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A novel deterministic SEIS model for the transmission of email viruses in growing communication networks is formulated. Interestingly, the model is different from classical SEIS models not only in the form, but also in the dynamical features. We study the equilibria and their stability and analyse the bifurcation dynamics of the model. In particular, we find that the virus-free equilibrium is locally asymptotically stable for any parameter values, which may attribute to the absence of the basic reproduction number. It is shown that the model undergoes a saddle-node bifurcation and admits the bistable phenomenon. Moreover, on the basis of the Lyapunov function, the domains of attraction of equilibria are estimated by solving an LMI optimization problem. Based on the above theoretical results, some effective strategies are also provided to control the propagation of the email viruses. Additionally, our results are confirmed by numerical simulations.

This paper proposed a radial basis function neural network optimization algorithm with a hybrid adaptive mutation particle swarm. During the optimization of RBF neural networks, the HAMPSO method is adopted to train the network structure and applied to solve the problems of the target localization. The HAMPSO algorithm is a dynamically adaptive optimization approach using uniform distribution mutation and Gaussian distribution mutation to escape local optima. We propose a HAMPSO method that can expedite convergence toward the global optimum during the iterations. In order to verify that the proposed HAMPSO-RBF approach has effect, comparisons with the RBF, genetic algorithm based RBF and PSO-based RBF approach are made. The computational results proved that the proposed HAMPSO-RBF approach exhibits much better and faster convergence performance in the training process as well as better prediction ability in the validation process than the results of other three approaches.

In the industrial CT detection systems, because of the restrictions of noises such as scattering noise, the traditional methods for CT reconstruction have high noise and low resolution, and cannot detect smaller flaws. So this paper has proposed using the algorithm of characteristic reconstruction to reconstruct the characteristic of object. About this algorithm, relying on the inner structure character of detected object, according to the character of wavelet function before and after RADON transform, the characteristic part of the image is reconstructed by prefiltering the projected data. Then some experiment show that the method can protrude the inner small flaw of detected object with the less sacrifice of the resolution of image background.

Computer users’ reactions to the outbreak of Internet worm directly determine the defense capability of the computer and play an important role in the spread of worm. In this paper, in order to characterize the impacts of adaptive user protection behaviors, an improved SIS model is proposed to describe the Internet worm propagation. The results of theoretical analysis indicate that the protective campaigns of users can indeed reduce the worm’s reproduction number to values less than one. But it may not be sufficient to eradicate the worm. In certain condition, a backward bifurcation leading to bistability can occur. These are new findings in the worm propagation model that bring new challenges to control the spread of the worm and further demonstrate the importance of user behaviors in controlling the worm propagation. Corresponding to the analysis results, defense and control strategies are provided.

A method that can fast, real-time get the projection matrix in ART reconstruction is proposed based on helical cone-beam CT trajectory in this paper. For a given ray this method can rapidly acquire the indices and corresponding lengths of the voxels passed through by this ray through sorting the intersections between the ray and mesh planes. Meanwhile we only store the projection matrix elements required for a given ray and release them after using them. By doing so the computation time and the storage space are significantly reduced. The experimental results show that this method can heighten the speed and better suppress noise.

Target localization based on time difference of arrival (TDOA) measurements has important applications in sonar, radar and sensor networks. This paper simply introduced the target localization principle of moving emitter and the position location algorithm. Further more presented an improved particle swarm optimization with stochastic inertia weight and adaptive mutation, and adopts it to solve the target localization problem according to the batch of continuous TDOA measurements. The experimental results show that the new algorithm has higher localization accuracy, better algorithm stability and faster convergence rate.

Ordered-subset simultaneous algebraic reconstruction technique (OS-SART) was studied by Ge Wang and Ming Jiang in 2004. It accelerate the convergence of SART, but it has some disadvantages, such as increasing the number of subsets accelerates iterative convergence, but there is a point beyond which image quality degrades due to a lack of statistical information within subset. In this paper, a new method of subset partition based on statistical test is proposed as an improved OS- SART (IOS-SART). IOS-SART can automatically adjust the number of the subsets for each iteration according to the statistical information content within subset demanded by user. Numerical simulation and application to practical data demonstrate that this algorithm converge faster and can provide high quality reconstructed images after a small number of iterations.