http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/134 2026-09-23T22:13:12Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13011 Revolutionizing Landmine Detection: The Metal and Boom Detecting Robotic System Atiyanpan, R.; Sumanasena, H.P.V.N.R.; Raafeek, A.R.M.; Canistus, G.A. The Metal and Boom Detecting Robotic System represents a significant advancement in addressing the critical need for efficient and safe landmine removal by leveraging the principles of electromagnetic induction. This innovative robotic platform integrates advanced sensors and sophisticated signal processing techniques to detect metallic objects and identify acoustic signals associated with explosive devices. By reducing human involvement, the system enhances the speed and accuracy of threat detection while simultaneously minimizing potential hazards. The primary objective of this project is to develop a cost-effective and user-friendly metal detector equipped with a buzzer for immediate alerting, facilitating the seamless detection and removal of hazardous landmines. A dedicated software program will be engineered for the PIC16F877A microcontroller, utilizing the C programming language and the MPLAB XC8 IDE. This software will be responsible for signal processing, metal detection, and displaying results on an LCD screen, ensuring real-time feedback and enhanced operational efficiency. This integrated approach offers a promising solution for the efficient and safe removal of landmines, thereby contributing significantly to humanitarian efforts in conflict-affected regions. The Metal and Boom Detecting Robotic System represents a pivotal advancement in landmine detection technology, providing a proactive and effective means to mitigate the devastating impact of landmines on civilian populations and facilitate post-conflict recovery and reconstruction efforts. 2024-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13010 Real-Time Smart Metering for Enhanced Demand Management Canistus, G.A.; Wickramage, I.W.B. The conventional energy meter lacks the ability to provide real-time power measurements and demand information crucial for modern demand management strategies. To address this gap, we developed a smart meter device capable of recording real-time power usage and presenting demand information to consumers via a visual display. Additionally, the device can transmit data directly to consumers' mobile phones in real-time. The fabrication process of the smart meter device involved integrating key components such as current and voltage sensors, a microcontroller, a clock module, a display, and a Wi-Fi module. The current sensor accurately measures AC currents ranging from -30A to +30A. The voltage sensor, specifically the ZMPT101B module, provides precise AC voltage measurements, making it suitable for domestic voltage levels. The microcontroller selected for the device, Arduino Pro Mini V2, offers compact design and essential features for data processing. A clock module (DS1307) ensures accurate timekeeping, crucial for real-time data analysis. The display, a 128x64 Graphic LCD, enables graphical representation of power usage data. Integration with a Wi-Fi module (NodeMCU ESP8266) facilitates real-time data transfer to consumers' mobile devices. The system architecture comprises four subsystems: power calculation, data transfer, online server, and user interaction. Communication between the smart meter and consumers' mobile devices is enabled through the Arduino Pro Mini and the Blynk IoT platform. The smart meter system architecture, including hardware and software components, offers an effective solution for real-time energy monitoring and demand management. 2024-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13009 IoT-Based Health Monitoring Device Thilakshan, R.; Ashfak, M.A.M.; Sampath, A.M.H.; Sajarupan, T.; Canistus, G.A. In today's modern healthcare environment, significant advancements in science and knowledge have been achieved through the implementation of Internet of Things (IoT) technology. However, many patients in rural areas face numerous challenges in managing their health conditions due to the lack of adequate medical facilities. These challenges are not limited to rural areas; urban dwellers also struggle to allocate time for their health needs, potentially leading to significant health issues in the future. The Internet of Things refers to objects embedded with sensors, software, and various technologies to interact and exchange data with other devices and systems over the internet. This research presents an electronic device capable of measuring essential human body parameters such as temperature (using LM35), heart rate, and oxygen level through Arduino technology. The primary technique employed in this process is the Blynk IoT web-based application. Ketone levels are identified through breath measurements. Initially, the patient's personal information, including name, gender, age, and mobile number, is collected, followed by the sensor-based checkup process. Upon completion, the patient receives a summarized report on their mobile device. If any result from the report exceeds the threshold value, the system alerts the patient to consult a doctor. The main features of this device include user-friendliness and portability, enabling individuals to operate it conveniently anytime and anywhere. 2024-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/13008 Intelligent Monitoring and Self-Maintenance System for Solar PV Systems: Design, Development, And Survey Based Analysis Thanushyani, J.; Piyarathna, S.M.; Kumari, R.G.M.W.; Canistus, G.A.; Sajarupan, T.; Amani, A.R.A.A. However, conventional PV monitoring and maintenance methods rely heavily on manual inspection, leading to inefficiencies, delayed fault detection, and reduced system performance. To address these challenges, this study presents an intelligent monitoring and self-maintenance system designed to enhance the performance and reliability of solar PV installations. The proposed approach is based on sensor-driven data acquisition, where multiple sensors, including a Light-Dependent Resistor (LDR), temperature sensor, light intensity sensor, rain sensor, and DHT11 humidity sensor, are integrated to continuously collect environmental and operational data such as panel temperature, ambient temperature, humidity, light intensity, rainfall duration, and dust accumulation. Data communication and control are achieved using the SIM800L GSM module and ESP32 Wi-Fi microcontroller, enabling real-time monitoring, automated alerts, and IoT-based management through Firebase, Google Sheets, and a custom Flutter mobile application. The 20x4 LCD display and ESP32 web server provide a user-friendly local interface, while the Flutter app offers remote access to data visualization and control. The system incorporates automatic cooling and cleaning mechanisms that activate based on sensor readings, reducing manual intervention and ensuring optimal power generation efficiency. Field testing conducted on fifteen solar PV systems across Sri Lanka demonstrated measurable improvements in energy output and a significant reduction in manual maintenance requirements. Furthermore, an AI-based analytical framework was introduced to evaluate performance trends and generate predictive maintenance recommendations using data from cloud storage platforms, supporting proactive maintenance decisions. This intelligent and cost-effective self-maintenance system minimizes human involvement, lowers operational costs, and enhances the lifespan and efficiency of solar panels, thereby improving the overall sustainability of solar PV systems. The proposed approach provides a sustainable and scalable solution for smart solar management, making it highly suitable for developing countries like Sri Lanka, where maximizing renewable energy utilization and reducing maintenance costs are critical objectives. 2026-01-01T00:00:00Z