Abstract
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.
Highlights
Nowadays, electromagnetic (EM) sensing is a powerful nondestructive examination tool under all-weather all-time operational condition, severing as a fundamental asset in science, engineering and military [1,2,3,4,5,6,7,8,9,10,11,12,13]
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
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
Summary
Electromagnetic (EM) sensing is a powerful nondestructive examination tool under all-weather all-time operational condition, severing as a fundamental asset in science, engineering and military [1,2,3,4,5,6,7,8,9,10,11,12,13]. The real-aperture strategy has nearly no requirements on data processing, but requires costly massive sensors [10,11,12,13] This situation becomes more and more serious with the ever-increasing demand for the high-frame-rate threedimensional imaging, since it is inevitably companied with dramatically increasing data rates that poses a heavy burden on the data acquisition, system communication and subsequent reconstruction algorithms. We here mean by the real-world setting that the target is in a really complicated indoor physical environment, and acquired signals are seriously disturbed by unknown co-channel interferences Another important contribution in this work is that a Bayesian inference framework for the developed EM metasurface camera has been proposed. The presented sensing strategy could open up a promising route toward smart community and beyond, and can be readily transposed to other frequencies and other types of wave phenomena
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