基于梯度矢量流snake与多尺度分析的血管内超声图像轮廓提取

提取血管内超声(IVUS)图像的血管内腔轮廓对冠状动脉疾病的诊断有积极意义。本文提出了一种基于梯度矢量流(GVF)snake与多尺度分析的轮廓提取新方法。针对传统GVF snake的轮廓初始化、抑制噪声和伪像干扰两个难点,本文方法在如下两个方面进行了改进:第一,利用序列图像的特性产生时间方差图并得到初始轮廓,从而实现轮廓提取过程的全自动化;第二,引入多尺度分析,使GVF snake在离散小波变换得到的多尺度图中形变,从而增强了算法的鲁棒性。对仿真图像和实际IVUS图像的实验表明,该方法在轮廓提取的精度方面优于传统的GVF snake方法。     

最优故障诊断系统设计

为改善系统的故障诊断性能，在设备设计阶段考虑增加故障诊断子系统是十分必要的．本文从信息论的观点，提出了确定系统中最优状态检测器数目及其安装位置的方法，研究结果为指导故障诊断系统的设计提供了科学依据．     

WAS IT THERE? DEALING WITH IMPERFECT DETECTION FOR SPECIES PRESENCE/ABSENCE DATA†

Species presence/absence surveys are commonly used in monitoring programs, metapopulation studies and habitat modelling, yet they can never be used to confirm that a species is absent from a location. Was the species there but not detected, or was the species genuinely absent? Not accounting for imperfect detection of the species leads to misleading conclusions about the status of the population under study. Here some recent modelling developments are reviewed that explicitly allow for the detection process, enabling unbiased estimation of occupancy, colonization and local extinction probabilities. The methods are illustrated with a simple analysis of presence/absence data collected on larvae and metamorphs of tiger salamander (Ambystoma tigrinum) in 2000 and 2001 from Minnesota farm ponds, which highlights that misleading conclusions can result from naive analyses that do not explicitly account for imperfect detection.     

Empirical null distribution-based modeling of multi-class differential gene expression detection

In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutation or theoretical null distribution-based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to lung transplant microarray data, we illustrate the competitive performance of the proposed method.     

Change Point Detection in The Skew-Normal Model Parameters

Bayesian inference under the skew-normal family of distributions is discussed using an arbitrary proper prior for the skewness parameter. In particular, we review some results when a skew-normal prior distribution is considered. Considering this particular prior, we provide a stochastic representation of the posterior of the skewness parameter. Moreover, we obtain analytical expressions for the posterior mean and variance of the skewness parameter. The ultimate goal is to consider these results to one change point identification in the parameters of the location-scale skew-normal model. Some Latin American emerging market datasets are used to illustrate the methodology developed in this work.     

基于粗糙集理论的入侵检测方法研究

为了克服入侵检测系统存在着在先验知识较少情况的推广能力差的问题,提出了基于粗糙集理论的入侵检测方法。利用粗糙理论,建立了系统调用短序列的检测模型并应用于sendmail调用序列检测。实验结果表明:它不需要全部的正常和异常的信息,在给出较少的正常和异常调用序列数据的情况下,能得到较为理想的检测效果。     

中等起伏目标最佳检测器的性能分析

推导出了中等起伏雷达目标最佳检测器主要性能参数的显式表达式。采用分析和模拟的方法将最佳检测器同广义特征滤波方法的性能进行了比较，结果说明这种针对部分相关目标的最佳检测方法具有更优良的检测性能。     

Outliers in multilevel data

This paper offers the data analyst a range of practical procedures for dealing with outliers in multilevel data. It first develops several techniques for data exploration for outliers and outlier analysis and then applies these to the detailed analysis of outliers in two large scale multilevel data sets from educational contexts. The techniques include the use of deviance reduction, measures based on residuals, leverage values, hierarchical cluster analysis and a measure called DFITS. Outlier analysis is more complex in a multilevel data set than in, say, a univariate sample or a set of regression data, where the concept of an outlying value is straightforward. In the multilevel situation one has to consider, for example, at what level or levels a particular response is outlying, and in respect of which explanatory variables; furthermore, the treatment of a particular response at one level may affect its status or the status of other units at other levels in the model.     

On shape detection in noisy images with particular reference to ultrasonography

We discuss the detection of a connected shape in a noisy image. Two types of image are considered: in the first a degraded outline of the shape is visible, while in the second the data are a corrupted version of the shape itself. In the first type the shape is defined by a thin outline of pixels with records that are different from those at pixels inside and outside the shape, while in the second type the shape is defined by its edge and pixels inside and outside the shape have different records. Our motivation is the identification of cross-sectional head shapes in ultrasound images of human fetuses. We describe and discuss a new approach to detecting shapes in images of the first type that uses a specially designed filter function that iteratively identifies the outline pixels of the head. We then suggest a way based on the cascade algorithm introduced by Jubb and Jennison (1991) of improving and considerably increasing the speed of a method proposed by Storvik (1994) for detecting edges in images of the second type.     

瑞利衰落信道下差分跳频同步多用户性能

在瑞利衰落信道和同步多用户环境下,分别针对逐符号检测和按序列检测两种信号检测方法,从理论上分析了差分跳频通信系统的误符号性能,同时做出相应的计算机仿真。仿真结果证实了差分跳频通信技术和按序列检测方法的结合,使系统的多用户性能得到了一定程度的提升。