There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time, we propose the simultaneous use of a smart metasurface imager and recognizer, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy, transforming measured microwave data into images of the whole human body, classifying specifically designated spots (hand and chest) within the whole image, and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities, smart homes, human-device interaction interfaces, health monitoring, and safety screening free of visual privacy issues.

Conventional wireless communication architecture, a backbone of our modern society, relies on actively generated carrier signals to transfer information, leading to important challenges including limited spectral resources and energy consumption. Backscatter communication systems, on the other hand, modulate an antenna's impedance to encode information into already existing waves but suffer from low data rates and a lack of information security. Here, we introduce the concept of massive backscatter communication which modulates the propagation environment of stray ambient waves with a programmable metasurface. The metasurface's large aperture and huge number of degrees of freedom enable unprecedented wave control and thereby secure and high-speed information transfer. Our prototype leveraging existing commodity 2.4 GHz Wi-Fi signals achieves data rates on the order of hundreds of Kbps. Our technique is applicable to all types of wave phenomena and provides a fundamentally new perspective on the role of metasurfaces in future wireless communication.

Electromagnetic (EM) sensing is a wide-spread contactless examination technique inscience, engineering and military. However, conventional sensing systems are mostly lack of intelligence, which not only require expensive hardware and complicated computational algorithms, but also pose important challenges for advanced in-situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition, and integrating it into a data-driven learnable data processing pipeline. This strategy allows to learn an optimal sensing chain in systematic sense of variational autoencoder, i.e., to jointly learn an optimal measurement strategy along with matching data post processing schemes. A three-port deep artificial neural network (ANN) is designed to characterize the measurement process, such that an optimal measurement strategy is adaptive to the subject of interest by controlling the programmable metasurface for manipulating the EM illuminations. We design and fabricate a proof-of-principle sensing system in microwave, and demonstrate experimentally its significance on the highquality imaging and high-accuracy object recognition from a remarkably reduced number of measurements. We faithfully expect that the presented methodology will provide us with a fundamentally new perspective on the design of intelligent sensing architectures at various frequencies, and beyond.

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The information-carrying programmable metasurfaces have found widespread applications in communication, sensing, and other related areas. However, there is a fundamental but unresolved problem, i.e., the rigorous understanding of the quantization of metasurface coding. Here, we theoretically investigate the performance difference between one-bit and continuous information-encoding metasurfaces. To this end, we derive analytical representations of system responses in various cases (single-input single-output, singleinput multiple-output, and multiple-input multiple-output). We analyze the impact of one-bit coding (in contrast to continuous coding) in terms of the resulting channel cross-talk and reduction of information capacity in various representative scenarios from wireless communication and holography. Our main finding indicates that the one-bit coding yields a satisfactory performance in most practical scenarios; we also show that in many cases there are smart ways to mildly relax optimization constraints in order to reduce the performance gap between the one-bit and continuous coding. We expect that our results can provide theoretical guidance for a large variety of metasurface-assisted information systems for electromagnetic waves and other wave phenomena.