Institut Supérieur des Études Technologiques en Communications de Tunis

This study presents a robust switching system for semi-autonomous vehicles enabling safe transitions between remote and local control modes. Using Design Science Research, the system was iteratively developed and validated through CARLA and MATLAB co-simulation. Key contributions include modular architecture, hybrid control logic, fault-tolerant communication, and fail-safe immobilization mechanisms meeting critical safety standards. Performance analysis demonstrated improved safety and reliability under simulated conditions. Future work will focus on real-world deployment and integration with fully autonomous platforms.

Bovine tuberculosis (bTB), caused by Mycobacterium bovis (M. bovis), remains a major zoonotic and economically burdensome disease worldwide. In Tunisia, where bTB has remained present for many years, Efforts to eliminate the disease have been slowed down by limited diagnostic tools and a lack of resources. The tuberculin Intra Dermal Reaction test (IDR), also known as Tuberculin Skin Test (TST), is still the main tool for surveillance, but its low sensitivity and specificity show the need for better diagnostic methods. This study evaluated the diagnostic performance of IDR, PCRMpb70 and three serological rapid tests: Quickvet Ab® (Ac1, detecting antibodies against a recombinant M.bovis antigen), Vetdiagnostix Ab® (Ac2, detecting antibodies against a recombinant MPB70/MPB83 fusion protein), and Vetdiagnostix Ag® (Monoclonal antibodies against Bovine IFN-γ detecting a native M. bovis antigen complex) in Tunisian cattle (n = 32). Based on AI, Bayesian Latent Class Model (BLCM) analysis, PCRMpb70 was identified as the most reliable reference standard due to its high sensitivity (Se) and perfect specificity (Sp) when assessed alongside other tests. Using PCRMpb70 as a proxy gold standard, supervised machine learning via the Random Forest algorithm was employed to assess the predictive performance of the individual and combined diagnostic tests. As a main result, a high bTB prevalence (46.37%) was confirmed in the tested animals, although prevalence estimates varied considerably depending on the diagnostic test used (average ± 9.76). The combinations IDR2/Ac2, IDR2/Ag, and IDR2/Ac2/Ag pair a second IntraDermal test (IDR2) with antibody tests for Ac2, Ag, or both, proved to be the most informative and complementary alternatives to PCR. These approaches provide practical and effective diagnostic options for field settings where access to molecular testing is limited or unavailable.

Robotic systems have significantly evolved, integrating Artificial Intelligence (AI) techniques to enhance autonomy, adaptability, and robustness. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, remain fundamental in many applications, yet AI-driven approaches are proving invaluable in tackling complex, non-linear, and uncertain environments.This special issue explores the latest advancements in AI-driven control techniques for robotic applications. The contributions cover a range of topics, including adaptive and learning-based control strategies, intelligent navigation, state estimation, and the integration of hybrid AI techniques for enhanced robotic performance.The articles included in this special issue address various challenges in robotic control and introduce innovative AI-based methodologies. Below, we highlight the key contributions:• Intelligent Control Techniques: Several articles explore the integration of AI-driven controllers, such as neural networks and fuzzy logic, to improve robotic decision-making and adaptability in dynamic environments.• State Estimation and Sensor Fusion: Advanced techniques like Kalman filters and particle filters are leveraged to enhance real-time localization and trajectory tracking, ensuring precise motion planning.• Hybrid Control Strategies: The fusion of classical and AI-based methods demonstrates how hybrid controllers can achieve robust performance, particularly in unstructured and uncertain scenarios.• Obstacle Avoidance and Path Optimization: Contributions discuss AI-enhanced navigation algorithms that enable robots to autonomously detect obstacles and compute optimal paths in real time.The first paper, "On Designing a Configurable UAV Autopilot for Unmanned Quadrotors," presents a control strategy for unmanned aerial vehicles (UAVs) based on an inner-outer loop design for attitude and altitude control. It leverages nonlinear feedback linearization, linear-quadratic regulator (LQR), sliding mode control (SMC), proportional-derivative (PD), and PID controllers to enhance stability and disturbance rejection. The study also introduces a PD-PID hybrid controller designed for tracking and surveillance of smoke and fire. Simulation results confirm the effectiveness of the proposed approach in improving UAV performance under various conditions. The second paper, "Fusion Inception and Transformer Network for Continuous Estimation of Finger Kinematics from Surface Electromyography (sEMG)," introduces FIT (Fusion Inception Transformer), a deep learning model for decoding sEMG signals to predict hand movements. By integrating Inception and Transformer networks, the model efficiently extracts both local and global features. Results on the Ninapro dataset show that FIT outperforms temporal convolutional networks (TCN), long short-term memory (LSTM) networks, and Bidirectional Encoder Representations from Transformers (BERT) in terms of estimation accuracy and computational efficiency, contributing significantly to advancements in human-computer interaction (HCI) technology.The third paper, "Cardioid Oscillator-Based Pattern Generator for Imitating Lower Limb Exoskeleton Behavior," proposes a pattern generator based on cardioid oscillators to mimic human gait characteristics such as periodicity, selfexcitation, and time-ratio asymmetry. The approach is validated through simulations and experiments, demonstrating that the generated trajectories closely resemble natural human gait. This method holds promise for exoskeleton control in rehabilitation and assistive applications.The fourth paper, "An Autonomous Mobile Robot Path Planning Strategy Using an Enhanced Slime Mold Algorithm (ESMA)," introduces ESMA, an improved optimization method for robotic path planning. By incorporating adaptive techniques for enhanced global search and an artificial potential field for dynamic obstacle avoidance, ESMA significantly reduces both path length and computation time. Comparative analysis against the standard Slime Mold Algorithm (SMA) and other optimization algorithms confirms its superiority, making it a valuable solution for real-world robotic navigation.The fifth paper, "Multi-User Motion Recognition Using sEMG via Discriminative Canonical Correlation Analysis and Adaptive Dimensionality Reduction," proposes a multi-user sEMG motion recognition framework designed to address variability in muscle signals across different users. By utilizing discriminative canonical correlation analysis (DCCA) and adaptive dimensionality reduction (ADR), the system projects feature sets into a uniform space, enhancing motion recognition accuracy. Experimental results demonstrate over 90% accuracy, highlighting its potential for rehabilitation and assistive technology applications.The convergence of AI and robotics presents exciting research opportunities. Future advancements may focus on reinforcement learning for real-time adaptation, neuromorphic computing for energy-efficient robotic control, and explainable AI (XAI) for transparent decision-making in safety-critical applications.This special issue highlights the growing role of AI in robotic applications and control. The featured articles provide valuable insights into emerging methodologies that push the boundaries of autonomous robotic systems. We thank all the contributors for their efforts in advancing this field.We express our gratitude to the authors, reviewers, and editorial team for their contributions to this special issue. Their efforts have ensured a high-quality collection of research in AI-driven robotics.

In this paper, we introduce a new stochastic geometry model, the ’Modified Matérn Cluster Process (MMCP),’ to provide an accurate representation of cell locations in a network. This model not only incorporates realistic constraints but also takes into account the clustered distribution of user equipment (UE). Specifically, UEs are assumed to cluster around small-cell base stations (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCBs</i>), which adhere to a minimum separation distance and are organized according to a Matérn Hard-Core Process (MHCP) II, ensuring efficient spatial distribution and reduced interference. To further enhance network performance, we propose a novel transmission scheme specifically designed to mitigate interference. This innovative approach results in the development of a tractable analytical framework, which includes a detailed assessment of the non-interference probability, thereby optimizing resource allocation strategies across the network. Within this framework, we derive expressions for the successful transmission probabilities for both uplink and downlink communications. Additionally, we explore the average ergodic capacity, a crucial metric for evaluating the overall efficacy of the three-dimensional (3-D) MMCP. These derivations enable a deeper understanding of the model’s impact on network performance. To validate our theoretical findings, we conduct extensive Monte Carlo simulations, which demonstrate the accuracy of our proposed model. Our results highlight the critical importance of accounting for user distribution when evaluating dynamic system performance.

<bold>Introduction:</bold> Despite a similar apnea hypopnea index (AHI), clinical and polygraphic features are different according to gender in obstructive sleep apnea hypopnea syndrome (OSAS). The aim of this study was to evaluate the severity of OSAS according to gender by the severity of respiratory events. <bold>Methods:</bold> Retrospective study including 602 patients followed for OSAS, confirmed by ventilatory polygraphy, at the pneumology department D of Abderrahmane Mami hospital in Ariana between 2015 and 2022. These patients were divided into 2 groups according to sex: G1 including 178 male patients and G2 including 424 female patients. <bold>Results:</bold> A female predominance was noted with a sex ratio of 2.37. OSAS was classified as mild in 30.1% of cases, moderate in 16.9% and severe in 53%. AHI was significantly higher in men (G1: 34.39/h ±22.40; G2: 30.29/h ±22.64; p=0.04). The mean duration of apneas and hypopneas were statistically more prolonged in men with respectively (G1: 20.41s ±6.62; G2: 17.57s ±6.13; p= 0.001) and (G1: 24.27s ±5.37; G2: 22.41s ±4.97; p= 0.001). No significant difference between the 2 groups concerning the oxygen desaturation index (ODI) (G1: 32.81/h ±22.17; G2: 30.28/h ±23.96; p=0.2). There was no significant correlation between mean apnea/hypopnea duration and ODI according to sex (p=0.07; p=0,3). However, the mean duration of apnea and hypopnea was statistically associated with AHI in men (p=0.02) and not associated in women (p=0.4). <bold>Conclusion:</bold> The average duration of apnea or hypopnea does not seem to have a significant impact on the ODI. However, this duration is proportionally correlated with the AHI in men.

Face recognition remains a crucial issue in computer vision with various applications. This paper introduces an adapted method to tackle the challenges posed by mask-wearing during the COVID-19 pandemic. We propose modifications to the bag of convolutional neural networks (BoCNN) framework, which combines CNNs and the bag of words (BoW) approach. Our main contribution is customizing the BoCNN algorithm to identify faces with masks by focusing on the visible facial regions, particularly the eyes and eyebrows. Facial landmarks are detected, and the region of interest (ROI) is extracted using techniques such as Media Pipe. A pre-trained CNN is then applied to sections within the ROI, enabling robust feature extraction that captures intricate details such as lighting variations and facial expressions while reducing the impact of mask occlusions. The extracted features are pooled to create a comprehensive representation for recognition. Extensive experiments on the labeled faces in the wild (LFW) dataset, including masked face images, demonstrate the superiority of our adapted BoCNN approach over traditional BoW and deep feature extraction methods, especially accurately recognizing masked faces. Additionally, we assess the generalizability of our method across multiple datasets and discuss potential limitations and future research directions. The proposed BoCNN-based solution proves effective in recognizing masked faces, making it highly relevant for applications in security, human-computer interaction, and various other domains affected by the COVID-19 pandemic.

The primary objective of this article is to establish an analytical framework for evaluating the rate-distortion and the outage probability performance in Internet-of-Things (IoT) systems based on lossy cooperative wireless communications. Two correlated sources transmit information through fading multiple access channels (MACs) with the assistance of a bit-flipping (BF) helper. To begin with, we derive a closed-form expression of the inner bound on the achievable rate-distortion region. To reduce computational complexity, we then propose an approximate method for calculating the outage probability based on the lossy source-channel separation theorem. Moreover, Monte-Carlo methods are adopted to evaluate the outage probability in MAC and orthogonal transmission schemes. Theoretically, approximate results are also compared with the exact results obtained by Monte-Carlo methods. It is shown that the gap between the approximate and exact performance curves decreases as the distortion requirement becomes smaller. Especially when the distortion requirement reduces to zero, the approximated outage probability is exactly the same as the results obtained by Monte-Carlo methods. In addition, we also present performance comparisons in terms of the outage probability between Rayleigh and Nakagami-m fading, and between BF helper and optimal helper.

IntroductionThe aim of this astudy was to describe the management strategy in workplace adopted when an employee is diagnosed with tuberculosis.MethodsA prospective intervention study was carried out in a Tunisian Occupational Medicine Group (OMG) after declaration of a bacilliferous tuberculosis case in a private sector company. Data was collected from the medical files of the index case and work colleagues. Surveys were carried out by the OMG, the Direction of Medical Inspection and Safety at Work (DMISW), the Anti-Tuberculosis Dispensary (ATD) and the Regional Health Directorate (RHG).ResultsA 24-year-old employee was diagnosed with tuberculosis. The employee was diagnosed with bacilliferous tuberculosis on the basis of X-ray images and the positivity of Koch’s bacillus in the sputum. He was hospitalized and treated. As soon as the diagnosis was confirmed, the case was reported to the DMISTW territorially competent, then to the RHG and the ATD. A survey within the company was carried by the occupational physician in order to identify the close contacts. The index case was a trainer in a garment factory with 1500 employees. The ventilation in the enterprise was artificial. The employee was working two shifts (2x8). The duration of the study was of five months and still continuing. The number of close contacts was 63 with two suspected cases. A prevention campaign for close contacts and screening process was carried out. Contacts were divided into groups of 10 people for consultations at ATD while continuing their work. They underwent a chest X-ray, Koch’s bacillus in sputum, tuberculin skin test (TST) and a Quantiferon test. One collegue was diagnised with tuberculosis.ConclusionThe role of the occupational physician is crucial in the early detection of this disease in collaboration with all the stakeholders specializing in the management of pulmonary tuberculosis.