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.

Controlling electromagnetic waves and information simultaneously by information metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart platforms to manipulate the wave–information–matter interactions without manual intervention by synergizing engineered ultrathin structures with active devices and algorithms, which evolve from the passive composite materials for tailoring wave–matter interactions that cannot be achieved in nature. Here, we review the recent progress of intelligent metasurfaces in wave–information–matter controls by providing the historical background and underlying physical mechanisms. Then we explore the application of intelligent metasurfaces in developing novel wireless communication architectures, with particular emphasis on metasurface-modulated backscatter wireless communications. We also explore the wave-based computing by using the intelligent metasurfaces, focusing on the emerging research direction in intelligent sensing. Finally, we comment on the challenges and highlight the potential routes for the further developments of the intelligent metasurfaces for controls, communications and computing.

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.

Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of “intelligence” in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step “over-the-air.” We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.