The virtual try-on technologies based on image synthesis mask strategy can better retain details of the clothing when the warped clothing is fused with the human body. However， because the position and structure of the human body and the clothing are difficult to align during the try-on process， the try-on result is likely to produce severe occlusion， affecting visual effect. In order to solve the occlusion in the try-on process， a U-Net based generator was proposed. In the generator， a cascaded spatial attention module and a channel attention module were added to the U-Net decoder， thereby achieving the cross-domain fusion between local features of warped clothes and global features of the human body. Formally， first， by predicting the Thin Plate Spline （TPS） conversion using the convolutional network， the clothing was distorted according to the target human body pose. Then， the dressed-on person representation information and the warped clothing were input into the proposed generator， and the mask image of the corresponding clothing area was obtained to render the intermediate result. Finally， the strategy of mask synthesis was used to synthesize the warped clothing with the intermediate result through mask processing to obtain the final try-on result. Experimental results show that the proposed method can not only reduce occlusion， but also enhance image details. Compared with Characteristic-Preserving Virtual Try-On Network （CP-VTON） method， the proposed method has the generated image with the average Peak Signal-to-Noise Ratio （PSNR） increased by 10.47%， the average Fréchet Inception Distance （FID） decreased by 47.28%， and the average Structural SIMilarity （SSIM） increased by 4.16%.

For the differences between Virtex-5 RocketIOTM GTP and Virtex-6 RocketIOTM GTX adjustments should be made on pre-emphasis/de-emphasis values and reception equalization values,the receiving terminal voltage and differential voltage transmission values, so as to adapt to the data communication between GTP and GTX. By using ChipsCope Pro_IBERT to measure the actual communication parameters to set GTP/GTX transceiver side, this paper defined the data frame structure through custom communication protocol, and designed a new RocketIO data transceiver interface controller. By analyzing the phenomenon of byte misalignment caused by some uncertainty such as high frequency instability, and clock drift, this paper added a data misalignment correction module in custom communication protocol, thus greatly reducing the error rate of data transmission. The experimental results show that high speed serial communication between Virtex-5 RocketIOTM GTP and Virtex-6 RocketIOTM GTX can be implemented, and the data transceiver interface controller is of data transmission stability, low bit error rata and good generality.

In order to solve the distortion of wool geometric dimension, resulting from image binarization with weak contour, an automatic binarization algorithm for Charge-Coupled Device （CCD） wool image was proposed with reference to a ramp-width-reduction approach based on intensity and gradient indices, using a classical global threshold method and a local one. In that algorithm, edge-pixel-seeking step was added and gray-adjusting factor was improved, with sobel operator and ramp edge model introduced, to increase processing efficiency and avoid human intervention. Besides, every sub image was processed by the mixed global and local threshold based on the analysis of Otsus and Bernsens methods to intensify edge details and decrease distortion. Compared with the traditional ways, the experimental results show that the new algorithm has good performance in automatic binarization with weak contour.

With the rapid growth of the Web text data, mining and analyzing these text data, especially the online review data posted by the users, can greatly help better understand the users' consuming habits and public opinions, and plays an important role in decision-making for the enterprises and the government. This survey first introduces the motivation, research problems and goals of sentiment analysis, and presents some basic technologies used in sentiment analysis. It then describes one of the major tasks in sentiment analysis, subjective sentence detection, by reviewing and analyzing some recent work in this area. Next, it focuses on another important task in sentiment analysis, opinion classification, and discusses two leading feature extraction techniques for opinion classification, sentimental word based and frequent pattern based methods. Furthermore, it also introduces several other relevant sentiment analysis problems. Finally, the paper summarizes the current status, remaining challenges, and future directions in the field of sentiment analysis.