The real-time monitoring data generated by high-speed trains during running are usually processed by variable-length coding compression technology, which is convenient for transmission and storage. However, this method will complicate the internal structure of the compressed data, so that the corresponding data decompression process must follow the composition order of the compressed data, which is inefficient. In order to improve the decompression efficiency of high-speed train monitoring data, a parallel decompression algorithm for high-speed train monitoring data was proposed with the help of the speculation technology. Firstly, the structural characteristics of high-speed train monitoring data were studied, and the internal dependence that affects data division was analyzed. Secondly, the speculation technology was used to clean up internal dependence, and then, the data were divided into different parts tentatively. Thirdly, the division results were decompressed in a distributed computing environment in parallel. Finally, the parallel decompression results were combined together. Through this way, the decompression efficiency of high-speed train monitoring data was improved. Experimental results showed that on the computing cluster composed of 7 computing nodes, compared with the serial algorithm, the speedup of the proposed speculative parallel algorithm was about 3, showing a good performance of this algorithm. It can be seen that this algorithm can improve the monitoring data decompression efficiency significantly.

In order to solve the problem of longitudinal projection distortion in non-overlapping regions of images caused by the quasi-homography warp algorithm for image stitching, an image stitching algorithm based on nonlinear constraint was proposed. Firstly, the nonlinear constraint was used to smoothly transit the image regions around the dividing line. Then, the linear equation of quasi-homography warp was replaced by a parabolic equation. Finally, the mesh-based method was used to improve the speed of image texture mapping and the method based on optimal stitching line was used to fuse the images. For images of 1 200 pixel×1 600 pixel, the time consumption range of texture mapping by the proposed algorithm is 4 s to 7 s, and the proposed algorithm has the average deviation degree of diagonal structure is 11 to 31. Compared with the quasi-homography warp algorithm for image stitching, the proposed algorithm has the time consumption of texture mapping reduced by 55% to 67%, and the average deviation degree of diagonal structure reduced by 36% to 62%. It can be seen that the proposed algorithm not only corrects the oblique diagonal structure, but also improves the efficiency of image stitching. Experimental results show that the proposed algorithm has better results in improving the visual effect of stitched images.

Intrusion detection model based on machine learning plays a vital role in the security protection of network environment. Aiming at the problem that the existing network intrusion detection model cannot fully learn the data features of network intrusion, the deep learning theory was applied to intrusion detection, and a deep network model with automatic feature extraction function was proposed. In this model, the dilated convolution was used to increase the receptive field of information and extract high-level features from it, the Gated Recurrent Unit (GRU) model was used to extract long-term dependencies between retained features, then the Deep Neural Network (DNN) was used to fully learn the data features. Compared with the classical machine learning classifier, this model has a higher detection rate. Experiments conducted on the famous KDD CUP99, NSL-KDD and UNSW-NB15 datasets show that the model has the performance better than other classifiers. Specifically, the model has the accuracy of 99.78% on KDD CUP99 dataset, the accuracy of 99.53% on NSL-KDD dataset, and the accuracy of 93.12% on UNSW-NB15 dataset.

Aiming at the characteristics of large passenger flow, complex and variable indoor environment in airport terminals, an indoor positioning algorithm based on Spectral Regression Kernel Discriminant Analysis (SRKDA) was proposed. In the offline phase, the Received Signal Strength (RSS) data of known location was collected, and the non-linear features of the Original Location Fingerprint (OLF) were extracted by SRKDA algorithm to generate a new feature fingerprint database. In the online phase, SRKDA was firstly used to process the RSS data of the point to be positioned, and then Weighted K-Nearest Neighbor (W KNN) algorithm was used to estimate the position. In positioning simulation experiments, the Cumulative Distribution Function (CDF) and positioning accuracies of the proposed algorithm under 1.5 m positioning accuracy are 91.2% and 88.25% respectively in two different localization scenarios, which are 16.7 percentage points and 18.64 percentage points higher than those of the Kernel Principal Component Analysis (KPCA)+W KNN model, 3.5 percentage points and and 9.07 percentage points higher than those of the KDA+W KNN model. In the case of a large number of offline samples (more than 1100), the data processing time of the proposed algorithm is much shorter than that of KPCA and KDA. The experimental results show that, the proposed algorithm can effectively improve the indoor positioning accuracy, save data processing time and enhance the positioning efficiency.

The construction of measurement matrix in compressive sensing varies widely and is on the development constantly. In order to sort out the research results and acquire the development trend of measurement matrix, the process of measurement matrix construction was introduced systematically. Firstly, compared with the traditional signal acquisition theory, the advantages of high resource utilization and small storage space were expounded. Secondly, on the basis of the framework of compressive sensing and focusing on four aspects:the construction principle, the generation method, the structure design of measurement matrix and the optimal method, the construction of measurement matrix in compressive sensing was summarized, and advantages of different principles, generations and structures were introduced in detail. Finally, based on the research results, the development directions of measurement matrix were prospected.

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.

For the localization problem in urban areas, where Global Positioning System (GPS) cannot provide the accurate location as its signal can be easily blocked by the high-rise buildings, a visual localization method based on vertical building facades and 2D bulding boundary map was proposed. Firstly, the vertical line features across two views, which are captured with an onboard camera, were matched into pairs. Then, the vertical building facades were reconstructed using the matched vertical line pairs. Finally, a visual localization method, which utilized the reconstructed vertical building facades and 2D building boundary map, was designed under the RANSAC (RANdom Sample Consensus) framework. The proposed localization method can work in real complex urban scenes. The experimental results show that the average localization error is around 3.6m, which can effectively improve the accuracy and robustness of self-localization of mobile robots in urban environments.

Because the traditional software development method does not provide the mechanism that separates the trace capture concern and other business concerns, the implementation codes of all the concerns tangle seriously. To solve this problem, we applied Aspect-Oriented Programming (AOP) in the research of software trace capture and studied a technique of trace capture that can wave the monitor requirement into the system without changing the source code. This technique can improve the modularity of software effectively. Base on it, we implemented a monitor tool named Software Runtime Tracer (SRT), which can be used to analyze system manner and find program bugs and enhance the trustworthiness of software as well.