Faculty Publications
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Publications by NITK Faculty
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Item HIL implementation of an islanding detection and an automatic mode switching for droop-based microgrid(Inderscience Publishers, 2022) Kulkarni, S.V.; Gaonkar, D.N.This paper presents the control schemes and performance study of parallel connected inverter based distributed generation sources (DGs) in microgrid for grid-connected and stand-alone modes of operation. This standalone mode of operation of inverter based DG system is mainly based on droop control scheme with the virtual complex impedance in the outer voltage loop. The microgrid load power is proportionally shared by the DGs according to their power ratings which features a good reliability and efficiency. Both the modes are switched automatically based on the Phase Locked Loop (PLL) phase error sin(γ – θ). This phase error is used to detect the islanding during disturbances in the system and also helps in seamless transfer between the modes. The PLL phase error response, islanding detection and mode switching are presented for various fault conditions. The hardware-in-the-loop (HIL) based platform is used to evaluate the performance of the microgrid in both the modes with islanding detection and automatic mode switching operation. © © 2022 Inderscience Enterprises Ltd.Item An automated deep learning pipeline for detecting user errors in spirometry test(Elsevier Ltd, 2024) Bonthada, S.; Pariserum Perumal, S.P.; Naik, P.P.; Mahesh, M.A.; Rajan, J.Spirometer is used as a major diagnostic tool for obstructive airway diseases and a monitoring tool for therapy response and disease staging over time. It is a sophisticated medical device employed to quantify flow and volume of air exhaled by a subject during a specific testing period. The essential metrics obtained from the spirometry test, play a crucial role in enabling healthcare professionals to thoroughly evaluate the respiratory health and condition of the individual under examination. Several spirometer measurements including Forced Vital Capacity (FVC) and Forced Expiratory Volume (FEV) serve as guidelines for diagnosis and prognosis of Chronic Obstructive Pulmonary Diseases (COPD) and asthma. However, user errors caused by different reasons, including improper handling of the equipment and poor performance during the maneuvers of the expiratory airflow, end up in incorrect treatment directions. To ensure accurate results, spirometry tests traditionally require the presence of a skilled professional to identify and address these errors promptly. A novel machine learning approach is proposed in this paper to automatically identify four such user errors based on Volume-Time and Flow-Volume graphs. By detecting specific errors and providing immediate feedback to patients, reliability and accuracy of spirometry results will be improved and the need for trained professionals will be reduced. The implementation facilitates the widespread adoption of spirometry, particularly in low-resource telemedicine settings. This work implements a binary classification model distinguishing between normal and error test samples, achieving a prediction accuracy of 93%. Additionally, a 4-way classification model is presented for identifying individual error sub-types, demonstrating a prediction accuracy of 94%. © 2023 Elsevier LtdItem High-Q Plasmonic Resonator for Volatile Organic Compound Detection(Institute of Electrical and Electronics Engineers Inc., 2025) Mehta, S.; Shivaputra, S.; Ramesh, S.; Mandi, M.V.; Singh, M.A hybrid plasmonic waveguide (HPWG)-based resonator designs are studied for on-chip detection of volatile organic compounds (VOCs). The HPWG, which combines dielectric and metallic layers, significantly enhances the confinement of electromagnetic field, leading to increased interaction between the guided light and the surrounding analytes. The system achieves high spectral sensitivity and narrow linewidth by integrating multiple microring resonators in a cascaded configuration. This is critical for distinguishing small changes in the refractive index (RI) associated with different VOCs. Finite element method (FEM) simulations demonstrate the superior sensing performance of a proposed device, showing a spectral sensitivity of 469.5 nm/RIU and a quality factor (QF) of 518.75. The compact design and high sensitivity make this sensor an excellent candidate for on-chip VOC monitoring in industrial safety, as well as portable breath sensors to detect VOC biomarkers for early disease diagnosis. © IEEE. 1973-2012 IEEE.Item Applying Multi-Modal Quantum Deep Learning Algorithms for Enhanced Fake News Detection(Jagiellonian University, 2025) Aishwarya, C.; Venkatesan, M.; Prabhavathy; Akanksha, D.The pervasive spread of fake news across digital platforms has prompted the development of advanced detection systems. This review surveys and compares state-of-the-art multimodal deep learning models, including SpotFake, BDANN, MVAE, EANN, and the attention-based model by Guo et al., across benchmark datasets such as Twitter and Weibo. We present detailed performance comparisons, with SpotFake achieving an accuracy of 86.1% on the Twitter dataset. Key contributions of this review include the introduction of taxonomy tables based on fusion strategy and model architecture, a critical comparison of early, late, and hybrid fusion mechanisms, and a comprehensive evaluation of cross-modal generalization capabilities. In addition, we explore recent efforts in Quantum Machine Learning (QML), highlighting variational quantum circuits and hybrid quantum-classical models as promising approaches for enhancing scalability and efficiency. This work serves as a roadmap for building robust, interpretable, and scalable fake news detection systems that integrate both classical and quantum techniques. Povzetek: Pregled primerja multimodalne modele za zaznavanje lažnih novic (SpotFake, BDANN, MVAE, EANN, Guo) na Twitterju in Weibou ter predstavi taksonomije fuzije in arhitektur. Obravnava tudi obetavne kvantne pristope, ki lahko izboljšajo skalabilnost in u?inkovitost prihodnjih sistemov. © (2026). All right reserved.