一种具有PVR功能的嵌入式机顶盒设计与实现

该文实现了一个具有个人视频录像(PVR)功能的机顶盒。该机顶盒具有边看边录、手动录像、文件管理等功能。具有较好的商业应用价值。     

PetaLinux操作系统在MicroBlaze系统中的移植

随着性能的逐渐提高以及成本的不断降低,FPGA在嵌入式领域的应用日益广泛。本文介绍了PetaLinux操作系统的特点和目录结构,并分析了基于MLD的BSP自动生成技术,最后总结了PetaLinux操作系统移植的具体方法。这对设计基于MicroBlaze处理器的嵌入式系统具有重要指导意义。     

Windows2003 Server双网卡多站点内外网访问解决方案

Windows2003 Server操作系统自带IIS Server以性能稳定,吞吐量高被很多单位选作WEB Server,本文讨论在高校校园网环境下,一台Windows2003 Server配置成多站点,双网卡,来解决内外网访问服务器的方案。     

Robust exponential stability conditions for retarded systems with Lipschitz nonlinear stochastic perturbations

This paper investigates robust mean‐square exponential stability of a class of uncertain stochastic state‐delayed systems with Lipschitz nonlinear stochastic perturbation. Based on Lyapunov–Krasovskii functional (LKF) method and free‐weighting matrix technique, some new delay‐dependent stability conditions are established in terms of linear matrix inequalities (LMIs). In order to reduce the conservatism, (1) the delay is divided into several segments, i.e. the delay decomposition method is applied; (2) cross terms estimation is avoided; (3) some information of the cross terms relationships which has not been involved in Reference (IET Control Theory Appl. 2008; 2(11):966–973) is considered. Moreover, from the mathematical point of view, the results obtained by free‐weighting matrix technique can be equivalently re‐formulated by simpler ones without involving any additional free matrix variables. The effectiveness of the method is demonstrated by numerical examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Z-SKY: an efficient skyline query processing framework based on Z-order

Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and skyline candidates, and a suite of algorithms, namely, (1) ZSearch, which processes skyline queries, (2) ZInsert, ZDelete and ZUpdate, which incrementally maintain skyline results in presence of source dataset updates, (3) ZBand, which answers skyband queries, (4) ZRank, which returns top-ranked skyline points, (5) k-ZSearch, which evaluates k-dominant skyline queries, and (6) ZSubspace, which supports skyline queries on a subset of dimensions. While derived upon coherent ideas and concepts, our approaches are shown to outperform the state-of-the-art algorithms that are specialized to address particular skyline problems, especially when a large number of skyline points are resulted, via comprehensive experiments.     

Algorithms for constrained <Emphasis Type="Italic">k</Emphasis>-nearest neighbor queries over moving object trajectories

An important query for spatio-temporal databases is to find nearest trajectories of moving objects. Existing work on this topic focuses on the closest trajectories in the whole data space. In this paper, we introduce and solve constrained k-nearest neighbor (CkNN) queries and historical continuous CkNN (HCCkNN) queries on R-tree-like structures storing historical information about moving object trajectories. Given a trajectory set D, a query object (point or trajectory) q, a temporal extent T, and a constrained region CR, (i) a CkNN query over trajectories retrieves from D within T, the k (≥ 1) trajectories that lie closest to q and intersect (or are enclosed by) CR; and (ii) an HCCkNN query on trajectories retrieves the constrained k nearest neighbors (CkNNs) of q at any time instance of T. We propose a suite of algorithms for processing CkNN queries and HCCkNN queries respectively, with different properties and advantages. In particular, we thoroughly investigate two types of CkNN queries, i.e., CkNN_P and CkNN_T, which are defined with respect to stationary query points and moving query trajectories, respectively; and two types of HCCkNN queries, namely, HCCkNN_P and HCCkNN_T, which are continuous counterparts of CkNN_P and CkNN_T, respectively. Our methods utilize an existing data-partitioning index for trajectory data (i.e., TB-tree) to achieve low I/O and CPU cost. Extensive experiments with both real and synthetic datasets demonstrate the performance of the proposed algorithms in terms of efficiency and scalability.     

Robustness of fuzzy operators in environments with random perturbations

In many applications, such as intelligent decision systems, there are usually random perturbations caused by the constant changing of real situations, thus the analysis of the robustness with respect to random perturbations is practically important. In the side of fuzzy methods, a corresponding problem arises: will a small random perturbation of fuzzy input cause a big variance of fuzzy output? This paper study the robustness of fuzzy schemes in environments with random perturbations. It focuses on fuzzy algebraic operators, and proposes two methods to analyze their robustness in environments with random perturbations. The effectiveness and features of the methods are shown by simulations.     

A power- and coverage-aware clustering scheme for wireless sensor networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering (PCC) is proposed, which can adaptively select cluster heads according to a hybrid of the nodes' residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size (and its standard deviation), number of nodes alive over time, total energy consumption, etc.     

Automatic text summarization based on latent semantic indexing

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.