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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Mouhsen, Ahmed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A new compact grounded coplanar waveguide slotted multiband planar antenna for radio frequency identification data applications Dakir, Rachid; Mouhsen, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3800-3807

Abstract

This research presents the development and conception of a new compact grounded coplanar waveguide fed slotted rectangular planar antenna with a multi-frequency band for radio frequency identification data (RFID) reader applications which is based on the antenna mono-band frequency to use for a various applications RFID to support a different operating range. The optimized of the final prototype designing operates a multiple frequency bands ranging from 0.7-1.1 GHz, 2.2-2.5 GHz and 5.4-6 GHz for 0.9/2.4 GHz and 5.8 GHz RFID operation bands which is adapted from ultra-high frequency band (0.9 GHz) to microwave frequency band (2.4-5.8 GHz) RFID systems. This antenna is implemented and printed on a FR4 substrate with a size of 30×50×1.6 mm3. The novel prototype includes of a radiator rectangular patch with a symmetrical slot and a U-slot with I-stub on ground plan. The principles parameters of the antenna have been studied optimized and miniaturized by using a two simulators CST Microwave Studio and advanced design system (ADS) to validate the simulation results before the planar antenna realization. The final structure is achieved and validated of the results measurement. Experimental results show that the proposed antenna with a small size has good and stable radiation and thus promising for a various RFID applications.
The implementation of an optimized neural network in a hybrid system for energy management Jarmouni, Ezzitouni; Mouhsen, Ahmed; Lamhamdi, Mohamed; Ennajih, Elmehdi; Ennaoui, Ilias; Krari, Ayoub
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp815-823

Abstract

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.
Deep neural network for lateral control of self-driving cars in urban environment El Farnane, Abdelhafid; Youssefi, My Abdelkader; Mouhsen, Ahmed; El Ihyaoui, Abdelilah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1014-1021

Abstract

The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control. 
Arabic vowels characterization and classification using the normalized energy in frequency bands Farchi, Mohamed; Tahiry, Karim; Mouhsen, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4395

Abstract

The main objective of this work is to conduct an acoustic study of Arabic vowels (/a/, /a:/, /u/, /u:/, /i/ and /i:/) in order to determine the most relevant characteristics that allow recognizing these vowels. The analysis of vowel spectrograms reveals that the energy distribution as a function of time and frequency clearly differs according to the considered vowel. Thus, we used the normalized energy in frequency bands to classify these vowels. Thereafter, we have exploited the obtained results to develop algorithms that allow the classification of vowels and the distinction of the long vowels from the short ones. The efficiency of these algorithms was evaluated by testing their performances on our Arabic corpus.
Nonlinear backstepping and model predictive control for grid-connected permanent magnet synchronous generator wind turbines Kassoumi, Adil El; Lamhamdi, Mohamed; Mouhsen, Ahmed; Fdaili, Mohammed; Aboudrar, Imad; Mouhsen, Azeddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5091-5105

Abstract

This research investigates and compares two nonlinear current-control strategies, backstepping control (BSC) and finite control set model predictive control (FCS-MPC) for machine-side and grid-side converters in grid-connected direct-drive permanent magnet synchronous generator (DD-PMSG) wind turbines. Addressing the control challenges in wind energy systems with varying speeds, the study aims to determine which strategy offers superior performance under identical operating conditions. The nonlinear BSC regulates stator and grid currents using Lyapunov-based techniques, while FCS-MPC leverages model predictions to select optimal switching states based on a cost function. A comprehensive simulation using MATLAB/Simulink is conducted, analyzing each controller’s transient behavior, steady-state response, torque ripple, and power quality total harmonic distortion (THD). Results show that FCS-MPC achieves faster convergence, lower overshoot, and superior power quality compared to BSC, though it requires higher computational resources. Statistical validation supports the robustness of FCS-MPC under parameter uncertainties. This work contributes a structured comparison of advanced nonlinear strategies for PMSG-based wind turbines and provides a foundation for future implementations in real-time embedded control systems. Future directions include experimental validation and hybrid model predictive controller- artificial intelligence (MPC-AI) control frameworks.
AI-MG-LEACH: investigation of MG-LEACH in wireless sensor networks energy efficiency applied the advanced algorithm Ouldzira, Hicham; Essaadoui, Alami; Hanine, Mustapha EL; Mouhsen, Ahmed; Mes-Adi, Hassane
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5080-5090

Abstract

Wireless sensor networks (WSNs) play a crucial role in data collection across various fields like environmental monitoring and industrial automation. The energy efficiency of these networks, powered by limited-capacity batteries, is key to their performance. Clustering protocols such as low- energy adaptive clustering hierarchy (LEACH) are widely used to optimize energy consumption. To enhance LEACH’s performance, MG-LEACH was introduced, improving cluster head selection to extend network lifespan. This study compares MG-LEACH with AI-MG-LEACH, which incorporates artificial intelligence (AI) to further improve energy efficiency by selecting cluster heads based on factors like residual energy. Simulations show AI-MG-LEACH reduces energy consumption, extends network life, and enhances data reliability, outperforming MG-LEACH.
Co-Authors Aboudrar, Imad Dakir, Rachid El Farnane, Abdelhafid El Ihyaoui, Abdelilah Ennajih, Elmehdi Ennaoui, Ilias Essaadoui, Alami Farchi, Mohamed Fdaili, Mohammed Hanine, Mustapha EL Jarmouni, Ezzitouni Kassoumi, Adil El Krari, Ayoub Lamhamdi, Mohamed Mes-Adi, Hassane Mouhsen, Azeddine Ouldzira, Hicham Tahiry, Karim Youssefi, My Abdelkader