基于SURF特征的高动态范围图像配准算法

同一场景的多曝光图像序列被广泛的应用于高动态范围图像（HighDynamicRangeImage）的合成中。但是,在多曝光图像序列的采集过程中,相机抖动、场景运动等因素会对合成图像的质量产生较大的影响。此外,离镜头较近的大目标往往由于显著的三维形状,在序列图中产生较大的视差效应,也会对合成图像产生消极影响。该文提出一种基于SURF特征点的三维图像配准算法,实验证明该算法在近距离大目标情形下较之传统配准算法MTB（MeanThresholdBitmap,均值二值化）可以获得更好效果。     

机房管理对策探微

本文介绍了几种最常见的机房管理手段并结合Windows2003 server平台上利用ISA Server2004提出了一套比较行之有效的网络管理方法.     

煤矿调度电话录音上传及远程回放的实现

文章介绍了煤矿现有的调度电话录音系统,设计了调度电话监控上传系统来实现录音文件的上传和调度电话查询系统实现录音文件的查询和远程回放。系统通过输入查询取代了传统的人工查询的工作方式,大大节约了生产调度成本,同时为煤矿人员考勤和定位也提供了准确依据。由于其配置容易、操作方便、实用性高,因此具有很好的维护性和推广性。     

嵌入式Linux下NAND flash上根文件系统的构建

针对嵌入式Linux操作系统,通常选择NAND Flash烧录根文件系统的情况.说明了常用嵌入式文件系统的类型及NAND flash的特点;通过在三星的ARM9 2410S平台上构建YAFFS和Cramfs文件系统为例,详细阐述MTD驱动层原理及文件系统的配置过程.     

几种硬盘驱动器的特点与发展

硬盘驱动器是计算机中重要的存储设备。本文介绍了温切斯特磁盘、固态硬盘和混合硬盘的特点及其发展过程。     

基于Web学生成绩管理系统的设计与实现

该系统基于校园网,采用ASP．NET＋SQL Server开发技术,解决了传统人工方式处理学生成绩信息的弊端,实现了学校人力资源的优化和学生成绩信息的科学管理,具有较高的实用价值。     

Static Bayesian games with finite fuzzy types and the existence of equilibrium

This paper studies a special game with incomplete information, in which the payoffs of the players are both random and fuzzy. Such a game is considered in the context of a Bayesian game with the uncertain types characterized as fuzzy variables. A static fuzzy Bayesian game is then introduced and the decision rules for players are given based on credibility theory. We further prove the existence of the equilibrium of the game. Finally, a Cournot competition model with fuzzy efficiency under asymmetric information is investigated as an application and some results are presented.     

A survey on algorithms for mining frequent itemsets over data streams

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful. Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas for future research.     

A computer program for estimating the re-transformed mean in heteroscedastic two-part models

The population of health care costs is typically skewed, heteroscedastic, and may include zero costs. Without proper accounting for these special distributional features, resulting prediction may be biased, and wrong inferences about the distribution of patients’ health care costs may be made. Welsh and Zhou [A.H. Welsh, X.H. Zhou, Estimating the retransformed mean in a heteroscedastic two-part model, J. Stat. Plan. Inference 136 (2006) 860–881] proposed a semi-parametric regression model, which addressed these special features. In this paper we developed a software program to implement this statistical method, which would provide better prediction of health care costs for clinical researchers.

Our program computed two mean estimators, their asymptotical standard deviation, confidence interval, and optional bootstrap confidence interval. Our program included user-friendly interactive mode and more efficient and flexible batch mode. It was written in free statistical computing language R and could be run on a wide variety of platforms.