Communications Internationales

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

Browse

Search Results

Now showing 1 - 10 of 854
  • No Thumbnail Available
    Item
    Thermopower in lutetium-substituted manganese sulfides
    (SPIE, 2025) Aplesnin, Sergey; Kharkov, Anton; Sitnikov, Maksim; Bandurina, Olga; Uvaev, Ilya; Voronova, Evgenia; Abdelbaki, Hichem
    hermoelectric effects in the solid solution Mn1-xLuxS (x ≤ 0.2) in a wide temperature range without a field and in a magnetic field of 12 kOe are studied. The temperatures at wich a change in the sign of the thermoelectric power and the maxima of the Seebeck coefficient below Neel temperature in a magnetic field are found. The thermal expansion coefficients are measured to establish a correlation with the thermoelectric power. The thermoelectric power anomalies are explained within the framework of the orbital glass model and a change in the type of magnetic order in a magnetic field in a magnetically ordered region
  • No Thumbnail Available
    Item
    A Binary Relevance Approach for Smart Antenna Selection in Massive MIMO Systems
    (Institute of Electrical and Electronics Engineers, 2025) Bouchibane, Fatima Zohra; Boutellaa, Elhocine; Tayakout, Hakim; Cherigui, Rahma; Bouazabia, Sarah
    Future transceivers are projected to incorporate massive antenna arrays, which could significantly increase power consumption. To mitigate this challenge, the antenna selection technique (AS) emerges as a viable solution. By strategically selecting a subset of antennas, the system power consumption can be significantly reduced without compromising the overall system performance. This paper proposes a novel AS approach for massive MIMO systems under real-world channel measurements. By employing the binary relevance technique (BR), a straightforward approach to multi-label (ML) learning that tackles the problem by treating each class label as an independent binary classification task, we formulate the AS problem as a ML classification task. We conducted simulations using SVM as the base learning algorithm to assess the performance of our proposed approach and compare results to the Multi Label convolutional neural network (ML-CNN) and convex relaxation based approaches (CVX). The binary relevance based SVM (SVM-BR) performance, while slightly below the suboptimal convex relaxation approach in terms of system capacity, remains competitive with the MLCNN under different antenna array configurations
  • No Thumbnail Available
    Item
    Optimized Trade-off Design of Gain and Noise Figure in LNAs for SDR-Based Compressed Spectrum Sensing
    (Institute of Electrical and Electronics Engineers Inc., 2025) Benzater, Hadj Abdelkader; Azrar, Arab; Lassami, Nacerredine; Teguig, Djamal; Zeraoula, Hamza
    This paper presents a comprehensive study on the design and validation of Low-Noise Amplifiers (LNAs) optimized for Software-Defined Radio (SDR)-based Cognitive Radio Networks (CRNs). Aimed at enhancing the Signal-to-Noise Ratio (SNR) and improving compressed sensing efficiency, we developed a MATLAB-based graphical user interface to facilitate LNA design. The GUI integrates analytical methods to calculate critical parameters, including available gain, reflection coefficients, and matching networks, ensuring accuracy through comparison with simulations in Advanced Design System software. The proposed method delivers a peak gain of 17.16 dB, representing an improvement of 3.71 dB, while maintaining a noise figure of 0.35 dB, which is only 0.13 dB higher than the minimum achievable value, demonstrating an optimized trade-off between gain and noise performance. Real-case LNA parameters were used to validate the design, with ADS simulations confirming a negligible deviation of 0.02 dB in gain. These results highlight the effectiveness of the proposed approach in improving the SNR (by 7 dB) and detection efficiency for SDR-based systems.
  • No Thumbnail Available
    Item
    Enhanced Signal Processing for Echo Detection Using Support Vector Machine, Weber’s Law Features, and Local Descriptors (LDP and LOOP)
    (Springer Science and Business Media, 2025) Hedir, Mehdia; Messaoui, Ali Zakaria; Messaoui, Aimen Abdelhak; Belaidi, Hadjira; Rouigueb, Abdenebi; Nemra, Abdelkrim
    Removing ground echoes from weather radar images is a critical task due to their substantial influence on the accuracy of processed meteorological data. These echoes often obscure the true atmospheric signals, particularly precipitation, which is essential for weather forecasting and analysis. In this study, we aim to develop advanced methods that not only eliminate ground echoes but also preserve precipitation signals, ensuring accurate meteorological observations. To achieve this, we explore the use of Local Descriptors based on Weber’s Law Descriptor (WLD) and combine it with the Local Binary Pattern (WLBP) descriptor, as well as introducing two novel descriptors: Local Directional Pattern (LDP) and Local Optimal-Oriented Pattern (LOOP). These descriptors are employed to capture various local features and patterns within the radar images that are crucial for distinguishing between ground echoes and precipitation. To automate the classification of these echo types, we leverage Support Vector Machine (SVM) classifiers, which have proven to be effective in high-dimensional pattern recognition tasks. Our proposed methods are rigorously tested at the Setif and Bordeaux sites, allowing for comprehensive evaluation under different weather conditions. The results from these tests demonstrate the effectiveness of the proposed techniques in accurately identifying and eliminating ground echoes while preserving precipitation. Specifically, the integration of LDP and LOOP significantly enhances the ability to differentiate between echoes, improving the robustness of the classifier in challenging environments. The outcomes indicate that these methods show considerable promise for practical applications in meteorological data processing, providing a reliable solution for improving the quality of weather radar data and supporting more accurate weather predictions.
  • No Thumbnail Available
    Item
    Advanced Trajectory Planning Technique for Unmanned Underwater Vehicle Navigation with Enhanced Fuzzy Logic Control and Obstacle Avoidance Strategy
    (Springer Science and Business Media, 2025) Demim, Fethi; Saghor, Sofian; Belaidi, Hadjira; Rouigueb, Abdenebi; Messaoui, Ali Zakaria; Benatia, Mohamed Akram; Chergui, Mohamed; Nemra, Abdelkrim; Allam, Ahmed; Kobzili, Elhaouari
    Trajectory planning plays a pivotal role in Unmanned Underwater Vehicles (UUVs), and this study addresses this aspect by employing Rapidly-exploring Random Trees (RRT) and a Fuzzy Logic Control (FLC). The investigation focuses on utilizing the RRT algorithm for waypoint generation in static environments. Leveraging Particle Swarm Optimization (PSO) enhances UUV control by optimizing FLC parameters, ensuring trajectory adherence to obstacle avoidance criteria. Through diverse experimental scenarios, the efficacy of the FLC regulator has been demonstrated, particularly in 3D waypoint navigation using Line-Of-Sight (LOS) guidance, showcasing accurate waypoint navigation, precise course maintenance, and effective pitch and yaw angle control for successful destination arrival. Moreover, this study highlights the increasing importance of RANS simulations in comprehending flow dynamics. It emphasizes a CFD-centric approach for design enhancement and aims to simulate 3D turbulent flow around UUV using ANSYS CFX code. This simulation evaluates appendage effects on overall drag and their interaction with the hull, effectively characterizing hydrodynamic behavior around the defined shape, aligning with study objectives.
  • No Thumbnail Available
    Item
    A Review on Advancement in PEM Fuel Cell Diagnosis Based on Machine Learning Techniques
    (Springer Science and Business Media, 2025) Kahia, Hichem; Boumerdassi, Selma; Belmeguenai, Aissa; Herbadji, Abderrahmane; Herbadji, Djamel
    Proton exchange membrane (PEM) fuel cell has attracted much attention due to its high efficiency and environmental friendliness, whose only reaction product is water. The main obstacles to large-scale deployment are premature fuel cell failures that limit the PEM fuel cell lifetime, which brings us back to many difficulties. In this study, we have defined and discarded the main methodological approaches to identify and diagnose FC. In this context, we have discussed the possible existing PEM fuel cell diagnosis methods that provide insight into FC performance, with the aim of improving diagnostic techniques and to deepen the understanding of the diagnostic behavior of PEMFC
  • No Thumbnail Available
    Item
    GPS Spoofing Attack Against UAVs: A Timeseries Dataset Case Study
    (Springer Science and Business Media, 2025) Mustapha, Mouzai; Amine, Riahla Mohamed
    Over the past few years, the world has witnessed a notable surge in the adoption of Unmanned Aerial Vehicles in civil and military applications, including border surveillance, search and rescue, agriculture and delivery. In contrast, this potential growth has been accompanied by the lack of necessary security mechanisms that respond to the threats and vulnerabilities posed by malicious actors. Therefore, in this study we investigate one of the stealthiest attacks that afflict the navigation system of UAVs named GPS Spoofing attack. We overview the different detection techniques existing in literature, and highlight machine learning based approaches dealing with time series data
  • No Thumbnail Available
    Item
    DotWise: A deep Learning-Powered Application for Efficient Black-To-Braille Text Conversion
    (Institute of Electrical and Electronics Engineers Inc., 2025) Zemouri, ET-Tahir; Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Sayah, Djamel Eddine
    Access to information and reading materials is crucial for the empowerment and education of visually impaired individuals, and Braille serves as a vital medium for this purpose. However, the production of Braille texts is often time-consuming and expensive, limiting the availability of resources. This paper presents an innovative automatic Braille recognition and conversion system, designed to significantly reduce both the time and cost involved in producing Braille texts. By automating the conversion of standard printed text into electronic Braille, the system addresses the challenges faced in Braille production, enhancing accessibility for the visually impaired. The core of the proposed system leverages deep learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, to recognize printed characters and convert them into Braille. Experimental results demonstrate a drastic reduction in conversion time, highlighting the efficiency of the approach. This system promises to expand the availability of reading materials for visually impaired individuals, making a meaningful impact on their knowledge acquisition and overall welfare
  • No Thumbnail Available
    Item
    Robust Trajectory Tracking Control of a Robotic Manipulator Using Fractional Order PID Controller
    (2025) Maouche, Malak; Garma, Abdelkader; Guesmi, Kamel
    This paper investigates the efficiency of FractionalOrder Proportional-Integral-Derivative (FOPID) control of a 2-degree-of-freedom (2-DOF) robotic manipulator for highprecision trajectory tracking. Although the standard PID controller offers a performance baseline, its integer-order structure often fails to adequately compensate for the inherent nonlinearities and dynamic coupling in multi-joint manipulators under real-time operation. By incorporating fractional-order calculus, the proposed FOPID controller provides additional tuning parameters that substantially expand the optimization space. Additionally, we present a comparative study of both controllers under various dynamic tracking scenarios of a disturbed system. Our findings indicate that the enhanced flexibility of the FOPID structure facilitates a more robust and optimized control performance, resulting in superior tracking accuracy and improved disturbance rejection. The obtained results highlight the practical value of advanced control strategies in meeting the high-performance requirements of the next generation of autonomous systems.
  • No Thumbnail Available
    Item
    Finite-horizon optimal LQ control design using artificial bee colony programming
    (Institute of Electrical and Electronics Engineers, 2025) Boudouaoui, Yassine; Habbi, Hacene; Maidi, Ahmed; Belharet, Karim
    Designing optimal control laws in closed analytical form is still showing challenging computational issues. This may even hold for moderately complex problems like the linear quadratic (LQ) control problem with finite horizon. This paper introduces a novel approach to LQ controller design based on artificial bee colony programming (ABCP). Solution to the matrix Riccati differential equation (MRDE) derived for the optimal control problem subject to linear dynamical system model is determined by means of ABCP method. Aiming at this, preparatory steps have been set and analyzed as essential part of the ABCP-based MRDE solver. The effectiveness of the proposed solver is investigated on a typical LQ control problem and compared to existing methods in literature. Evidence of superiority is shown through numerical evaluation of the method convergence and solution accuracy