Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

High‐error approximate dictionary search using estimate hash comparisons

A method for finding all matches in a pre‐processed dictionary for a query string q and with at most k differences is presented. A very fast constant‐time estimate using hashes is presented. A tree structure is used to minimize the number of estimates made. Practical tests are performed, showing that the estimate can filter out 99% of the full comparisons for 40% error rates and dictionaries of up to four million words. The tree is found to be efficient up to a 50% error rate. Copyright © 2006 John Wiley & Sons, Ltd.     

灵活运用数据库设计理论

简介了关系模型、键、完整性规则、E-R模型、函数依赖、模式分解和关系模式的范式等数据库技术基础和规范化设计理论，从键和完整性规则的实现、数据冗余的特殊情况和字典设计等方面论述了在实际设计工作中如何灵活运用关系数据库设计理论。     

Unseen users, unknown systems: Computer design for a scholar's dictionary

The Dictionary of Old English computing systems have provided access since the 1970s to a database of approximately three million running words. These systems, designed for a variety of machines and written in a variety of languages, have until recently been planned with computing center billing algorithms in mind. With personal workstations emphasis has shifted to building more elegant user interfaces and to providing the entire DOE database to editors around the world. While the shift from sequential files to random access files and the provision of extensive development tools have changed some of the design process, error checking and protection of the database against accidental intrusion have remained as central issues.Richard L. Venezky is Unidel Professor of Educational Studies and professor of Computer and Information Sciences at the University of Delaware. He was formerly professor and chair of Computer Sciences at the University of Wisconsin. His research interests include writing systems, literacy, knowledge representation, and computer-assisted instruction. Among his recent publications are The Subtle Danger: Reflections on the Literacy Abilities of America's Young Adults (Princeton, NJ: ETS, 1987), and Steps Towards a Modern History of American Reading Instruction (Review of Research in Education, 1986, vol. 13, 129–70).     

无理数字典码的测试数据压缩方案

提出了一种无理数字典码的测试数据压缩方案,能有效地压缩芯片测试数据。方案利用无理数序列建立字典,编码时只需进行相容性检查,无需填充无关位,简化了操作步骤;同时,选择局部压缩率最大的一组数据作为最终编码结果,保证压缩效果可以达到局部最优。对ISCAS 89标准电路Mintest集的实验结果显示,该方案的压缩效果优于传统压缩方法,如Golomb码、FDR码、混合定变长码等。     

稀疏卷积非负矩阵分解的语音增强算法

非平稳噪声和低信噪比条件下提高增强语音质量一直以来都是语音增强研究的难题。近年来,卷积非负矩阵分解在语音增强算法中成功应用,本文进一步考虑语音信号在时频域的稀疏性,提出了稀疏卷积非负矩阵分解(Sparse Convolutive Nonnegative Matrix Factorization, SCNMF)的语音增强算法。该算法包括训练和增强两个阶段。训练阶段通过SCNMF算法分别对纯净语音和噪声的频谱进行训练,得到纯净语音和噪声字典,并将其作为增强阶段的先验信息。增强阶段首先通过SCNMF算法对带噪语音的频谱进行分解,然后利用纯净语音和噪声联合字典对语音编码矩阵进行估计,重构增强语音。本文通过实验仿真分析了稀疏因子对增强语音质量的影响。实验结果表明,在非平稳噪声和低信噪比条件下,本文算法增强效果均优于多带谱减、非负矩阵分解、卷积非负矩阵分解等传统的算法。     

稀疏表示分类中遮挡字典构造方法的改进

针对稀疏表示分类算法中遮挡字典维数高且无冗余的问题,提出一种遮挡字典构造方法.首先通过图像分块得到各级的遮挡基图像;然后将所有互不相同的遮挡基图像按字典顺序转化为向量,并用这些向量作为遮挡字典的列,从而构造出维数相对较低且具有一定冗余度的遮挡字典.实验结果表明,该方法不仅明显提高了稀疏表示分类算法对遮挡人脸的识别率,而且还能通过减少图像的分块级数降低稀疏分解的耗时量,提高运算效率.     

基于方程式逆序数的软件水印算法*

针对方程式重排序等算法存在的程序运行速度、信息隐藏量等问题,提出基于方程式操作数系数排列逆序数的软件水印算法。重新排列那些可以相互交换的操作数,使各操作数的系数按照一定次序排列。通过排列的逆序数和二进制数的一一对应关系来隐藏水印。这一方法无须向程序中添加任何代码,并且能有效提高隐藏数据的效率,程序的规模和速度并不受影响。     

一个新的基于口令的密钥协商协议

以不经意多项式计算作为核心组件,提出了一个基于口令的密钥协商协议PSKA-I,该协议能够抵抗字典攻击但只能工作于认证模型。为解决协议PSKA-I这一缺陷,根据BCK安全模型设计了消息传输认证器,将协议PSKA-I转换为非认证模型中的安全协议PSKA-Ⅱ。上述协议口令的安全性由不经意多项式计算予以保证。与GL协议相比,该协议的通信及计算复杂度明显降低。     

结合自适应稀疏表示和全变分约束的图像重建

In view of the limitation of fixed complete orthogonal transformation, represented by two-dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction, this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint.The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning. It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation, thus reducing the sampling rate and improving the quality of image reconstruction. Finally, the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem. Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate.