Electromagnetic metamaterial agent.

Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans.

Language-controllable programmable metasurface empowered by large language models.

Programmable metasurface has become a prominent tool in various areas including control, communication, computing, and so on, due to its unique capability in the electromagnetic (EM) manipulation. However, it is lack of the intelligence in the sense that it usually requires the manual intervention, and thus makes it hard to behavior as the human process. To endow the programmable metasurface with the intelligence, we here proposed the concept of the language-controllable programmable metasurface for autonomous EM manipulations by exploring the notable capability of large language models (LLMs) in attaining the human-like intelligence.

Semantic-Electromagnetic Inversion With Pretrained Multimodal Generative Model.

Across diverse domains of science and technology, electromagnetic (EM) inversion problems benefit from the ability to account for multimodal prior information to regularize their inherent ill-posedness. Indeed, besides priors that are formulated mathematically or learned from quantitative data, valuable prior information may be available in the form of text or images. Besides handling semantic multimodality, it is furthermore important to minimize the cost of adapting to a new physical measurement operator and to limit the requirements for costly labeled data.

Semantic regularization of electromagnetic inverse problems.

Solving ill-posed inverse problems typically requires regularization based on prior knowledge. To date, only prior knowledge that is formulated mathematically (e.g.

Microwave Speech Recognizer Empowered by a Programmable Metasurface.

Speech recognition becomes increasingly important in the modern society, especially for human-machine interactions, but its deployment is still severely thwarted by the struggle of machines to recognize voiced commands in challenging real-life settings: oftentimes, ambient noise drowns the acoustic sound signals, and walls, face masks or other obstacles hide the mouth motion from optical sensors. To address these formidable challenges, an experimental prototype of a microwave speech recognizer empowered by programmable metasurface is presented here that can remotely recognize human voice commands and speaker identities even in noisy environments and if the speaker's mouth is hidden behind a wall or face mask. The programmable metasurface is the pivotal hardware ingredient of the system because its large aperture and huge number of degrees of freedom allows the system to perform a complex sequence of sensing tasks, orchestrated by artificial-intelligence tools.

Intelligent indoor metasurface robotics.

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs (motorized vehicles, airborne drones, etc.

Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision.

The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize the smart beam tracking and wireless communications.

Three-Dimensional Encoding Approach for Wearable Tactile Communication Devices.

In this work, we presented a novel encoding method for tactile communication. This approach was based on several tactile sensory characteristics of human skin at different body parts, such as the head and neck, where location coordinates in the three-dimensional (3D) space were clearly mapped in the brain cortex, and gentle stimulations of vibrational touching with varied strengths were received instantly and precisely. For certain applications, such as playing cards or navigating walk paths for blinded people, we demonstrated specifically designed code lists with different patterns of tactile points in varied temporal sequences.

Intelligent electromagnetic metasurface camera: system design and experimental results.

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.

Harmonic information transitions of spatiotemporal metasurfaces.

Facilitated by ultrafast dynamic modulations, spatiotemporal metasurfaces have been identified as a pivotal platform for manipulating electromagnetic waves and creating exotic physical phenomena, such as dispersion cancellation, Lorentz reciprocity breakage, and Doppler illusions. Motivated by emerging information-oriented technologies, we hereby probe the information transition mechanisms induced by spatiotemporal variations and present a general model to characterize the information processing capabilities of the spatiotemporal metasurface. Group theory and abstract number theory are adopted through this investigation, by which the group extension and independent controls of multiple harmonics are proposed and demonstrated as two major tools for information transitions from the spatiotemporal domain to the spectra-wavevector domain.

Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing.

Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time 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.

Information Metamaterial Systems.

Metamaterials have great capabilities and flexibilities in controlling electromagnetic (EM) waves because their subwavelength meta-atoms can be designed and tailored in desired ways. However, once the structure-only metamaterials (i.e.

Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals.

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.

Information theory of metasurfaces.

We propose a theory to characterize the information and information processing abilities of metasurfaces, and demonstrate the relation between the information of the metasurface and its radiation pattern in the far-field region. By incorporating a general aperture model with uncertainty relation in -space, we propose a theory to predict the upper bound of information contained in the radiation pattern of a metasurface, and reveal the theoretical upper limit of orthogonal radiation states. The proposed theory also provides guidance for inverse design of the metasurface with respect to given functionalities.

Smart metasurface with self-adaptively reprogrammable functions.

Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing. From passive to active, metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic (EM) wavefields. However, the programmable metasurfaces require manual control to switch among different functionalities.

Intelligent metasurface imager and recognizer.

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.

Machine-learning reprogrammable metasurface imager.

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.

Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images.

Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis.

Spin-Controlled Multiple Pencil Beams and Vortex Beams with Different Polarizations Generated by Pancharatnam-Berry Coding Metasurfaces.

We propose to design coding metasurfaces based on the Pancharatnam-Berry (PB) phase. The proposed PB coding metasurface could control circularly polarized components of incident waves, by encoding geometric phase into the orientation angle of coding particles to generate 1-bit and multibit phase responses. We perform digital convolution operations on scattering patterns of the PB coding metasurface to reach flexible controls of the circularly polarized waves, forming spin-controlled multiple beams with different polarizations in free space, such as pencil beams and vortex beams carrying orbital angular momentum.

Controlling Energy Radiations of Electromagnetic Waves via Frequency Coding Metamaterials.

Metamaterials are artificial structures composed of subwavelength unit cells to control electromagnetic (EM) waves. The spatial coding representation of metamaterial has the ability to describe the material in a digital way. The spatial coding metamaterials are typically constructed by unit cells that have similar shapes with fixed functionality.

Electromagnetic reprogrammable coding-metasurface holograms.

Metasurfaces have enabled a plethora of emerging functions within an ultrathin dimension, paving way towards flat and highly integrated photonic devices. Despite the rapid progress in this area, simultaneous realization of reconfigurability, high efficiency, and full control over the phase and amplitude of scattered light is posing a great challenge. Here, we try to tackle this challenge by introducing the concept of a reprogrammable hologram based on 1-bit coding metasurfaces.

Extraordinary optical transmission through a rectangular hole filled with extreme uniaxial metamaterials.

This Letter presents a theory of extraordinary optical transmission (EOT) through a rectangular hole filled with the extreme uniaxial metamaterials with infinite longitudinal components of permittivity (ϵ) and permeability (μ). We demonstrate theoretically and numerically that a number of high-order transverse electromagnetic (TEM) modes can be supported by such a structure, and that, more interestingly, their normalized transmittance can be remarkably enhanced due to the Fabry-Perot resonance effect. A set of illustrative examples has been provided to demonstrate that such an EOT property could be explored for the purpose of subwavelength-resolution imaging.

Information entropy of coding metasurface.

Because of their exceptional capability to tailor the effective medium parameters, metamaterials have been widely used to control electromagnetic waves, which has led to the observation of many interesting phenomena, for example, negative refraction, invisibility cloaking, and anomalous reflections and transmissions. However, the studies of metamaterials or metasurfaces are mainly limited to their physical features; currently, there is a lack of viewpoints on metamaterials and metasurfaces from the information perspective. Here we propose to measure the information of a coding metasurface using Shannon entropy.

Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface.

Real-time high-resolution (including super-resolution) imaging with low-cost hardware is a long sought-after goal in various imaging applications. Here, we propose broadband single-shot and single-sensor high-/super-resolution imaging by using a spatio-temporal dispersive metasurface and an imaging reconstruction algorithm. The metasurface with spatio-temporal dispersive property ensures the feasibility of the single-shot and single-sensor imager for super- and high-resolution imaging, since it can convert efficiently the detailed spatial information of the probed object into one-dimensional time- or frequency-dependent signal acquired by a single sensor fixed in the far-field region.