Intelligent electromagnetic metasurface camera: system design and experimental results

Abstract

Electromagnetic (EM) sensing is uniquely positioned among nondestructive examination options, which enables us to see clearly targets, even when they visually invisible, and thus has found many valuable applications in science, engineering and military. However, it is suffering from increasingly critical challenges from energy consumption, cost, efficiency, portability, etc., with the rapidly growing demands for the high-quality sensing with three-dimensional high-frame-rate schemes. To address these difficulties, we propose the concept of intelligent EM metasurface camera by the synergetic exploitation of inexpensive programmable metasurfaces with modern machine learning techniques, and establish a Bayesian inference framework for it. Such EM camera introduces the intelligence over the entire sensing chain of data acquisition and processing, and exhibits good performance in terms of the image quality and efficiency, even when it is deployed in highly noisy environment. Selected experimental results in real-world settings are provided to demonstrate that the developed EM metasurface camera enables us to see clearly human behaviors behind a 60 cm-thickness reinforced concrete wall with the frame rate in order of tens of Hz. We expect that the presented strategy could have considerable impacts on sensing and beyond, and open up a promising route toward smart community and beyond.

Keywords

• deep learning
• electromagnetic sensing
• programmable metasurfaces