Concerning the secondary attack problem of virus in cloud computing， data center and other virtual network-based environments， the virus propagation and immune mechanism under the background of dynamic platform defense was studied， and a heterogeneous backup based network virus defense method was proposed. Firstly， the process of secondary attack of redundant backup was analyzed， and the law of virus action was summarized. At the same time， combined with the idea of dynamic platform defense， the heterogeneous platform state node was introduced， and a Susceptible-Escaped-Infected-Removed-Heterogeneous-Susceptible （SEIRHS） virus propagation model was proposed. Secondly， the local stability at the equilibrium point of the model was proved by using the Routh-Hurwitz stability criterion， and the basic reproductive number was solved. Finally， the proposed model was compared with the traditional Susceptible-Infected-Removed （SIR） and Susceptible-Escaped-Infected-Removed （SEIR） models through simulation analysis， the stability of the model was verified， and the effect of virus propagation influencing factors on virus spread scale was discussed. The simulation results show that the proposed model can objectively reflect the propagation law of virus in the network， and effectively improve the network’s defense effect against the virus by reducing the node degree， increasing the Infected-Heterogeneous （I-H） state transition probability， and reducing the probability of being hidden by the virus during backup， etc.

Bilevel programming involves two optimization problems located at upper-level （leader） and lower-level （follower）. The constraint domain of the leader is determined by the follower implicitly， the leader objective dominates in a bilevel optimization procedure， and the follower objective must be optimized with respect of the follower variables. The hierarchical structure of the bilevel optimization problem causes large computational complexity. Especially， the frequent computations of the follower can accumulate a large amount of computational cost. In order to solve this kind of problem effectively， an evolutionary algorithm based on approximation technique was developed. Firstly， a multi-population co-evolution approach was applied， and the crossover and the mutation operators were used respectively to balance the exploitation and exploration capabilities of the algorithm. Secondly， based on the sensitivity analysis theory， an approximation evaluation method for new individuals was designed to reduce the computation frequency of the follower carried out by the algorithm. The demonstration results of the approximate effect of a numerical example show that most positions of the approximate offspring individuals and the exact offspring individual are mostly coincident. In addition， the results on 10 common examples show that the proposed algorithm can find better optimal solutions than the multi-valued mapping algorithm. CPU time comparison shows that the approximate technique improves the speed of finding the optimal solution effectively， thereby reducing the running time. Therefore， the effectiveness of the approximate technique adopted by the algorithm is demonstrated.

Max Weighted Queue (MWQ) control policy based on the theory of Lyapunov optimization is a cross-layer control policy that achieves queue stability and optimal delay performance. For the real-time and delay-sensitive demand in Device-to-Device (D2D) communication services, the MWQ algorithm, in which the Channel State Information (CSI) of PHY layer and the Queue State Information (QSI) of MAC layer are collectively considered, makes the maximum system throughput as the objective function and controls the power of D2D nodes dynamic. In this paper, a novel MWQ algorithm in the D2D communication was proposed. Compared to the algorithm with fixed power, the CSI-based algorithm and the QSI-based algorithm, the MWQ algorithm can decrease the average delay about 0.5 s when the average packets arrival rate exceeds 10 Mb/s and require less 26 dB power while having the same average delay. So the MWQ algorithm can achieve a good performance and have a reference to obtain low latency in D2D communication.

In order to improve image retrieval performance, this paper proposed a new image retrieval algorithm based on motif and color features. The color image edge gradient was detected, and by means of edge gradient image transform, a motif image was obtained. Adopting the gravity center of motif image as the datum point, the distances of all points were calculated to the datum point to get the motif center distance histogram. The all motifs of the motif image were projected in four different directions to get motif projective histogram. Color image was uniformly quantized into 64-color space from RGB space to obtain the color histogram. The above three histograms described image features for image retrieval. The experimental results show that the algorithm has high precision and recall.

The types of embedded device platform become more and more diversified. In order to make GIS systems developed run on several kinds of platforms, this paper put forward a cross-platform embedded GIS data model. This model isolated the data processing part, which was irrelevant to display, and based on which, it was further divided into higher layer interface model and lower layer data engine model. This model is able to not only meet user's requirement of secondary development but also cross several operating systems and integrate several heterogeneous GIS data resources. Finally, the platform-independence of the model is certified, through the implementation of a traffic navigation system.

Task assignment problem is a typical NP problem. Particle Swarm Optimization (PSO) algorithm was used to solve task assignment problem. The model of task assignment problem was formulated and the detailed solution for solving task assignment problem based on PSO algorithm was illuminated. To get better optimization results, an improved PSO algorithm named IPSO including variance mechanism and local updating mechanism was presented. Examples and simulation experiences demonstrate that the IPSO algorithm is effective in solving task assignment problem.