Browsing by Author "Messaoui, Ali Zakaria"

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Advanced Trajectory Planning and 3D Waypoints Navigation of Unmanned Underwater Vehicles Based Fuzzy Logic Control with LOS Guidance Technique
(Science and Technology Publications, Lda, 2023) Demim, Fethi; Belaidi, Hadjira; Rouigueb, Abdenebi; Messaoui, Ali Zakaria; Louadj, Kahina; Saghour, Sofian; Benatia, Mohamed Akram; Chergui, Mohamed; Nemra, Abdelkrim; Allam, Ahmed; Kobzili, Elhaouari
Trajectory planning is a critical action for achieving the objectives of Unmanned Underwater Vehicles (UUVs). To navigate through complex environments, this study investigates motion trajectory planning using Rapidlyexploring Random Trees (RRT) and Fuzzy Logic Control (FLC). Our goal is to explore the use of the RRT trajectory planning algorithm to generate waypoints in a known static environment. In this case, the UUV’s planned trajectory can meet the required conditions for obstacle avoidance. By using various objective functions, the model can be solved, and the corresponding control variables can be adjusted to effectively accomplish the requirements of underwater navigation. This technique has been successfully applied in various experimental scenarios, demonstrating the effectiveness of the FLC regulator. For instance, The 3D waypoint navigation challenge has been tackled by implementing the Fuzzy Controller, which utilizes the robust Line-Of-Sight (LOS) guidance technique. Experimental results demonstrate that the FLC regulator efficiently navigates through the waypoints, maintains an accurate course, controls the pitch and yaw angles of the UUV, and successfully reaches the final destination.
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.
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.
Enhancing Echo Processing Through the Integration of Support Vector Machine and Weber’s Law Descriptors
(SciTePress, 2024) Hedir, Mehdia; Demim, Fethi; Messaoui, Ali Zakaria; Messaoui, Aimen Abdelhak; Belaidi, Hadjira; Rouigueb, Abdenebi; Nemra, Abdelkrim
Removing ground echoes from weather radar images is a topic of great importance due to their significant impact on the accuracy of processed data. To address this challenge, we aim to develop methods that effectively eliminate ground echoes while preserving the precipitation, which is a crucial meteorological parameter. To accomplish this, we propose to test Local Descriptors based on Weber’s law (WLD), as well as descriptors that combine Weber’s law with Local Binary Pattern (WLBP), using Support Vector Machine (SVM) classifiers to automate the recognition of both types of echoes. The proposed methods are rigorously tested at the sites of Setif and Bordeaux to evaluate their effectiveness in accurately identifying the ground echoes and precipitation. The results of our experiments demonstrate that the proposed techniques are highly effective in eliminating ground echoes while preserving the precipitation, and can be considered satisfactory for practical applications in meteorological data processing.
Improving the License Plate Character Segmentation Using Naïve Bayesian Network
(Science and Technology Publications, Lda, 2023) Rouigueb, Abdenebi; Demim, Fethi; Belaidi, Hadjira; Messaoui, Ali Zakaria; Benatia, Mohamed Akrem; Djamaa, Badis
Character segmentation plays a pivotal role in automatic license plate recognition (ALPR) systems. Assuming that plate localization has been accurately performed in a preceding stage, this paper mainly introduces a character segmentation algorithm based on combining standard segmentation techniques with prior knowledge about the plate’s structure. We propose employing a set of relevant features on-demand to classify detected blocks into either character or noise and to refine the segmentation when necessary. We suggest using the naïve Bayesian network (NBN) classifier for efficient combination of selected features. Incrementally, one after one, high computational cost features are computed and involved only if the low-cost ones cannot decisively determine the class of a block. Experimental results on a sample of Algerian car license plates demonstrate the efficiency of the proposed algorithm. It is designed to be more generic and easily extendable to integrate other features into the process.
Secured Communication of Speech Signal Using the Discrete Cosine Transform Based on Hyperchaos-System
(Science and Technology Publications, Lda, 2023) Aissaoui, Noureddine; Demim, Fethi; Rouigueb, Abdenebi; Belaidi, Hadjira; Messaoui, Ali Zakaria; Louadj, Kahina; Nemra, Abdelkrim; Allam, Ahmed; Saidi, Yasmine; Sadoudi, Said
This paper proposes a novel approach that combines chaos-based encryption and Discrete Cosine Transform (DCT) to ensure high-level speech security and robustness against attacks. In this approach, the encryption process is based on Lorenz’s hyperchaotic system, which utilizes the One Time Pad approach to encrypt the speech DCT coefficients. The effectiveness of this approach has been validated through experiments on two PCs interconnected via real-time serial communication links (USB-RS232), which showed that the original speech is effectively hidden, and the proposed solution is highly resistant to possible attacks. Moreover, the proposed solution can be implemented in real-time applications using technologies such as FPGA.
Smooth Sliding Mode Control Based Technique of an Autonomous Underwater Vehicle Based Localization Using Obstacle Avoidance Strategy
(Science and Technology Publications, Lda, 2023) Demim, Fethi; Rouigueb, Abdenebi; Belaidi, Hadjira; Messaoui, Ali Zakaria; Bensseghieur, Khadir Lakhdar; Allam, Ahmed; Benatia, Mohamed Akram; Nouri, Abdelmadjid; Nemra, Abdelkrim
Navigating underwater environments presents serious challenges in control and localization technology. The successful navigation of uncharted territories requires autonomous maneuvers that achieve goals while avoiding obstacles, posing a significant problem to be addressed. Detection-based control using sensor data and obstacle avoidance technology are vital for the autonomy of Autonomous Underwater Vehicles (AUVs). This study focuses on developing a control method based on Sliding Mode Control (SMC) and utilizing an imaging sonar sensor for obstacle avoidance. The proposed approach includes a controller for pitch and depth control, enabling avoidance of stationary objects. A Gaussian potential function is employed to guide the AUV’s maneuvers and avoid obstructions. Numerous simulation results evaluate the control performance of the AUV in realistic simulation conditions, assessing accuracy and stability. The experimental in simulation results demonstrate the excellent performance of our approach in navigating various obstacles such as gentle rise, steep drop-off, and underwater walls, using seafloor environment simulation models.