岛式FPGA手动布局布线的实现

针对一款岛式FPGA芯片VS1000开发了布局布线工具VA,并实现了图形界面.为了使得FPGA芯片能够更灵活地满足实际电路设计的需求及实现用户对电路的人为控制,在VA中加入了手动布局布线功能,使得使用者可以灵活地移动逻辑单元,逻辑模块及其他模块并实现局部布线功能.     

基于HFSM模型的GUI自动测试

针对图形用户界面（GUI）软件的层次性,采用分层有限自动机（HFSM）模型对被测GUI软件建模,将GUI测试转换为预期HFSM和实际HFSM的一致性测试。模型自动搜索GUI控件,提供接口,采用均匀设计思想自动产生测试数据,由测试数据驱动测试执行。根据GUI软件的特点,采用增量方式进行模型构造和测试,从而避免错误积累。     

基于事件交互图的算法与研究

图形用户界面的质量直接影响整个软件系统的有效性和实用性,一般采用提取模型的方法对图形用户界面进行测试,目前常用的模型为事件流图和事件交互图,但是事件流图转换成事件交互图的算法较为复杂,为此,提出一种转换算法,对图形用户界面事件进行明确划分,利用模态窗口事件的特殊性优化原有算法。测试实例结果表明,该算法可用于图形用户界面,且与Memon算法相比,能更高效地获得更准确的数据。     

Finding a Clear Path: Structuring Strategies for Visualization Sequences

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers’ perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.     

数据仓库实现过程及在线分析

数据仓库技术是近几年来出现并迅速发展的一种技术,它可以充分利用数据仓库中已存储的信息,帮助决策者进行决策。本文在介绍数据仓库的定义和数据仓库决策支持系统原理的基础上,详尽地讨论了数据仓库中的数据抽取与转换方法,并对数据仓库实现过程进行了实例探索和在线分析,最后根据数据挖掘方法进行了实际的应用。本文就是采用微软SQL Server 2000数据仓库技术设计了一商业用的数据仓库,使读者可清楚了解数据仓库的决策支持系统的实现过程。     

A generic parallel processing model for facilitating data mining and integration

To facilitate data mining and integration (DMI) processes in a generic way, we investigate a parallel pipeline streaming model. We model a DMI task as a streaming data-flow graph: a directed acyclic graph (DAG) of Processing Elements (PEs). The composition mechanism links PEs via data streams, which may be in memory, buffered via disks or inter-computer data-flows. This makes it possible to build arbitrary DAGs with pipelining and both data and task parallelisms, which provide room for performance enhancement. We have applied this approach to a real DMI case in the life sciences and implemented a prototype. To demonstrate feasibility of the modelled DMI task and assess the efficiency of the prototype, we have also built a performance evaluation model. The experimental evaluation results show that a linear speedup has been achieved with the increase of the number of distributed computing nodes in this case study.     

测绘元数据的框架设计和基于RDF/XML的表达

论述元数据技术在测绘信息共享和管理中的重要作用,分析当前测绘元数据库建设的现状,指出研究测绘元数据表达的重要意义。根据测绘数据的组织结构特点,设计测绘元数据框架,提出利用RDF／XML技术表达测绘元数据的思想。最后总结利用RDF表达测绘元数据的优点。     

Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called Insights Feed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.     

AVOCADO: Visualization of Workflow–Derived Data Provenance for Reproducible Biomedical Research

A major challenge in data‐driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi‐step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node‐link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif‐based aggregation. Based on user action and graph attributes, a modular degree‐of‐interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest‐driven adaptive approach to provenance visualization allows users to review and communicate complex multi‐step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.     

Repast:基于Agent建模仿真的可扩展平台

建模仿真平台为基于Agent建模理论和方法的研究创造了条件.在研究当前流行的基于Agent建模仿真平台不足的基础之上,介绍了一种新的基于Agent建模仿真平台Repast,详细阐述了该平台的设计目标、框架结构及运行机制等内容,并结合一个实例简要说明了Repast模型的设计与实现过程.该平台移植性和可扩展性强,使用方便,易于学习,方便了应用基于Agent建模仿真方法对复杂适应系统及复杂性的研究及探索,同时也为复杂适应系统的研究提供新的思路.     

Multisensor fusion for linear control systems with asynchronous, Out-Of-Sequence and erroneous data

This paper presents a set of new centralized algorithms for estimating the state of linear dynamic Multiple-Input Multiple-Output (MIMO) control systems with asynchronous, non-systematically delayed and corrupted measurements provided by a set of sensors. The delays, which make the data available Out-Of-Sequence (OOS), appear when using physically distributed sensors, communication networks and pre-processing algorithms. The potentially corrupted measurements can be generated by malfunctioning sensors or communication errors. Our algorithms, designed to work with real-time control systems, handle these problems with a streamlined memory and computational efficient reorganization of the basic operations of the Kalman and Information Filters (KF & IF). The two versions designed to deal only with valid measurements are optimal solutions of the OOS problem, while the other two remaining are suboptimal algorithms able to handle corrupted data.     

Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web

The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database—directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently—in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.     

A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application

Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.     

Building a geodatabase for mapping hydrogeological features and 3D modeling of groundwater systems: Application to the Saguenay-Lac-St.-Jean region, Canada

Understanding and managing groundwater resources require the integration of a large amount of high-quality data from a variety of sources. Due to the limitations in accessing information related to groundwater and subsurface conditions, the gathering of available existing information is of crucial importance when conducting a successful hydrogeological study. Here, we present an approach for the development of an exhaustive and comprehensive groundwater database through (1) the gathering of relevant sources of information relating to groundwater, and (2) the application of a quality control process in order to screen the data for accuracy and quality. This hydrogeological database is then implemented within a GIS (geographic information system) framework coupled to a Relational Database Management System (RDBMS) as a personal geodatabase (ESRI format) GIS technology. Once established, the spatial database allows a user to request the relevant data required for a specific hydrogeological study. In addition, stratigraphic data stored within the spatial database may be utilized for constructing 3D subsurface hydrostructural models. In order to achieve this objective, the software Arc Hydro Groundwater combined with the ArcGIS spatial database is shown to be appropriate for the 3D structural representation of aquifers (groundwater reservoirs). The innovative contribution of this approach in building 3D hydrostructural subsurface models from a spatial database resides in simplifying the required step-by-step processes by considering a unified compatible combination of “RDBMS-ArcGIS-Arc Hydro Groundwater” technologies. The proposed methodology is illustrated using data from an ongoing project aimed at developing an inventory of the groundwater resources of the Saguenay-Lac-Saint-Jean region, Quebec (Canada).     

Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling

Remote sensing models based on light use efficiency (LUE) provide promising tools for monitoring spatial and temporal variation of gross primary production (GPP) at regional scale. In most of current LUE-based models, maximal LUE (ε_max) heavily relies on land cover types and is considered as a constant, rather than a variable for a certain vegetation type or even entire eco-region. However, species composition and plant functional types are often highly heterogeneous in a given land cover class; therefore, spatial heterogeneity of ε_max must be fully considered in GPP modeling, so that a single cover type does not equate to a single ε_max value. A spatial dataset of ε_max accurately represents the spatial heterogeneity of maximal light use would be of significant beneficial to regional GPP models. Here, we developed a spatial dataset of ε_max by integrating eddy covariance flux measurements from 14 field sites in a network of coordinated observation across northern China and satellite derived indices such as enhanced vegetation index (EVI) and visible albedo to simulate regional distribution of GPP. This dynamic modeling method recognizes the spatial heterogeneity of ε_max and reduces the uncertainties in mixed pixels. Further, we simulated GPP with the spatial dataset of ε_max generated above. Both ε_max and growing season GPP show complex patterns over northern China that reflect influences of humidity, green vegetation fractions, and land use intensity. “Green spots” such as oasis meadow and alpine forests in dryland and “brown spots” such as build-up and heavily degraded vegetation in the east are clearly captured by the simulation. The correlation between simulated GPP and EC measured GPP indicate that the simulated GPP from this new approach is well matched with flux-measured GPP. Those results have demonstrated the importance of considering ε_max as both a spatially and temporally variable values in GPP modeling.     

A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies

Leaf area index (LAI) is an important variable needed by various land surface process models. It has been produced operationally from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a look-up table (LUT) method, but the inversion accuracy still needs significant improvements. We propose an alternative method in this study that integrates both the radiative transfer (RT) simulation and nonparametric regression methods. Two nonparametric regression methods (i.e., the neural network [NN] and the projection pursuit regression [PPR]) were examined. An integrated database was constructed from radiative transfer simulations tuned for two broad biome categories (broadleaf and needleleaf vegetations). A new soil reflectance index (SRI) and analytically simulated leaf optical properties were used in the parameterization process. This algorithm was tested in two sites, one at Maryland, USA, a middle latitude temperate agricultural area, and the other at Canada, a boreal forest site, and LAI was accurately estimated. The derived LAI maps were also compared with those from MODIS science team and ETM+ data. The MODIS standard LAI products were found consistent with our results for broadleaf crops, needleleaf forest, and other cover types, but overestimated broadleaf forest by 2.0-3.0 due to the complex biome types.     

多源空间数据的共享与集成模式研究

在研究现有多格式空间数据共享与集成模式的基础上,借鉴通用数据访问(UniversalDataAccess,UDA)技术为传统多源数据提供通用数据访问的思路,作者提出了基于通用空间数据引擎(UniversalSpatialDataEngine,USDE)的空间数据共享与集成模式。通用空间数据引擎由数据引擎管理器及相应的一系列数据引擎构成,其中数据引擎管理器负责数据引擎的智能调用,并为客户GIS软件提供统一的数据访问接口;各数据引擎负责对具体数据访问接口的实现。利用COM技术,作者设计出了基于目前几种流行的GIS数据格式的USDE,并成功地实现了对几种不同格式空间数据的集成应用。     

Exploring Items and Features with IF,FI‐Tables

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an I^F‐Table, showing all items with a selection of features; and an F^I‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.     

面向SAP系统的透明加解密方法

针对广泛应用于国内的企业资源计划( ERP)商用管理软件———数据处理系统应用与产品( SAP)系统存在的本地文件导出安全性问题,建立高度匹配于SAP系统特点及特定进程的文件透明加解密系统,在驱动层针对SAP系统关联的特定进程涉及的新建、读、写文件等操作进行捕获,通过卷上下文与流上下文提取缓存信息,依据所捕获的文件操作类型,完成不同的缓存置换;在应用层实现密钥的获取与分发。同时实现了文件透明加解密系统的客户化定制,通过配置可以实现对SAP系统特定进程,导出文件类型甚至特定文件的指定。实验结果表明,该系统在对用户无干扰无需人工介入的前提下,能自动与SAP系统无缝对接,对导出文件透明加解密,并限定导出文件仅能在本地计算机中使用;最终有效地与SAP系统本身自带的用户名登录、权限管控等安全控制机制整合联动,完善了SAP系统在外部商业数据输出安全性管控上的缺失,提升企业对商业数据安全使用、输出的管控及保障能力。     

基于模型的嵌入式系统软件工程语言AADL与UML的比较

随着嵌入式系统规模、复杂度和性能需求的提升,嵌入式系统开发的重点从代码级提前到模型级,模型驱动体系结构成为嵌入式系统开发的主流。统一建模语言（uML）和结构分析和设计语言（AADL）是模型驱动系统工程的标准,从方法、扩展机制和应用领域三个方面对两者进行研究和比较,讨论模型分析、模型转换等相关技术。最后,探讨了UML和AADL的发展与研究方向