Nadeem Rather
Papers
2
Total Citations
27
H-Index
2
About
Nadeem Rather is a rising researcher at the forefront of chipless Radio Frequency Identification (RFID) technology, with a specific focus on merging hardware sensing with advanced computational intelligence. His key research areas include automated data acquisition, sensor tag design, and the application of machine learning for signal processing. Rather’s most impactful contribution is the pioneering use of Deep Learning (DL) and Machine Learning (ML) regression models to decode data from chipless RFID sensor tags. By applying these techniques to measured Radar Cross Section (RCS) data, his 2023 paper on deep-learning-assisted robust detection has already garnered 20 citations, demonstrating its immediate relevance to solving the field’s core challenge of reliable tag reading. Furthermore, Rather has made significant strides in experimental methodology. His novel robot-based data acquisition system, which integrates a Raspberry Pi with a universal arm robot, achieved a remarkable 98% improvement in data acquisition efficiency. This work, cited 7 times, provides a scalable, automated solution for high-throughput testing, a critical enabler for future chipless RFID deployments in industrial sensing and IoT applications.
Research Focus
Key Achievements
Top Papers
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- 2