The overall performance of traditional network file system is affected by software overhead when using high-speed storage device. Therefore, a method of constructing a file system using SPDK (Storage Performance Development Kit) was proposed, and a prototype of a network file system RUFS (Remote Userspace File System) was realized on this basis. In this system, the directory tree structure of file system was simulated and the metadata of file system were managed by using key-value storage, and the file contents were stored by using SPDK. Besides, RDMA (Remote Direct Memory Access) technology was used to provide file system service to clients. Compared with NFS+ext4, on 4 KB random access, RUFS had the read and write bandwidth performance increased by 202.2% in read and 738.9% respectively, and had the average read and write latency decreased by 74.4% and 97.2% respectively; on 4 MB sequential access, RUFS had the read and write bandwidth performance increased by 153.1% and 44.0% respectively. RUFS had significant advantages over NFS+ext4 on most metadata operations, especially on the operation of folder creation, RUFS had the bandwidth performance increased by about 5 693.8%. File system service with lower latency and higher bandwidth can be provided by this system via making full use of the performance advantages of the high-speed network and high-speed storage device.

Aiming at the problem of poor detail and visibility in current color coding methods of single polarization Synthetic Aperture Radar (SAR), a color feature coding method was proposed. Firstly, texture features were extracted from a single-polarized SAR image. Secondly, each feature was quantized to 0 to 255. Then an RGB color was assigned to each gray level to generate a color feature map. Finally, the importance of features calculated by random forest was sorted; the pseudo-color graphs were generated by each three dimensional feature corresponding to the R, G, and B channels. Based on the presented color feature coding method, a new classification method was proposed. Firstly, the pseudo color map with the best geographical separability was selected according to the visual effect, and then segmented by the Statistical Region Merging (SRM) segmentation algorithm. Secondly, all the RGB pseudo color maps were used as the classification features, and a random forest was used as the classifier and obtain the preliminary results. At the end, a relative majority vote was made on the preliminary results and the final classification results were obtained. In the method verification, two sets of TerraSAR-X single-polarization SAR data were used. By comparing the corresponding grayscale image with HIS-based color coding method, the color image information entropy generated by the proposed color feature coding method was greatly improved, and the classification accuracy of each type of ground features for two data sets was greatly improved. It is demonstrated that the proposed algorithm preserves more details for more color information, and it is more conducive to visualization and terrain classification, which indicating the proposed color feature coding method is feasible.

Separability Index (SI) can be used to select effective classification features, but in the case of multi-dimensional features and good separability of geology, the use of separability index for feature selection can not effectively remove redundancy. Based on this, a method of feature selection and multi-layer Support Vector Machine (SVM) classification was proposed by using separability index and Sequential Backward Selection (SBS) algorithm. Firstly, the classification object and features were determined according to the SIs of all the ground objects under all the features, and then based on the classification accuracies of the objects, the SBS algorithm was used to select the features again. Secondly, the features of next ground objects were determined by the separability index of remaining objects and the SBS algorithm in turn. Finally, the multi-layer SVM was used for classification. The experimental results show that the classification accuracy of the proposed method is improved by 2% compared with the method of multi-layer SVM classification where features are selected only based on the SI, and the classification accuracy of all kinds of objects is higher than 86%, and the running time is half of the original method.

Web applications are under the threat from malicious host problem just as native applications. How to ensure the core algorithm or main business process's security of Web applications in browser-side has become a serious problem needed to be solved. For the problem of low effectiveness to resist dynamic analysis and cumulative attack in present JavaScript code protection methods, a JavaScript code Protection based on Temporal Diversity (TDJSP) method was proposed. In order to resist cumulative attack, the method firstly made the JavaScript program obtain the diverse ability in runtime by building program's diversity set and obfuscating its branch space. And then, it detected features of abnormal execution environments such as debuggers and emulations to improve the difficulty of dynamic analysis. The theoretical analyses and experimental results show that the method improves the ability of JavaScript program against the converse analysis. And the space growth rate is 3.1 (superior to JScrambler3) while the delay time is in millisecond level. Hence, the proposed method can protect Web applications effectively without much overhead.