Yongjian Fu

Diffractive neural networks (NNs) have garnered attention for directly implementing wireless signal processing at the physical layer. However, they are limited by a constrained weight learning space and activation functions, which restricts their data processing capabilities. To address this, we propose an RF circuit-based weighted diffraction NN (WDNN) that rivals digital NNs in processing ability. We design a weighted asymmetric RF coupler unit that, when stacked into a network, enables diffractive propagation with arbitrary connection weights. Additionally, an activation module is introduced that utilizes RF amplifiers operating in their nonlinear regions. We validate the effectiveness of the proposed WDNN through three tasks: 32-level amplitude modulated (AM) signal decoding, 31-class angle of arrival (AoA) estimation, and 2-class Wi-Fi based fall detection. After training, WDNN achieves the accuracy of 98.5%, 93.7%, and 90.8% in the AM decoding, AoA estimation, and fall detection tasks, respectively; while the diffractive NN SOTA achieves only 21.6%, 16.9%, and 63.3%. We also implement the prototypes of WDNN and SOTA, and real-world experimental results demonstrate that our method achieves an average accuracy improvement of up to 76.85% across various tasks compared to SOTA.

The demand for ultra-high-resolution imaging in mobile phone photography is continuously increasing. However, the image resolution of mobile devices is typically constrained by the size of the CMOS sensor. Although deep learning-based super-resolution (SR) techniques have the potential to overcome this limitation, existing SR neural network models require large computational resources, making them unsuitable for real-time SR imaging on current mobile devices. Additionally, cloud-based SR systems pose privacy leakage risks. In this paper, we propose M3Cam, an innovative and lightweight SR imaging system for mobile phones. M3Cam can ensure high-quality 16\texttimes SR image (4\texttimes in both height and width) visualization with almost negligible latency. In detail, we utilize an optical image stabilization (OIS) module for lens control and introduce a new modality of data, namely gyroscope readings, to achieve high-precision and compact optical flow estimation modules. Building upon this concept, we design a multi-frame-based SR model utilizing the Swin Transformer. Our proposed system can generate a 16\texttimes SR image from four captured low-resolution images in real-time, with low computational load, low inference latency, and minimal reliance on runtime RAM. Through extensive experiments, we demonstrate that our proposed multi-modal optical flow model significantly enhances pixel alignment accuracy between multiple frames and delivers outstanding 16\texttimes SR imaging results under various shooting scenarios. Code and dataset are available at: https://github.com/liangjindeamo-yuer/M3CAM

Acoustic imaging is attractive due to its ability to work under occlusion, different lighting conditions, and privacy-sensitive environments. Existing acoustic imaging methods require large transceiver arrays or device movement, which makes it challenging to use in many scenarios. In this paper, we develop a novel acoustic imaging system for low-cost devices with few speakers and microphones without any device movement. To achieve this goal, we leverage a 3D-printed passive acoustic metasurface to significantly enhance the diversity of the measurement data, thereby improving the imaging quality. Specifically, we jointly design the transmission signal, transceivers’ beamforming weights, metasurface, and imaging algorithm to minimize the imaging reconstruction error in an end-to-end manner. We further develop a scheme to dynamically adapt the imaging resolution based on the distance to the target. We implement a system prototype. Using extensive experiments, we show that our system yields high-quality images across a wide range of scenarios.

Wireless charging has achieved promising progress in recent years. However, the severe bottlenecks are the small charging range and poor flexibility. This paper presents ChargeX to enable smart and long-range wireless charging for small mobile devices. ChargeX incorporates emerging smart metasurface into the magnetic resonance coupling-based wireless charging to extend the charging range and accommodates the mobility of charging device. Unlike previous endeavors in metasurface-assisted wireless charging that focused on simulation, ChargeX makes efforts across software and hardware to meet three crucial requirements for a practical wireless charging system: (i) realize high-freedom and accurate metasurface control under the premise of low loss; (ii) obtain real-time feedback from the receiver and make effective manipulation for transmitted magnetic flux; and (iii) generate a proper AC signal source at the desired frequency band. We developed a prototype of ChargeX, and evaluated its performance through controlled experiments and real-world phone charging. Extensive experiments demonstrate the great potential of ChargeX for long-range and flexible wireless charging with a compact receiver design.

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.