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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Abounada, Abdelouahed
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Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor Bariko, Smail; Klilou, Abdessamad; Abounada, Abdelouahed; Arsalane, Assia
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp552-559

Abstract

Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.
Hybrid ANN-PSO MPPT with high-gain boost converter for standalone photovoltaic systems Byar, Mohcine; Chbirik, Ghizlane; Brahmi, Abdennabi; Abounada, Abdelouahed
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9451

Abstract

Standalone photovoltaic (SPV) systems play a critical role in delivering clean energy to remote areas; however, maintaining consistent maximum power point tracking (MPPT) under dynamic environmental conditions remains a significant challenge. This paper proposes a hybrid artificial neural network–particle swarm optimization (ANN-PSO) based MPPT algorithm, integrated with a high-gain boost converter (HGBC), to overcome these limitations. The hybrid approach leverages the predictive capacity of ANN and the global optimization strength of PSO to achieve accurate and rapid tracking of the maximum power point under fluctuating irradiance. In addition, the high-gain converter improves voltage amplification and reduces power losses, improving overall system efficiency. The simulation results in MATLAB/Simulink confirm that the proposed system achieves a 99.7% tracking efficiency, faster convergence than conventional MPPT techniques, and significantly reduced power ripple. These results indicate that the proposed strategy can improve energy harvesting and operational stability in SPV applications. In addition, it offers a scalable and cost-effective solution suitable for off-grid electrification, particularly in rural and underdeveloped regions, contributing to global renewable energy goals.
Optimization of ANN-based DC voltage control using hybrid rain optimization algorithm for a transformerless high-gain boost converter Byar, Mohcine; Abounada, Abdelouahed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i3.pp1711-1720

Abstract

This paper introduces an adaptive voltage regulation technique for a transformerless high-gain boost converter (HGBC) integrated within standalone photovoltaic systems. A neural network controller is trained and fine-tuned using the rain optimization algorithm (ROA) to achieve improved dynamic behavior under variable solar conditions. The proposed ROA-ANN framework continuously updates the duty cycle to ensure output voltage stability in real time. Validation was carried out using MATLAB–OrCAD co-simulation under multiple scenarios. Comparative results highlight superior performance of the ROA-ANN controller in terms of convergence speed, overshoot minimization, and steady-state response, outperforming conventional PID and ANN-based methods.
Co-Authors Arsalane, Assia Bariko, Smail Brahmi, Abdennabi Byar, Mohcine Chbirik, Ghizlane Klilou, Abdessamad