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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Robotics and Control Systems
Mohamed Abouelela
American University of the Middle East
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Low-Cost Active Monitoring of Attendance using Passive RFID Technology Wael A Farag; Mohamed Abouelela
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25168

Abstract

In this paper, a smart attendance system for students attending schools is proposed. The proposed attendance system is based on Radio Frequency Identification (RFID) technology to facilitate automation and convenience. The proposed RFID Attendance System (RFID-AS) should be used by school administration to ensure safety for students as well as using it for grading and evaluation purposes. After careful study, passive RFID technology is selected to be used by the proposed system for its reasonable cost. The main components of the system are an RFID tag, an RFID reader, Visual Studio (XAF Tool), and SQL Server to compare the data from the RFID tag with the students’ database to record attendance automatically. A Graphical User Interface (GUI) is developed using Visual Studio (XAF Tool) to allow parents and school faculty to log in and browse the students’ records. Students will pass the classroom door, which will have an integrated RFID reader device to read their RFID. The paper discusses the design of the solution as well as the testing scenarios.
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%.
Co-Authors Magdy Helal Wael A Farag Wael Farag