4 publications
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Published Subscription Review Article
Digital Twin and Machine Learning Framework for Predictive Maintenance of Nuclear Power SystemsBy Shweta Zambare, Shivani Shendage, Pratiksha Surve, Nilophar Shaikh, Vaibhav Godase
Abstract: Nuclear power systems contain safety-critical components whose degradation can affect availability, reliability, and maintenance planning. Conventional maintenance strategies are largely based on scheduled inspection, threshold monitoring, and condition-based assessment, which may not fully exploit the large volume of continuously generated plant data. Digital Twin (DT) technology provides a mechanism for maintaining a synchronized virtual representation of a physical nuclear system, while machine learning (ML) can extract degradation patterns from multivariate …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article →
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Published Subscription Review Article
AI-Assisted Design of a Compact UWB Antenna with Tunable Band-Notch CharacteristicsBy Shweta Zambare, Shivani Shendage, Pratiksha Surve, Nilophar Shaikh, Vaibhav Godase
Abstract: Ultra-wideband antennas require wide impedance bandwidth while maintaining stable radiation characteristics and suppressing interference from coexisting narrowband wireless services. Conventional optimization of UWB antenna geometries generally requires repeated full-wave electromagnetic simulations, making parameter exploration computationally expensive. This paper proposes an artificial-intelligence-assisted methodology for designing a compact UWB monopole antenna with a tunable band-notch characteristic. The proposed antenna uses a microstrip-fed planar monopole printed on an FR-4 substrate, while a tunable …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article →
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Published Subscription Review Article
AI-Assisted Design of a Compact Bandpass Microwave Filter for 5G/6G ApplicationsBy Shweta Zambare, Shivani Shendage, Pratiksha Surve, Nilophar Shaikh, Vaibhav Godase
Abstract: The rapid development of fifth generation (5G) and emerging sixth generation (6G) wireless communication systems require compact, low-loss, highly selective and frequency-stable radio- frequency front-end components. Microwave bandpass filters play an important role in selecting desired communication channels while suppressing out-of-band interference. However, conventional filter design generally requires repeated electromagnetic simulation and manual optimization of geometrical parameters, resulting in considerable computational time. This paper presents an artificial intelligence (AI)-assisted methodology …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article →
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Published Subscription Review Article
Machine Learning Based Optical Fiber Fault Detection and Localization Using OTDR SignalsBy Shweta Zambare, Shivani Shendage
Abstract: Optical fiber networks provide high bandwidth, low transmission loss, and immunity to electromagnetic interference, making them fundamental to modern communication infrastructure. However, fiber faults caused by cable cuts, excessive bending, connector degradation, splicing defects, and physical disturbances can significantly affect network reliability. Optical time-domain reflectometry (OTDR) is widely used for identifying and locating such faults, but conventional OTDR interpretation often depends on threshold-based signal processing and manual analysis. This study …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 41–52 Read article →