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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Emerging Science Journal
Jarrah, Amin
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Economics, Econometrics & Finance Environmental Science

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FPGAs memory synchronization and performance evaluation using the open computing language framework Almomany, Abedalmuhdi; Jarrah, Amin
International Journal of Reconfigurable and Embedded Systems (IJRES) 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/ijres.v13.i1.pp33-40

Abstract

One advantage of the open computing language (OpenCL) software framework is its ability to run on different architectures. Field programmable gate arrays (FPGAs) are a high-speed computing architecture used for computation acceleration. This work develops a set of eight benchmarks (memory synchronization functions, explained in this study) using an OpenCL framework to study the effect of memory access time on overall performance when targeting the general FPGA computing platform. The results indicate the best synchronization mechanism to be adopted to synthesize the proposed design on the FPGA computation architecture. The proposed research results also demonstrate the effectiveness of using a task-parallel model approach to avoid using high-cost synchronization mechanisms within proposed designs that are constructed on the general FPGA computation platform.
Real-Time FPGA-Based ADAS Solution for Driver Drowsiness Detection and Autonomous Stopping Almomany, Abedalmuhdi; Marouf, Zaid; Jarrah, Amin; Sutcu, Muhammad
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-023

Abstract

This study addresses driver drowsiness, a leading cause of traffic accidents, by developing a real-time Advanced Driver Assistance System that integrates biometric detection and autonomous vehicle control. The objective of this study is to enhance road safety through the early detection of drowsiness and automated intervention. The proposed system detects signs of drowsiness by monitoring facial and ocular features using a real-time video stream. Once a predefined threshold is exceeded, an audible alert is triggered. If the driver remains unresponsive, the system gradually reduces the vehicle’s speed and initiates an automated stop procedure. Methodologically, the system employs OpenCV for image processing and a convolutional neural network for lane detection and vehicle control. It is implemented on a high-performance hardware platform using field-programmable gate arrays programmed via Vivado High-Level Synthesis to ensure low-latency operation. The results confirm the system’s real-time capability, accuracy in drowsiness detection, and effective vehicle control under drowsy driving conditions. The system’s novelty lies in its combination of biometric monitoring, deep learning, and hardware acceleration to provide faster and more reliable intervention than existing Advanced Driver Assistance System technologies. This integration sets a new benchmark for proactive road safety measures.
Co-Authors Almomany, Abedalmuhdi Marouf, Zaid Sutcu, Muhammad