Research Publications

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    MISO M-Ppm for Integrated-Receiver SWIPT with Pulse Shaping and PSO-Based Beamforming
    (Institute of Electrical and Electronics Engineers, 2026-06-12) Vithanage, G. S; Jayakody, D. N.K; Sabapathy, S
    Integrated-receiver (IntRx) simultaneous wireless information and power transfer (SWIPT) enables low-power Internet of Things devices by eliminating energy-intensive radio frequency (RF) front-end components at the receiver. This paper investigates a multiple-input single-output (MISO) SWIPT system employing M-ary pulse position modulation (M-PPM), where high-amplitude time-localized pulses exploit rectifier nonlinearity via increased peak-to-average power ratio (PAPR). Transmit beamforming is employed to enhance harvested DC power, and the impact of pulse shaping is analyzed using rectangular and raised-cosine (RC) pulses with varying roll-off factors. Monte Carlo simulations under random channel realizations show that the proposed MISO M-PPM architecture achieves harvested energy gains of up to 37.8% compared to single-antenna transmission. Furthermore, RC pulse shaping consistently outperforms rectangular pulses, with harvested energy increasing with the roll-off factor. To enable beamforming without increasing receiver complexity, a particle swarm optimization (PSO)-based transmit beamforming method is proposed, using received power as the sole fitness metric and requiring no phase estimation at the receiver. Beamforming coherence is characterized by using the standard deviation of received signal phases. An analytical model is developed to estimate the expected number of PSO iterations required to satisfy a target coherence level as a function of swarm size, enabling efficient allocation of computational resources.
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    Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases
    (Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, D
    The manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.
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    AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being
    (Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, S
    The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregiv