Sparse-view projection can reduce the scan does and scan time of Cone-Beam Computed Tomography （CBCT） effectively but brings a lot of streak artifacts to the reconstructed images. Sinogram inpainting can generate projection data for missing angles and improve the quality of reconstructed images. Based on the above， a Residual Encoder-Decoder Generative Adversarial Network （RED-GAN） was proposed for sinogram inpainting to reconstruct sparse-view CBCT images. In this network， the U-Net generator in Pix2pixGAN （Pix2pix Generative Adversarial Network） was replaced with the Residual Encoder-Decoder （RED） module. In addition， the conditional discriminator based on PatchGAN （Patch Generative Adversarial Network） was used to distinguish between the repaired sinograms from the real sinograms， thereby further improving the network performance. After the network training using real CBCT projection data， the proposed network was tested under 1/2， 1/3 and 1/4 sparse-view sampling conditions， and compared with linear interpolation method， Residual Encoder-Decoder Convolutional Neural Network （RED-CNN） and Pix2pixGAN. Experimental results indicate that the sinogram inpainting results of RED-GAN are better than those of the comparison methods under all the three conditions. Under the 1/4 sparse-view sampling condition， the proposed network has the most obvious advantages. In the sinogram domain， the proposed network has the Root Mean Square Error （RMSE） decreased by 7.2%， Peak Signal-to-Noise Ratio （PSNR） increased by 1.5% and Structural Similarity （SSIM） increased by 1.4%； in the reconstructed image domain， the proposed network has the RMSE decreased by 5.4%， PSNR increased by 1.6% and SSIM increased by 1.0%. It can be seen that RED-GAN is suitable for high-quality CBCT reconstruction and has potential application value in the field of fast low-dose CBCT scanning.

In order to deal with the channel fading in Underwater Wireless Sensor Networks (UWSN) changing randomly in time-space-frequency domain, underwater cooperative communication model with relays was proposed in this paper to improve reliability and obtain diversity gain of the communication system. Based on the new model, a relay selection algorithm for UWSN was proposed. The new relay selection algorithm used new evaluation criteria to select the best relay node by considering two indicators: channel gain and long delay. With the selected relay node, source node and relay nodes could adjust their sending power by the power allocation algorithm which was based on the principle of minimizing the bit error rate. In a typical scenario, by comparing with the traditional relay selecting algorithm and equal power allocation algorithm, the new algorithm reduces the delay by 16.7% and lowers bit error rate by 1.81dB.