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Abstract: Motion is a common phenomenon in the real world so the consistence of virtual and real motion is one of hot focuses in augmented reality system. This paper mainly analyzes virtual and real motion blur consistence in augmented reality system, and mainly do improvement in two aspects, one is how to get the motion blur parameters, the other is how to render motion blur. From the available literature it can be seen that, most achieved methods to get motion blur parameters are based on machine vision. But this paper first acquires the motion trajectory by magnetic tracking, and then gets motion blur parameters. Considering the available literature on motion blur rendering methods, they mainly contain convolution and accumulator caching and so on. Due to rotation translation compound motion blur, this paper does rendering in advance based on improved mathematical model, and then render virtual object through looking up table in augmented reality system. Experimental results show that the new virtual and real motion blur consistence render algorithm has low time complexity and achieve the desired effect in simulation environment and real scenes. [Copyright &y& Elsevier]

Abstract: The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm. If the ambient excitation can be modeled as a nonstationary white noise in the form of a product model, then the nonstationary cross randomdec signatures of structural response evaluated at any fixed time instant are shown theoretically to be proportional to the nonstationary cross-correlation functions, which is of the same form as the free-vibration decay or impulse response of the original system. The practical problem of insufficient data samples available for evaluating nonstationary randomdec signatures can be approximately resolved by first extracting the amplitude-modulating function from the response and then transforming the nonstationary responses into stationary ones. However, the error involved in the approximate free-decay response would generally lead to a distortion in the modal identification. In the present paper, we also further propose that, if the ambient excitation can be modeled as a zero-mean nonstationary process, without any additional treatment of transforming the original nonstationary responses, the nonstationary cross randomdec signatures of structural response are shown in the same mathematical form as that of free vibration of a structure, from which modal parameters of the original system can thus be identified. Numerical simulations, including one example of using the practical excitation data, confirm the validity of the proposed method for identification of modal parameters from nonstationary ambient response data only. [Copyright &y& Elsevier]

Abstract: In this paper, we first propose two learning automata based decentralized dynamic guard channel algorithms for cellular mobile networks. These algorithms use learning automata to adjust the number of guard channels to be assigned to cells of network. Then, we introduce a new model for nonstationary environments under which the proposed algorithms work and study their steady state behavior when they use learning algorithm. It is also shown that a learning automaton operating under the proposed nonstationary environment equalizes its penalty strengths. Computer simulations have been conducted to show the effectiveness of the proposed algorithms. The simulation results show that the performances of the proposed algorithms are close to the performance of guard channel algorithm that knows all the traffic parameters. [Copyright &y& Elsevier]