Windows下微机与单片机联机通信系统设计

本文介绍了在Windows 95下利用Delphi软件实现与 80 98单片机进行联机串行通信的原理和方法 ,经过硬、软件的设计 ,只需利用该联机通信系统就能在Windows下直接完成对80 98单片机的控制和操作 ,实现了对 80 98单片机的集中管理     

多媒体邮件系统中语音邮件的开发

介绍了一个自行研制的基于Ｎｏｖｅｌｌ网络的多媒体邮件系统的功能和构成，详细阐述了语音邮件的设计与实现方法。     

Simulation of extremes of diffusions

The authors consider the problem of simulating the times of events such as extremes and barrier crossings in diffusion processes. They develop a rejection sampler based on Shepp [Shepp, Journal of Applied Probability 1979; 16:423–427] for simulating an extreme of a Brownian motion and use it in a general recursive scheme for more complex simulations, including simultaneous simulation of the minimum and maximum and application to more general diffusions. They price exotic options that are difficult to price analytically: a knock‐out barrier option with a modified payoff function, a lookback option that includes discounting at the risk‐free interest rate, and a chooser option where the choice is made at the time of a barrier crossing. The Canadian Journal of Statistics 38: 738–755; 2010 © 2010 Statistical Society of Canada     

Contaminated normal modeling with application to microarray data analysis

A contaminated beta model $(1-\gamma) B(1,1) + \gamma B(\alpha,\beta)$ is often used to describe the distribution of $P$ ‐values arising from a microarray experiment. The authors propose and examine a different approach: namely, using a contaminated normal model $(1-\gamma) N(0,\sigma^2) + \gamma N(\mu,\sigma^2)$ to describe the distribution of $Z$ statistics or suitably transformed $T$ statistics. The authors then address whether a researcher who has $Z$ statistics should analyze them using the contaminated normal model or whether the $Z$ statistics should be converted to $P$ ‐values to be analyzed using the contaminated beta model. The authors also provide a decision‐theoretic perspective on the analysis of $Z$ statistics. The Canadian Journal of Statistics 38: 315–332; 2010 © 2010 Statistical Society of Canada     

An efficient computational approach for prior sensitivity analysis and cross‐validation

Prior sensitivity analysis and cross‐validation are important tools in Bayesian statistics. However, due to the computational expense of implementing existing methods, these techniques are rarely used. In this paper, the authors show how it is possible to use sequential Monte Carlo methods to create an efficient and automated algorithm to perform these tasks. They apply the algorithm to the computation of regularization path plots and to assess the sensitivity of the tuning parameter in g‐prior model selection. They then demonstrate the algorithm in a cross‐validation context and use it to select the shrinkage parameter in Bayesian regression. The Canadian Journal of Statistics 38:47–64; 2010 © 2010 Statistical Society of Canada     

基于DSP的JPEG图像压缩算法的研究

本文讨论了JPEG2000的相关核心算法,它是基于EBCOT算法,使用DWT,采用两层编码策略,能够获得较好的压缩率。     

Model‐based clustering of longitudinal data

A new family of mixture models for the model‐based clustering of longitudinal data is introduced. The covariance structures of eight members of this new family of models are given and the associated maximum likelihood estimates for the parameters are derived via expectation–maximization (EM) algorithms. The Bayesian information criterion is used for model selection and a convergence criterion based on the Aitken acceleration is used to determine the convergence of these EM algorithms. This new family of models is applied to yeast sporulation time course data, where the models give good clustering performance. Further constraints are then imposed on the decomposition to allow a deeper investigation of the correlation structure of the yeast data. These constraints greatly extend this new family of models, with the addition of many parsimonious models. The Canadian Journal of Statistics 38:153–168; 2010 © 2010 Statistical Society of Canada     

Small area estimation of poverty indicators

The authors propose to estimate nonlinear small area population parameters by using the empirical Bayes (best) method, based on a nested error model. They focus on poverty indicators as particular nonlinear parameters of interest, but the proposed methodology is applicable to general nonlinear parameters. They use a parametric bootstrap method to estimate the mean squared error of the empirical best estimators. They also study small sample properties of these estimators by model‐based and design‐based simulation studies. Results show large reductions in mean squared error relative to direct area‐specific estimators and other estimators obtained by “simulated” censuses. The authors also apply the proposed method to estimate poverty incidences and poverty gaps in Spanish provinces by gender with mean squared errors estimated by the mentioned parametric bootstrap method. For the Spanish data, results show a significant reduction in coefficient of variation of the proposed empirical best estimators over direct estimators for practically all domains. The Canadian Journal of Statistics 38: 369–385; 2010 © 2010 Statistical Society of Canada     

State‐space model for proxy‐based millennial reconstruction

It is important to study historical temperature time series prior to the industrial revolution so that one can view the current global warming trend from a long‐term historical perspective. Because there are no instrumental records of such historical temperature data, climatologists have been interested in reconstructing historical temperatures using various proxy time series. In this paper, the authors examine a state‐space model approach for historical temperature reconstruction which not only makes use of the proxy data but also information on external forcings. A challenge in the implementation of this approach is the estimation of the parameters in the state‐space model. The authors developed two maximum likelihood methods for parameter estimation and studied the efficiency and asymptotic properties of the associated estimators through a combination of theoretical and numerical investigations. The Canadian Journal of Statistics 38: 488–505; 2010 © 2010 Crown in the right of Canada