多站时差频差高精度定位技术

针对多站时差频差联合定位系统,建立了定位模型,推导了定位误差表达式并作了仿真分析.仿真结果表明,在观测站构型较差的情况下,多站时差频差联合定位与纯时差或频差定位相比可提高定位精度.本文同时提出了利用相参脉冲串实现雷达信号高精度频差测量方法,推导了无模糊频率估计的充要条件及相参脉冲串测频的CRLB,仿真及实验室测试结果证明了在一定条件下可实现对相参雷达信号的Hz级测量精度,对工程应用具有一定的价值.     

一种快速车牌定位算法

提出一种快速的车辆牌照定位算法,对获取的车辆图像进行相应预处理后,根据图像的纹理特征,分析牌照区域字符分布情况及变化规律,利用车牌号码区域像素值的变化频率,在一个特定范围内进行水平和垂直方向上的定位,从而准确地得到车牌区域。实验结果表明,本文提出的算法能够较好地快速定位车辆图像中车牌区域。     

浅析网络传播环境下开源软件与知识产权

开源软件从一直面临知识产权问题,而且随着开源软件的发展,开源软件与知识产权的冲突愈加激烈。在网络传播环境下,解决这种冲突最好的办法是制定并遵循“开放下”的规则。     

CAD/CAE公共服务平台中资源共享智能型系统研究

在产业技术公共服务平台的影响和带动下,随着其用户规模不断扩大和信息技术的发展,越来越要求平台服务向智能化发展,有必要对其中之一的资源共享智能型系统建设进行探讨。     

采油厂信息建设整合提升配套技术研究

通过采油厂的信息化建设与实践,形成了采油厂信息建设整合提升配套技术体系;围绕勘探开发、生产运行、经营管理等三条主线全力推进信息应用的集成整合;形成分领域、按专业整合形成专业部门信息应用的整体解决方案。为采油厂信息化建设的科学发展、快速发展奠定了理论、技术、实施等基础。     

Segmentation and recognition of phonetic features in handwritten Pitman shorthand

There is a wish to be able to enter text into mobile computing devices at the speed of speech. Only handwritten shorthand schemes can achieve this data recording rate. A new, overall solution to the segmentation and recognition of phonetic features in Pitman shorthand is proposed in this paper. Approaches to the recognition of consonant outlines, vowel and diphthong symbols and shortforms, which are different components of Pitman shorthand, are presented. A new rule is introduced to solve the issue of smooth junctions in the consonant outlines which was normally the bottleneck for recognition. Experiments with a set of 1127 consonant outlines, 2039 vowels and diphthongs and 841 shortforms from three shorthand writers have demonstrated that the proposed solution is quite promising. The recognition accuracies for consonant outlines, vowels and diphthongs, and shortforms achieved 75.33%, 96.86% and 91.86%, respectively. From the evaluation of 461 outlines with smooth junction, the introduction of the new rule has a great positive effect on the performance of the solution. The recognition accuracy of smooth junction improves from 37.53% to 93.41% given a writing time increase of 14.42%.     

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

Real-time crowd motion planning

Real-time crowd motion planning requires fast, realistic methods for path planning as well as obstacle avoidance. In a previous work (Morini et al. in Cyberworlds International Conference, pp. 144–151, 2007), we introduced a hybrid architecture to handle real-time motion planning of thousands of pedestrians. In this article, we present an extended version of our architecture, introducing two new features: an improved short-term collision avoidance algorithm, and simple efficient group behavior for crowds. Our approach allows the use of several motion planning algorithms of different precision for regions of varied interest. Pedestrian motion continuity is ensured when switching between such algorithms. To assess our architecture, several performance tests have been conducted, as well as a subjective test demonstrating the impact of using groups. Our results show that the architecture can plan motion in real time for several thousands of characters.

Typology of teacher perception toward distance education issues – A study of college information department teachers in Taiwan

The popularity of distance education has grown rapidly over the last decade in Taiwan’s higher education, yet many fundamental teaching–learning issues are still in debate. While teacher–student interaction is a key success factor in distance education, little work has been done on the teachers. The intent of this research was to clarify teacher’s perceptions on key distance education issues and to develop a discernible typology of different groups of teachers based on their perceptions. Because there may be a gulf between teachers and the technology used in distance education, the target of this study was teachers in information related departments in Taiwan’s colleges, who were more familiar with current technology. Factor analysis and cluster analysis were used to derive the typology. Five higher-level issue constructs emerged from the factor analysis: learning effect, customization, administrative challenges, geographic and resource integration, and instructional design challenges. Four groups of teachers, namely the skeptics, the optimists, the mild-promising group, and the outlier, were identified using cluster analysis of teachers’ perceptions on these five higher-level issue constructs. The profiles of the four groups of teachers were summarized and implications were discussed, which should provide useful insights to the policy makers of higher education on distance education decisions.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.