Communications Internationales

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

Browse

Start date: 2000Has files: Yes

Search Results

Now showing 1 - 10 of 643
  • Thumbnail Image
    Item
    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
  • Thumbnail Image
    Item
    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).
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Deep Learning Models to Analyze Sentiments of People Regarding New Vaccines
    (Institute of Electrical and Electronics Engineers, 2025) Khoudi, Asmaa; Draoui, Yasmine; Aoutou, Nadjet
    The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic
  • Thumbnail Image
    Item
    BOA based SVR analysis for Predicting the Strength of Subgrade soil for Pavement Design
    (LGCH, 2023) Bouguedra, Billel; Sandjak, Khaled; Ouanani, Mouloud
    This paper introduces a hybrid of the Bayesian optimization algorithm (BOA) and support vector regression (SVR) as a new modelling tool for the California Bearing Ratio (CBR) prediction of subgrade soil for pavement design. For this purpose, an experimental database was utilized to generate the hybrid BOA-SVR model of indirect estimation of the CBR using routinely collected soil properties. The database consists of 238 experimental datasets collected from soil tests carried out in the northern region of Algeria. To develop the model, all hyperparameters were optimised using the BOA technique. It was found that the average, median, standard deviation, minimum, maximum and interquartile range of the expected values of the developed hybrid model are very close to the experimental results. Results revealed that the hybrid BOA-SVR model predict the CBR of the tested subgrade soils with a coefficient of determination of 89% and mean squared error of 5.77. Comparisons with conventional and other machine learning models showed that BOA-SVR hybrid model predictions are more accurate and robust than those of other models
  • Thumbnail Image
    Item
    Synthesis, Characterization, and in Silico ADMET Evaluation of Transition Metal Complexes Based on Ortho-Phenylenediamine and Its Derivatives
    (ISRES, 2025) Kichou, Noura; Guechtouli, Nabila; Taferguennit, Manel; Ighilahriz, Karima
    A series of cobalt (II), nickel (II), and zinc(II) complexes were synthesized using orthophenylenediamine and its two substituted derivatives (methyl- and nitro-ortho-phenylenediamine) as ligands. These complexes were isolated and characterized using various analytical techniques, including Elemental analysis, infrared (IR) and UV-Visible spectroscopy, gravimetry, and conductimetry. Conductimetric analysis revealed that all the complexes exhibit a non-electrolytic behavior in solution, indicating the absence of free ions in the medium. IR spectroscopic studies allowed the identification of the coordination modes of the ligands to the metal centers. Comparison of the IR spectra of the complexes with those of the free ligands highlighted the involvement of the amine (-NH₂) groups in coordination with the metal, confirming their role as the primary coordination sites. UV-Visible spectroscopic analysis was used to determine the geometry of the complexes. The observed absorption bands are characteristic of an octahedral coordination around the metal ions, which is consistent with the expected electronic transitions for these systems. In recent years, the integration of computational methodologies has considerably enhanced the ability to predict the toxicity and pharmacokinetic behavior of bioactive compounds, thereby streamlining the early stages of drug discovery. Within this framework, the present study investigates the ADMET profiles - Absorption, Distribution, Metabolism, Excretion, and Toxicity as well as the drug-likeness properties of the synthesized ligands and their corresponding transition metal complexes.
  • Thumbnail Image
    Item
    Verified Path Indexing
    (pringer Science and Business Media Deutschland GmbH, 2025) Chaabani, Mohamed; Robillard, Simon
    The indexing of syntactic terms is a key component for the efficient implementation of automated theorem provers. This paper presents the first verified implementation of a term indexing data structure, namely a formalization of path indexing in the proof assistant Isabelle/HOL. We define the data structure, maintenance operations, and retrieval operations, including retrieval of unifiable terms, instances, generalizations and variants. We prove that maintenance operations preserve the invariants of the structure, and that retrieval operations are sound and complete
  • Thumbnail Image
    Item
    Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions
    (Avestia Publishing, 2025) Ferroudji, Hicham; Al-Ammari, Wahib A.; Barooah, Abinash; Hassan, Ibrahim; Hassan, Rashid; Hassan, Rashid; Gomari, Sina Rezaei; Rahman, Mohammad Azizur
    Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification