Research Papers - Department of Electrical and Electronic Engineering

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Start date: 2010

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Now showing 1 - 10 of 128
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    PublicationOpen Access
    Performance Enhancement of Cooperative NOMA in Satellite Communication Through Combining Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2026) Ashwini K; Singh, A; Jagadeesh V.K; Shenoy, R; Jayakody, D. N.K
    This article studies the analysis of the key performance parameters in the proposed cooperative Non-Orthogonal Multiple Access (NOMA) system considering the satellite network. NOMA can be coupled with standard relaying procedures to increase the overall capabilities of the wireless communication system. The system under consideration consists of two users, one near and a far user, and a satellite. The paper explores by using a Decode and Forward (DF) relaying mechanism at the user closer to the satellite, which will forward the signal received from the satellite user that is distant from it. In separate case studies, the far user combines the direct and indirect transmission signals using Maximal Ratio Combining (MRC) and Selection Combining (SC) techniques. The expressions for outage, average Bit Error Rate (BER), and capacity are derived for MRC and SC methods. It is also derived and approximated, and closed-form expressions are obtained. The simulation results of these analytical expressions reveal that the MRC method of combining outperforms the SC technique in terms of outage, BER, and ergodic capacity.
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    PublicationOpen Access
    Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach
    (John Wiley and Sons Ltd, 2026-04-10) Jathunga, T; Rajapaksha, S; Jayasinghe, S; Abeygunawardena, N
    This study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi-robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including (Formula presented.), Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms (Formula presented.), RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi-robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.genetic algorithm
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    PublicationOpen Access
    Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills
    (Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, Rohan S; Wijesinghe, R, E; Herath H.M.L.P.B; Nalin de Silva K.M.; de Silva, Rohini M; Rajapaksha, Suranga M; Wijenayake, U; Manatunga, Danushika C
    The existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.
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    PublicationOpen Access
    PV-Assisted Charging for Electric Three-Wheelers in Sri Lanka: A Comparative TCO and Sensitivity Analysis
    (Algerian Centre for the Development of Renewable Energy, 2026-05-24) Abeygunawardena, Nuwanthi; Jathunga, T; Rodrigo, M
    This research explores the potential of solar-powered electric three-wheelers as a sustainable and cost-effective transportation solution in Sri Lanka. Traditional three-wheelers contribute to air pollution and fuel dependency, while electric three-wheelers offer a cleaner alternative. By integrating solar power, these vehicles can further reduce emissions and operational costs. The study examines the economic benefits of three types of three-wheelers, covering fuel-based, electric, and solar-powered electric three-wheeler categories. Survey-based findings are used to calculate the total cost of ownership of tuk-tuks. Fuel and electricity costs, operational and maintenance costs, and more parameters are incorporated for the study. The findings suggest that solar-powered electric three-wheelers offer a promising solution for sustainable transportation solutions for the Sri Lankan context.
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    PublicationEmbargo
    High-resolution optical imaging for sustainable fish freshness and safety assessment
    (Elsevier GmbH, 2026-07) Madhubhashini, M. N; Kahandawala, B, S; Sandaruwan, H.H.P. B; Silva, B. N; Wijenayake, U; Wijesinghe, R. U
    Fish freshness evaluation is crucial to ensure consumer safety, and rapid assessment is essential for effective and accurate quality control. To overcome the limitations of the gold standards, such as lack of structural depth information, high-time consumption, and labor-intensiveness, high-resolution Optical Coherence Tomography (OCT) was employed for real-time monitoring of fish freshness non-invasively. Microstructural changes of eye and skin of Indian Anchovies ( Stolephorus indicus ) specimens were considered as the main freshness parameters during refrigeration storage. Both eye and skin tissues exhibited decreased internal scattering, loss of clarity, boundary weakening, and gradual structural degradations through the OCT observations. The quantitatively assessed variance intensity, entropy, energy, and edge density clearly revealed the internal tissue disruption over storage time due to protein denaturation, oxidative damage, and fluid imbalance. The findings of this study indicate that OCT shows an insightful correlation with microbiological and biochemical spoilage processes, enabling the advanced identification of subtle microstructural changes in fish skin and eye, even at a prior stage of deterioration. Such capability offers an objective and rapid freshness evaluation approach that could greatly benefit supply chain management and post-harvest seafood quality monitoring.
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    PublicationOpen Access
    Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills
    (Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B.; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, U; Manatunga, D.C
    The existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.
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    PublicationOpen Access
    YOLO-MOTF: Motion-temporal fusion for dynamic object detection with a moving camera for assistive wheelchairs
    (Elsevier B.V., 2026-03-09) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
    Dynamic object detection is fundamental to advancing vision-based navigation systems, particularly in environments where the camera itself is in motion. Despite progress in detection algorithms, existing approaches often struggle with challenges such as egomotion, short-term occlusions, temporal discontinuities, and computational cost. This paper presents YOLO-MOTF, a novel knowledge-based model that integrates spatial features with motion cues, especially for operation under moving camera conditions. The framework incorporates a hybrid motion compensation strategy to suppress camera-induced distortions and an occlusion handling buffer to preserve object trajectories through discontinuities. Additionally, a motion attention gating mechanism selectively reinforces moving object predictions by intersecting fused motion masks with semantic outputs. The proposed system achieves an F1 score of 88.6% and a 93% reduction in flow processing compared to dense flow methods, underscoring its robustness and efficiency in dynamic environments. Beyond theoretical contributions, the model demonstrates direct applicability in real-world knowledge-based decision systems, including healthcare applications such as assistive wheelchair navigation, as well as assistive robotics, autonomous navigation, and surveillance.
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    PublicationEmbargo
    Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
    (Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
    In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
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    PublicationEmbargo
    Fly-Energy Ecosystem: A Game-Theoretic Hybrid SWIPT Framework for UAV-Assisted Rural Wireless Systems
    (Institute of Electrical and Electronics Engineers Inc., 2026) Sooriarachchi, V.P; Jayakody, D. N.K; Muthuchidambaranathan P.
    The increasing use of IoT and related solutions in rural environments brings the growing need for energy-efficient and energy-aware solutions. This paper proposes a novel Stack-elberg game-theory-assisted hybrid wireless energy harvesting approach for unmanned aerial vehicle (UAV), which incorporates SimultaneousWireless Information and Power Transfer (SWIPT) systems designed specifically for remote and rural environments with conventional wireless power transfer (WPT). A multi-UAVs, multi-user scenario is considered where UAVs collect information from ground-level users while simultaneously providing WPT to the users. The proposed framework enables sustainable operation of remote monitoring systems in rural areas where conventional power infrastructure is limited or unavailable, contributing to more resilient and energy-efficient IoT deployments in challenging environments. The simulation results show that the proposed method achieves scalable performance and significant improvements in SINR and energy harvesting efficiency.
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    PublicationOpen Access
    Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills
    (Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, U
    The existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.