The X-ray phase contrast Computed Tomography (CT) can produce high contrast images by the X-ray phase information alteration, which comes forth after the X-ray passes through the sample, and it is highly favorable to the imaging of light elements and can get much higher contrast resolution than the absorption contrast CT. Grating-based Differential Phase Contrast CT (DPC-CT) shows great clinical prospects due to the possibility of using a conventional X-ray source, but the X-ray radiation dose issue limits its clinical applications. Concerning such inadequacies, an image reconstruction method for DPC-CT named DD-L1 was proposed. This algorithm combined Compressive Sensing (CS) theory with CT iterative reconstruction technique and introduced distance driven forward and backward projection computation strategy. The experimental results show that DD-L1 algorithm can generate tomographic images of higher quality even when the projection data is incomplete.

To solve the problem of poor portability and low load capacity in network management system based on C/S model, a kind of Ethernet Passive Optical Network (EPON) network performance management system based on Web was designed and implemented. The system used SNMP4J library to develop underlying application, adopted Struts+Spring+Hibernate (SSH) framework based on Model-View-Controller (MVC) model to realize separation of user interface, application business logic and data access logic, and realized the acquisition, statistical analysis and display of the system related performance parameters. The test results indicate that performance alarm reporting delay and single operation response time of the system are less than that in the "China Telecom mobile service network management system norms—total volume (v1.1)" standard.

By analyzing the deficiency of the existing network virus models, referring to the reality and considering the infectious disease models in biology, an improved Susceptible-Infected-Removed (SIR) computer virus propagation model with pre-immune measures was put forward. The authors considered the varying number of nodes and analyzed its impact on the spread of the virus in the network. In addition, several related dynamic properties were analyzed. The numerical simulation results demonstrate that improving pre-immune rate and controlling the nodes flow in the network can effectively constrain virus prevalence. And this model has inspiration to predict and prevent from computer virus propagation.