Recent Submissions
Joint Resource Allocation and Network Slicing in Hybrid RF/VLC Systems: A Proportional-Fairness Approach
(Institute of Electrical and Electronics Engineers Inc., 2026-08-01) Rajahrajasingh, H; Jayakody, D. N. K.; Peha, J. M
This article presents a joint resource allocation and network slicing framework for hybrid radio frequency (RF) and visible light communication (VLC) systems based on a proportional-fairness (PF) objective. The proposed approach formulates a convex optimization problem that jointly allocates bandwidth resources across multiple slices and access technologies to balance aggregate throughput and user fairness. A relaxed PF solution is first derived via projected gradient optimization, followed by two practical rounding strategies: a simple threshold-based method and a greedy assignment heuristic that produce integer allocations with low complexity. Simulation results for a 20 MHz hybrid RF/VLC system (10 MHz/band) demonstrate that the proposed PF-based allocation achieves aggregate sum-rates of approximately 450-500 Mb/s with Jain's fairness indices greater than 0.9, thus outperforming static and rounding baselines by 10%-15% in throughput while maintaining high fairness. The analysis is further extended to asymmetric RF/VLC bandwidth conditions, demonstrating that the proposed PF-based slicing maintains high fairness while scaling throughput effectively. These results confirm that the PF formulation effectively captures the throughput fairness tradeoff and provides near-optimal slicing performance under realistic indoor bandwidth and power constraints.
OcupHI: knowledge-driven colorimetric interpretation framework for high-precision real-time ocular pH diagnostics
(Springer Nature, 2026-08-12) Kahandawala, B, S; Sandaruwan, H. H. P. B; Liyanage, P; Dassanayake, R.S; Costha, N.P; Liyanage, R.N; Wijenayake, U; Wijesinghe, R.E; Silva, B.N; Manatunga, D.C
Ocular injuries due to chemical spills pose a substantial concern, representing 10–22% of all ocular trauma. Although precise detection of ocular pH is crucial for determining the optimal medical treatment, many existing methods remain invasive, biased, or insufficiently precise. Reliance on subjective visual assessment of subtle color differences limits the objectivity and hinders high-throughput analysis. Therefore, an advanced colorimetric knowledge-driven ocular pH detection method was developed using a biosensor (OcupHI) based on a Clitoria ternatea (Butterfly Pea) anthocyanin sensing agent. The proposed work delivers fast, high-precision, and easily measurable pH prediction across clinically relevant ranges, while supporting real-time decision support for eye physicians. The pH range from 1 to 12 was tested and compared with six different anthocyanin concentrations: 5, 10, 20, 30, 40, and 50 ppm, and five different machine learning models, namely, Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM). The results revealed that the 40 ppm anthocyanin concentration trained with the XGBoost model produced the most accurate ocular pH values, achieving superior performance with an overall accuracy of 96%, a significantly higher F1-score for early detection. Experimental validation clearly demonstrates strong predictive accuracy, robustness, and interpretability, highlighting the potential for next-generation ocular diagnostics. Further research findings support Sustainable Development Goal (SDG) 3 – good health and well-being through a real-time ocular pH monitoring kit, and SDG 12 – responsible consumption and production by optimizing the use of the natural colorant anthocyanin for sensor development.
An Integrated Smart Framework for Post-Harvest Optimization and Market Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Gamage U.V.A; Wimalarathna B.P.K; Dharmappriya W.A.I.U.; Rathnayaka S.J.; Tissera, W; Rupasinghe, S; De Silva, H; Priyadarshana W.H.D.
Post-harvest losses in Sri Lanka's fruit and vegetable supply chain remain a critical challenge, attributed to the absence of integrated quality assessment tools, real-time market intelligence, and accessible decision support systems tailored to local conditions. Existing approaches address these problems in isolation, leaving smallholder farmers without a unified platform for quality grading, price forecasting, post-harvest advisory, and cultivation planning. This paper presents CropShield, a novel four-component AI framework designed to address these gaps for Sri Lankan agricultural stakeholders. The first component employs YOLOv8 for fruit detection followed by MobileNetV2 fine-tuned on a papaya dataset for defect classification across six categories and maturity classification across three stages, with Grad-CAM explainability and a Random Forest recommendation engine integrated with Department of Agriculture knowledge. The second component delivers price forecasting across eleven crop varieties using a hybrid ensemble of ARIMAX, XGBoost, and LightGBM trained on HARTI market data from 2008 to 2025. The third component provides a bilingual post-harvest risk advisory assistant supporting Sinhala and English, integrating real-time weather data with an NLP-driven prediction engine, with SHAP-based explainability for transparent advisory outputs. The fourth component implements a Random Forest-based crop suitability and yield estimation model using district-level agronomic data with SHAP explainability for interpretable crop recommendations. The defect detection model achieved 95.65% accuracy and F1-score under clean conditions and 93.48% under robust augmentation, while the price forecasting model achieved R2=0.986 and MAPE=3.16%. CropShield delivers a scalable, explainable, and farmer-accessible platform for evidence-based agricultural decision-making across Sri Lanka
Voice Agentic Smart Reader Application In Sinhala and Tamil For Visually Impaired People
(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Siriwardana B.J.A; Hewagama A.D; Aluwihare M.C; Sharumathan M; Weerasinghe, M; Abeywardhana, L
This paper proposes an intelligent smart reading system to improve access to documents, news articles, and printed content for visually impaired and reading-challenged users, particularly in low-resource languages such as Sinhala and Tamil. Although recent advances in computer vision, speech processing, and large language models have enabled effective document understanding, existing systems often operate independently and provide limited support for non-visual navigation, interactive information access, and expressive audio output. The proposed system integrates four main components into a unified framework. First, a voice-guided navigation module enables users to capture documents without visual assistance by providing real-time audio guidance, ensuring proper alignment and reliable image acquisition for text recognition. Second, a Tamil document understanding and question answering module processes document content and supports accurate, context-aware information retrieval using a retrieval-based approach, while also providing personalized content recommendations. Third, a voice-assisted Sinhala reading module allows users to navigate documents and access information using natural voice commands and semantic processing. Finally, an emotional text-to-speech module generates expressive speech in Sinhala and Tamil, improving the naturalness and clarity of audio output. Experimental results demonstrate that the proposed system improves document capture accuracy, navigation efficiency, information retrieval performance, and speech quality. Overall, the system provides a practical, accessible, and user-centered solution for intelligent document interaction in underrepresented languages.
ML-Based System to Detect GPS Spoofing and Signal Jamming via Signal Logs
(Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Harshani S.U.E; Wickramasinghe V.D.A; Muthukuda M.A.D.H.N; Siriwardhane H.H.D.V.; Siriwardana, D; Wijesooriya, A
This paper describes the design, implementation, and analysis of a machine learning-based and forensic-grade desktop system to detect GPS spoofing attacks and signal jamming attacks. The system processes telemetry logs collected from Unmanned Aerial Vehicles (UAVs) and other GNSS-enabled systems to detect any malicious signal manipulations and disruptions. It utilizes a pipeline comprising modules: GPS logging, feature extraction, and an unsupervised machine learning detection engine based on Isolation Forest, One-Class SVM, and LSTM Autoencoder models. The system learns the normal behavioral patterns and is trained on actual GPS data, therefore, identifying the previously unknown attacks. One of the contributions is that forensic concepts, such as hash-SHA-256, chain-of-custody logging, and read-only processing, are factored into the human process, thus supporting evidence integrity and traceability. The system generates elaborate visual and textual reports, giving a user-friendly timeline of the attack with severity ratings. Through the experiment with real and synthetic interfered datasets, the system is found to be effective in predictably distinguishing between spoofing (jumping coordinates and unrealistic kinematics) and jamming (significantly lost signal and large drift variance) with substantial detection. This tool offers an essential feature to cybersecurity forensic investigators, drone operators, and other critical infrastructure defenders to diagnose and record GNSS susceptibility.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.