Journal of Advances in Engineering and Technology [JAET]
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The Journal of Advances in Engineering and Technology (JAET) is an international, open access, double blind peer-reviewed journal. It is published by the Faculty of Engineering of Sri Lanka Institute of Information Technology (SLIIT). The JAET aims at fostering research and development work in Engineering and Technology and bringing researchers on to a common platform. Furthermore, JAET will also accept review articles on appropriate subject areas including concept papers of academic opinions, book reviews, etc. for publication therein.
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Item Open Access Solar Hotspot Detection Using VHDL-Simulated Fixed-Point SVM: A Methodology Toward FPGA Realization(Faculty of Engineering, 2026-03) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, YEarly detection of thermal hotspots in photovoltaic modules is critical to ensuring their efficiency, safety, and longevity. This study presents a complete end-to-end methodology for implementing a fixedpoint Medium Gaussian Support Vector Machine classifier using VHDL for a Field Programmable Logic Array. The approach begins with feature extraction from thermal images of healthy and defective solar panels, which focuses on MPEG-7 descriptors. The study shows that high impact for hotspot detection comes from blue chrominance contrast. A medium Gaussian SVM model is trained in MATLAB and converted to a fixed-point Q1.15 format for hardware compatibility. Key parameters, including support vectors, Lagrange multipliers, bias, and kernel scale, are extracted and verified in a custom Python environment to ensure numerical alignment with MATLAB results. The validated model is then implemented in synthesizable VHDL. It is verified using GHDL and the GNU Tool Kit waveform viewer, confirming bit-accurate hardware behaviour. Results show classification accuracy exceeding 99.3% with negligible performance loss due to quantization. The design achieves deterministic latency through an FSM-based structure and parallel feature processing for a 300-support vector and 222-feature system. This method enables low-power, real-time inference on a UAV-based edge platform, primarily focusing on drones.Item Open Access Machine Learning-Based Early Warning Systems for Urban Floods: A Case Study in Nilwala Basin(Faculty of Engineering, 2026-01) Abayapala A.I.; Lindamulla L.M.L.K.BThis study pioneers the integration of Graph Neural Networks (GNNs) into flood forecasting systems, extending the predictive horizon from short-term forecasts to 7 days by effectively capturing spatial dependencies between rainfall stations. Focusing on the flood-prone regions of Matara and Galle districts within the Nilwala Basin, the research addresses the limitations of conventional forecasting methods by leveraging historical hydrological data, including daily rainfall records from six key stations and flow data from Pitabeddara. A hybrid machine learning framework combining Random Forest (RF) and K-Nearest Neighbors (KNN) models was developed to predict river discharge using rainfall data, overcoming challenges posed by limited water level data. The inclusion of GNNs introduces a novel approach to modeling complex spatial relationships, enabling improved accuracy in long-term flood prediction, particularly during extreme events. The proposed system demonstrates significant advancements in predictive reliability, offering a timely and accurate early warning tool to enhance disaster preparedness and risk management in the Nilwala Basin. This research underscores the transformative potential of datadriven methodologies in addressing the challenges of flood-prone regions.Publication Open Access Pneumonia Detection and Lung Disease Assessment from Chest X-rays: Developing A Diagnostic Support System(SLIIT, Faculty of Engineering, 2025-01) Jayawardena, C.A; Wedasingha, N; Kolambage, N; Perera, SThis research, dedicated to developing an accurate and efficient pneumonia detection system from Chest X-Ray images, highlights the significance of automated tools in enhancing healthcare diagnostics. Its significance lies in the fact that pneumonia is a prevalent respiratory condition that requires timely and accurate diagnosis for effective medical intervention. The project's objective was to make use of convolutional neural networks and image analyses to create an automated diagnostic tool that could assist healthcare professionals in identifying pneumonia with precision and efficiency. To achieve this, the system initially made use of two custom deep learning architectures but ultimately used a pretrained CheXNet-based model, developed by using transfer learning. This choice was made by considering CheXNet’s proven performance in identifying pneumonia and other pulmonary conditions. The project's results proved promising, with the CheXNet-based model achieving high diagnostic accuracy and providing valuable insights into the presence of pneumonia. The system's architecture, using deep learning and the use of DICOM images, demonstrated its effectiveness in improving the accuracy and efficiency of pneumonia diagnosis. Based on the results, this paper further demonstrates a web-based application for interaction with the system. Additionally, it provides information on the work that could be done in the future. Thus, this research contributes to the growing field of medical image analysis and highlights the significance of automated tools in enhancing healthcare diagnostics. The project's outcomes are meant to pave the way for more efficient and accessible methods for pneumonia detection, ultimately benefiting both healthcare providers and patients.