to-peer architecture using ad hoc mobile networks, leveraging the capability of mobile devices to communicate directly with each other in peer-to-peer mode. SESNs are ephemeral by nature, dissolving once participants disperse from the event location. However, for future analysis, some content may be retrievable from an external server after the event. A potential concern is that collaborative content creation within SESNs resembles crowdsourcing. However, in SESNs, the service provider may retain control over the data even after the event concludes. This centralized management of user-generated content could pose risks to user anonymity. To address these concerns, we propose a blockchain-based architecture that certifies transactions and ensures data anonymity in a decentralized manner. The proposed architecture demonstrates its robustness through a performance evaluation and a comprehensive security analysis. Our solution guarantees data integrity, confidentiality, privacy, anonymity, and network resilience. Additionally, blockchain technology is employed to ensure SESN compliance with the General Data Protection Regulation (GDPR).
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    Design of Sliding Mode Control Applied to Inverted Cart-Pendulum for Good Stability Performances
    (2025) Miloudi, Lalia; Toubal Maamar, Alla Eddine; Elamri, Oumaymah; Benabdallah, Tassadit
    This paper proposes a resilient sliding mode control (SMC) strategy for the stabilization of a cart-pendulum system, tackling significant issues in nonlinear control, including parametric uncertainties and external disturbances. The suggested solution uses a two-step process: first, an open-loop energy-based swing-up to lift the pendulum, and then a closedloop SMC phase to keep it stable. The designed controller uses a saturation function to reduce chattering, which is different from methods that depend on linearized models or complicated gain tuning. The simulation results show that the accuracy is very high, with settling times of about 5 seconds for the pendulum angle and 7 seconds for the cart position. The controller works well even when the system mass and disturbances change by 10%, as long as the cart can only move ±0.5 m and the control forces can only be ±10 N. Stability is reached from the most unfavorable initial condition, the pendulum's downward-hanging position, with a steady-state error of under 1% in essential state variables. This work offers a computationally efficient and adaptive solution, appropriate for real-time applications in robotics and aerospace where resilience to nonlinear dynamics and uncertainty is essential.
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    Load Frequency Control in Two Area Power Systems in A Smart Grid Environment
    (IEEE, 2024) Faradji, Mohamed; Madani Layadi, Toufik; Ilhami, Colak
    Load Frequency Control (LFC) is a critical aspect of power system stability, ensuring that the frequency and tieline power flow remains within acceptable limits. In this paper, we investigate LFC in a two-area system with the integration of demand response (DR) loops. The DR loops allow for dynamic adjustments of load demand based on real-time system conditions. Our study focuses on optimizing the proportional-integral-derivative (PID) controller used in the LFC system. To achieve this, we perform a comparative analysis of three optimization algorithms: Artificial bee colony (ABC), particle swarm optimization (PSO), and Aquila Optimization (AO). These algorithms are applied to tune the PID controller parameters, aiming to enhance system performance, reduce frequency deviations, and minimize control efforts. Simulation results demonstrate the effectiveness of the proposed approach. The optimized PID controller, combined with DR, significantly improves system response during load disturbances. Furthermore, the comparative study sheds light on the strengths and weaknesses of each optimization algorithm, providing valuable insights for future LFC implementations. Overall, our work contributes to the advancement of LFC strategies in interconnected power systems, emphasizing the role of demand response and optimization techniques in achieving robust and efficient control
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    Comparative Evaluation of StyleGAN3-Based Augmentation Strategies for Enhanced Medical Image Classification
    (CEUR-WS, 2025) Touazi, Faycal; Gaceb, Djamel; Tadrist, Amira; Bakiri, Sara
    Deep learning models for medical image classification face significant challenges due to class imbalance and the limited availability of annotated datasets, particularly for rare diseases. Traditional data augmentation techniques, such as rotation, translation, etc., often fail to provide sufficient diversity to perform a good classification for minor classes. To address this issue, various strategies have been explored, including oversampling, undersampling, cost-sensitive learning, and synthetic data generation using generative adversarial networks (GANs). In this study, we evaluate the impact of using a generative AI based approaches and demonstrate that the most effective strategy is to combine synthetic augmentation with traditional methods. Specifically, we employ StyleGAN3 to generate high-fidelity synthetic images that, when integrated with traditional data-augmentation techniques, may improve the performance of deep learning models on medical image classification. We validate our method on datasets, including COVID-19 chest X-rays and HAM10000. Experimental results show that this hybrid approach leads to an improvement in classification accuracy, particularly for minority classes, surpassing standalone augmentation strategies. Our findings highlight the potential of AI-driven synthetic data generation as a complementary solution to traditional augmentation, offering a more balanced and diverse dataset for medical image analysis.
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    SVPWM-Based Control of a Three-Phase Five-Level NPC Inverter for Grid-Connected Solar Power System
    (Institute of Electrical and Electronics, 2025) Elamri, Oumaymah; Toubal Maamar, Alla Eddine; Oukassi, Abdellah; El Kharki, Abdellah; Hammoudi, Abderazek; Mekhilef, Saad
    This study focuses on analyzing a photovoltaic system for energy production and its integration into the grid. Take into account the key grid parameters, including frequency, three-phase system symmetry, and voltage waveforms. Non-sinusoidal voltages can cause interference that affects the operation of networked equipment. To address this issue, a three-phase five-level neutral-point-clamped inverter is incorporated into the system, utilizing the space vector pulse width modulation technique for control. The control strategy of the converter is presented in detail. The study was carried out utilizing Matlab/Simulink, and the simulation outcomes demonstrate the efficiency of this control approach for renewable energy applications