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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 Intelligent Safety and Security System for Vehicles Based on GSM Communication and IoT Network for Real-Time Tracking Marhoon, Hamzah M.; Alanssari, Ali Ihsan; Basil, Noorulden
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19652

Abstract

In recent years, the surge in car theft cases, often linked to illicit activities, has become a growing concern. Simultaneously, countries grappling with oil shortages have shifted towards converting vehicles to run on liquid propane gas, presenting new safety challenges for car owners. This paper introduces a novel integrated intelligent system designed to address the challenges of car theft and safety concerns associated with gas-based vehicles. By seamlessly integrating these concerns into a single system, it aims to achieve significantly improved performance compared to traditional alarm systems. The proposed system consists of three primary parts: the car security subsystem, an Internet of Things (IoT)-based real-time car tracking subsystem, and the car safety subsystem. Utilizing key technologies such as the Arduino Microcontroller, Bluetooth module, vibration sensor, keypad, solenoid lock, GSM module, NodeMCU microcontroller, GPS module, MQ-4 gas sensor, flame sensor, temperature sensor, and Bluetooth module, the system aims to provide a comprehensive solution for the mentioned issues. Furthermore, the vibration sensor plays a crucial role in identifying unauthorized vehicle operations. Its significance lies in detecting the vibrations emanating from the running engine. Concurrently, other modules and sensors are utilized for real-time tracking and enhancing vehicle safety. These measures include safeguarding against incidents like fire outbreaks or gas leaks within the gas container. Finally, after assembling the system, a practical test was conducted, yielding favourable performance results. This paper describes a meaningful step towards improving the protection and safety for the cars, simultaneously addressing the stealing prevention and gas-related accident alleviation.
A New Hybrid Intelligent Fractional Order Proportional Double Derivative + Integral (FOPDD+I) Controller with ANFIS Simulated on Automatic Voltage Regulator System Mohammed, Abdullah Fadhil; Marhoon, Hamzah M.; Basil, Noorulden; Ma'arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1336

Abstract

In the dynamic realm of Automatic Voltage Regulation (AVR), the pursuit of robust transient response, adaptability, and stability drives researchers to explore novel avenues. This study introduces a groundbreaking approach—the Hybrid Intelligent Fractional Order Proportional Derivative2+Integral (FOPDD+I) controller—leveraging the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). The novelty lies in the comparative analysis of three scenarios: the AVR system without a controller, with a traditional PID controller, and with the proposed FOPDD+I-based ANFIS. By fusing ANFIS with a hybrid controller, we forge a unique path toward optimized AVR performance. The hybrid controller, based on FOPID (Fractional Order Proportional Integral Derivative) principles, synergizes individual integral factors with ANFIS, augmenting them with a doubled derivative factor. The ANFIS design employs a hybrid optimization learning scheme to fine-tune the Fuzzy Inference System (FIS) parameters governing the AVR system. To train the fuzzy inference system, we utilize a Proportional-Integral-Derivative (PID) simulation of the entire AVR system, capturing essential data over approximately seven seconds. Our simulations, conducted in MATLAB/Simulink, reveal impressive performance metrics for the FOPDD+I-ANFIS approach: Rise time: 1.1162 seconds, settling time: 0.5531 seconds, Overshoot: 0%, Steady-state error: 0.00272, These results position our novel approach favorably against existing works, underscoring the transformative potential of intelligent creation in AVR control.
Selection and Evaluation of Robotic Arm based Conveyor Belts (RACBs) Motions: NARMA(L2)-FO(ANFIS)PD-I based Jaya Optimization Algorithm Fadhil Mohammed, Abdullah; Basil, Noorulden; Abdulmaged, Riyam Bassim; Marhoon, Hamzah M.; Ridha, Hussein Mohammed; Ma'arif, Alfian; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1243

Abstract

Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)-FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology.
Systematic Review of Unmanned Aerial Vehicles Control: Challenges, Solutions, and Meta-Heuristic Optimization Basil, Noorulden; Sabbar, Bayan Mahdi; Marhoon, Hamzah M.; Mohammed, Abdullah Fadhil; Ma'arif, Alfian
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1596

Abstract

Unmanned Aerial Vehicles (UAVs) are powerful tools with vast potential, yet they face significant challenges. One of the primary issues is flight endurance, limited by current battery technology. Researchers are exploring alternative power sources, including hybrid systems and internal combustion engines, and considering docking stations for battery exchange or recharging. Beyond endurance, UAVs must address safety, efficient path planning, payload capacity balancing, and flight autonomy. The complexity increases when considering swarming behaviour, collision avoidance, and communication protocols. Despite these challenges, research continues to unlock UAVs’ potential, with path planning optimization significantly advanced by meta-heuristic algorithms like the Cuckoo Optimization Algorithm (COA). Whereas, meta-heuristic algorithms can be defined as system-level strategies that are used to seek suboptimal solutions to optimization problems. It uses heuristic approaches together with the exploration/exploitation scheme in order to effectively employ within large solution spaces. However, dynamic environments still present difficulties. UAVs have evolved beyond recreational use, becoming essential in industries like agriculture, delivery services, surveillance, and disaster relief. By resolving issues related to autonomy, battery longevity, and security, the benefits of UAV technology can be fully optimized. This systematic review emphasizes the importance of continuous innovation in UAV research to overcome these challenges.
Transformer Models in Deep Learning: Foundations, Advances, Challenges and Future Directions Mangkunegara, Iis Setiawan; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13053

Abstract

Transformer models have significantly advanced deep learning by introducing parallel processing and enabling the modeling of long-range dependencies. Despite their performance gains, their high computational and memory demands hinder deployment in resource-constrained environments such as edge devices or real-time systems. This review aims to analyze and compare Transformer architectures by categorizing them into encoder-only, decoder-only, and encoder-decoder variants and examining their applications in natural language processing (NLP), computer vision (CV), and multimodal tasks. Representative models BERT, GPT, T5, ViT, and MobileViT are selected based on architectural diversity and relevance across domains. Core components including self-attention mechanisms, positional encoding schemes, and feed-forward networks are dissected using a systematic review methodology, supported by a visual framework to improve clarity and reproducibility. Performance comparisons are discussed using standard evaluation metrics such as accuracy, F1-score, and Intersection over Union (IoU), with particular attention to trade-offs between computational cost and model effectiveness. Lightweight models like DistilBERT and MobileViT are analyzed for their deployment feasibility. Major challenges including quadratic attention complexity, hardware constraints, and limited generalization are explored alongside solutions such as sparse attention mechanisms, model distillation, and hardware accelerators. Additionally, ethical aspects including fairness, interpretability, and sustainability are critically reviewed in relation to Transformer adoption across sensitive domains. This study offers a domain-spanning overview and proposes practical directions for future research aimed at building scalable, efficient, and ethically aligned. Transformer-based systems suited for mobile, embedded, and healthcare applications.
A Comparative Study of Fuzzy Logic Controller, ANFIS, and HHOPSO Algorithms in the LEACH Protocol for Optimising Energy Efficiency and Network Longevity in Wireless Sensor Networks Shafeeq Bakr, Zaid; Hassan, Reem Falah; Al-Tahir, Sarah O.; Basil, Noorulden; Ma'arif, Alfian; Marhoon, Hamzah M.
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1918

Abstract

This research provides a thorough analysis of the algorithms used in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for Wireless Sensor Networks (WSNs) to apply Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Harris Hawks Optimisation-Particle Swarm Optimisation (HHOPSO). The primary aim of this paper is to compare and measure these methods by how they save energy, prolong the network’s lifetime and choose the best cluster heads. We look at major indicators such as First Node Death (FND) and the number of rounds when 80% and 50% of nodes are still working, by testing 100 simulated network nodes. The HHOPSO is shown to do a better job at keeping node batteries alive and, at length the network in operation than both Fuzzy Logic and ANFIS. Moreover, ANFIS is more effective than Fuzzy Logic, because it can learn better from data. It is found that HHOPSO helps LEACH become more efficient and effective, contributing new information about how to manage energy and network performance in Wireless Sensor Networks. The document shows the effectiveness of advanced algorithms in keeping sensor networks running longer and offers ideas on how to evaluate them in various network settings.
A Systematic Design of a Low-Cost Real-Time Vehicle Tracking System for Enhanced Security and Location Monitoring Marhoon, Hamzah M.; Sabah, Sabah Ali; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Mohammed, Raghad Jassim; Fadhil Abbas, Riyam
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.219

Abstract

The rapid development of vehicle tracking technology has significantly enhanced the safety and security of vehicles worldwide. This paper presents the design and implementation of a real-time vehicle tracking system utilising Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technologies, based on the Arduino Uno platform. The proposed system enables vehicle owners to continuously monitor their vehicle's location, receiving instant SMS notifications for unauthorised movement, speed violations, and historical location data. The system's core components include an Arduino Uno microcontroller, interfaced with a SIM900A GSM module and a NEO-6 M GPS module, enabling real-time tracking via SMS alerts and Google Maps integration. Key features include automatic alerts for unauthorised car startups, exceeding speed limits, live tracking requests, and location history retrieval. The system stores the last five GPS coordinates in EEPROM memory and offers a user-friendly interface for retrieving data via SMS. The integration of Google Maps enhances the tracking experience by providing a visual representation of the vehicle’s location. This solution offers a cost-effective and reliable means of vehicle monitoring, contributing to improved vehicle security and owner peace of mind.
Implementation of Filter Bank Multicarrier Transmitter Using Universal Software Radio Peripheral Abdulhussein, Ali A.; Abdullah, Hikmat N.; Marhoon, Hamzah M.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13876

Abstract

Particularly in 5G and beyond, Filter Bank Multicarrier (FBMC) modulation is becoming more widely acknowledged as a potent substitute for traditional Orthogonal Frequency Division Multiplexing (OFDM) in upcoming wireless communication systems. FBMC is robust in situations impacted by multipath fading, synchronisation errors, and spectral leakage because of its improved spectral efficiency, superior time-frequency localisation, and removal of cyclic prefix. The Universal Software Radio Peripheral (USRP) N210, combined with GNU Radio version 3.,7 is used in this paper to design and implement a working FBMC transmitter. The system architecture supports 32 and 64 subcarrier allocations that can be changed to accommodate different communication scenarios, allowing for real-time signal generation and transmission. While SDR hardware was used for transmission and reception, software was used to develop the entire signal processing chain, including modulation, prototype filtering, and transmission. To evaluate performance metrics like constellation accuracy, spectrum containment, and signal quality, a number of experiments were carried out. These tests validate the viability of the suggested SDR-based architecture in real-world settings by confirming the successful generation and over-the-air transmission of FBMC signals. Notably, the work tackles important real-time implementation issues like subcarrier reconfigurability, synchronisation overhead, and hardware limitations. Along with its usefulness, this implementation lays the groundwork for future improvements by incorporating clever optimization algorithms like Harris Hawks Optimization, Particle Swarm Optimization, and Genetic Algorithms. Aspects like filter design, subcarrier spacing, and power efficiency can all be enhanced by utilising these algorithms. According to the results, the suggested system is in a good position to be implemented in adaptive and cognitive radio applications, where resilience to changing channel conditions and effective spectrum utilization is essential.
Metaheuristic-Driven Optimisation of Support Vector Regression Models for Precision Control in Unmanned Aerial Vehicle Systems Marhoon, Hamzah M.; Omar, Rasha Khalid; Al-Rammahi, Hussein; Al-Tahir, Sarah O.; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Agajie, Takele Ferede
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14251

Abstract

Unmanned Aerial Vehicle (UAV) systems are deployed in dynamic and uncertain environments where many traditional control structures, including Proportional–Integral–Derivative (PID) and Linear Quadratic Regulator (LQR) controllers, are unable to provide stability and adaptation. In order to overcome these shortcomings, this work presents a hybrid Support Vector Regression (SVR) model optimised with the Eagle Strategy-Particle Swarm Optimisation (ES-PSO). The proposed framework is tested with high-fidelity simulated flight data on a quadcopter platform, in which throttle, pitch, roll and yaw are provided as control variables and altitude, velocity and orientation are provided as outputs. The ES-PSO algorithm is an algorithm that optimises the global and local hyperparameters of the SVR and makes it more effective at capturing nonlinear dynamics of the input-output process under both nominal and perturbed flight conditions. To compare with benchmarking, standalone SVR, Neural Networks, Decision Trees, Naive Bayes and K-Nearest Neighbour models were executed using the same simulation parameters with no metaheuristic optimisation, and it was made fair. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Percentage Error (MPE) quantitative assessments illustrate that the ES-PSO-SVR model has the lowest error in prediction and the highest tracking accuracy compared to all baseline techniques. These results demonstrate how metaheuristic-based learning systems can be used to drive forward the creation of adaptive and intelligent UAV control systems that can perform effectively in challenging operational conditions.
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 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