Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable performance in detection and classification tasks. Nevertheless, their feature extraction cannot consider both local and global information, so the detection and classification performance can be further improved. In addition, more and more deep learning networks are designed as more and more complex, and the amount of computation and storage space required is also significantly increased. This paper proposes a combination of CNN and transformer, and designs a local feature enhancement module and global context modeling module to enhance the cascade network. While the local feature enhancement module increases the range of feature extraction, the global context modeling is used to capture the feature maps' global information. To decrease the model complexity, a shared sublayer is designed to realize the sharing of weight parameters between the adjacent convolutional layers or cross convolutional layers, thereby reducing the number of convolutional weight parameters. Moreover, to effectively improve the detection performance of neural networks without increasing network parameters, the optimal transport assignment approach is proposed to resolve the problem of label assignment. The classification loss and regression loss are the summations of the cost between the demander and supplier. The experiment results demonstrate that the proposed Combination of CNN and Transformer with Shared Sublayer (CCTSS) performs better than the state-of-the-art methods in various datasets and applications.

In the advanced digital terrestrial/television multimedia broadcasting (DTMB-A) standard, a preamble based on distance detection (PBDD) is adopted for robust synchronization and signalling transmission. However, traditional signalling detection method will completely fail to work under severe frequency selective channels with ultra-long delay spread 0dB echoes. In this paper, a novel transmission parameter signalling detection method is proposed for the preamble in DTMB-A. Compared with the conventional signalling detection method, the proposed scheme works much better when the maximum channel delay is close to the length of the guard interval (GI). Both theoretical analyses and simulation results demonstrate that the proposed algorithm significantly improves the accuracy and robustness of detecting the transmitted signalling.