基于FT245RL和FPGA的6路数据采集系统设计

针对大规模数据采集过程中存在数据量大与传输速度慢的矛盾,文章设计了基于FT245RL和FPGA的6路数据采集系统;该设计采用Xilinx公司的FPGA作为整个系统的核心,控制A/D转换器实现6路模拟数据的同步采集,并将转换后的数字信号存储到Flash中,最终通过USB2.0转换芯片将数据传输到上位机;试验表明,该数据采集系统能够满足对大容量数据实时采集、存储和上传的要求,具有工作稳定,可靠性高,传输速度快的特点,能够广泛应用于大规模数据的采集.     

基于CPLD和USB技术的电力负荷特性在线采集系统设计

为了实现电力系统综合负荷特性数据的在线采集,提出了一种基于CPLD实现多通道数据采集USB传输系统的设计方案.本采集系统以CPLD为核心,通过控制A/D和USB芯片实现整个数据的采集过程,从而有效地减少了电路板上芯片数量,降低了系统故障几率,进而提高了系统运行的稳定性和设计的灵活性.借助于USB传输方式进一步提高了数据传输系统的稳定性和适应性,解决了传统数据采集系统在传输速度和通道数量之间的瓶颈问题.计算机仿真和现场运行均验证了该系统能够满足电力系统负荷建模所需数据采集的要求.     

高速数据采集系统的USB接口设计

针对工程领域对数据采集的多类别数据、高速采集和传输、实时性的要求,设计基于USB接口的高速数据采集系统,并有FPGA+DSP搭配进行采集系统的逻辑控制、接口控制和信号处理,另有一块AD转换板进行模数转换;重点阐述USB接口设计的原理和实现,包括USB固件程序设计、驱动程序设计和USB应用程序设计;经过传输256*256的灰度图像、测试USB通道数据传输速度和使用采集板采集方波信号的测试,证明系统能够实现数据采集的任务,有很快的数据传输速度。     

基于LabVIEW串口通信的多路数据采集系统

本文介绍了一种基于LabVIEW软件平台的单片机多路数据采集系统的设计原理和过程。本设计选用ATmega 128单片机组成的系统作为前端数据采集系统进行多路数据采集,通过RS-485串口通信将数据传输至上位机;利用LabVIEw强大的数据处理和显示功能对采集的数据进行实时处理、分析、显示和存储。     

一种光纤陀螺测试数据采集系统设计

光纤陀螺是基于Sagnac效应的新型全固态光学陀螺,它具有起动时间短、动态范围宽及寿命长等突出优点,目前已成为捷联式惯性导航系统中理想的惯性器件;在光纤陀螺的性能测试中,大多采用RS-232作为通信接口,数据采集速率慢;介绍了一种光纤陀螺测试数据采集方案,给出了采集系统的软硬件设计;系统通过高速A/D转换芯片对陀螺数据进行采集,利用USB总线作为数据传输通信接口,可实现光纤陀螺测试数据的高速率、大吞吐量采集,并可提高光纤陀螺的测试可靠性.     

AD7674和C8051F060构成的数据采集系统

随着数字信号处理技术的发展,人们对数据采集系统的采样精度和传输速度要求越来越高。本文介绍的数据采集系统设计方案采用了ADI公司的高精度18位SAR型数模转换芯片AD7674与Silicon Laboratories公司C8051F060单片机构成一个数据采集系统,并利用并口模式实现C8051F060单片机与AD7674之间的高速数据传输,给出了部分源程序。     

PCIE数据采集系统的驱动程序开发

为了提高数据传输速度和准确性,研制了一套基于PCIE接口的数据采集系统。该系统运用了模块化设计思路,包括数据接收卡、数据传输卡和软件驱动三部分。简要介绍了自行研制的数据采集卡的基本原理和构成,重点研究了在Windows XP系统环境下利用DriverStudio开发PCIE设备驱动程序的主要方法步骤、DMA方式进行数据传输和事件通知的实现方法。经过上位机测试,该系统稳定可靠,所开发的驱动程序完全可以实现数据的高速传输。     

多传感器图像采集处理系统的设计与实现

为了完成一个多传感器图像采集处理系统的设计、软硬件实现,采用参数同步配置方法提高多CMOS图像传感器同步曝光精度.采用基于Bayer颜色滤波阵列图像的残差集近无损压缩方法提高图像数据传输速度.系统三传感器曝光达到了微秒级同步精度.约为课题任务要求的1/29.在保证重建图像质量精度的前提下,数据传输速度提高了约4.4倍.给出了系统部分最终测试结果.     

一种基于RS-485总线的多路温度数据采集系统设计

介绍一种基于RS-485总线的温度数据采集系统,应用于混凝土施工控制中,数据采集模块挂接在RS-485总线上构成RS485总线网络,PC机与其进行数据通信,系统实现了对工业现场多路温度数据的采集、管理、显示与打印等功能,软件设计采用面向对象分析设计技术-Booch方法设计了项目类、图形类、工具类和数据类等,使系统模块化好,通用性强。     

三值光计算机解码器的数据采集控制系统

针对三值光计算机解码器数据采集问题,设计开发了一种基于USB2.0的数据采集控制系统,实现了对解码器数据采集设备的自动控制、数据传输、设备状态和数据显示.为了提高数据采集系统的效率和可靠性,在控制系统中采用了异步通信模式和事务处理机制.实验结果表明,系统使用方便、控制灵活、控制效果良好,保证了数据采集系统工作高效稳定.     

One-to-many data transformations through data mappers

The optimization capabilities of RDBMSs make them attractive for executing data transformations. However, despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produce several output tuples for a single input tuple cannot be expressed in that way.

To overcome this limitation, we propose to extend Relational Algebra with a new operator named data mapper. In this paper, we formalize the data mapper operator and investigate some of its properties. We then propose a set of algebraic rewriting rules that enable the logical optimization of expressions with mappers and prove their correctness. Finally, we experimentally study the proposed optimizations and identify the key factors that influence the optimization gains.     

Efficient data reduction in multimedia data

As the amount of multimedia data is increasing day-by-day thanks to cheaper storage devices and increasing number of information sources, the machine learning algorithms are faced with large-sized datasets. When original data is huge in size small sample sizes are preferred for various applications. This is typically the case for multimedia applications. But using a simple random sample may not obtain satisfactory results because such a sample may not adequately represent the entire data set due to random fluctuations in the sampling process. The difficulty is particularly apparent when small sample sizes are needed. Fortunately the use of a good sampling set for training can improve the final results significantly. In KDD’03 we proposed EASE that outputs a sample based on its ‘closeness’ to the original sample. Reported results show that EASE outperforms simple random sampling (SRS). In this paper we propose EASIER that extends EASE in two ways. (1) EASE is a halving algorithm, i.e., to achieve the required sample ratio it starts from a suitable initial large sample and iteratively halves. EASIER, on the other hand, does away with the repeated halving by directly obtaining the required sample ratio in one iteration. (2) EASE was shown to work on IBM QUEST dataset which is a categorical count data set. EASIER, in addition, is shown to work on continuous data of images and audio features. We have successfully applied EASIER to image classification and audio event identification applications. Experimental results show that EASIER outperforms SRS significantly. Surong Wang received the B.E. and M.E. degree from the School of Information Engineering, University of Science and Technology Beijing, China, in 1999 and 2002 respectively. She is currently studying toward for the Ph.D. degree at the School of Computer Engineering, Nanyang Technological University, Singapore. Her research interests include multimedia data processing, image processing and content-based image retrieval. Manoranjan Dash obtained Ph.D. and M. Sc. (Computer Science) degrees from School of Computing, National University of Singapore. He has worked in academic and research institutes extensively and has published more than 30 research papers (mostly refereed) in various reputable machine learning and data mining journals, conference proceedings, and books. His research interests include machine learning and data mining, and their applications in bioinformatics, image processing, and GPU programming. Before joining School of Computer Engineering (SCE), Nanyang Technological University, Singapore, as Assistant Professor, he worked as a postdoctoral fellow in Northwestern University. He is a member of IEEE and ACM. He has served as program committee member of many conferences and he is in the editorial board of “International journal of Theoretical and Applied Computer Science.” Liang-Tien Chia received the B.S. and Ph.D. degrees from Loughborough University, in 1990 and 1994, respectively. He is an Associate Professor in the School of Computer Engineering, Nanyang Technological University, Singapore. He has recently been appointed as Head, Division of Computer Communications and he also holds the position of Director, Centre for Multimedia and Network Technology. His research interests include image/video processing & coding, multimodal data fusion, multimedia adaptation/transmission and multimedia over the Semantic Web. He has published over 80 research papers.     

大数据环境下的数据安全性探讨

随着互联网的高速发展,特别是近年来云计算、物联网等新兴技术的出现,社交网络等服务的广泛应用,人类社会的数据的规模正快速地增长,大数据时代已经到来。如何获取,分析大数据已经成为广泛的问题。但随着带来的数据的安全性必须引起高度重视。本文从大数据的概念和特征说起,阐述大数据面临的安全挑战,并提出大数据的安全应对策略。     

Scattered data interpolation of Radon data

Linear combinations of translates of a given basis function have long been successfully used to solve scattered data interpolation and approximation problems. We demonstrate how the classical basis function approach can be transferred to the projective space ℙ ^d−1. To be precise, we use concepts from harmonic analysis to identify positive definite and strictly positive definite zonal functions on ℙ ^d−1. These can then be applied to solve problems arising in tomography since the data given there consists of integrals over lines. Here, enhancing known reconstruction techniques with the use of a scattered data interpolant in the “space of lines”, naturally leads to reconstruction algorithms well suited to limited angle and limited range tomography. In the medical setting algorithms for such incomplete data problems are desirable as using them can limit radiation dosage.     

Compression-based data mining of sequential data

The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process. Data mining algorithms should have as few parameters as possible. A parameter-light algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics, learning, and computational theory hold great promise for a parameter-light data-mining paradigm. The results are strongly connected to Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen lines of code. We will show that this approach is competitive or superior to many of the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/XML/video datasets. As a further evidence of the advantages of our method, we will demonstrate its effectiveness to solve a real world classification problem in recommending printing services and products. Responsible editor: Johannes Gehrke     

Fuzzy data mining for time-series data

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.     

Test data regeneration: generating new test data from existing test data

Existing automated test data generation techniques tend to start from scratch, implicitly assuming that no pre‐existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre‐existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search‐based test data regeneration, a technique that can generate additional test data from existing test data using a meta‐heuristic search algorithm. The proposed technique is compared to a widely studied test data generation approach in terms of both efficiency and effectiveness. The empirical evaluation shows that test data regeneration can be up to 2 orders of magnitude more efficient than existing test data generation techniques, while achieving comparable effectiveness in terms of structural coverage and mutation score. Copyright © 2010 John Wiley & Sons, Ltd.     

数据竞争的规制：数据的利用与数据保护的协调

自互联网出现以来,数据保护一直是个难题。当社交媒体网站在数字市场上大展拳脚的那一刻,对用户数据和信息的保护让决策者们不得不保持警惕。在数字经济时代的背景下,数据逐渐成为企业提升竞争力的重要要素,围绕着数据展开的市场竞争越来越多。数字经济时代,企业对数据资源的重视与争夺,将网络平台权利与用户个人信息保护、互联网企业之间有关数据不正当竞争的纠纷和冲突,推上了风口浪尖。因此,如何协调和把握数据的合理利用和保护之间的关系,规制不正当竞争行为,以求在数字经济快速发展的洪流中,占据竞争优势显得尤为重要。文章将通过分析数据的二元性,讨论数据在数字经济时代的价值,并结合反不正当竞争法和实践案例,进一步讨论数据利用和保护的关系。