Eavesdropping poses a critical threat to the confidentiality and integrity of voice communications. In recent years, techniques have advanced beyond traditional microphone-based methods toward more intelligent approaches, such as leveraging millimeter-wave sensing to detect the subtle vibrations induced by speakers and reconstruct voice information without direct audio capture. Despite their technical feasibility, these methods remain constrained by limited working ranges—typically only several meters—rendering them impractical for real-world stealthy eavesdropping. In this work, we propose MoiréEar, the first long-range passive eavesdropping system based on moiré patterns. The key idea is to exploit the amplification capability of moiré patterns, which amplify the minute vibrations induced by acoustic signals by hundreds of times, enabling long-range eavesdropping. To make the proposed method even more practical and stealthy, we develop new theoretical foundations that relax the strict requirements for generating moiré patterns. Specifically, our approach enables the use of irregular stripe structures (e.g., commonly seen barcodes) instead of standard moiré gratings to generate moiré patterns. We implement our design using a low-cost photodiode instead of cameras, achieving real-time eavesdropping with lightweight signal processing. Comprehensive experiments show that the system can extract intelligible audio at a distance of up to 90 m, outperforming the state of the art by an order of magnitude in range. We believe this new eavesdropping modality can inspire a wide range of IoT applications.

Millimeter-wave (mmWave) technology offers significant potential for high-bandwidth, low-latency communication industrial applications. However, mmWave faces several challenges, such as limited range, susceptibility to blockage, and slow beam alignment. For this, mmWave access points (APs) are often deployed at high densities, which leads to increased costs. In this paper, we introduce mmProjector, the first cost-effective reflector, which is non-reconfigurable with a static structure but can serve mobile industrial robots. The key idea is to reshape the reflected waves only along the robots’ movement trajectories using mmProjector and make the phase of reflected wave constructively added along the trajectories, improving reflection gain with limited incident waves. To achieve this, we develop an analytical model grounded in electromagnetic theory and propose sub-optimal algorithms for the deployment and shape design of reflectors. We prototype mmProjector and deploy it in a real-world airplane assembly factory. Experiments demonstrate that mmProjector operates effectively at 60 GHz, delivering up to 1.8 Gbps of bandwidth in non-line-of-sight (NLOS) scenarios, while achieving up to 75% reduction in costs.

Millimeter-wave (mmWave) communication offers a promising solution for high-throughput, low-latency unmanned aerial vehicle (UAV) networks. However, maintaining strong received signal strength (RSS) remains a challenge due to UAV mobility. While existing studies have largely focused on beam alignment, they often overlook another critical issue: polarization mismatch caused by UAV orientation changes. This problem is particularly severe in cost-sensitive commercial off-the-shelf (COTS) mmWave devices, which typically employ linearly polarized (LP) antenna arrays. Our measurements reveal that even with perfect beam alignment, UAV orientation can still cause significant signal degradation due to polarization mismatch. To address this challenge, we propose PolarFix, a practical metasurface solution that enables real-time polarization matching without requiring any modifications to existing transceiver hardware. Specifically, we design a linear-to-circular polarization (L2C) metasurface that transforms linearly polarized (LP) waves into circularly polarized signals, allowing LP antennas to maintain consistent signal power despite changes in UAV orientation.

With the advancement and maturation of 5G technology, its network slicing capabilities play a crucial role in delivering high-quality network services for industrial Internet of Things (IoT). However, effectively applying 5G network slicing to meet continuously changing network demands remains a challenging endeavor. We propose a heuristic-based dynamic adaptive network slicing deployment algorithm aimed at addressing both resource utilization efficiency and dynamics of network service requests during deployment. We first establish a system model for the network slicing deployment process, framing the deployment issue as a nonlinear integer programming problem. Subsequently, we introduce a two-stage deployment algorithm designed to accommodate the variability of network requests. Finally, we conduct numerical simulations and experiments to assess the algorithm based on criteria such as average deployment cost, number of Virtual Network Functions, and service acceptance rate. Results demonstrate that the proposed algorithm effectively manages resource utilization efficiency and dynamic issues of network service requests.

Millimeter-wave (mmWave) communication has great potential for high rates and low latency but suffers from severe non-line-of-sight (NLOS) blockage and high cost of beam alignment. These problems exacerbate for mobile industrial robots. Most existing methods use reconfigurable intelligent surface (RIS) to change the channel environment, whose practical applications are hindered by high cost. This paper introduces low-cost mmFlower, consisting of an array of 3D-printing reflector units whose orientation can be adjusted mechanically. We first construct a theoretical reflection model, bridging the mechanical parameters and communication metrics. mmFlower works in two modes according to whether it needs real-time reconfiguration. On the one hand, based on our uniform-RSS (received signal strength) projection algorithm, mmFlower remains static and reshapes mmWave into arbitrary projection trajectory along the robot movement. It maximizes mmWave allocation to the more frequently visited areas and realizes seamless mmWave coverage. On the other hand, mmFlower can dynamically track mmWave pencil beams on robots, providing higher reflection gain. Extensive evaluations in an airplane assembly factory show the superiority of mmFlower on reflection gain (supporting up to 1.8 Gbps in NLOS), flexibility to different trajectories of robots, robustness to deployment deviation, etc.

Admittance control is an effective technique for enhancing compliance with the interaction between the exoskeleton and the wearer. In practice, obtaining the desired admittance model parameters is a challenging task under various interaction scenarios with different wearers, especially when the human impedance parameters are indeterminate. In this paper, we propose a hierarchical optimal admittance control consisting of a higher-level admittance learning and a lower-level position control. First, finding the desired admittance model parameters is reformulated as a linear quadratic regulator (LQR) problem, and then a policy iteration (PI) algorithm is proposed to solve the derived LQR problem even with unknown system dynamics and human impedance parameters. With the obtained admittance model, the virtual trajectory is regulated to adapt the human-robot interaction (HRI) torque. Finally, an unknown system dynamics estimator (USDE) is integrated into a lower-level position controller to accommodate the lumped system uncertainties. This approach reduces the computational burden of admittance control while improving the tracking performance of position control. Comparative simulations are conducted to validate the effectiveness of the proposed hierarchical admittance control, and multiple knee joint flexion/extension movement experiments with surface electromyographic (sEMG) measurements are also carried out on three subjects to evaluate its improved compliance performance.