https://ir.kdu.ac.lk/handle/345/8354 2026-04-08T13:46:52Z https://ir.kdu.ac.lk/handle/345/8880 Integration of Image Processing with Underwater Rover to Monitor Coral Growth. Adhipaththu, WAMPL; Abeysinghe, AMMHB; Ranasinghe, BY; Sameera, UVH; Thilakarathne, BLS This research outlines the integration of image processing with an Aquabot, an underwater rover to monitor the growth of corals. Designed system composed of underwater vehicle, a floating station for communication and coral monitoring unit. This monitoring unit can classify the coral type and also to identify available diseases on the coral. Communication occurs through a combination of wired and cloud methods. The coral diseasedetection unit was evaluated on a data set of 121 images containing 489 instances of coral disease and the coral varieties detection model was evaluated on a dataset of 124 images containing 492 instances of coral varieties. These images are taken from corals reefs and coral nurseries around Sri Lanka. In both cases the models identified the diseases and varieties correctly with overall confidence levels of 33.1% and 59.6% respectively. The two models achieved box precision of 0.346 and 0.679, the box recallsof 0.331 and 0.558, the mean average precision (mAP50) of 0.295 and 0.596 and the mean average precision at IoUthresholds of 0.5 to 0.95 (mAP50-95) of 0.188 and 0.425 respectively. The results of the evaluations show that both models are effective for respective tasks. This research introduces a combination of AquaBot and an image processing system, which is capable of real time monitoring and identification of corals and its diseases. 2024-09-29T00:00:00Z https://ir.kdu.ac.lk/handle/345/8878 Designing a Portable Solar Power Station for Outdoor Activities Eranda, HAS; Rathnayake, RAIT; Rodrigo, PMP; Karunarathne, KGNS; Karunadasa, JP Portable solar power stations with compact solar panels and lithium-ion batteries are useful for outdoor activities like camping and hiking for clean and renewable energy. This paper presents the development of a backpack style portable solar power station with weight less than 6 kg, that delivers 500 W for 30 minutes after one charge. It uses a 100 Ah, 3.7 V, lithium-ion battery with foldable solar panels and delivers sinusoidal 230 V, 50 Hz, AC output through standard socket outlets. Multiple outputs are provided to enable several devices to take power simultaneously, for example charging multiple devices. Solar panel output is fed to a push-pull converter that produces constant 400 V DC voltage for an H-bridge inverter which is operated with sinusoidal PWM to produce sinusoidal, constant voltage, and constant frequency AC output. The overall design is compact, sustainable, and adaptable to various weather conditions, to provide a green option for outdoor enthusiasts for a better outdoor experience 2024-09-29T00:00:00Z https://ir.kdu.ac.lk/handle/345/8877 Enhancing Electrical Grid Reliability through Predictive Cycle Detection with Graph Neural Networks WIJEKOON, WMKGVB; HETTIARACHCHI, HPPP This paper represents end-end study focused on improving the grid reliability with the application of Graph Neural Networks (GNNs). Graph Representation of the electrical grid which yields the model of nodes of substations and transformers interconnection of power lines constructed by the data from the National Grid Electricity System Operator (ESO) Data Portal. Based on their connections, node feature updating and encoding by predict grid reliability with multi-layered Graph Attention Network (GAT) was employed. In predicting failure regions, proposed model with rigorously trained and tested state shows higher accuracy compared to existing methods. Results of the model signifies the model capability to efficiently manage large-scale data with actionable insight generation for specific usecases such as predictive maintanance, which ensure the resilience of modern power systems and integrating renewable energy in the modern power system. 2024-09-29T00:00:00Z https://ir.kdu.ac.lk/handle/345/8876 Edge Computing using FPGA with the Deployment of Neural Networks for General Purpose Application Perera, Kevini; Hettihewa, Chamod; Wickramasinghe, Manupa; Sandanayake, Ashan; Rajapaksha, Chamali; Pathirana, Pubudu Artificial intelligence and deep learning are gaining traction in edge computing to extract insights from Internet of Things (IoT) devices. Hardware accelerators like Field Programmable Gate Arrays (FPGAs) accelerate deep learning efficiently due to their energy efficiency, parallelism, flexibility, and reconfigurability. However, resource constraints of FPGAs pose deployment challenges. This research explores hardware-accelerated applications’ dynamic deployment on the Kria KV260 platform with a Xilinx Kria K26 system-on-module, equipped with a Zynq multiprocessor system-on-chip. It presents an innovative solution to dynamically reconfigure deep neural networks by running multiple neural networks and Deep Processing Units concurrently. This research advances Edge Computing using FPGAs to facilitate efficient deployment of Neural Networks in resource constrained edge environments 2024-09-29T00:00:00Z