The uplink timing synchronization is indispensable for establishing a reliable link between the base station and the user equipment (UE). To tackle this problem, a new random access preamble (RAP) waveform is designed for cellular communication systems operating in high-mobility scenarios for orthogonal time frequency space systems. Meanwhile, we also propose a scheme to detect access UEs and estimate the corresponding round-trip propagation delay (i.e., timing advance (TA)). Concretely, the RAP root sequence consists of chirp sequences in the delay-Doppler domain, and the set of available RAPs is generated by cyclic shifting the root sequence. Then, the cross-correlation in the fractional Fourier domain is carried out to achieve the RAP detection and TA estimation of the access UEs. Exhaustive numerical simulation results corroborate that the proposed scheme has a superior RAP detection and TA estimation performance and exhibits sufficient robustness regarding Doppler shifts and the number of UEs.

To overcome the limitations of traditional monitoring methods, which are restricted to periodic inspections, this study proposes a real-time method for identifying metal corrosion damage patterns to monitor the condition of Q235 steel corrosion based on acoustic emission (AE). Firstly, AE technology was utilized to monitor the corrosion process of Q235 steel plates in simulated industrial marine environment in real-time. Wavelet packet energy spectrum coefficients, closely related to the damage mechanism, were extracted from the acquired signals. A feature matrix was then constructed using principal component analysis (PCA) to eliminate redundant information and enhance computational efficiency. The K-means clustering algorithm was then applied to classify the AE signals into three classifications: the signals of mode 1 correspond to bubble rupture, the signals of mode 2 to pit growth and expansion, and the signals of mode 3 to the detachment of corrosion products and oxide film rupture. A damage pattern recognition model based on a convolutional neural network (CNN) was developed, enabling the real-time recognition of other unknown AE signals generating during the corrosion process of Q235 steel, and it exhibited satisfactory performance in accurately identifying corrosion-related acoustic emission patterns.