从《Visual Basic程序设计》谈课堂教学改革

从计算机二级等级考试出发,根据《Visual Basic程序设计》课程自身的特点,对《Visual Basic程序设计》的教学方法进行改革。改革的基本原则:课堂上应以学生为本,注重激发学生的学习兴趣,帮助学生体验成功,增强自信。具体做法:不拘泥于课本,从顺序上对教学内容进行大胆地调整,将教学内容进行有机分解、组合,应用案例教学法。     

转变课堂语言提高学习效率

作为一名信息技术教师不仅要有扎实的专业知识,还要能灵活运用教学语言来调动学生的学习积极性,在新课程改革过程中,不仅要保持原有的用词准确规范、表达简明扼要外,还要注意语言表达的几种转变:由命令式转变为商讨式、由评判式转变成建议鼓励式、由灌输式转变为引导式、由统一的语言转变为开放的语言、由单一的评价转变为评价的多元化,使得我们的教学语言更符合新课程标准,更加有效地提高课堂教学效果。     

构建基于XML的在线备忘录

XML能够使用得Internet上的数据交换变得更方便,XML与数据库一样,都能够作为信息存储的途径。当数据量不大的时候,比如完成一个在线备忘录,XML作为数据存储具有关系数据库所无法比拟的优势。在asp.net中能够利用.net类库提供的类及方法轻松地构建一个基于XML的在线备忘录。     

浅谈EDA软件在数电教学中的应用

《数字电子技术》是一门很重要的专业基础课,将EDA软件应用到数字电路的教学与实验中,对于数字电路的理论教学、实验教学、课程设计等环节都有显著的帮助,利于学生们更好的掌握这门课程,取得了很好的教学和实验效果。     

Interaction of ISPs: Distributed Resource Allocation and Revenue Maximization

The Internet is a hierarchical architecture comprising heterogeneous entities of privately owned infrastructures, where higher level Internet service providers (ISPs) supply connectivity to the local ISPs and charge the local ISPs for the transit services. One of the challenging problems facing service providers today is how the profitability can be increased while maintaining good service qualities as the network scales up. In this work, we seek to understand the fundamental issues on the "interplay" (or interaction) between ISPs at different tiers. Although the local ISPs (which we term peers) can communicate with each other by purchasing the connectivity from transit ISPs, there stands an opportunity for them to set up private peering relationships. Under this competitive framework, we explore the issues on 1) the impact of peering relationship; 2) resource distribution; 3) revenue maximization; and 4) condition for network upgrade. First, a generalized model is presented to characterize the behaviors of peers and the transit ISP, in which their economic interests are reflected. We study how a peer can distributively determine its optimal peering strategy. Furthermore, we show how a transit ISP is able to utilize the available information to infer its optimal pricing strategy, under which a revenue maximization is achieved. Two distributed algorithms are proposed to help ISPs to provide a fair and efficient bandwidth allocation to peers, avoiding a resource monopolization of the market. Last, we investigate the above issues in a "many-peers region," that is, when we scale up the network. We provide insightful evidence to show that the ISPs can still gain profits as they upgrade the network infrastructures. Extensive simulations are carried out to support our theoretical claims.     

DocXS distributed operator construction and execution system

In the field of computer vision and pattern recognition, data processing and data analysis tasks are often implemented as a consecutive or parallel application of more-or-less complex operations. In the following we will present DocXS, a computing environment for the design and the distributed and parallel execution of such tasks. Algorithms can be programmed using an Eclipse-based user interface, and the resulting Matlab and Java operators can be visually connected to graphs representing complex data processing workflows. DocXS is platform independent due to its implementation in Java, is freely available for noncommercial research, and can be installed on standard office computers. One advantage of DocXS is that it automatically takes care about the task execution and does not require its users to care about code distribution or parallelization. Experiments with DocXS show that it scales very well with only a small overhead. The text was submitted by the authors in English. Steffen Wachenfeld received B.Sc. and M.Sc. (honors) degrees in Information Systems in 2003 and 2005 from the University of Muenster, Germany, and an M.Sc. (honors) degree in Computer Science in 2003 from the University of Muenster. He is currently a research fellow and PhD student in the Computer Science at the Dept. of Computer Science, University of Muenster. His research interests include low resolution text recognition, computer vision on mobile devices, and systems/system architectures for computer vision and image analysis. He is author or coauthor of more than ten scientific papers and a member of IAPR. Tobias Lohe, M.Sc. degree in Computer Science in 2007 from the University of Muenster, Germany, is currently a research associate and PhD student in Computer Science at the Institute for Robotics and Cognitive Systems, University of Luebeck, Germany. His research interests include medical imaging, signal processing, and robotics for minimally invasive surgery. Michael Fieseler is currently a student of Computer Science at the University of Muenster, Germany. He has participated in research in the field of computer vision and medical imaging. Currently he is working on his Master thesis on depth-based image rendering (DBIR). Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from the University of Bern, Switzerland. In 2002 he became an associate professor at the Technical University of Berlin, Germany. Since October 2002 he has been a full professor at the University of Münster, Germany. He has coauthored and coedited two books published by Springer and has served as the co-guest-editor of two special issues in international journals. Currently, he is the Coeditor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. In addition he also serves on the editorial advisory board of the International Journal of Neural Systems and the editorial board of IEEE Transactions on Systems, Man, and Cybernetics—Part B, the International Journal of Image and Graphics, Electronic Letters on Computer Vision and Image Analysis, and Pattern Recognition. His research interests include medical image analysis, vision-based man-machine interface, 3D image analysis, structural pattern recognition, and mobile multimedia. He is a member of IEEE and a Fellow of IAPR.     

HBA: Distributed Metadata Management for Large Cluster-Based Storage Systems

An efficient and distributed scheme for file mapping or file lookup is critical in decentralizing metadata management within a group of metadata servers. This paper presents a novel technique called Hierarchical Bloom Filter Arrays (HBA) to map filenames to the metadata servers holding their metadata. Two levels of probabilistic arrays, namely, the Bloom filter arrays with different levels of accuracies, are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, whereas the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Both arrays are replicated to all metadata servers to support fast local lookups. We evaluate HBA through extensive trace-driven simulations and implementation in Linux. Simulation results show our HBA design to be highly effective and efficient in improving the performance and scalability of file systems in clusters with 1,000 to 10,000 nodes (or superclusters) and with the amount of data in the petabyte scale or higher. Our implementation indicates that HBA can reduce the metadata operation time of a single-metadata-server architecture by a factor of up to 43.9 when the system is configured with 16 metadata servers.     

A Constrained Line Search Optimization Method for Discriminative Training of HMMs

In this paper, we propose a novel optimization algorithm called constrained line search (CLS) for discriminative training (DT) of Gaussian mixture continuous density hidden Markov model (CDHMM) in speech recognition. The CLS method is formulated under a general framework for optimizing any discriminative objective functions including maximum mutual information (MMI), minimum classification error (MCE), minimum phone error (MPE)/minimum word error (MWE), etc. In this method, discriminative training of HMM is first cast as a constrained optimization problem, where Kullback-Leibler divergence (KLD) between models is explicitly imposed as a constraint during optimization. Based upon the idea of line search, we show that a simple formula of HMM parameters can be found by constraining the KLD between HMM of two successive iterations in an quadratic form. The proposed CLS method can be applied to optimize all model parameters in Gaussian mixture CDHMMs, including means, covariances, and mixture weights. We have investigated the proposed CLS approach on several benchmark speech recognition databases, including TIDIGITS, Resource Management (RM), and Switchboard. Experimental results show that the new CLS optimization method consistently outperforms the conventional EBW method in both recognition performance and convergence behavior.     

A model reference robust control with application to a flight control system design

In this paper, a new model reference robust control scheme for plants with relative degree greater than one is proposed for the purpose of coping with the “explosion of complexity” problem in control law design. The key of the scheme is that we use a first‐order filter to link each subsystem, so that the derivative of some nonlinear functions can be avoided. As a result, the final control law can be greatly simplified. The application of this scheme to a flight control system shows that both the tracking performance and the control effort can be improved significantly in comparison with earlier research. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

图像匹配技术在套印误差自动检测中的应用

准确检测套印偏差是印刷机自动套印系统中最为关键的技术;针对印刷色标图案的特点,提出了一种新的基于图像匹配的套印误差检测方法,通过相关匹配处理获取各单色色标的位置,并巧妙利用色标圆线的直径确定了系统的标称分辨率,进而计算出各色组问纵向和横向的套印误差;文中详细给出了算法原理和实验结果;相对于经典的边缘检测方法,图像匹配技术充分利用了色标图案的整体信息;实验结果表明,该方法对像素灰度的变化和系统噪声具有鲁棒性,检测精度高。