Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is considered an alternative to conventional orthogonal frequency division multiplexing (OFDM) to meet the various requirements proposed by future communication networks. Among the different perspectives on the merits of FBMC-OQAM and OFDM, a straightforward metric is the bit error probability (BEP). This paper presents a general analytical framework for BEP evaluation that is applicable to FBMC-OQAM and OFDM systems in both Rayleigh and Rician multipath fading channels. Explicit BEP expressions are derived for Gray-coded pulse amplitude modulation (PAM) and square quadrature amplitude modulation (QAM) signals with arbitrary constellation sizes. The theoretical analysis results show excellent agreement with the numerical simulation results in different channel scenarios.

Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.