Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing

Patterns
Hao-Yang LiLianlin Li

Abstract

Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.

Citations

Aug 11, 2020·IScience·Tie Jun CuiQiang Cheng
May 1, 2021·Sensors·Hamidreza TaghvaeeAlbert Cabellos-Aparicio
Jun 3, 2021·Sensors·Michał RapczyńskiAyoub Al-Hamadi
Aug 7, 2021·Physical Review Letters·Michael Del HougnePhilipp Del Hougne
Nov 10, 2020··Qian MaTie Jun Cui

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Software Mentioned

MATLAB
TensorFlow
ANN
Tensor Flow 50
ADAM

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