Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-019-09103-2
Title: Machine-learning reprogrammable metasurface imager
Authors: Li, L.
Ruan, H.
Liu, C.
Li, Y. 
Shuang, Y.
Alù, A.
Qiu, C.-W. 
Cui, T.J.
Issue Date: 2019
Publisher: Nature Publishing Group
Citation: Li, L., Ruan, H., Liu, C., Li, Y., Shuang, Y., Alù, A., Qiu, C.-W., Cui, T.J. (2019). Machine-learning reprogrammable metasurface imager. Nature Communications 10 (1) : 1082. ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-019-09103-2
Rights: Attribution 4.0 International
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. © 2019, The Author(s).
Source Title: Nature Communications
URI: https://scholarbank.nus.edu.sg/handle/10635/209519
ISSN: 2041-1723
DOI: 10.1038/s41467-019-09103-2
Rights: Attribution 4.0 International
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