Improving the image quality of IntraVascular Optical Coherence Tomography （IVOCT） through guidewire artifact removal can assist physicians in diagnosing cardiovascular diseases more accurately， which reduces the probabilities of misdiagnosis and missed diagnosis. Aiming at the difficulties of complex structure information and a large proportion of artifact areas in IVOCT images， a Structure-Enhanced Transformer Network （SETN） using Generative Adversarial Network （GAN） architecture was proposed for guidewire artifact removal of IVOCT images. Firstly， based on the ORiginal Image （ORI） backbone generation network for extracting texture features， the generator of GAN was combined with RTV （Relative Total Variation） image enhanced generation network in parallel to obtain image structure information. Next， during the artifact area reconstruction of ORI/RTV image， Transformer encoders focusing on the temporal/spatial domain information respectively were introduced to capture the contextual information and the correlation between texture/structure features of IVOCT image sequence. Finally， the structural feature fusion module was used to integrate the structural features of different levels into the decoding stage of the ORI backbone generation network， so that the generator was cooperated with the discriminator for completing the image reconstruction of the guidewire artifact area. Experimental results show that the guidewire artifact removal results of SETN are excellent in both texture and structure reconstruction. Besides， the improvement of IVOCT image quality after guidewire artifact removal is positive for both vulnerable plaque segmentation and lumen contour extraction tasks of IVOCT image.

Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections， which has complex spatio-temporal correlation. For the lack of correlation analysis on traffic flow data， capturing small changes but ignoring long-term time characteristics in traditional spatio-temporal residual models， a short-term traffic flow prediction model for urban road network based on improved spatio-temporal residual Convolutional Neural Network （CNN） was proposed. In the proposed model， the original traffic flow data was transformed into traffic grid data， and Pearson Correlation Coefficient （PCC） was used to analyze the correlation of traffic grid data， so as to determine the periodic series and adjacent series with high correlation. At the same time， the periodic series model and the adjacent series model were established， and Long Short-Term Memory （LSTM） network was introduced as the hybrid model to extract the time characteristics and capture the long-term time characteristics of the two series. Experimental results on Chengdu taxi dataset show that the proposed model can predict traffic flow better than benchmark models of LSTM， CNN and the traditional residual model. When the evaluation index is Root Mean Square Error （RMSE）， the average prediction accuracy of traffic road network in the test set is improved by 25.6%， 13.3% and 3.2% respectively.

The current applications of network coding in single-hop wireless network retransmission are based Single Sender Multiple Receiver (SSMR) scenes. Therefore, this paper proposed a retransmission mechanism named NCWRM (Network Coding Wireless Retransmission Mechanism) which can be used in multiple sender multiple receiver networks. Each node in the network can be either a sender or a receiver. The node can broadcast a coded packet which is combined by multiple lost packets in the second retransmission after packet failed in transmission and the first retransmission. Multiple recipients can simultaneously get their lost packets by decoding the coded packet, which can effectively improve the efficiency of retransmission. Theoretical analysis and simulation results show that NCWRM algorithm can significantly improve system saturation throughput, while reducing overhead and packet loss rate.