Estimation of parameters of the Makeham distribution using the least squares method

The Makeham distribution has been used to describe human mortality and establish actuarial tables. The hazard function is defined by μ(t)=A+BC^t$\mu (t)=A+B{C}^t$, we use the least squares type estimation to estimate the parameters of Makeham distribution in this paper. Seven cases are considered, when A, B, C are known or unknown, respectively. Also, we evaluated the mean square errors of these estimators.     

Numerical approximation of solution of nonhomogeneous backward heat conduction problem in bounded region

In this paper we consider a numerical approximation of solution of nonhomogeneous backward heat conduction problem (BHCP) in bounded region based on Tikhonov regularization method. Error estimate at t=0$t=0$ for this method is provided. According to the error estimate, a selection of regularization parameter is given. Meanwhile, a numerical implementation is described and the numerical results show that our algorithm is effective.     

Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems

An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor.     

Scalable search-based image annotation

With the popularity of digital cameras, more and more people have accumulated considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation algorithm that is analogous to information retrieval. First, content-based image retrieval technology is used to retrieve a set of visually similar images from a large-scale Web image set. Second, a text-based keyword search technique is used to obtain a ranked list of candidate annotations for each retrieved image. Third, a fusion algorithm is used to combine the ranked lists into a final candidate annotation list. Finally, the candidate annotations are re-ranked using Random Walk with Restarts and only the top ones are reserved as the final annotations. The application of both efficient search techniques and Web-scale image set guarantees the scalability of the proposed algorithm. Moreover, we provide an annotation rejection scheme to point out the images that our annotation system cannot handle well. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.     

Perceptual image preview

Image preview is a convenient way to browse large or multiple images on small displays. However, current signal-level image resampling algorithms may remove many features of interest in the preview image. In this paper, we propose perceptual image preview which retains more perceptual features such that users can inspect features of interest by viewing the preview image only and without zooming in. This technology has two components, structure enhancement and perceptual feature visualization. Structure enhancement enhances the image structure while suppressing subtle details using a gradient modulation method, thus making the succedent perceptual features more apparent. For perceptual feature visualization, features of interest detected in the picture is visualized on the structure enhanced preview image. We demonstrate with two examples of most commonly used image quality features, image blur and noise. The effectiveness of the proposed method is validated by experimental results.     

基于控制点影像库的控制点自动选取初探

地面控制点的快速选取与定位是几何纠正的重要环节之一,鉴于控制点快速、自动选点的迫切需求,国内外已经开始考虑局部性和全国性控制点影像库的建立.本文首先介绍了控制点影像库设计的原则,然后基于影像库进行控制点的自动选取并给出了匹配流程,最后通过实验验证其有效性.     

P2P网络中的社区结构发现方法

针对P2P网络的分布式特征,提出了一种可扩展的分布式社区发现方法PDC,采用节点Power值度量社区结构,并选择中心节点,其中包括新的社区度量方法和新的中心节点选择算法。仿真实验结果证明,与FDC和CDC方法相比,和其他算法相比PDC的社区发现效果率比FDC和CDC至少提高了510%,但是产生的消息数量却很少比FDC少一个数量级,具有良好的可扩展性。     

基于集员滤波的双归一化数据重用盲均衡算法

数据重用可以有效地加快盲均衡算法的收敛速度,但会带来噪声放大的问题。针对这种情况,该文将数据重用方法应用到基于实虚部分开处理的改进常模盲均衡算法中,采用集员滤波克服应用数据重用时引起的噪声放大问题,推导出一种收敛速度较快的盲均衡算法。实验仿真表明,该算法具有较快的收敛速度,适用于短时信号处理。     

基于OGRE和ODE的碰撞检测在巷道漫游系统中的研究

为了提高交互漫游系统中动态人物与静态场景对象之间的碰撞检测性能,提出了将动态人物采用包围盒的方法,与地面、巷道等静态物体采用三角网格面的方法相结合的思想,并将其应用于OGRE和ODE环境的巷道漫游系统中。结果表明,此方法提升了碰撞检测性能,满足了虚拟现实中实时性的要求。