Communications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11
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Item 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, AbdenourAutonomous 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.Item 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, AbdelAccurate 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.Item Achievable Rates of Full Duplex Cooperative Relay Selection-Based Machine Learning(IEEE, 2025) Belaoura, Widad; Althunibat, Saud; Mazen, Hasna; Qaraqe, Khalid; Ammuri, RulaMachine 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.Item Valuation of Physical Layer Security Under Jamming Attacks Utilizing RIS(2025) Refas, Souad; Meraihi, Yassine; Ivanova, Galina; Baiche, Karim; Cherif, Amar Ramdane; Acheli, DalilaVehicular 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.Item 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, SaadThis 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 applicationsItem Optimized Control of Single-Stage Grid-Connected PV Inverters with Embedded ANN based MPPT(Institute of Electrical and Electronics, 2025) Triki, Yacine; Bechouche, Ali; Seddiki, Hamid; Abdeslam, Djaffar OuldThis paper presents a control strategy for single-stage grid-connected photovoltaic (PV) inverters. The objective of this strategy is to address the two primary challenges of this topology: maximizing power extraction through a novel maximum power point tracking (MPPT) method and regulating both active and reactive power injection. The proposed strategy includes two main components. First, an innovative MPPT algorithm based on adaptive linear neurons (ADALINE) is used to maximize the extracted power. This method is built on the fact that at the maximum power point, the derivative of the PV output power with respect to the panel voltage equals zero. ADALINE is chosen for its simplicity, quick convergence, and online learning capability. Second, a simplified control method is developed to manage grid connection and regulate active and reactive power by adjusting the inverter's voltage amplitude and phase. This approach is significantly simpler than existing methods, which involve more complex calculations. Simulation tests demonstrate the strategy's effectiveness, showing the MPPT's ability to adapt under varying conditions and ensuring optimal power extraction. Additionally, the obtained results highlight the effective regulation of both active and reactive power. The injected current demonstrates high quality with low total harmonic distortion. This ensures compliance with grid standards while significantly improving system stabilityItem Optimizing Smart City Street Lighting: A Hybrid IoT-SAC Approach(Institute of Electrical and Electronics, 2025) Tercha, Wassila; Chekired, Fathia; Tadjer, Sid Ahmed; Canale, LaurentThe confluence of artificial intelligence (AI) and the internet of things (IoT) is fast changing the concept of smart cities. Smart street lighting is only one example of the great opportunities this potent combination presents for enhancing urban infrastructure. While previous studies have looked into the possibility of combining IoT and Soft Actor-Critic (SAC) for this goal, this work takes a different tack. A simulated Internet of Things system that replicates real-world sensor data is used in our work. The SAC algorithm receives data from this system on variables like ambient light levels, weather, and vehicle and pedestrian traffic. The SAC algorithm modifies street light operation patterns and brightness dynamically within this controlled environment. This enables us to fine-tune the hybrid strategy so that it strikes a balance between user comfort and energy efficiencyItem Mössbauer Spectroscopy Analysis of FeCo Nano-Particles Alloy Synthesized by Hydrothermal Method(Trans Tech Publications, 2025) Halimi, Mohamed Walid; Guittoum, Abderrahim; Hemmous, Messaoud; Bouelkreb, ImaneMössbauer spectroscopy (MS), was used to characterize the synthesized materials prepared from the elemental powders by hydrothermal method which are binary iron-based nanoparticles (NPs) Fe15Co85 and Fe10Co90 alloys. The transmission 57Fe Mössbauer spectra were measured at room temperature RT (T~300K), using 57Co γ-ray source with an Activity ~ 1.85 GBq (50 mCi). The analysis of Mössbauer spectra curves was by using WinNormos with two subspectra with the “Site” option and then with the “Dist” option in order to learn more about hyperfine interactions and parameters such as isomer shifts IS, quadruple splitting QS and hyperfine magnetic field Bhf. MS results observe only one Zeeman sextet with a relative area of ~77.125% with parameter Bhf = 32.727 T and line width Γ=0.693 mm/s for Fe10Co90, and a relative area of ~84.719% with parameter Bhf = 34.354 T and line width Γ=1.043 mm/s for Fe15Co85 and one broad singlet which confirms the body-centered cubic structure BCC. The main contribution to the spectra comes from the magnetic sextet which is assigned to ferromagnetic FeCo phase which is the dominant one while the singlet is assigned to paramagnetic phase. As a result of the analysis of the distributions hyperfine magnetic field the average values of the hyperfine parameters of the Mössbauer spectra were obtained Fe10Co90 = 28.3363 T and Fe15Co85 = 31.4657 T. Therefore, it is observed that the increasing of the cobalt concentration decreases the hyperfine field. The results observe indicates Co concentration dependence, where for Co-rich alloys (Fe10Co90) the FCC (face-centered cubic structure) contributing to the decrease in Bhf due to the absence of BCC. the obtained NPs most likely to be in disordered structure A2Item Transformer-Based Approach for Intrusion Detection System(Institute of Electrical and Electronics, 2025) Senoussi, Nour El Houda; Salmi, Cheikh; Banouh, Nassim; Khalfi, AdemIntrusion Detection Systems (IDS) have been used for years to protect enterprise hosts from cyberattacks. Traditional IDSs are usually based on simple methods, such as signatures or heuristics, that do not adapt to reactions against new threats that are constantly increasing. The objective of this paper is to develop an IDS based on a deep learning technique which is transformers. Unlike conventional models and thanks to their self-attention mechanism, transformers are characterized by an excellent ability to support complex patterns by very accurately modeling the context in sequential data. A host-based dataset containing system logs and network activities is used to train the transformer model that forms the core of the developed IDS. A detailed evaluation is used to compare our approach against existing methods based on machine learning and deep learning, showing significant improvements in precision, recall, and false positive rate. These results are very encouraging for developing robust IDSs that can be fine-tuned in real time to take into account new attacksItem Performance Enhancement of IRS-Aided Coded Index Modulation for D2D Communications Systems(MS-Editions, 2025) Kirouani, Abderrezzak; Boukhalfoun, Leila; Behidj, NassimaIntelligent Reflecting Surface (IRS) is regarded as an innovative solution for enhancing reliability and spectrum efficiency for the future 6G wireless networks. In this paper, we aim to improve the spectrum efficiency and error performance of device-to-device (D2D) systems over Rician fading channels by jointly using IRS and coded index modulation (IM) concept. Specifically, at the transmitter, the system uses the spreading code’s index to transmit extra information, thereby improving transmit diversity without requiring additional power consumption. At the receiver, a maximum likelihood (ML) detection process is formulated. Simulation results demonstrate that the IRS-aided Coded IM solution achieves significantly better error performance and spectral efficiency enhancements compared to conventional D2D systems without IRS and/or without coded IM under different system parameters.