Pasting the Radio Frequency Identification (RFID) tag on the shell of computer so that to trace the location of computer in real time has been the most frequently used method for terminal location management. However, RFID tag would lose the direct control of the computer when it is out of the authorized area. Therefore, the terminal location management system based on the firmware and RFID was proposed. First of all, the authorized area was allocated by RFID radio signal. The computer was allowed to boot only if the firmware received the authorized signal of RFID on the boot stage via the interaction between the firmware and RFID tag. Secondly, the computer could function normally only if it received the signal of RFID when operation system is running. At last, the software Agent of location management would be protected by the firmware to prevent it from being altered and deleted. The scenario of the computer out of the RFID signal coverage would be caught by the software Agent of the terminal; and the terminal would then be locked and data would be destroyed. The terminal location management system prototype was deployed in the office area to control almost thirty computers so that they were used normally in authorized areas and locked immediately once out of authorized areas.

Focusing on the issues that the Principal Component Analysis (PCA) related dimensionality reduction methods are limited to deal with nonlinear distributed datasets and have poor robustness, a new dimensionality reduction method named Robust Cosine-Euclidean Metric (RCEM) was proposed. Considering that Cosine Metric (CM) can handle the outliers efficiently and Euclidean distance can well maintain variance information of samples, the CM was used to describe the geometric characteristics of neighborhood and the Euclidean distance was used to depict the global distribution of dataset. This new proposal method retained local information of dataset while achieving the unification of local and global structure, thus it increased the robustness of local dimensionality reduction algorithm and helped avoiding the problem of small sample size cases. The experimental results on Corel-1000 dataset showed that the retrieval average precision of RCEM was 5.61% higher than that of Angle Optimization Global Embedding (AOGE), and the retrieval time of RCEM was decreased by 42% compared with dimensionality reduction free method. The results indicate that RCEM can improve the efficiency of image retrieval without decreasing the retrieval accuracy, and it can be effectively applied to Content-Based Image Retrieval (CBIR).

To extract features from Deep Packet Inspection (DPI) data and perform mobile application recommendation, using the DPI data collected from Internet Service Provider (ISP) in Jiangsu Telecom, the access history data of active users defined by the communications operator was processed by matrix factorization recommendation (including Singular Value Decomposition (SVD) and Non-negtive Matrix Factorization (NMF)), SVD recommendation and factorization machine recommendation algorithms for mobile phone application recommendation. The results show that factorization machine algorithm achieves better performance, it means that factorization machine algorithm can better describe the latent connection in the user-item relationship.

The available tools for detecting network information can hardly meet the demands of acquiring the completeness and precision of network information for the researchers.The information fusion technology was applied to collect the network information using several detecting tools.The information from different detecting tools was fused in different layers.In data layer,the fuzzy logical statistic method was adopted to identify system type and network device,and in logic layer,the most credible information was obtained with the support of system knowledge database.