Machine-learning reprogrammable metasurface imager

Lianlin Li (), Hengxin Ruan, Che Liu, Ying Li, Ya Shuang, Andrea Alù (), Cheng-Wei Qiu () and Tie Jun Cui ()
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Lianlin Li: Peking University
Hengxin Ruan: Peking University
Che Liu: Southeast University
Ying Li: National University of Singapore
Ya Shuang: Peking University
Andrea Alù: City University of New York
Cheng-Wei Qiu: National University of Singapore
Tie Jun Cui: Southeast University

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: Abstract Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized solution for an entire data set, realizing storage and transfer of full-resolution raw data in dynamically varying scenes. High-accuracy image coding and recognition are demonstrated in situ for various image sets, including hand-written digits and through-wall body gestures, using a single physical hardware imager, reprogrammed in real time. Our electronically controlled metasurface imager opens new venues for intelligent surveillance, fast data acquisition and processing, imaging at various frequencies, and beyond.

Date: 2019
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DOI: 10.1038/s41467-019-09103-2

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