Machine-learning reprogrammable metasurface imager

By Lianlin Li, Hengxin Ruan, Ying Li, Ya Shuang, Andrea Alù, Cheng-Wei Qiu, T. Cui. Nature Communications, 2019

527 citations recorded by the source corpus.

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. Conventional imagers require time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing. Here, the authors demonstrate a real-time digital-metasurface imager that can be trained in-situ to show high accuracy image coding and recognition for various image sets.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/4f2b9cb774489c1a600c224c75edb8da07a24064

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In Biomedical Engineering

In Electrical and Electronic Engineering

In Electronic, Optical and Magnetic Materials

Citations in the corpus

Deep learning for the design of photonic structures (2020, background): “Other examples of generative models applied in photonics research have also been reported.”
Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges (2021, methodology): “Unsupervised subspace methods such as principal components analysis are applied to metasurface-based sensing and imaging [41].”
Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, methodology): “Conventional machine learning methods, such as Bayesian learning [106], clustering [107], and manifold learning [104], are also very helpful in solving photonic design problems.”
Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts (2019, methodology): “2, the RIS is achieved by the b-bit re-programmable meta-material, which has been implemented as a set of radiative elements layered on a guiding structure following the wave guide techniques, forming a 2-dimensional (2D) planar antenna array [7].”