基于星敏感器的姿态优化估计算法

对于采用星敏感器的航天器姿态确定问题,提出了一种快速的姿态优化估计方法;首先,根据Rodrigues参数和观测向量之间的线性关系,引入四元数,构造了不同的非奇异的优化准则;其次,借助四元数,求解了基于该准则的姿态优化算法;误差协方差分析和数值仿真结果表明,该算法与著名的QUaternion ESTmation(QUEST)算法相比,在相同的操作系统环境下,具有相同的姿态估计精度,但计算速度更快,对两种算法选取间隔为2000次的仿真步长,统计数据长度为100个采样点,最后对结果加权求平均值,统计结果显示仅为QUEST的三分之一;这种姿态优化估计方法具有一定的工程应用价值。     

基于NIOSⅡ软核处理器的嵌入式测试系统软硬件设计研究

介绍了利用NIOSⅡ软核处理器设计嵌入式测试系统的两类系统架构,详细讲述了基于NIOSⅡ软核处理器的嵌入式测试系统软硬件设计方法;最后结合EP2C8Q-208C8型FPGA芯片,利用Verilog语言描述A/D芯片的工作时序逻辑,利用NIOSⅡ软核处理器设计串口处理单元,将A/D采集的数据通过串口发送到计算机显示。实践表明,利用NIOS II软核处理器设计嵌入式测试系统,具有开发周期短,系统集成度高,功能灵活多样等特点,与传统利用单片机设计嵌入式测试系统相比,具有时钟频率高、运行速度快、调试方便等特点,是一种值得推广的嵌入式测试系统设计方法。     

大时延遥操作系统的波变量双边自适应控制

针对通信时延对遥操作系统稳定性和透明性的影响,研究了一种基于双边自适应控制和波变量理论的控制方法。通过设计波控制器保证通信传输模块的无源性,在保证系统稳定的基础上,调节波阻抗系数来提高系统的透明性,并在时延10 s的情况下进行主从端速度、位置和力的跟踪仿真实验,结果表明该方法和已有的双边自适应方法相比既能保证系统稳定且透明性好,达到较好的控制效果。     

基于QT4.7的ARM嵌入式Linux的游戏程序开发实现

在嵌入式系统中,ARM处理器与嵌入式Linux操作系统结合得愈加紧密,并广泛应用于社会的各行各业。同时,面对人机交互效率的需求不断提高,越来越多的嵌入式设备开始采用较为复杂的GUI系统,在此过程中QT得到了广泛的应用。本文通过介绍五子棋游戏开发,可以使读者了解基于QT4.7的ARM嵌入式Linux的应用程序开发实现过程。     

一种摄像机标定系统的设计与实现

对摄像机标定系统进行研究和设计,给出了基于Matlab的单个摄像机标定和立体标定的流程和实现方法,给出了基于棋盘格长度的标定精度方法的实现步骤。该标定系统具有简单、快捷、精度高等优点,是一种简便实用的标定方法。     

μC／OS—Ⅲ在Cortex—M3处理器上的移植

为了将μC／OS-Ⅲ移植到Cortex—M3处理器上,选用RealView MDK作为软件开发平台,针对Cortex—M3处理器特性编写了移植所需的C语言和汇编语言源代码,并验证了移植的正确性。移植后的μC／OS-Ⅲ能够稳定运行于Cortex～M3处理器上。该移植对大部分Cortex—M3处理器具有通用性,对其他架构处理器的μC／OS-Ⅲ移植具有参考作用。     

CBR系统中一种实例相似度算法的改进

本文对CBR系统中实例相似度的算法进行了改进,在传统实例相似性算法的基础上加入实例属性缺失度因子和实例可复用性因子。因此在实例检索中考虑实例检索结果的精确度和实例的可复用度,进而使检索的精确度提高和修改难度降低,最终提高CBR系统的精确度和实用性。     

Roll‐to‐roll UV embossing process applied for light bar‐based sub‐wavelength gratings for backlight

This study proposes a roll‐to‐roll process‐based sub‐wavelength grating, which is attached on a light bar to turn the side‐lit red/green/blue (620, 520, and 450 nm) incident rays into a uniformly and normally output white light with high illuminance from the light bar's surface. On the basis of the rigorous coupling wave analysis, the relationship between the first‐order transmission/reflection efficiency and the pitch of the gratings with different shapes was analyzed. The optimal design can effectively reduce the coupling length and enhance the white color balance for display applications.     

Delay‐dependent passivity and passification for uncertain Markovian jump systems with time‐varying delays

This paper deals with the problems of passivity analysis and passivity‐based controller design for Markovian jump systems with both time‐varying delays and norm‐bounded parametric uncertainties. Firstly, new delay‐dependent conditions for the considered system to be passive are obtained by using a mode‐dependent Lyapunov functional and by introducing some slack variables. These conditions are expressed by means of LMIs that are easy to check. It is shown through a numerical example that the obtained passivity conditions are less conservative than the existing ones in the literature. Secondly, the passification problem is investigated. On the basis of the obtained passivity conditions, dynamic output‐feedback controllers are designed, which ensure that the resulting closed‐loop system is passive. The effectiveness of the proposed design method is demonstrated by a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.     

Scalable influence maximization for independent cascade model in large-scale social networks

Influence maximization, defined by Kempe et al. (SIGKDD 2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (SIGKDD 2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this article, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread—it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.