Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Applied Engineering and Technological Science (JAETS) International Journal of Robotics and Control Systems
Magdy Helal
American University of the Middle East
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Finding and Tracking Automobiles on Roads for Self-Driving Car Systems Wael Farag; Mohamed Abouelela; Magdy Helal
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1022

Abstract

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.
On The Assembly Line Balancing Problem: A Simplified Perspective With The Precedence Matrix Magdy Helal; Kaushik Nag; Rifat Ozdemir
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5597

Abstract

The assembly line balancing problem (ALBP) has been an attractive research area for decades; however, the industrial application of the research findings remains limited. This can be attributed to the complexity of solution methods, restrictive assumptions, and the numerous variants of the problem in the real-world settings. This article describes using the precedence matrix as the basis for developing simplified, more practical analysis frameworks for the ALBP. We introduce algorithms to construct the precedence matrix from basic assembly problem data and implement it within a spreadsheet model, and to utilize this matrix in assigning the assembly tasks to workstations while ensuring assignment feasibility. The structure and potential uses of the precedence matrix-based framework are presented in detail. Furthermore, we compare selected line balancing priority rules using the proposed framework to demonstrate its effectiveness. The results suggest that the precedence matrix model is a straightforward, efficient tool for line balancing analysis, that can offer substantial support to both industry practitioners and academic researchers.
Co-Authors Kaushik Nag Mohamed Abouelela Rifat Ozdemir Wael Farag