We present MetaAI, a novel wireless computing paradigm that integrates neural network computation directly into wireless signal propagation. Unlike traditional approaches that treat wireless channels as mere data conduits, MetaAI transforms them into active computing elements through programmable metasurfaces, enabling concurrent data transmission and neural network processing. By leveraging the inherent linearity of both wireless propagation and neural networks, our design resolves the fundamental mismatch between sequential wireless transmission and parallel neural computation, while supporting efficient multi-sensor late-stage data fusion. We implemented MetaAI using metasurfaces at both dual-band (2.4/5 GHz) and single-band (3.5 GHz) frequencies. Extensive experiments demonstrate robust performance across diverse classification tasks, achieving 82.8% average accuracy (up to 89.8%) even with a simple linear architecture. Multi-sensor fusion further improves accuracy by up to 27.06%. MetaAI represents a fundamental shift in Edge AI architecture, where wireless infrastructure becomes an integral part of the computing pipeline.

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%.

Wi-Fi technology has emerged as a promising solution for contact-free sensing owing to the pervasiveness of Wi-Fi signals in indoor environments. However, Wi-Fi sensing faces several fundamental issues, including limited sensing range and unstable orientation-dependent sensing performance, hindering the widespread adoption of Wi-Fi sensing in real-life scenarios. In this paper, we propose RISensing, a novel system that leverages Reconfigurable Intelligent Surfaces (RIS) to address these two fundamental issues of Wi-Fi sensing and bring Wi-Fi sensing one step closer to real-world adoption. Unlike prior Wi-Fi sensing works which typically rely on a single target reflection signal to capture the target movement, RISensing utilizes two target reflection signals, i.e., the direct target reflection signal and RIS-based target reflection signal, to boost the sensing capability. RISensing characterizes the RIS-based target reflection signal, and constructively combines it with the direct target reflection. We evaluate the sensing performance of RISensing in various environments, including corridor, office and lab. Extensive experiments demonstrate RISensing can improve the sensing range of Wi-Fi from 4 m to 23 m, and effectively mitigate the orientation-dependent issue.

Contact-free finger gesture recognition unlocks plenty of applications in smart Human-Computer Interaction (HCI). However, existing solutions either require users to wear sensors on their fingers or use continuously monitored cameras, raising concerns regarding user comfort and privacy. In this paper, we propose mmFinger, an accurate and robust mmWave-based finger gesture recognition system that can extend the range of available custom commands. The core idea is that mmFinger leverages the finger tapping pattern as a basic gesture and encodes different number combinations of the basic gesture like Morse code. To enable reliable recognition across different locations and for various users, we carefully design a robust feature Dop-profile to effectively characterize finger movements. Furthermore, by leveraging the multi-views provided by multiple antennas of radar, we develop an adaptive weighted feature fusion network to enhance the system's robustness. Finally, we devise a novel sequence prediction network to enable the system to recognize new gestures without retraining. Comprehensive experiments demonstrate that mmFinger can achieve an average recognition accuracy of 92% for 36 predefined gestures and 88% for 5 new user-defined commands, and is robust against finger location and user diversity.

Gas sensing plays a key role in detecting explosive/toxic gases and monitoring environmental pollution. Existing approaches usually require expensive hardware or high maintenance cost, and are thus ill-suited for large-scale long-term deployment. In this paper, we propose Gastag, a gas sensing paradigm based on passive tags. The heart of Gastag design is embedding a small piece of gas-sensitive material to a cheap RFID tag. When gas concentration varies, the conductivity of gas-sensitive materials changes, impacting the impedance of the tag and accordingly the received signal. To increase the sensing sensitivity and gas concentration range capable of sensing, we carefully select multiple materials and synthesize a new material that exhibits high sensitivity and high surface-to-weight ratio. To enable a long working range, we redesigned the tag antenna and carefully determined the location to place the gas-sensitive material in order to achieve impedance matching. Comprehensive experiments demonstrate the effectiveness of the proposed system. Gastag can achieve a median error of 6.7 ppm for CH4 concentration measurements, 12.6 ppm for CO2 concentration measurements, and 3 ppm for CO concentration measurements, outperforming a lot of commodity gas sensors on the market. The working range is successfully increased to 8.5 m, enabling the coverage of many tags with a single reader, laying the foundation for large-scale deployment.

Acoustic metasurface was recently proposed to enhance the performance of acoustic communication and sensing. While promising, there are two issues hindering the adoption of acoustic metasurface for real-life usage. The first issue is that configurable metasurface is still expensive and unscalable. The second issue is that it is difficult for an acoustic metasurface to work in a large frequency range. In this paper, we present a wideband and configurable acoustic metasurface for the first time. We show that with a large number of metasurface elements, a cheap and simple two-state element design can achieve performance very close to that achieved by expensive continuous-state elements. We also fine-tune the geometric parameter of the element structure to support similar phase changes across a large frequency range, laying the foundation to enable wideband acoustic metasurface. Extensive experiments show that our system can achieve an average signal strength improvement of 7.5 dB and 10.5 dB in LoS and NLoS scenarios respectively with the help of a metasurface with a size of 17.6 × 17.6 cm. Two representative sensing applications (i.e., respiration sensing and gesture recognition) and one communication case study are employed to show the effectiveness of the metasurface.

As the most natural and convenient way to communicate with people, speech is always preferred in Human-Computer Interactions. However, voice-based interaction still has several limitations. It raises privacy concerns in some circumstances and the accuracy severely degrades in noisy environments. To address these limitations, silent speech recognition (SSR) has been proposed, which leverages the inaudible information (e.g., lip movements and throat vibration) to recognize the speech. In this paper, we present EarSSR, an earphone-based silent speech recognition system to enable interaction with human and device without a need for vocalization. The key insight is that when people are speaking, their ear canals exhibit unique deformation patterns and the corresponding deformation patterns are related to words/letters even without any vocalization. We utilize the built-in microphone and speaker of an earphone to capture the ear canal deformation. Ultrasound signals are emitted and the reflected signals are analyzed to extract the signal features corresponding to speech-induced ear canal deformation for silent speech recognition. We design a two-channel hierarchical convolutional neural network to achieve fine-grained letter/word recognition. Our extensive experiments show that EarSSR can achieve an accuracy of 82% for single alphabetic letter recognition and an accuracy of 93% for word recognition.

Phased array beamforming has been extensively explored as a physical layer primitive to improve the secrecy capacity of wireless communication links. However, existing solutions are incompatible with low-profile IoT devices due to cost, power and form factor constraints. More importantly, they are vulnerable to eavesdroppers with a high-sensitivity receiver. This paper presents Protego, which offloads the security protection to a metasurface comprised of a large number of 1-bit programmable unit-cells (i.e., phase shifters). Protego builds on a novel observation that, due to phase quantization effect, not all the unit-cells contribute equally to beamforming. By judiciously flipping the phase shift of certain unit-cells, Protego can generate artificial phase noise to obfuscate the signals towards potential eavesdroppers, while preserving the signal integrity and beamforming gain towards the legitimate receiver. A hardware prototype along with extensive experiments has validated the feasibility and effectiveness of Protego.

Millimeter-wave (mmWave) technology is emerging as the most promising candidate to support a wide range of applications with high data rate demand. However, due to the high directivity of mmWaves, its links are highly susceptible to barriers from walls and the movement of people. To address these issues, this paper introduces a low-cost and reconfigurable metasurface placed in the environment to reshape and resteer mmWave beams. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, the metasurface can reshape and resteer mmWave beams, thereby enabling a fast mmWave beam relay through the wall or redirects the beam power to another direction when a human body blocks the line-of-sight path. Preliminary simulated results show our designed metasurface can perform accurate beam steering within a field-of-view of [-60°, 60°]. And even with the small-size prototype (16 × 16 array of unit-cells), the metasurface enables up to 10.8 dB signal strength improvement.

Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to "reshape" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.

Human respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.

Target material identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in material identification. This paper introduces WiMi, a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. We also design a new material feature which is only related to the material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained material identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.