This internship project presents the design and simulation of a conformal metasurface array with Right-Hand Circular Polarisation (RHCP), operating at GPS L1/L2/L5 and NavIC S-band frequencies. The array demonstrates tri-band resonances with strong reflection dips and axial ratio below 3 dB, ensuring reliable GNSS performance. Equivalent circuit modelling using ADS and electromagnetic simulations in CST validated resonance behaviour and polarisation purity. The work shows potential for UAV-based Reconfigurable Intelligent Surfaces (RIS), enabling enhanced navigation accuracy and adaptability in complex environments.

This internship focused on the design, simulation, and analysis of advanced metasurface architectures tailored for GNSS applications. A tri-band transmissive metasurface was developed at 5 GHz, 6 GHz, and 8.5 GHz, along with a wideband reflective metasurface covering 7.7–13.8 GHz. Further, an Artificial Magnetic Conductor (AMC) was designed and integrated with a patch antenna to enhance efficiency, while a reconfigurable liquid-metal metasurface enabled reflective–transmissive switching with frequency agility. The results demonstrate compact, multifunctional, and adaptive metasurfaces as promising solutions for next-generation GNSS and RF front-end systems

This internship focused on developing a data-driven system to model and predict Total Electron Content (TEC) in the Indian Equatorial Ionospheric Anomaly (EIA) region using GNSS receiver data. The work involved preprocessing raw data, engineering temporal and statistical features, and training multiple machine learning and deep learning models including Random Forest, XGBoost, MLP, LSTM, and BiLSTM. A FastAPI-based backend automated the data pipeline, while a web dashboard was built for real-time visualization, prediction analysis, and error evaluation. The developed system provides a robust, scalable platform for accurate TEC forecasting, supporting GNSS-based positioning, navigation, and atmospheric research.

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This internship focused on developing a GPS-based timing synchronization system for smart grids using PYNQ-Z2 FPGA boards as distributed base stations. The system processed GPS PPS signals in FPGA fabric for sub-microsecond timestamping and utilized the ARM processor for parsing NMEA data. A centralized server validated timestamp integrity through drift analysis and supported real-time monitoring via MQTT-based communication and dashboard visualization. The work demonstrated scalable, secure, and accurate time synchronization, enhancing smart grid reliability and resilience.

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