Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Hamad Dahou
Ibn Tofail University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
A New Photovoltaic Blocks Mutualization System For Micro-Grids Using An Arduino Board And Labview Abdelkader Mezouari; Rachid Elgouri; Mohammed Alareqi; Khalid Mateur; Hamad Dahou; Laamari Hlou
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 9, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (465.229 KB) | DOI: 10.11591/ijpeds.v9.i1.pp98-104

Abstract

The photovoltaic systems are often employed into micro-grids; Micro-grids are small power grids designed to provide a reliable and better power supply to a small number of consumers using renewable energy sources. This paper deals with DC micro-grids and present a new system of monitoring and sharing electricity between homes equipped with photovoltaic panels (PV) in the goal to reduce the electrical energy waste. The system is based on dynamic sharing of photovoltaic blocks through homes in stand-alone areas, using an arduino board for controlling the switching matrix. The LABVIEW program is used to further process and display collected data from the system in the PC screen. A small-scale prototype has been developed in a laboratory to proof the concept. This prototype demonstrates the feasibility and functionality of the system.
A microsystem design for controlling a DC motor by pulse width modulation using MicroBlaze soft-core Abdelkarim Zemmouri; Anass Barodi; Hamad Dahou; Mohammed Alareqi; Rachid Elgouri; Laamari Hlou; Mohammed Benbrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1437-1448

Abstract

This paper proposes a microsystem based on the field programmable gate arrays (FPGA) electronic board. The preliminary objective is to manipulate a programming language to achieve a control part capable of controlling the speed of electric actuators, such as direct current (DC) motors. The method proposed in this work is to control the speed of the DC motor by a purely embedded architecture within the FPGA in order to reduce the space occupied by the circuit to a minimum and to ensure the reliability of the system. The implementation of this system allows the embedded MicroBlaze processor to be installed side by side with its memory blocks provided by Xilinx very high-speed integrated circuit (VHSIC) hardware description language (VHDL), Embedded C. The control signal of digital pulse-width modulation pulses is generated by an embedded block managed by the same processor. This potential application is demonstrated by experimental simulation on the Vertix5 FPGA chip.
Deep learning–based real time speed limit sign detection with YOLOv12 on edge AI platforms for embedded ADAS Mohammed Chaman; Anas El Maliki; Youssef Natij; Hamad Dahou; Abdelkader Hadjoudja
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11128

Abstract

This research examined a real-time speed-limit sign detection framework based upon deep learning using the YOLOv12 neural network, optimized for the use of small edge devices that are embedded advanced driver assistance systems (ADAS). You only look once version 12 (YOLOv12) achieved a remarkable detection performance, while maintaining efficient computation, utilizing significantly optimized lightweight attention modules with an R-ELAN backbone capable of small and partially occluded detection. A custom dataset comprising 23,000 annotated images was prepared and augmented to ensure robustness under varying conditions. Model training utilized quantization-aware techniques and optimization via TensorRT and ONNX Runtime. Deployment and performance were rigorously evaluated on resource-constrained edge platforms, specifically NVIDIA Jetson Nano and Raspberry Pi 5. Experimental results demonstrated exceptional detection performance, achieving a precision of 99.0%, recall of 99.1%, and mean average precision (mAP@50) of 99.2%, confirming YOLOv12’s suitability for reliable, real-time ADAS implementation in intelligent transportation and autonomous vehicles.
Co-Authors Abdelkader Hadjoudja Abdelkader Mezouari Abdelkarim Zemmouri Anas El Maliki Anass Barodi Khalid Mateur Laamari Hlou Mohammed Alareqi Mohammed Benbrahim Mohammed Chaman Rachid Elgouri Youssef Natij