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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Healthcare and Biomedical Science (JHBS)
Ahmad Syahrin Idris
University of Southampton Malaysia
Biochemistry, Genetics & Molecular Biology Control & Systems Engineering Electrical & Electronics Engineering Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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A hybrid simulation and hardware approach for a regenerative braking system in an electric motorcycle Faris Anwar Amir Faisal; Siti Fauziah Toha; Nurul Muthmainnah Mohd Noor; Ahmad Syahrin Idris; Mohamad Osman Tokhi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1265-1278

Abstract

Conventional electric motorcycles mostly depend on mechanical braking systems that dissipate kinetic energy as heat, resulting in significant energy losses, frequent battery recharging, and reduced operational efficiency. To address these limitations, a regenerative braking system (RBS) is designed and developed to recover and store kinetic energy during braking phases. The proposed RBS integrates a brushless DC (BLDC) motor that serves as a propulsion and energy regenerative unit, a lithium-ion battery for energy storage, and an Arduino microcontroller for real-time control and seamless system integration. A hybrid methodology combining MATLAB/Simulink simulations and hardware prototyping was adopted to evaluate system performance under various operating conditions. The simulation results demonstrated effective braking torque generation and back electromotive force (EMF) recovery to validate the system’s ability to convert kinetic energy into storable electrical energy. The proposed RBS achieved a theoretical energy recovery efficiency of approximately 70%, attributed to internal resistance and motor back EMF variations. These findings demonstrates the potential of regenerative braking in improving the energy efficiency of electric motorcycles, extending battery life, and reducing dependency on external charging. Furthermore, this study establishes a foundation for future RBS development incorporating lightweight materials, cost-effective components, and intelligent control strategies that can contribute to advancing sustainable and energy-efficient urban mobility solutions.
A Data-Centric Approach to HEK Cell Microscopic Image Segmentation using Multi-Scaling U-Net Syiham Fakhrulradzi Abdul Aziz; Ahmad Syahrin Idris; Siti Fauziah Toha; Izyan Mohd Idris; Muhammad Fauzi Daud; Azam Ahmad Bakir; Low Siow Yong
Journal of Healthcare and Biomedical Science Vol. 4 No. 2 (2026): Journal of Healthcare and Biomedical Science (JHBS)
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/jhbs.v4i2.4402

Abstract

Accurate and reproducible cell culture monitoring is important in biomedical research and regenerative medicine, yet manual assessment of cell confluency and morphology remains subjective and prone to inter-observer variability. Although deep learning approaches have been widely applied to cell segmentation, their systematic application to Human Embryonic Kidney (HEK) cells using data-centric methodologies remains underexplored. This study addresses this gap by implementing a Multi-Scaling U-Net (MSUNet) architecture combined with a data-centric workflow that emphasizes improving data quality through Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing, Total Variation Denoising (TVD), and iterative expert-guided annotation refinement. The scope of analysis was limited to HEK293T cell images captured at 10x magnification using phase-contrast microscopy. The optimized model achieved an Intersection over Union (IoU) of 0.8980 after applying the data-centric approach, representing a 15.1% relative improvement over the baseline model trained without preprocessing or annotation refinement. These findings provide empirical evidence that systematic data quality improvement constitutes a key contributing factor to segmentation performance, offering a reproducible methodology for automated cell confluency measurement in resource-constrained laboratory settings.
Co-Authors Azam Ahmad Bakir Faris Anwar Amir Faisal Izyan Mohd Idris Low Siow Yong Mohamad Osman Tokhi Muhammad Fauzi Daud Nurul Muthmainnah Mohd Noor Siti Fauziah Toha Syiham Fakhrulradzi Abdul Aziz