考虑亚像素配准误差的超分辨率图像复原

超分辨率图像复原作为第二代图像复原方向,已成为目前国际图像复原界的一个研究热点.一般来说,超分辨率图像复原是一个病态问题,可以结合图像的先验信息,使其成为良态的,这需要有效的规整化算法.但是,规整化参数的选择多数情况是通过经验确定的,且现有的一些计算规整化参数的方法又过于繁琐.本文讨论了亚像素配准误差引入的情况下噪声的统计模型,利用Miller规整的思想给出了简易可行的规整化参数计算方法.这种规整化参数计算方法能够自适应地根据配准误差和观测噪声局部调整由于配准误差导致的失真.仿真结果表明得到的规整化参数能使规整化算法有效收敛.     

一种改进的基于约束关系的XML查询重写算法研究

查询重写是解决数据集成、查询优化和物理层数据独立性等问题的关键技术.以往工作主要集中在关系数据模型方面.最近Michigan大学Timber研究小组提出一种全新的基于约束的XML查询重写算法.然而,该算法未考虑存在内定谓词情况下的重写问题,应用范围受到一定限制.在原算法的重写思想基础上,提出了一种基于约束的XML查询重写的改进算法.通过引入映射规则中的约束条件,消除阻碍重写的Skolem函数,从而解决内定谓词问题,增大原算法的应用范围.证明了改进算法的正确性.性能分析和测试结果表明,改进算法并不增加实质性的性能代价.     

Mathematical formulation of provisioning of connections with advance reservation in metro WDM ring networks using reconfigurable OADMs (ROADMs) with tuning constraint

Optical add/drop multiplexers (OADMs) can significantly reduce the cost of metro optical wavelength-division multiplexing (WDM) ring networks by allowing traffic to bypass intermediate nodes without expensive opto-electro-opto (O-E-O) conversion. Some traditional OADMs, called fixed OADMs (FOADMs), can only add/drop traffic on a specific wavelength. Reconfigurable Optical Add/Drop Multiplexers (ROADMs) are emerging, which can add/drop traffic onto/from different wavelengths at different time. ROADMs provide desirable flexibility, enable fast provisioning of dynamic traffic, and save capital expenditure (CapEx) and operational expenditure (OpEx). In order to be cost-effective, some ROADMs employ architectures that tune the ROADM continuously from one wavelength to another, crossing through all the wavelengths in-between, which may cause interference to the connections, if any, on those wavelengths being crossed. In order to prevent existing connections from being interrupted, a constraint needs to be imposed that ROADMs cannot cross working wavelengths when tuning. In this study, the design and the benefits of metro optical WDM network architectures using ROADMs and the impact of this tuning constraint on the performance of the network are investigated. Mathematical formulation of the problem of provisioning of connections with advance reservation, in which the arrival time and departure time of all the connections are known in advance, is presented, and results for a small network are shown.     

二阶连续的四次有理插值样条及其应用

对有理插值样条有关问题进行了分析,并在此基础上构造了一种带参数的分母为线性的四次有理插值样条.把四次有理插值样条函数的连续性降为C2连续就可以提供额外的自由度,这对于控制曲线的形状具有较大的灵活性.     

Preference update for e-commerce applications: Model,language, and processing

It is likely that customers issue requests based on out-of-date information in e-commerce application systems. Hence, the transaction failure rates would increase greatly. In this paper, we present a preference update model to address this problem. A preference update is an extended SQL update statement where a user can request the desired number of target data items by specifying multiple preferences. Moreover, the preference update allows easy extraction of criteria from a set of concurrent requests and, hence, optimal decisions for the data assignments can be made. We propose a group evaluation strategy for preference update processing in a multidatabase environment. The experimental results show that the group evaluation can effectively increase the customer satisfaction level with acceptable cost. Peng Li is the Chief Software Architect of didiom LLC. Before that, he was a visiting assistant professor of computer science department in Western Kentucky University. He received his Ph.D. degree of computer science from the University of Texas at Dallas. He also holds a B.Sc. and M.S. in Computer Science from the Renmin University of China. His research interests include database systems, database security, transaction processing, distributed and Internet computer and E-commerce. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China in 1996, and a Master Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the PhD degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu’s research interests include distributed systems, grid computing, information security, mobile computing, and scientific computing. His PhD research work focus on the data management in secure and high performance data grid. He is a student member of the IEEE. I-Ling Yen received her BS degree from Tsing-Hua University, Taiwan, and her MS and PhD degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at the University of Texas at Dallas. Dr. Yen’s research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce, and self-stabilizing systems. She had published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA, and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Co-Chair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She is a member of the IEEE. Zhonghang Xia received the B.S. degree in applied mathematics from Dalian University of Technology in 1990, the M.S. degree in Operations Research from Qufu Normal University in 1993, and the Ph.D. degree in computer science from the University of Texas at Dallas in 2004. He is now an assistant professor in the Department of Computer Science, Western Kentucky University, Bowling Green, KY. His research interests are in the area of multimedia computing and networking, distributed systems, and data mining.     

Constrained multi-variable generalized predictive control using a dual neural network

Multi-variable generalized predictive control algorithm has obtained great success in process industries. However, it suffers from a high computational cost because the multi-stage optimization approach in the algorithm is time-consuming when constraints of the control system are considered. In this paper, a dual neural network is employed to deal with the multi-stage optimization problem, and bounded constraints on the input and output signals of the control system are taken into account. The dual neural network has many favorable features such as simple structure, rapid execution, and easy implementation. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. In addition, the dual network model can yield the exact optimum values of future control signals while many other neural networks only obtain the approximate optimal solutions. Hence the multi-variable generalized predictive control algorithm based on the dual neural network is suitable for industrial applications with the real-time computation requirement. Simulation examples are given to demonstrate the efficiency of the proposed approach.     

On Semismooth Newton’s Methods for Total Variation Minimization

In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton’s methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms. The research of this author is supported in part by Hong Kong Research Grants Council Grant Nos. 7035/04P and 7035/05P, and HKBU FRGs. The research of this author is supported in part by the Research Grant Council of Hong Kong. This work was started while the author was visiting Department of Applied Mathematics, The Hong Kong Polytechnic University. The research of this author is supported in part by The Hong Kong Polytechnic University Postdoctoral Fellowship Scheme and the National Science Foundation of China (No. 60572114). Michael Ng is a Professor in the Department of Mathematics at the Hong Kong Baptist University. As an applied mathematician, Michael’s main research areas include Bioinformatics, Data Mining, Operations Research and Scientific Computing. Michael has published and edited 5 books, published more than 140 journal papers. He is the principal editor of the Journal of Computational and Applied Mathematics, and the associate editor of SIAM Journal on Scientific Computing. Liqun Qi received his B.S. in Computational Mathematics at Tsinghua University in 1968, his M.S, and Ph.D. degree in Computer Sciences at University of Wisconsin-Madison in 1981 and 1984, respectively. Professor Qi has taught in Tsinghua University, China, University of Wisconsin-Madison, USA, University of New South Wales, Australia, and The Hong Kong Polytechnic University. He is now Chair Professor of Applied Mathematics at The Hong Kong Polytechnic University. Professor Qi has published more than 140 research papers in international journals. He established the superlinear and quadratic convergence theory of the generalized Newton method, and played a principal role in the development of reformulation methods in optimization. Professor Qi’s research work has been cited by the researchers around the world. According to the authoritative citation database ISIHighlyCited.com, he is one of the world’s most highly cited 300 mathematicians during the period from 1981 to 1999. Yu-Fei Yang received the B.Sc., M.S. and Ph.D. degrees in mathematics from Hunan University, P. R. China, in 1987, 1994 and 1999, respectively. From 1999 to 2001, he stayed at the University of New South Wales, Australia as visiting fellow. From 2002 to 2005, he held research associate and postdoctoral fellowship positions at the Hong Kong Polytechnic University. He is currently professor in the College of Mathematics and Econometrics, at Hunan University, P. R. China. His research interests includes optimization theory and methods, and partial differential equations with applications to image analysis. Yu-Mei Huang received her M.Sc. in Computer science from Lanzhou University in 2000. She is now pursuing her doctoral studies in computational mathematics in Hong Kong Baptist University. Her research interests are in image processing and numerical linear algebra.     

The finite horizon investor problem with a budget constraint

We study a model that incorporates a budget constraint in a decision making problem. Our goal is to maximize the expected wealth, where in each time period we can either stop the business getting our current wealth or to continue one additional time period and getting a random revenue. We show that when the wealth is scalar, the problem is NP-hard and we provide an FPTAS. However, when the wealth is vector with at least two components the problem cannot be approximated.     

FOE estimation: Can image measurement errors be totally “corrected” by the geometric method?

For the FOE estimation, there are basically three kinds of estimation methods in the literature: algebraic, geometric, and the maximum likelihood-based ones. In this paper, our attention is focused on the geometric method. The computational complexity of the classical geometric method is usually very high because it needs to solve a non-linear minimum problem with many variables. In this work, such a minimum problem is converted into an equivalent one with only two variables and accordingly a simplified geometric method is proposed. Based on the equivalence of the classical geometric method and the proposed simplified geometric method, we show that the measurement errors can at most be “corrected” only in one of the two images by geometric methods. In other words, it is impossible to correct the measurement errors in both of the two images. In addition, we show that the “corrected” corresponding pairs by geometric methods cannot in general meet some of the inherent constraints of corresponding pairs under pure camera translations. Hence, it is not proper to consider the “corrected” corresponding pairs as “faithful” corresponding pairs in geometric methods, and the estimated FOE from such pairs is not necessarily trustworthier. Finally, a new geometric algorithm, which automatically enforces the inherent constraints, is proposed in this work, and better FOE estimation and more faithful corresponding pairs are obtained.     

基于约束规划的最优浇次计划模型研究

基于约束规划提出最优连铸浇次计划模型.模型在以往研究的基础上增加了板坯厚度约束和交货期约束;为不同钢级赋予不同的量化值,按照组浇工艺要求规定的钢级范围来确定能够组成同一浇次的钢级量化允许值范围,将组浇工艺要求直接量化;模型不仅确定浇次的组成,而且确定浇次的顺序和炉次在浇次内的顺序.根据生产实际随机生成数据所做的实验表明所提出的方法是有效的.