Reconfigurable Web wrapper agents

Web wrapper agents exploit online Web data sources, facilitating information integration and reuse. With the DeepSpot Agent Toolbox, users can automate virtually all types of Web browsing sessions simply by browsing the target Web sites.     

基于遗传算法的分布式异构数据库的查询优化

对于分布武异构数据库，查询优化问题是其核心问题。本文用基于位、值结构树型编码的遗传算法对分布武异构数据库进行查询优化，取得了很好的效果。     

一种三层结构的WEB数据库访问平台的设计与实现

近年来，利用ASP及ADO技术实现WEB到数据库的访问是开发WEB应用系统常见的方法，但是这种访问方式具有Client/Server两层结构的缺点。笔者运用DCOM技术及客户端组件技术，设计一种WEB环境下的数据库访问平台，实现了真正的WEB三层结构方式。我们利用Inprise Delphi开发工具实现该平台的功能，并使已有ASP程序只要经过简单修改，就可移植到该平台运行。     

网络环境下会计信息系统的内部控制

本文探讨网络环境下电算化会计信息系统的内部控制出现的新变化、新特点等，并对出现的新问题提出了相应对策。     

基于Notes的OA系统的通用设计方案研究

文章主要研究了基于LotusNotes的办公自动化系统的通用设计方案及整体框架,并且详细阐述了以工作流为基础的系统功能模块、数据组织模式、系统运行方式以及保证系统安全的一系列相应措施。     

通过脉冲耦合神经网络来增强图像

图像增强是指对图像进行加工,以获得更“好”的视觉效果的一种图像处理技术。这种“好”的评价是由图像的观察者给出的。该文提出了一个通过脉冲耦合神经网络来增强图像,使图像的边缘变得更清晰的方法,并用边缘检测算子对其结果进行了验证。实验表明这种方法具有良好的效果。     

一个基于XML的教案制作工具的设计

学习资源共享对于电子教案制作有着重要意义。文章给出了一个基于XML的以学习资源共享为核心的教案制作工具的设计。介绍了组成教案的几种学习资源组件,描述了这些学习资源组件之间的关系,以及XML在实现这些学习资源组件上的作用。给出了教案设计制作方式。另外还给出了工具的系统框架。     

一个基于SNMP协议的网络设备性能分析系统

该文介绍了在校园网环境，基于SNMP，设计并开发网络设备性能分析系统。该系统主要针对交换机Dlink 5016和3Com 3300的接口性能及其系统性能进行数据的采集、处理和分析。系统采用VC 实现。     

Efficient mining of intertransaction association rules

Most of the previous studies on mining association rules are on mining intratransaction associations, i.e., the associations among items within the same transaction. We extend the scope to include multidimensional, intertransaction associations. In a database of stock price information, an example of such an association is "if (company) A's stock goes up on day one, B's stock will go down on day two but go up on day four:" whether we treat company or day as the unit of transaction, the items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple properties in the same rule, so that multidimensional intertransaction associations can also be defined and discovered. Mining intertransaction associations pose more challenges on efficient processing than mining intratransaction associations because the number of potential association rules is extremely large. We introduce the notion of intertransaction association rule and develop an efficient algorithm, FITI (first intra then inter), for mining intertransaction associations, which adopts two major ideas: 1) an intertransaction frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart; and 2) a special data structure is built among intratransaction frequent itemsets for efficient mining of intertransaction frequent itemsets.     

Deformable M-Reps for 3D Medical Image Segmentation

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures—each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure.A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects.The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.