Automated manufacturing is the cornerstone of the Industrial Internet of Things (IIoT) ecosystem, where vibration monitoring technology is a critical tool for maintaining industrial machinery. The prevailing approach mostly employs inertial measurement units (IMUs), lasers, and cameras, each demonstrating deployment constraints. In recent years, millimeter-wave (mmWave) radar has shown high vibration measurement performance, but it faces challenges in accurately localizing vibrating objects and determining observation points. This study introduces a new system called VibCamera, which leverages the mmWave vibration measurement technology with computer vision (CV) algorithms for vibration monitoring. With the positional assistant of CV semantic segmentation, the radar can accurately determine sufficient observation points, thereby achieving precise measurement with high directionality. VibCamera includes two camera modes, RGB-only and RGB+depth, and solves two technical challenges: 1) integrating multimodal information for vibration target localization and 2) extracting high-quality vibration signals in interference environments. VibCamera provides more consistent and precise outcomes without the need for physical contact. The experimental results indicate that the RGB-only mode has amplitude and frequency errors below$27.04 \; \mu \rm m$and 0.22 Hz, respectively, with a 90% probability, and the RGB+depth mode has errors below$23.72 \; \mu \rm m$and 0.21 Hz.

Non-intrusive hand tracking with mmWave radar technology is important in various Human-Computer Interaction (HCI) scenarios. However, existing mmWave-based solutions require users to be stationary and restrict a fixed hand motion area, which limits application flexibility and user experience. This paper proposes a novel mmWave-basedMobileHandTracking (MHTrack) system, which tracks user's hand gestures during walking. MHTrack focuses on tracking bothabsolutehand trajectory in the global coordinate system andrelativehand trajectory to the body. Specifically, we propose a wake-up mechanism for hand motion capture, in which hand point cloud can be recognized even under body interference and noise. We propose a hand tracking strategy named local spatial update, which overcomes the sparsity and instability of point clouds, to obtain absolute hand trajectory. Subsequently, we propose a hand anchor correction method to suppress anchor offset and remove the impact of body movement from absolute hand trajectory, thereby obtaining relative hand trajectory. As a case study, we project the relative hand trajectory onto a 2D image and feed it into a gesture recognition model to recognize the gestures. We conduct extensive experiments to evaluate the performance of MHTrack. Results demonstrate a 3D hand trajectory tracking error of$3.6cm$in an area of$3.2m\times 4.8m$and a gesture recognition accuracy of$99\%$with 30 gesture classes.

Integrated AI and Communication have been identified as crucial elements for IMT2030 by ITU-R. The integration of communication and computing in the RAN will facilitate deploying AI technology closer to mobile devices and enable AI computing collaboration with reduced latency. This paper primarily investigates the collaboration of AI inference computation between mobile devices and base stations, with a specific focus on the joint optimization of communication and computing resources in a multi-user environment. A communication and computing integrated scheduling algorithm is proposed to ensure optimal system performance in terms of inference accuracy and computation latency. Simulation results validate the effectiveness of the algorithm, demonstrating superior performance compared to the baseline algorithm.

Abstract With the 5G worldwide deployment, the scale of vertical applications is innovated benefit from 5G technologies including MEC (Multi‐access Edge Computing), network slicing, etc. Especially for healthcare, 5G had been used for COVID‐19 protection and intelligent medical processing. However, limited by the hospital's traditional information infrastructures, those 5G‐based healthcare applications are hard to be deployed and most only for demonstration, also isolated from the existing medical systems. So what is the next generation of smart healthcare information infrastructures is the key issue for the long‐term development of 5G healthcare applications. Even though the standardized 5G MEC framework has been widely used in many vertical scenarios, it is also hard to satisfy hospital‐specific requirements such as hospital‐dedicated deployment, medical data security, and various network connections, etc. This paper proposes a 5G‐based architecture for smart healthcare information infrastructure, a new network element iGW (industry gateway) is defined, and the smart healthcare dedicated cloud platform iMEP (industry multi‐access edge platform) is also introduced here, making it possible to satisfy both the hospital‐specific requirements and the long‐term evolution. Meanwhile, the implementation methodology and the corresponding field test results are presented, which show the significant network performance gain achieved by the proposed new system structure.