Parallelizing particle filters with butterfly interactions

The bootstrap particle filter (BPF) is the cornerstone of many algorithms used for solving generally intractable inference problems with hidden Markov models. The long-term stability of the BPF arises from particle interactions that typically make parallel implementations of the BPF nontrivial. We propose a method whereby particle interaction is done in several stages. With the proposed method, full interaction can be accomplished even if we allow only pairwise communications between processing elements at each stage. We show that our method preserves the consistency and the long-term stability of the BPF, although our analysis suggests that the constraints on the stagewise interactions introduce errors leading to a lower convergence rate than standard Monte Carlo. The proposed method also suggests a new, more flexible, adaptive resampling scheme, which, according to our numerical experiments, is the method of choice, displaying a notable gain in efficiency in certain parallel computing scenarios.     

电生理诊断中实时数字滤波器的设计与实现

根据视觉电生理信号的特点，采用计算机软件方法实现了一种消除人体５０Ｈｚ工频干扰的实时带阻滤波器．     

振动对微波振荡器电性能的影响初探

着重研究微波振荡器在机械振动干扰下发生结构谐振时,其输出特性呈现明显变化的物理现象.从压控振荡器、场效应振荡器、体效应振荡器、磁控管等的机、电性能联合动态试验分析得出:当振动干扰频率与微波振荡器结构谐振频率一致时,即使干扰能量不大,也会使微波振荡器的输出发生很大变化.在应用振动理论分析微波振荡器的结构谐振频率后,提出了机、电性能之间互相影响的几点初步看法.     

三角形取样鉴相数字合成器锁相环中的混沌

研究三角形取样鉴相数字合成器锁相环中的混沌现象。其方法是先对三角形取样鉴相数字合成器锁相环系统作离散化处理,得到相应的离散化系统模型;系统中的环路滤波器采用RC积分滤波器,鉴相器采用三角形鉴相特性;通过验证离散化系统相应的低维系统存在快返反射点,证明了当系统方程满足一定条件时,原系统有混沌输出。最后导出了混沌与系统参数间的关系式。     

保图像细节的DWAWMTMF算法

提出双窗自适应加权中值-截尾均值滤波算法。根据信号结构特征设计出自适应计算滤波参数的方法,该方法在抑制加性混合噪声和保图像细节等方面有优良的性能,综合性能优于3LH+、DWMTMF、ACWM。     

微波毫米波辐照对生物体血液系统的影响

介绍了微波毫米波的生物医学效应，观察了大功率微波对生物体外用血像的损伤作用和低功率毫米波的治疗作用，发现大功率微波照射对血液粘度、红细胞聚集性和变形性、血小板聚集性有严重的杀伤作用，而低功率毫米波对特定部位的有效照射能够对白细胞和骨髓细胞产生保护作用，能减轻化疗和放疗期白细胞的下降，提高机体对化学药物治疗和放射剂量的承受力。     

高温超导薄膜微波表面特性的研究

研究和设计了“终端反射式带状线谐振器”测试系统,配合测量数据处理软件包,对高温超导薄膜的微波表面特性进行了测试分析。     

试论英语教学中的“情感过滤”

“情感过滤”和“输入”是影响外语习得的两大重要因素。该文首先论述了“情感过滤”对外语习得和学习的作用机制 ,然后分析了引起“情感过滤”的四大情感因素 ,并提出怎样减少或消除学习者的“情感过滤”来提高英语教学质量的方法。     

Prediction and forecasting in linear models with measurement error

We consider measurement error models within the time series unobserved component framework. A variable of interest is observed with some measurement error and modelled as an unobserved component. The forecast and the prediction of this variable given the observed values is given by the Kalman filter and smoother along with their conditional variances. By expressing the forecasts and predictions as weighted averages of the observed values, we investigate the effect of estimation error in the measurement and observation noise variances. We also develop corrected standard errors for prediction and forecasting accounting for the fact that the measurement and observation error variances are estimated by the same sample that is used for forecasting and prediction purposes. We apply the theory to the Yellowstone grizzly bears and US index of production datasets.     

Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We have employed weak priors based on prewar data. The estimation makes use of the Gibbs sampler and the Metropolis algorithm.