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All Journal International Journal of Research and Applied Technology (INJURATECH) Journal of Power, Energy, and Control
Muhammed, Aniru Abudu
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Design and Implementation of a Microcontroller-Based Adaptive Four-Way Traffic Light Control System for Traffic Optimization Muhammed, Aniru Abudu; Gregory, Omoruyi; Aigbodion, Emmanuella Osose
Journal of Power, Energy, and Control Vol. 1 No. 2 (2024)
Publisher : MSD Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62777/pec.v1i2.34

Abstract

This paper presents the design and construction of a microcontroller-based four-way traffic light control system aimed at optimizing traffic flow by automatically adjusting signal timing based on traffic density at each intersection. The system is built around an Arduino ATmega328 microcontroller inter-faced with break beam infrared (IR) sensors (transmitters and receivers) and LED displays. The IR sensors are installed on both sides of the lanes at regulated intervals to detect traffic density. The system is powered by a 12V DC battery and a 5V, 3A power supply is provided using a buck converter IC (LM2596), which steps down the 12V from the battery to 5V, 3A. This 5V power is used to run the Arduino microcontroller and the Darlington pair ICs for current sinking and sourcing. As vehicles pass through the areas monitored by the IR sensors, the traffic density is measured for each opposing lane, allowing the system to determine which lane should be prioritized for traffic flow. The corresponding LED indicators are then activated accordingly.
Physics-Informed Artificial Intelligence for Adaptive Wireless Channel Modelling in Fifth-Generation (5G) Networks Muhammed, Aniru Abudu; Muhammed, Hibah
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate wireless channel modeling is fundamental to the design and optimization of fifth-generation (5G) communication systems. Traditional geometry-based stochastic models (GBSMs) and empirical formulations, while effective in static environments, often fail to capture the nonlinear, non-stationary, and environment-dependent propagation behaviours inherent in modern multi-antenna and millimeter-wave systems. This study introduces a physics-informed AI hybrid framework that fuses physical propagation principles with deep learning architectures, enabling channel modeling that is interpretable, adaptive, and data-efficient. Using large-scale datasets including DeepMIMO, COST (Cooperation in Science and Technology) 2100, and New York University (NYU) Wireless, the model integrates Physics-Informed Neural Networks (PINNs) and Convolutional Neural Networks (CNNs) to simultaneously capture spatial, temporal, and frequency-domain relationships under realistic propagation environments. Reinforcement and federated learning layers enable real-time adaptation and decentralized training across multiple base stations while preserving data privacy. Experimental results demonstrate substantial improvements over benchmark models such as 3GPP (3rd Generation Partnership Project) TR 38.901, COST 2100, and QuaDRiGa  (QUAsi Deterministic RadIo Channel GenerAtor), achieving an RMSE of 1.72 dB and NMSE of –20.6 dB, corresponding to a 25–30% accuracy gain. Visual analyses of power delay profiles, residual error distributions, and spatial correlation maps confirm the model’s robustness and physical consistency. The proposed framework offers a scalable, interpretable, and adaptive paradigm for next-generation wireless channel modeling, paving the way toward intelligent, self-optimizing, and 6G-ready communication networks that bridge the gap between physics-based theory and AI-driven modeling.
Co-Authors Aigbodion, Emmanuella Osose Gregory, Omoruyi Muhammed, Hibah