A novel algorithm based on the Newton projection scheme [l] is proposed to improve the convergence behavior of circuit simulation. The computation experiment shows that the number of iterations to achieve convergence in Newton-Raphson method can be cut by as much as twenty nine-fold, and the convergence behavior of the Newton iteration is made much more robust using this algorithm, Moreover, the implementation of the proposed algorithm in conventtiional circuit simulators such as SPICE is very easily done. Both the theoretical background and actual implementation of the algorithm are discussed. The experimental circuit and simulation results are also shown.Newton iteration. This method can lead to a significant cut of the number of iterations and a robust convergence behavior for the Newton-Raphson method, The preliminary computation experime:nt on several circuit3 shows that the number of iteration is reduced by a factor of eight to twenty nine.In section II, the theoretical background for our algorithm, called the Newton Projection Method, or NPM, is presented. An implementation of the method in conventional circuit simulators is then illustrated in the subsequent section with SPICE as an example. The experimental circuit and computation results are shown and discussed in Section IV, Finally, the conclusions are drawn.

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Abstract. Image aesthetic analysis is a new direction of computer vision, whose purpose is to simulate the visual perception and aesthetic criterion of human being to assess aesthetic value of a given image. Nowadays, due to the popularization of smart phones with built-in cameras, functions like automatic image management and aesthetic guidance in mobile devices are valuable and in great demand. In order to remedy the gap between the large amount of computation and the hardware limitation of mobile devices, an image aesthetic classification and evaluation system using cloud computing is built in this paper. The time consuming parts such as feature extraction and machine learning algorithms are deployed on the virtual machine in the cloud server, while the simple part such as user interface is left for client. In addition, to make full use of the cloud server, a parallel-processing strategy of feature extraction is put to use in the system The result shows that our approach achieves a promising accuracy and is well correlated with the subjective aesthetics evaluation of human. And the system is more efficient and easier to be used in mobile devices with the help of cloud computing.