Browsing by Author "Gunawardana, K. D. B. H."

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A cost-effective and adaptable queue management system to increase efficiency in patient queue management
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Adhikari, A. M. N. D. S.; Gunarathna, T. G. L.; Bandara, K. D. Y.; Gunawardana, K. D. B. H.; Seneviratne, J. A.; Perera, M. H. M. T. S.
Healthcare systems worldwide, particularly in resource-limited settings like Sri Lanka, face significant challenges related to high patient volumes and constrained resources. These challenges often lead to extended wait times and reduced patient satisfaction. This study presents an innovative, adaptable queue management system designed to replace inefficient manual methods, enhance operational efficiency, and optimise patient flow. Scalable to meet the needs of both small clinics and large hospitals, the system functions across various connectivity scenarios, ensuring flexibility in diverse environments. The system comprises patient, doctor, and administrative interfaces. Upon patient registration, a QR code will be generated, and the patient can use the QR code to check-in. A printed queue token will be issued when a patient checks-in. Doctors can manage their queues and access real-time patient information. Administrators oversee overall system operations, including advertisement management and key performance indicator (KPI) tracking, to monitor and enhance healthcare delivery in addition to having the ability to add, remove, or edit users. Built on a robust technology stack that includes HTML, CSS, JavaScript, PHP, SQLite3 for database management, and AES-256-CBC encryption for secure data handling, the system is designed for reliability and scalability. Embedded ESP32 devices with OLED displays and LEDs provide offline functionality, while multicast DNS (mDNS) ensures seamless device connectivity to local networks without requiring Internet access which is critical for rural healthcare facilities. The system features a custom-built algorithm, leveraging Random Forest Regression, to analyse historical and real-time queue data. This allows for precise queue time estimates and significantly improves staff and patient planning. The system outperforms the traditional manual systems, which lack both real-time prediction capabilities and efficiency. The system performance was meticulously improved using various optimisation techniques such as batch processing, database indexing, and algorithm optimisation, which led to an execution time of 22 seconds to be brought down to 1.5 seconds on a 1.4 million row data set, where the execution involved processing, sorting, encrypting, decrypting, and storing data. A one-tailed t-test was performed to compare the execution times of test runs with optimisation and without optimisation. There was a significant difference in execution times between test runs without optimization (M = 21.84, SD = 1.16) and execution times between test runs with optimization (M = 1.52, SD = 0.28); t(43) = 107.76, p < 0.001. The system was validated for 10 years of sample data and the results demonstrate that the system is robust and responsive under real-world conditions. Continuous validation is ongoing in diverse healthcare environments to further assess its impact on optimizing queue management, resource allocation, and patient satisfaction. This scalable and adaptable system represents a substantial advancement in healthcare management, offering a transformative solution to meet the evolving needs of healthcare facilities despite scarce infrastructure.
Enhancing UPS battery life through C-rate control with a supercapacitor-assisted battery management system
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Ramesh, J. M. D. A.; Gunawardana, K. D. B. H.; Piyumal, P. L. A. K.; Ranaweera, A. L. A. K.; Kalingamudali, S. R. D.
Battery management systems (BMS) are essential for optimizing the efficiency and dependability of batteries. BMS employs various methodologies and techniques for state of health (SOH) determination, state of charge (SOC) estimation, cell balancing, voltage regulation, current regulation, and overload prevention. This study aims to create a Supercapacitor Assisted Battery Management System (SCABMS) to enhance battery performance and lifespan using a C-rate controlled system by analyzing battery dynamics and load demands. The suggested Battery Management System (BMS) incorporates supercapacitors (SCs) to effectively handle sudden increases in power demand, thereby lessening stress on the main battery and improving its overall lifespan. Previous research indicates that reducing the Crate extends battery lifespan. The C-rate control method is used to manage battery discharge, with the system functioning in two modes depending on the load current. The first is when the load is drawing less than the set current where the load current is completely drawn from the battery without the SC assistant. When the load current exceeds the set current value, the current control circuit begins to limit the battery current for the selected value. In this scenario, the load voltage drops, and the parallel connected buck-boost converter is used to fix the load voltage into the rated value by supplying the rest of the load current. The buck-boost converter output voltage is fixed for the load voltage. The implemented prototype incorporates supplementary functionalities encompassing cell balancing, voltage regulation, current regulation, and overload protection within its BMS. The real-time current and voltage monitoring system was integrated into BMS to ensure a constant C-rate in charging/discharging cycles. Also, the battery's operating periods with and without the Battery Management System (BMS) under the same load circumstances should be compared. This technology successfully reduces power fluctuations and guarantees safe equipment shutdown in case of a power outage. The results indicate a well-maintained c-rate and show the potential of integrating SCABMS in high-power-demanding situations, subject to improvements in efficiency and practicality.
Innovative fence monitoring system to mitigate human-elephant conflict
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Bodaragama, B. T. P.; Athawuda, A. H. C.; Gunawardhana, M. A. W. S. N. T.; Senanayake, S. V.; Leanage, H. B.; Gunawardana, K. D. B. H.; Seneviratne, J. A.
Human-elephant conflict (HEC) poses a significant threat to communities and wildlife, prompting the development of an innovative standalone device to enhance electric fence monitoring and mitigate associated risks. This research introduces a system that determines breakage location by measuring fence capacitance, inductance, and resistance, and uses a mathematical model to map changes of these parameters to change of the fence length. This approach enables remote detection of both open and short circuit breakdowns without relying on expensive, failure-prone active nodes along the entire fence. The device can identify the distance to the breached location along the length of the fence approximately and immediately. It will send this alert via SMS to designated contacts using a GSM module, providing real-time monitoring and rapid response capabilities. This key feature ensures timely alerts and quick responses to potential breaches, enhancing the fence's effectiveness in preventing elephants from entering villages and reducing HEC incidents. The standalone nature of the solution simplifies installation and maintenance, eliminating the need for additional wiring or complex infrastructure, thereby significantly reducing overall costs associated with fence monitoring while increasing reliability and efficiency. Furthermore, the device functions accurately by minimizing the effects of weather changes, ensuring consistent performance in various environmental conditions. This innovative breakage detection system represents a significant advancement in fence monitoring technology for wildlife conservation, addressing many shortcomings of traditional solutions by offering a cost-effective, efficient, and reliable method for mitigating human-elephant conflict. The research underscores the potential of integrating advanced technology with traditional conservation methods to create more sustainable and effective strategies for managing human-wildlife conflicts, ultimately improving the effectiveness of electric fences in deterring elephants and reducing the incidence of fatalities and crop damage. Testing on a 150m fence demonstrated promising results, with the system achieving nearly 80% accuracy in detecting and locating both open circuit and short circuit breakages, as verified through manual simulations and observations recorded in the device's test results.
IoT-enabled intelligent pedestrian crossing signal light system with violation tracking
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Rupasinghe, R. A. I. M.; Ranasinghe, R. A. J. B.; Moragoda, Y. G. D.; Navodya, W. D. I.; Premasiri, R. H. M. D.; Chethana, E. J. K. S.; Seneviratne, J. A.; Gunawardana, K. D. B. H.
The urban pedestrian crossing environment presents numerous challenges in ensuring the safety of pedestrians and maintaining smooth traffic flow. Traditional pedestrian signaling systems operate on fixed timings and have limited capabilities, making it difficult to manage the complexities of modern urban traffic effectively. This research introduces an innovative system for pedestrian crossing signal lights integrated with violation tracking and real-time data analytics to improve pedestrian safety and smooth traffic flow. This encompasses computer vision for pedestrian detection, machine learning (ML) for predictive analysis, adaptive signal light timers, sirens for violation deterrence, and IoT components for seamless real-time operation. The presented methodology combines real-time pedestrian detection, adaptive signal light timing, weather detection, and IoT integration so that all these subsystems work smoothly. The issues resolved include integrating image processing with hardware, selecting an efficient pedestrian detection model, optimizing camera angles for accurate detection, and transitioning from an Arduino to a Raspberry Pi 4 Model B. The Raspberry Pi offered better processing power, enabling faster and more complex data handling. A case study was done at a location proximate to the University of Kelaniya, and the average crossing time taken for the pedestrian crossing was recorded as 18.5 seconds, which can be factored using databases with larger data sets and simple ML models based on the day of the week. The issues that were resolved include integrating image processing with hardware, selecting an appropriate pedestrian detection model such as a Convolutional Neural Network (CNN) that works well within the outdoor environment, setting optimal camera angles for accurate pedestrian detection, and transitioning from an Arduino to a Raspberry Pi 4 Model B for enhanced processing capabilities. Integrating image processing with hardware posed challenges due to the need for real-time data transmission and processing, which required seamless communication between the software and hardware components. The pedestrian detection model was chosen based on its accuracy, speed, and ability to perform well in varying lighting and weather conditions. The transition to the Raspberry Pi 4 Model B, with its superior processing power and memory compared to the Arduino, allowed the system to handle more complex tasks, such as real-time data analysis and multiple input streams, significantly improving performance and efficiency. A custom dataset of overhead views of pedestrians was created, with images acquired from a similar environment within the university, manually labelling the images and achieving an 80% accuracy after training on Google Collab. The real-time data processing system is vital in making dynamic signal timing changes, tracking violations to encourage safe pedestrian behavior, and managing pedestrian and vehicle traffic flow. These findings endorse the broader adoption of intelligent systems, for innovative city projects toward safer and more efficient urban environments.
Wireless pager system for enhancing emergency communication in hospital environment
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Gunarathna, T. G. L.; Adhikari, A. M. N. D. S.; Bandara, K. D. Y.; Gunawardana, K. D. B. H.; Seneviratne, J. A.; Perera, M. H. M. T. S.
Maintaining fast and efficient communication between hospital staff is critical to ensure patient safety during emergencies. However, challenges such as the lack of Global System for Mobile Communications (GSM) signals in countries like Sri Lanka and the risk of using cable communication during hazardous weather conditions further complicate emergency communication. This paper proposes a wireless pager system utilizing LoRa (Long Range) technology to facilitate seamless interaction between doctors, nurses, and other supportive and administrative staff in a hospital. LoRa operates on sub-gigahertz frequencies, providing robust signal penetration and extended range, making it ideal for hospital environments where walls and infrastructure often disrupt traditional signals. The proposed system consists of three primary modules: the Ward Module, Central Hub, and Doctor Module. The Ward Module, placed in hospital wards, allows nurses to trigger emergency alerts by selecting an available doctor. It also provides status updates on message delivery and doctors' responses. The Central Hub acts as the system's control center, maintaining a database of doctors and wards, managing doctor availability, registering new entries, and logging communication transactions. It utilizes a web-based application to handle and collect data, which runs on the Central Hub, streamlining data management and access. The Hub also backs up data to the cloud and stores it locally during internet outages, synchronizing once the connection is restored. The Doctor Modules enable doctors to log their presence by selecting their ID from a list obtained from the Central Hub. This login data is updated in the Central Hub and shared with the Ward Modules. Upon receiving an emergency alert, doctors can respond by accepting, canceling, or forwarding the message, with the updated status being communicated back to the Ward Module. The system was tested in a simulated hospital environment using two Ward Modules, two Doctor Modules, and a Central Hub, covering a 200m distance. Both the Ward and Doctor Modules were built using ESP32 microcontrollers with LoRa modules operating at 433 MHz, while the Central Hub was developed using a Raspberry Pi single board computer with a LoRa module. The system demonstrated reliable performance, maintaining stable communication across the test range. It also demonstrated potential for larger hospitals, with extended range possible through proper antenna configuration. A 96% success rate was recorded, with message transmission in under 2 seconds. While LoRa offers robust long-range communication with low power use, its limited bandwidth poses challenges for large data transmission. However, for emergency pager systems, the trade-off between power efficiency and data capacity is acceptable. The system operates independently of traditional communication infrastructure, providing hospitals with a sustainable and resilient solution for emergency communication. It streamlines emergency response in hospital wards by enabling realtime communication and status updates between staff, ensuring fast and accurate transmission of critical information. This enhances the efficiency of interventions and improves patient care outcomes.