Browsing by Author "Bennia, Fatima"

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Comparative study between EMD, EEMD, and CEEMDAN based on De-Noising Bioelectric Signals
(Institute of Electrical and Electronics Engineers, 2024) Bennia, Fatima; Moussaoui, Siham; Boutalbi, Mohammed Chaker; Messaoudi, Noureddine
In synch with the artificial intelligence era and particularly in the biomedical field, biomedical signals like electrocardiographic (ECG), electromyographic (EMG), and Electroencephalogram (EEG) are being used in various applications, such as artificial hand and arterial pressure. However, identifying a patient's ailment is still a challenge. In this paper, we have utilized three empirical mode decomposition techniques to minimize the impact of additive noises on noninvasive biomedical signals. These methods are the classical empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with additive noise (CEEMDAN). Using the correlation coefficient, we conducted an extensive simulation and detailed comparative study between the noisy and reconstructed signals. The results show that the CEEMDAN method is the most effective in reducing noise compared to the other two methods.
Contribution to improving the efficiency of a wireless power transfer system using artificial intelligence techniques
(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2024) Bennia, Fatima; Boudouda, Aimad(Directeur de thèse)
Wireless Power Transfer (WPT) technology is an innovative method for powering devices without physical wires, which has been used here to provide power to bioimplantable devices. The main design constraints are to achieve maximum transfer efficiency while keeping the implant size small enough to be suitable for the living subject's body. Magnetic Resonant Coupling Wireless Power Transfer (MRCWPT), which uses pairs of inductor coils in the external and implant circuits, is a method actively researched for this type of power transmission. The objective of this thesis is to design and optimize a high-efficiency WPT receiving coil for biomedical applications. Traditionally, optimizing WPT systems based on mathematical equations or numerical models is often time-consuming and may not yield optimal designs. To address these limitations, this thesis introduces a novel approach that integrates a machine-learning model with metaheuristic methods for design and optimization. The primary goal is to maximize the transfer efficiency for an implantable coil with dimensions of 20 mm and a transfer distance of 30 mm, operating at a frequency of 13.56 MHz. To achieve this goal, we firstly identified the critical geometric coil parameters that significantly influence the WPT system's efficiency. A model-based Artificial Neural Network (ANN) was then constructed and trained on a comprehensive dataset generated through Finite Element Method (FEM) simulations. This model predicts efficiency based on geometric coil parameters, eliminating the need for complex calculations. Subsequently, two metaheuristic algorithms: the Genetic Algorithm (GA) and the Coyote Optimization Algorithm (COA), were employed to find the optimal parameters that maximize efficiency. The proposed ANN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, this WPT coil design approach significantly enhances transfer efficiency by up to 76% while drastically reducing computation time compared to conventional methods
Iterative method based optimization of wireless power transfer for biomedical implants
(IEEE, 2023) Bennia, Fatima; Boudouda, Aimad; Nafaa, Fares
Magnetic resonance coupling wireless power transfer (WPT) systems have gained increasing interest as an effective method for powering implantable medical devices (IMDs). Power transfer efficiency is a very important characteristic of WPT systems. It is characterized by the coupling and quality factors of the WPT coils. The maximum power transfer efficiency is obtained for high coupling and quality factors. It greatly depends on the geometrical parameters of the WPT coils. This paper presents an iterative procedure to design and optimize an implant coil restricted to a small size of 20mm at a distance of 30 mm, with an operating frequency of 13.56 MHz within the ISM frequency band. The proposed method aims to find the optimal geometrical parameters that maximize the power transfer efficiency. The designed coils give a high efficiency of about 83%. Finally, the finite element simulation with Ansys Maxwell 3D validates the obtained results
Optimal design of wireless power transfer coils for biomedical implants using machine learning and meta-heuristic algorithms
(Springer Nature, 2024) Bennia, Fatima; Boudouda, Aimad; Nafa, Fares
The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D.
Wireless Power Transfer Optimization Using Meta-heuristic Algorithms
(IEEE, 2024) Bennia, Fatima; Boudouda, Aimad; Nafa, Fares
The importance of Wireless Power Transfer (WPT) technology in biomedical implants to mitigate the risk of regeneration has increased significantly in recent years. WPT systems are dependent on key parameters such as the coupling coefficient (K), quality factor (Q), and mutual inductance (M), which play a crucial role in determining power transfer efficiency. These parameters are closely related to the geometric characteristics of the coils involved. Therefore, this study explores various meta-heuristic algorithms to search for optimal parameters that maximize power transfer efficiency. The initial results demonstrate that these algorithms perform well across different iterations. To confirm these findings, the study conducted comprehensive validation using Ansys Maxwell software to verify the optimal values obtained through optimization.