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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Marhoon, Hamzah M.
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Design and Implementation of an RFID-Based Smart Parking Control System with Infrared Sensors and Queueing-Theory Traffic Modelling Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.261

Abstract

The growing trends in the need for automated and trusted parking control have fostered the enhancement of smart systems that can do effective access control, precise occupancy identification, and effective traffic control. This paper provides both the design, implementation, and analytical evaluation of a low-cost smart parking control system based on Radio Frequency Identification (RFID)- based vehicle authentication, Infrared (IR) sensor-based slot monitoring, and servo-controlled gate actuation based on an Arduino-based embedded architecture. A working prototype is created to exemplify a one-level parking system, where IR sensors are used to detect live availability slots, an RFID module is used to provide authenticated access, and a Liquid Crystal Display (LCD) device is used to show occupants of the slot. In order to come up with a strict performance evaluation, Queueing Theory is adopted by modelling the entrance gate as a service system of M/M/1. This analytical model can be used to measure waiting times, queue time, and system usage quantitatively at different rates of arrival. Measurements conducted during experimental evaluation involve the accuracy of IR detection, RFID authentication latency, servo response time, system reliability, error-rate characterization, and analysis of energy consumption of each hardware component. These results indicate high accuracy in operation, fast authentication, consistent actuation operation, and low power consumption, applicable when a device is compact or battery powered. The queueing-based modelling also substantiates the fact that the system operates efficiently at the levels below saturation points.
Understanding Large Language Models: A Review Wulandari, Annastasya Nabila Elsa; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
Co-Authors Abdulhussein, Ali A. Abdullah, Hikmat N. Abdulmaged, Riyam Bassim Agajie, Takele Ferede Al-Rammahi, Hussein Al-Tahir, Sarah O. Alanssari, Ali Ihsan Alfian Ma’arif Basil, Noorulden Fadhil Abbas, Riyam Fadhil Mohammed, Abdullah Hassan, Reem Falah Listyantoro, Fiki Mangkunegara, Iis Setiawan Mohammed, Abdullah Fadhil Mohammed, Raghad Jassim Omar, Rasha Khalid Purwono Purwono, Purwono Ridha, Hussein Mohammed Sabah, Sabah Ali Sabbar, Bayan Mahdi Shafeeq Bakr, Zaid Sharkawy, Abdel-Nasser Suwarno, Iswanto Tarik, Benmessaoud Mohammed Wulandari, Annastasya Nabila Elsa