SLIIT Journal Publications
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Publication Open Access Highly Efficient 3D Object Transmission System for HTC Services in 6G Networks(Faculty of Engineering, 2026-03) Svechnikov, D; Volkov, A; Marochkina,A; Muthanna, A; Kouhceryavy, AIn recent years, advancements in technology have brought forth a new frontier in visual communication. Holography is a technique that captures and reproduces three-dimensional (3D) images with an unprecedented level of realism and depth, has emerged as a groundbreaking method for conveying visual information. Unlike traditional images and videos, holography recreates scenes with full parallax, enabling viewers to perceive objects from various angles. The transmission of holographic images presents both exciting possibilities and unique challenges. To this end, this article conducts a comparative analysis of a previously developed application system for transmitting dynamic 3D human movements with a ready-made solution for transmitting 2D video streams in order to provide conference calling services. The network characteristics of the systems were collected and compared. The opportunities that programs currently provide and will provide in the future are examined.Publication Open Access Task Scheduling Problem in Fog Computing Environment with improved Memetic algorithm(Faculty of Engineering, 2026-03) Thang, D.V; Artem, V; Muthanna, A; Vorozheikina, O; Koucheryavy, AThe task scheduling problem in fog computing is one of the key challenges in the development of fog computing within next-generation communication networks. Addressing this challenge requires balancing processing performance with resource constraints while meeting network conditions. Given the distributed and heterogeneous nature, as well as the dynamic topology, optimally allocating tasks to fog nodes is a complex issue. To contribute to solving this problem, we propose a task scheduling method based on an improved Memetic algorithm. The proposed method leverages the strengths of evolutionary algorithms and local search, while incorporating a task restructuring mechanism, to enhance allocation efficiency and task processing in the fog computing environment. Simulation experiments demonstrate that the proposed method outperforms genetic algorithms, Round-Robin, Greedy methods, and the Ant Colony Optimization algorithm in terms of efficiency. This study provides a fresh, simpler approach that aligns with network conditions while still achieving the desired performance.Publication Open Access Analysis of Thermal Performance of Shell and Tube Heat Exchangers: A Correlation and CFD Based Approach(Faculty of Engineering, 2026-03) Ushettige, S.A.P; Wimalsiri, W.K.; Hikkaduwa,H.G.S.Shell and tube heat exchangers are devices which are widely adopted in thermal systems for the transfer of thermal energy due to both performance and reliability factors. Given their application in energy-intensive systems, the design and sizing of these devices have become a rapidly growing field. Traditionally, empirical correlations which were based on experimental results were used for thermal sizing and design. This was replaced by computational fluid dynamics (CFD) modelling given its ability to model and visualize flow, expanding the horizon of possibilities for design and performance optimization. Recently, CFD has been combined with numerical methods such as non-linear leastsquares regression to develop correlations that predict thermal performance based on input design parameters. However, the application of this integrated method for shell and tube heat exchangers is limited. This study will model a single-pass TEMA E-type shell and tube heat exchanger using ANSYS Fluent ®. CFD simulations are used to explore the effect of turbulence on thermal performance by varying both the inlet mass flow rate and the central baffle spacing. Steady state simulations are conducted for four models with six, eight, ten, and twelve baffles. The results of CFD modelling are then combined with non-linear least squares regression in the MATLAB Curve Fitter Toolbox ® to develop four sets of correlations in the form of 𝑁𝑢 = 𝐶. 𝑅𝑒𝑎. 𝑃𝑟𝑏 . Reasonably confident results were obtained in the final fitted data; however, relatively high 95% confidence interval widths were evident for certain fitted coefficients leaving space for improvement in the model. The study highlights that combining CFD with tools such as nonlinear least squares regression aids both engineers and designers in the thermal design process of shell and tube heat exchangers eliminating the need to limit design based on empirical correlations.Publication 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.Publication Open Access Circular Economy Practices in Road Rehabilitation and Development in Sri Lanka(Faculty of Engineering, 2026-03) De Alwis, L. M.; Gunarathna, K A N; Kalugala, C.The integration of Circular Economy principles into road rehabilitation and development offers a sustainable alternative to traditional linear construction practices. In Sri Lanka, where road infrastructure is crucial to economic growth and connectivity, the Circular Economy offers opportunities to reduce reliance on virgin materials, minimise environmental impact, and enhance longterm cost efficiency. This study explored applicable Circular Economy strategies, assessed current implementation practices, identified key challenges, and proposed viable solutions to support Circular Economy adoption in the Sri Lankan Road sector. A mixed-methods approach was employed, combining a comprehensive literature review with data from semi-structured expert interviews and a questionnaire survey of construction professionals. The findings indicate that while awareness of the Circular Economy is growing, its practical application remains limited due to barriers such as the absence of standardized technical guidelines, insufficient government incentives, limited stakeholder knowledge, logistical challenges in material sourcing and storage, and reluctance to shift from traditional methods. Data also highlights substantial potential benefits, including cost savings, reduced construction waste, increased material efficiency, and environmental improvements. Participants emphasized the importance of pilot projects, training programs, and policy support in promoting Circular Economy practices. To overcome existing barriers, the study recommends the development of clear Circular Economy specifications, financial incentives, capacity-building initiatives, and the establishment of centralized recycling infrastructure. These strategic actions can facilitate the transition toward a more circular and sustainable approach in Sri Lanka’s Road construction and maintenance sectors.Publication Open Access Macaranga peltata Leaf Extract Mediated Green Synthesis of Iron Nanoparticles and Their Application in Organic Dye Removal(Faculty of Engineering, 2026-03) Dissanayake D.M.K.N.; Perera M.A.D; Karunaratne M.S.A; Pahalagedara M.NThis study presents an eco-friendly method for synthesizing iron nanoparticles (FeNPs) using Macaranga peltata leaf extract, and evaluate their potential for degrading the organic dye methyl orange (MO). The synthesis exploits phytochemicals in the leaf extract as natural reducing and stabilizing agents. The synthesized FeNPs were characterized using UV-Vis spectroscopy, FTIR, XRD, and SEM, confirming amorphous structure and particle sizes ranging from 34–94 nm. Catalytic activity was evaluated via MO degradation experiments, achieving 85.16% efficiency within 200 minutes. The study demonstrates a sustainable wastewater treatment solution using green nanotechnology.Publication Open Access Improving Post-Harvest Rice Drying Efficiency through a Low-Cost Halogen Dryer Design for Rural Communities(Faculty of Engineering, 2026-01) Sadeepa, S; Thilakarathna, RSmall-scale rice farmers in Sri Lanka often depend on traditional sun-drying methods, which are inefficient, weather-dependent, and contribute to significant post-harvest losses. This research focuses on the conceptual design and evaluation of a low-cost wet rice dryer using halogen lamps as the heat source, aimed at improving drying efficiency before milling. Field surveys were conducted to identify the common challenges faced by rural farmers, including uneven drying, weather interruptions, and grain rejection by millers due to high moisture content. Based on the survey results, key user requirements were identified, including low operating cost, simple structure, and potential for multicrop drying. Based on the survey results, key user requirements were identified, including low operating costs, a simple structure, and the potential for multi-crop drying. A conceptual design was developed accordingly, with a drying chamber and tray system optimized for 1 cm thick rice layers. The full assembly was modeled in 3D using CAD software, allowing for virtual evaluation of airflow, heat source positioning, and accessibility. Finite Element Analysis (FEA) was applied to simulate the mechanical response of the tray under typical loads, confirming its structural soundness. Preliminary thermal experiments were conducted using a controlled test box setup to evaluate the heating performance of a 1000W halogen lamp. The system successfully achieved drying temperatures up to 82°C, which are suitable for surface moisture reduction. Temperature trends were recorded over time, and manual quality checks showed promising results for further development. These findings indicate the technical feasibility of the design and its potential to improve post-harvest efficiency in rural settings. The study provides a foundation for future stages of prototype fabrication, sensor integration, and field validation.Publication Open Access Mobile Applications Usage and Awareness Among Sri Lankan Small Business Owners(Faculty of Engineering, 2026-01) Vidana Pathirana Y.K.; Munasinghe B.Many Sri Lankan small business owners (SBO) today use mobile applications to manage their businesses. The relatedness of the purpose of the mobile applications that the SBO communities commonly use to the objectives and nature of their business (i.e. specifically designed for small businesses), the awareness among the SBOs in finding applications that can serve their business purposes, or their awareness of the importance of using dedicated applications for small business’ purposes is an area that has not been investigated. This paper presents the findings of a pilot study on current trends and the nature of mobile application usage among Sri Lankan small business owners to understand the level of awareness they have on choosing mobile applications tailored for small businesses, and their expectations for features in such applications . It also investigates SBO demand for integrated, SBO-specific mobile solutions. The findings show that the Sri Lankan SBO community has either not considered these facts in their mobile applications usage or has found it difficult to manage their businesses using existing mobile applications because those applications are not designed to cater to their specific needs. This paper also summarizes the nature of mobile application usage among SBOs from several perspectives, providing meaningful insights for mobile application developers about this competitive community of users.Publication Open Access Synergistic Charge Dynamics and Light Harvesting in TiO₂/MgO Composites for Efficiency Enhancement in CdS Quantum Dot-Sensitized Solar Cells(Faculty of Engineering, 2026-01) Ajward, N.F.; Fernando,J.V.P.; Perera, V.P.S.Quantum dot-sensitized solar cells (QDSSCs) represent a promising advancement in renewable energy technologies, with recent improvements achieving power conversion efficiencies close to 6%. Structurally similar to dye-sensitized solar cells (DSSCs), QDSSCs employ quantum dots (QDs) as sensitizers that absorb photons and inject excited electrons into the conduction band of a wide-bandgap semiconductor electrode, while the redox electrolyte removes the generated holes and completes the circuit through regeneration at the counter electrode. Quantum dots composed of materials such as CdS, CdSe, PbS, and InP are increasingly studied for use in QDSSCs, offering the advantage of tunable optical band gaps through particle size manipulation. This adaptability enhances QDSSCs’ design potential, enabling the integration of third-generation solar cell configurations, including multiple exciton generation (MEG), to further improve energy conversion efficiency. Despite these advancements, QDSSC performance is currently limited by issues such as reduced photovoltage and recombination losses at the TiO₂-QD-electrolyte interface. This study investigates the effect of MgO incorporation into TiO₂ photoanodes on the photovoltaic performance of CdS QDSSCs, with particular attention to the fill factor (FF) and overall cell efficiency. MgO is expected to act as an interfacial passivation layer suppressing combination and improving charge-selective transport. In addition, MgO may enhance light scattering within the photoanode, thereby improving light harvesting and short-circuit current density. In this study, MgO powder was incorporated in specific mass ratios with TiO₂, followed by the application of CdS quantum dots (QDs) on the TiO₂/MgO composite layer using the SILAR method. Results indicated a significant improvement in the fill factor (FF) at an optimal MgO-to-TiO₂ ratio, attributed to synergistic effects of MgO on interface stabilization, reduced recombination, and enhanced charge transport. The optimized MgO-modified TiO₂ films achieved a current density of 1.95 mA cm-2, voltage of 437 mV, and power of 0.121 mW (active area = 0.49 cm²), reaching an efficiency of 0.311 % (18.7% higher than TiO₂/CdS QDSCs), with improved interfacial impedance, Incident Photon to Current Efficiency (IPCE), and FF of 0.374.Publication 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.