The dislocation of ring segments in shield tunnels poses adverse effects on tunnel structural stability and waterproofing. Current methods can only detect limited types of dislocation from circular tunnels, posing challenges in meeting practical requirements. This paper introduces a dislocation detection method that applicable to shield tunnels of various shapes, enabling the detection of both inter-ring and intra-ring dislocations. Firstly, a point cloud lossless unfolding method is introduced, allowing for the unfolding of tunnel point clouds of any cross-section shape without compromising point cloud features. Subsequently, a subway tunnel point cloud segmentation workflow is proposed, enabling the direct application of image deep learning networks to tunnel point clouds. The bolts are then identified as markers for dislocation measurement, and a method for bolt area of interest positioning and pairing is introduced. This method calculates dislocation value through fitting reference surfaces from area of interest. A series of experiments conducted on circular and quasi-rectangular shield tunnels totaling 2.45 km and containing 20 billion points demonstrate that the proposed segmentation workflow, coupled with an image semantic segmentation model, achieves a maximum mIoU of 90.2%, which is 10% higher than directly segmenting tunnel point clouds. The proposed method went through repeated accuracy tests and compared with other methods, including total station measurement, RANSAC plane fitting and scanline method. The results of our proposed method demonstrate high precision and stability, with a measurement accuracy RMSE of 0.89 mm, MAE of 0.69 mm, and efficiency 20 times higher compared to total station measurement.

Due to the public nature of wireless environments, legitimate communication systems are generally under the threat of Pilot Spoofing Attack (PSA). The malicious user may manipulate channel estimation by emitting the same pilot information, leading to the biased estimation of Channel State Information (CSI) and the degraded secrecy capacity. Although various methods have been proposed for combating PSA, the ubiquitous mobility of legitimate and malicious users introduces complex time-varying characteristics, making previous static PSA detection methods less attractive. In this paper, we present a novel location-awareness dynamical PSA detection mechanism. To model the complex dynamical behaviors, a Random Finite Set (RFS) is formulated to jointly describe the mobile positions and uncertain attack status. On this basis, we design a joint PSA detection and user localization algorithm relying on the sequential Bayesian inference. As such, the inherent correlations in mobile patterns can be exploited to effectively enhance PSA detection probability and localization accuracy. Numerical simulation results validate that the new method significantly improves PSA detection and CSI estimation accuracy compared with state-of-the-art counterparts, therefore the information leakage problem is greatly alleviated. Our new approach thus has the great potential in the emerging mobile scenarios by effectively enhancing the physical-layer secured transmissions.

The advanced 6G Technology benefits the Internet of Things (IoT) in various applications. As one essential application scenario, smart grid (SG) incorporates communication and management techniques and promises an efficient and intelligent power system, whereby cognitive radio (CR) is believed to be an essential tool for better resource utilization in power generation and delivering processes. In the CR-assisted IoT in SG scenarios, channel detection (CD) will play an essential role to accurately sense the available channel resource. However, for SG scenarios, high-accuracy CD may become a challenging task in complex power supply environments with unexpected impulsive noise (IN) and channel fading, which will significantly affect the signal statistical property. To address this problem, we propose a novel CD mechanism in the context of the wireless environment with IN and random channel fading. To be specific, taking the wireless channel status, IN and time-variant fading into account, a novel quaternary hypothesis testing model (QHTM) is formulated to describe the detection task, and by which a new dynamic state-space model (DSM) is developed to capture the dynamical behavior of the CD system. On this basis, a joint detection and estimation algorithm based on Bayesian statistical inference is devised to accomplish the CD task. Benefiting from the jointposterioridistribution estimation procedure, our algorithm can not only accurately detect the unknown channel status, but also estimate the real-time channel state information (CSI), thereby eliminating their effects on the detection performance. Numerical simulation results validate the proposed CD mechanism.