Scalable and Lightweight Approach to Toll Collection Management Using Amplitude Shift Keying (ASK) Modulation Technique (Published)
Toll collection systems utilizing modulation techniques encounter significant challenges related to signal interference and environmental conditions. The precise transmission and reception of signals are critical for modulation techniques, but they can be disrupted by physical obstacles, weather variations, and interference from other electronic devices, leading to signal degradation and potential errors. Moreover, the complexity inherent in these systems necessitates advanced infrastructure and ongoing maintenance, resulting in elevated operational expenses. Addressing these challenges requires the implementation of robust technical solutions, rigorous testing procedures, and continuous maintenance to ensure the efficient and secure operation of toll collection systems. This study aims to develop an efficient, cost-effective, scalable, and secure toll collection system using Amplitude Shift Keying (ASK) modulation. ASK modulation leverages amplitude variations to facilitate data transmission between RFID tags and readers, enabling seamless and efficient vehicle passage through toll points. The selection of the Arduino Uno microcontroller is based on its affordability and reliability, while the RC522 RFID reader and tags are chosen for their compatibility and performance. Real-time feedback is provided through an OLED display, and the MG996r metal gear servo is utilized for operating the toll barrier. ASK modulation offers several advantages in toll collection systems. Its simplicity facilitates easy implementation and reduces overall system costs, making it a financially viable option. The technique’s use of binary data representation ensures efficient and reliable data transmission while the design enhances scalability and simplifies maintenance requirements.
Keywords: Microcontroller, amplitude shift keying, intelligent systems, modulation techniques, radio frequency identification (RFID)
Dynamic Control and Performance Evaluation of Microcontroller-Based Smart Industrial Heat Extractor (Published)
Dynamic control and performance evaluation of a microcontroller-based smart industrial heat extractor involves the implementation of control strategies and the assessment of its performance under dynamic operating conditions. Performance evaluation aims to assess the effectiveness and efficiency of the microcontroller-based smart industrial heat extractor under dynamic conditions. Industrial heat extraction systems are often complex, involving multiple components, sensors, actuators, and control algorithms. Understanding and modelling the dynamic behaviour of these systems can be challenging, especially when considering factors like heat transfer rates, thermal delays, and interactions between different system elements. The effectiveness of dynamic control is significantly dependent on precise and dependable measurements from sensors. Sensors deployed in industrial settings may encounter severe environmental conditions, which can result in possible inaccuracies, drift, or even malfunctions. The objective of this research is to propose a simulated methodology for verifying the efficacy of a microcontroller-driven intelligent heat extractor utilised in industrial settings. The execution of experiments or tests within industrial environments can be a costly and time-intensive endeavour, and may entail potential hazards. The efficacy of a smart industrial heat extractor in practical industrial settings can be ascertained through the simulation of its evaluation process, thereby mitigating potential risks. The model designed and simulated in this work utilises an integrated temperature sensor to determine the ambient temperature and transmits a signal to the Arduino UNO microcontroller when the temperature sensor detects variant temperatures ranges. The evaluation is performed by comparing the behaviour and performance of the simulated system with predefined performance metrices.
Keywords: Microcontroller, Neural network, cooling system, heat extraction, sensor.