Recent Submissions
Development of a MEMS-based Earthquake Dataset using the Raspberry Shake Network in New Zealand
(Sri Lanka Institute of Information Technology, 2026-05-21) Samiha, T.Z; Ravishan, D
Traditional seismic monitoring is often limited by the high cost of instrumentation and logistical barriers, hindering the expansion of earthquake monitoring networks especially in
under-resourced regions. Low-cost MEMS-based sensors offer a scalable alternative, but require specialized datasets to train machine learning models adapted to their unique noise characteristics and sensitivity profiles. To address this, we systematically collected waveforms from approximately 4,000 earthquakes (magnitude 2.7 to the highest recorded) recorded across 89 Raspberry Shake stations in New Zealand from 2020–2025. Events were matched to nearby stations based on epicentral distance criteria (100 km for M 2.7–5.5, 150 km for M>5.5). A staged filtering pipeline using PhaseNet, EQTransformer, and GPD models, cross-validated with theoretical TauP arrivals, was applied to ensure phase pick quality across three confidence tiers. The final curated dataset comprises 918 high-confidence waveforms with validated P and S wave arrivals, alongside approximately 16,035 total waveform records spanning all quality tiers. This dataset addresses the critical scarcity of labeled training data for low-cost seismic instrumentation, enabling the development of phase pickers specifically calibrated for MEMS sensors.
A Novel Sample-Splitting Receiver Architecture for SWIPT: Design and Resource Allocation
(Institute of Electrical and Electronics Engineers, 2026-06-08) Vithanage, G. S; Jayakody, D.N.K; Muthuchidambaranathan P.; Dinis, R
This paper presents a novel sample splitting (SS) technique for simultaneous wireless information and power transfer (SWIPT), addressing the inefficiencies of conventional power splitting (PS) and time switching (TS) methods. Unlike traditional approaches, SS directly samples the received signal after it is captured by the antenna. Subsequently, the sampled signal is used for information decoding (ID), and the residual component is redirected towards energy harvesting (EH) by means of a single-pole double-throw (SPDT) switching mechanism. A digital receiver architecture is designed to implement SS, and its performance is evaluated against PS and TS through Monte Carlo simulations over Rayleigh fast fading channels. The results demonstrate an improved bit error rate (BER) and harvested power trade-off, with SS achieving maximum EH gains of approximately 189% over PS and 106% over TS under the considered system parameterization, providing its greatest advantage in ID-prioritized scenarios where the EH branch is most constrained
HireGenius: Automated Interviewing System for Software Engineers
(Springer Science and Business Media Deutschland GmbH, 2026-08-01) Hewamadduma N.A.; Nalinka G.K; Mahawaththa N.T.M.A.S.M; Rosa S.R.T.L; De Silva D.I.; Gunathilake P.
Recruiting the right software engineers is a critical challenge, with traditional manual screening being time-consuming, subjective, and often inconsistent. Recruiters typically rely on Curriculum vitae reviews and interviews, which lack the depth needed for evaluating technical roles. For software engineers, it is essential to assess programming skills, academic performance, and personality traits. To overcome these limitations, this study developed an automated candidate selection and interview system using artificial intelligence, natural language processing, and deep learning. Ensemble learning and artificial intelligence models incorporating natural language processing were used to rank candidates and predict job match percentages. Top-ranked individuals were further evaluated through analysis of GitHub profiles, LinkedIn activity, and academic transcripts using machine learning and natural language processing techniques. Each candidate’s technical skills, experience, and education were assessed to generate accurate shortlists for technical interviews. These shortlisted candidates then participated in an automated interview process powered by advanced natural language processing and deep learning. A gamified human resource interview system was introduced, leveraging a machine learning model and structured scoring criteria to identify the best-fit candidates while streamlining and enhancing the hiring process.
Identify Dyscalculia, Dysgraphia Learning Disabilities in Deaf and Mute Primary Students and Help to Improve Learning Abilities
(Institute of Electrical and Electronics Engineers, 2026-01-22) Perera, G; Neththasinghe, H; Rasanjana, D; Thalakotunna, Y; Krishara, J; Rajendran, K
Learning disabilities, particularly Dyscalculia and Dysgraphia, significantly hinder students' academic, social, and future occupational outcomes. Deaf and mute primary students face amplified challenges due to limited availability of specialized educational resources and tools that cater to their unique communication needs. This research presents an innovative, intelligent learning environment designed specifically to identify and mitigate Dyscalculia and Dysgraphia among deaf and mute primary students. Leveraging advanced artificial intelligence, computer vision, and machine learning (ML) technologies, the developed system incorporates Sinhala Sign Language (SLSL) and interactive, adaptive learning methodologies. The identification process employs Convolutional Neural Networks (CNNs) to analyze handwritten numerical inputs and sign language gestures, categorizing students based on the severity of their conditions. Subsequently, personalized, engaging instructional activities facilitate gradual skill development and continuous improvement. Robust data privacy measures ensure ethical standards, while automated tracking provides actionable insights for educators and parents. Initial findings indicate significant improvements in student performance and engagement, demonstrating the system's efficacy in delivering inclusive, culturally relevant, and accessible educational interventions tailored for deaf and mute students.
LegalVision: A Knowledge-Driven AI Framework for Legal Understanding and Trust Assessment
(Institute of Electrical and Electronics Engineers, 2026-06-12) Sharan K; Wicramasinghe D.A.T.N.; Maxwell L.Y; Sivanuja S; Kuruppu, D.S; Dissanayake, A
Legal documents are often lengthy, complex, and written in highly technical language, making them difficult for citizens and even legal professionals to interpret efficiently. This creates a need for an intelligent legal support system that can improve accessibility, transparency, and trust in document understanding. This study proposes LegalVision, a knowledgedriven AI framework that integrates multi-perspective legal summarization and visualization, explainable legal reasoning, clause-level bias and risk evaluation, and a dynamic legal knowledge graph for property law documents. This research is conducted within the Sri Lankan legal context using a dataset collected from real Sri Lankan legal documents, including property-related deeds and agreements. The framework processes legal texts through clause segmentation, entity and relation extraction, perspective-based summary generation, infographic visualization, risk classification, and graphsupported reasoning, while preserving links to the original clauses for traceability within a single platform. Therefore, this research contributes a unified and explainable legal AI framework that supports both legal professionals and nonexpert users in understanding property law documents more accurately and efficiently.
The SLIIT Research Document Archive (RDA) is the institutional repository of SLIIT, managed by the SLIIT Library. The primary purpose of SLIIT RDA is to manage, store, and disseminate SLIIT research output with its community and beyond, reaching the wider public. This plays a pivotal role in preserving the academic legacy of the institute.
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.