Browsing by Author "Moulahcene, Fateh"

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    Denoising of medical signals using fir filter
    (Université d'Oum El Bouaghi, 2024) Zeghadi, Khansa; Moulahcene, Fateh
    L'électrocardiographie (ECG) est un outil de diagnostic essentiel dans le domaine de la santé, utilisé pour surveiller et analyser l'activité électrique du coeur. Cette thèse étudie l'utilisation de filtres numériques dans MATLAB et VHDL pour débruiter les signaux ECG, en traitant la contamination par le bruit qui peut compromettre la précision de l'évaluation cardiaque. L'étude conçoit et simule des filtres, qui sont ensuite implémentés sur une plateforme FPGA à l'aide de la chaîne d'outils Vivado. Les performances du système sont évaluées, démontrant des améliorations significatives dans la réduction du bruit, améliorant la fiabilité de l'analyse du signal ECG et favorisant de meilleurs résultats cliniques.
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    Designing and Implementation of a multi-purpose UAV
    (Université d'Oum El Bouaghi, 2024) Derghal, Safa; Djaballah, Badr; Moulahcene, Fateh
    This thesis presents the design and implementation of an Unmanned Aerial Vehicle (UAV) quadcopter, developed to perform multiple tasks including delivery, fires detection and firefighting, as well as plant disease detection and treatment. It introduces a full approach to the design and implementation of control system for six degrees of freedom, four rotors unmanned aerial vehicle known as quadcopter. Initially, different forces acting on the system and aerodynamic effects are analyzed. Then, the mathematical model for simulation and control of such systems is detailed as well as the design methodology for the Quadcopter. Based on the mathematical model, linear and nonlinear control techniques are used to design and simulate various controllers along this work. Next, the model is developed and tested in Matlab\Simulink environment. Lastly, it discusses the step-by-step construction of the Arduino-based quadcopter, and the upgrade to an Ardupilot system. The UAV's practical applications are demonstrated through deployment scenarios in delivery, fire management, and agriculture. Object detection for forest fire prevention and image classification algorithms for plant disease identification are implemented and evaluated. This research contributes to the field by providing a robust framework for the design and application of versatile UAVs, addressing both theoretical and practical challenges, and proposing enhancements for future development.