Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing

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. Here, these challenges are tackled with a frugal and multimodal semantic-EM inversion technique. The key ingredient is a multimodal generator of reconstruction results that can be pretrained, being agnostic to the physical measurement operator. The generator is fed by a multimodal foundation model encoding the multimodal semantic prior and a physical adapter encoding the measured data. For a new physical setting, only the lightweight physical adapter is retrained. The authors' architecture also enables a flexible iterative step-by-step solution to the inverse problem where each step can be semantically controlled. The feasibility and benefits of this methodology are demonstrated for three EM inverse problems: a canonical two-dimensional inverse-scattering problem in numerics, as well as three-dimensional and four-dimensional compressive microwave meta-imaging experiments.

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., sparsity of the unknown) or implicitly learned from quantitative data can be used for regularization. Thereby, semantically formulated prior knowledge derived from human reasoning and recognition is excluded. Here, we introduce and demonstrate the concept of semantic regularization based on a pre-trained large language model to overcome this vexing limitation. We study the approach, first, numerically in a prototypical 2D inverse scattering problem, and, second, experimentally in 3D and 4D compressive microwave imaging problems based on programmable metasurfaces. We highlight that semantic regularization enables new forms of highly-sought privacy protection for applications like smart homes, touchless human-machine interaction and security screening: selected subjects in the scene can be concealed, or their actions and postures can be altered in the reconstruction by manipulating the semantic prior with suitable language-based control commands.

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. Relying solely on microwave data, the system avoids visual privacy infringements. The developed microwave speech recognizer can enable privacy-respecting voice-commanded human-machine interactions is experimentally demonstrated in many important but to-date inaccessible application scenarios. The presented strategy will unlock new possibilities and have expectations for future smart homes, ambient-assisted health monitoring, as well as intelligent surveillance and security.

WiFi-based non-contact human presence detection technology

In the swiftly evolving landscape of Internet of Things (IoT) technology, the demand for adaptive non-contact sensing has seen a considerable surge. Traditional human perception technologies, such as vision-based approaches, often grapple with problems including lack of sensor versatility and sub-optimal accuracy. To address these issues, this paper introduces a novel, non-contact method for human presence perception, relying on WiFi. This innovative approach involves a sequential process, beginning with the pre-processing of collected Channel State Information (CSI), followed by feature extraction, and finally, classification. By establishing signal models that correspond to varying states, this method enables the accurate perception and recognition of human presence. Remarkably, this technique exhibits a high level of precision, with sensing accuracy reaching up to 99[Formula: see text]. The potential applications of this approach are extensive, proving to be particularly beneficial in contexts such as smart homes and healthcare, amongst various other everyday scenarios. This underscores the significant role this novel method could play in enhancing the sophistication and effectiveness of human presence detection and recognition systems in the IoT era.

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.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ∼2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

RadarSense: Accurate Recognition of Mid-Air Hand Gestures with Radar Sensing and Few Training Examples

Microwave radars bring many benefits to mid-air gesture sensing due to their large field of view and independence from environmental conditions, such as ambient light and occlusion. However, radar signals are highly dimensional and usually require complex deep learning approaches. To understand this landscape, we report results from a systematic literature review of ( N = 118) scientific papers on radar sensing, unveiling a large variety of radar technology of different operating frequencies and bandwidths, antenna configurations, but also various gesture recognition techniques. Although highly accurate, these techniques require a large amount of training data that depend on the type of radar. Therefore, the training results cannot be easily transferred to other radars. To address this aspect, we introduce a new gesture recognition pipeline that implements advanced full-wave electromagnetic modeling and inversion to retrieve physical characteristics of gestures that are radar independent, i.e. , independent of the source, antennas, and radar-hand interactions. Inversion of radar signals further reduces the size of the dataset by several orders of magnitude, while preserving the essential information. This approach is compatible with conventional gesture recognizers, such as those based on template matching, which only need a few training examples to deliver high recognition accuracy rates. To evaluate our gesture recognition pipeline, we conducted user-dependent and user-independent evaluations on a dataset of 16 gesture types collected with the Walabot, a low-cost off-the-shelf array radar. We contrast these results with those obtained for the same gesture types collected with an ultra-wideband radar made of a vector network analyzer with a single horn antenna and with a computer vision sensor, respectively. Based on our findings, we suggest some design implications to support future development in radar-based gesture recognition.

Huygens' metasurface-based surface plasmon coupler with near-unit efficiency

Surface plasmon polaritons (SPPs) and their counterparts at low frequency (i.e., spoof SPPs) have been attracting a lot of attention recently due to their potential application for routing information with high speeds and bandwidth. To further develop integrated plasmonics, a high-efficiency surface plasmon coupler is required for full elimination of the intrinsic scattering and reflection when exciting the highly confined plasmonic modes, but a solution to this challenge has remained elusive so far. To take on this challenge, here we propose a feasible spoof SPP coupler based on a transparent Huygens' metasurface, which is able to realize more than 90% efficiency in near- and far-field experiments. To be specific, electrical and magnetic resonators are designed separately on both sides of the metasurface to satisfy the impedance-matching condition everywhere, leading to full conversion of plane wave propagation into surface wave propagation. Moreover, a well-optimized plasmonic metal which is able to support an eigen SPP is designed. This proposed high-efficiency spoof SPP coupler based on a Huygens' metasurface may pave the way for the development of high-performance plasmonic devices.

Origami Microwave Imaging Array: Metasurface Tiles on a Shape-Morphing Surface for Reconfigurable Computational Imaging

Origami is the art of paper folding that allows a single flat piece of paper to assume different 3D shapes depending on the fold patterns and the sequence of folding. Using the principles of origami along with computation imaging technique the authors demonstrate a versatile shape-morphing microwave imaging array with reconfigurable field-of-view and scene-adaptive imaging capability. Microwave/millimeter-wave based array imaging systems are expected to be the workhorse for sensory perception of future autonomous intelligent systems. The imaging capability of a planar array-based systems operating in complex scattering conditions have limited field-of-view and lack the ability to adaptively reconfigure resolution. To overcome this, here, deviations from planarity and isometry are allowed, and a shape-morphing computational imaging system is demonstrated. Implemented on a reconfigurable Waterbomb origami surface with 22 active metasurface panels that radiate near-orthogonal modes across 17-27 GHz, capability to image complex 3D objects in full details minimizing the effects of specular reflections in diffraction-limited sparse imaging with scene adaptability, reconfigurable cross-range resolution, and field-of-view is demonstrated. Such electromagnetic origami surfaces, through simultaneous surface shape-morphing ability (potentially with shape-shifting electronic materials) and electromagnetic field programmability, opens up new avenues for intelligent and robust sensing and imaging systems for a wide range of applications.

Light-driven single-cell rotational adhesion frequency assay

The interaction between cell surface receptors and extracellular ligands is highly related to many physiological processes in living systems. Many techniques have been developed to measure the ligand-receptor binding kinetics at the single-cell level. However, few techniques can measure the physiologically relevant shear binding affinity over a single cell in the clinical environment. Here, we develop a new optical technique, termed single-cell rotational adhesion frequency assay (scRAFA), that mimics in vivo cell adhesion to achieve label-free determination of both homogeneous and heterogeneous binding kinetics of targeted cells at the subcellular level. Moreover, the scRAFA is also applicable to analyze the binding affinities on a single cell in native human biofluids. With its superior performance and general applicability, scRAFA is expected to find applications in study of the spatial organization of cell surface receptors and diagnosis of infectious diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s43593-022-00020-4.

Intelligent wireless walls for contactless in-home monitoring

Human activity monitoring is an exciting research area to assist independent living among disabled and elderly population. Various techniques have been proposed to recognise human activities, such as exploiting sensors, cameras, wearables, and contactless microwave sensing. Among these, the microwave sensing has recently gained significant attention due to its merit to solve the privacy concerns of cameras and discomfort caused by wearables. However, the existing microwave sensing techniques have a basic disadvantage of requiring controlled and ideal settings for high-accuracy activity detections, which restricts its wide adoptions in non-line-of-sight (Non-LOS) environments. Here, we propose a concept of intelligent wireless walls (IWW) to ensure high-precision activity monitoring in complex environments wherein the conventional microwave sensing is invalid. The IWW is composed of a reconfigurable intelligent surface (RIS) that can perform beam steering and beamforming, and machine learning algorithms that can automatically detect the human activities with high accuracy. Two complex environments are considered: one is a corridor junction scenario with transmitter and receiver in separate corridor sections and the other is a multi-floor scenario wherein the transmitter and receiver are placed on two different floors of a building. In each of the aforementioned environments, three distinct body movements are considered namely, sitting, standing, and walking. Two subjects, one male and one female perform these activities in both environments. It is demonstrated that IWW provide a maximum detection gain of 28% in multi-floor scenario and 25% in corridor junction scenario as compared to traditional microwave sensing without RIS.

Dielectric metalens for miniaturized imaging systems: progress and challenges

Lightweight, miniaturized optical imaging systems are vastly anticipated in these fields of aerospace exploration, industrial vision, consumer electronics, and medical imaging. However, conventional optical techniques are intricate to downscale as refractive lenses mostly rely on phase accumulation. Metalens, composed of subwavelength nanostructures that locally control light waves, offers a disruptive path for small-scale imaging systems. Recent advances in the design and nanofabrication of dielectric metalenses have led to some high-performance practical optical systems. This review outlines the exciting developments in the aforementioned area whilst highlighting the challenges of using dielectric metalenses to replace conventional optics in miniature optical systems. After a brief introduction to the fundamental physics of dielectric metalenses, the progress and challenges in terms of the typical performances are introduced. The supplementary discussion on the common challenges hindering further development is also presented, including the limitations of the conventional design methods, difficulties in scaling up, and device integration. Furthermore, the potential approaches to address the existing challenges are also deliberated.

Artificial Intelligence in Meta-optics

Recent years have witnessed promising artificial intelligence (AI) applications in many disciplines, including optics, engineering, medicine, economics, and education. In particular, the synergy of AI and meta-optics has greatly benefited both fields. Meta-optics are advanced flat optics with novel functions and light-manipulation abilities. The optical properties can be engineered with a unique design to meet various optical demands. This review offers comprehensive coverage of meta-optics and artificial intelligence in synergy. After providing an overview of AI and meta-optics, we categorize and discuss the recent developments integrated by these two topics, namely AI for meta-optics and meta-optics for AI. The former describes how to apply AI to the research of meta-optics for design, simulation, optical information analysis, and application. The latter reports the development of the optical Al system and computation via meta-optics. This review will also provide an in-depth discussion of the challenges of this interdisciplinary field and indicate future directions. We expect that this review will inspire researchers in these fields and benefit the next generation of intelligent optical device design.

Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

Replacing electrons with photons is a compelling route toward high-speed, massively parallel, and low-power artificial intelligence computing. Recently, diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking in the visible. The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25 × 10⁶ mm^-2 multiplied by the number of channels. The metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor, providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision, autonomous driving, and precision medicine.

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. Here, we propose an analytic compressed iterative deep (ACID) framework to address this challenge. ACID synergizes a deep network trained on big data, kernel awareness from compressed sensing (CS)-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the ACID reconstruction is accurate, is stable, and sheds light on the converging mechanism of the ACID iteration under a bounded relative error norm assumption. ACID not only stabilizes an unstable deep reconstruction network but also is resilient against adversarial attacks to the whole ACID workflow, being superior to classic sparsity-regularized reconstruction and eliminating the three kinds of instabilities.

Meta-programmable analog differentiator

We present wave-based signal differentiation with unprecedented fidelity and flexibility by purposefully perturbing overmoded random scattering systems such that zeros of their scattering matrices lie exactly at the desired locations on the real frequency axis. Our technique overcomes limitations of hitherto existing approaches based on few-mode systems, both regarding their extreme vulnerability to fabrication inaccuracies or environmental perturbations and their inability to maintain high fidelity under in-situ adaptability. We demonstrate our technique experimentally by placing a programmable metasurface with hundreds of degrees of freedom inside a 3D disordered metallic box. Regarding the integrability of wave processors, such repurposing of existing enclosures is an enticing alternative to fabricating miniaturized devices. Our over-the-air differentiator can process in parallel multiple signals on distinct carriers and maintains high fidelity when reprogrammed to different carriers. We also perform programmable higher-order differentiation. Conceivable applications include segmentation or compression of communication or radar signals and machine vision.

Meta-hologram enabled by a double-face copper-cladded metasurface based on reflection-transmission amplitude coding

Here, we propose a double-face copper-cladded meta-hologram that can efficiently manipulate the amplitude of electromagnetic waves in both transmission and reflection spaces, depending on the polarization state of the incident electromagnetic wave. The proposed meta-hologram is validated by encoding the transmission-reflection amplitude information of two independent images into a single metasurface. The holographic images obtained from measurements agree qualitatively with simulation results. The proposed metasurface presents a novel, to the best of our knowledge, scheme for electromagnetic wavefront control in the whole space and overcomes the limitations of narrow frequency band operation.

Deeply Subwavelength Localization with Reverberation-Coded Aperture

Accessing subwavelength information about a scene from the far-field without invasive near-field manipulations is a fundamental challenge in wave engineering. Yet it is well understood that the dwell time of waves in complex media sets the scale for the waves' sensitivity to perturbations. Modern coded-aperture imagers leverage the degrees of freedom (d.o.f.) offered by complex media as natural multiplexor but do not recognize and reap the fundamental difference between placing the object of interest outside or within the complex medium. Here, we show that the precision of localizing a subwavelength object can be improved by several orders of magnitude simply by enclosing it in its far field with a reverberant passive chaotic cavity. We identify deep learning as a suitable noise-robust tool to extract subwavelength localization information encoded in multiplexed measurements, achieving resolutions well beyond those available in the training data. We demonstrate our finding in the microwave domain: harnessing the configurational d.o.f. of a simple programmable metasurface, we localize a subwavelength object along a curved trajectory inside a chaotic cavity with a resolution of λ/76 using intensity-only single-frequency single-pixel measurements. Our results may have important applications in photoacoustic imaging as well as human-machine interaction based on reverberating elastic waves, sound, or microwaves.

A Baseline for Cross-Database 3D Human Pose Estimation

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.

Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today's society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.

Deep-learning-enabled inverse engineering of multi-wavelength invisibility-to-superscattering switching with phase-change materials

Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i.e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices. However, traditionally the design process is quite complicated, which involves complex structures with many choices of synthetic constituents and dispersions. Here, we demonstrate that a well-trained deep-learning neural network can handle these issues efficiently, which can not only forwardly predict scattering spectra of multilayer nanoparticles with high precision, but also inversely design the required structural and material parameters efficiently. Moreover, we show that the neural network is capable of finding out multi-wavelength invisibility-to-superscattering switching points at the desired wavelengths in multilayer nanoparticles composed of metals and phase-change materials. Our work provides a useful solution of deep learning for inverse design of nanoparticles with dynamic scattering spectra by using phase-change materials.

Secure Deep Learning for Intelligent Terahertz Metamaterial Identification

Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification.

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., passive metamaterials) are fabricated, their functions will be fixed. To control the EM waves dynamically, active devices are integrated into the meta-atoms, yielding active metamaterials. Traditionally, the active metamaterials include tunable metamaterials and reconfigurable metamaterials, which have either small-range tunability or a few numbers of reconfigurability. Recently, a special kind of active metamaterials, digital coding and programmable metamaterials, have been presented, which can realize a large number of distinct functionalities and switch them in real time with the aid of field programmable gate array (FPGA). More importantly, the digital coding representations of metamaterials make it possible to bridge the digital world and physical world using the metamaterial platform and make the metamaterials process digital information directly, resulting in information metamaterials. In this review article, we firstly introduce the evolution of metamaterials and then present the concepts and basic principles of digital coding metamaterials and information metamaterials. With more details, we discuss a series of information metamaterial systems, including the programmable metamaterial systems, software metamaterial systems, intelligent metamaterial systems, and space-time-coding metamaterial systems. Finally, we introduce the current progress and predict the future trends of information metamaterials.

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. 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.