Fast method for SAR echo simulation of a three-dimensional ground scene

Due to huge calculation of SAR echo simulation of a three-dimensional (3D) ground scene, a fast simulation method is proposed. First, the data of a 3D ground scene are fractionally interpolated based on the Fractional Brownian Motion (FBM) model. Second, the data interpolated are divided into lots of small facets whose precision meets the simulation requirement, the backscattering coefficients of the small facets are calculated. Third, the shaded area is judged by the comparison method of overlook angles. Three parallel levels are analyzed and the kernel function is designed. Finally, the computing method of reduction adding and external thread are also utilized under the framework of GPU in order to get high efficiency. With the methods mentioned above, the imaging result of experimental DEM data verifies the validity and superiority of the proposed method.     

探究用电检查管理中营配信息集成的重要作用

随着我国经济和科学技术的不断发展,用电检查也应从传统的实地管理模式转为自动化、信息化的管理模式。详细阐述、分析了营配信息集成在用电检查工作中的作用。     

网络环境下的信息技术课程整合教学

基于信息技术的课程整合作为小学信息技术教学的发展方向之一,越来越受到信息技术科任老师的关注。本文对基于网络环境的信息技术课程整合优势、网络环境在课程整合中的利用策略、网络环境下的信息技术课程整合的核心活动等方面进行了分析,探讨了如何利用网络技术促进信息技术课与其它学科的整合。     

浅谈信息技术与数学学科的整合

信息技术的发展,使人们的学习和交流打破了过去的时空界限,为人类能力的提高和发挥作用带来了新的空间。通过信息技术与数学学科的整合,让数学更贴近生活,突破教学重点难点,提高了学生的发散性思维能力,让我们看到信息技术在教学中的重要性。     

TAPI软电话通信系统的模块化设计

首先分析了TAPI协议的结构、通信编程原理和编程应用环境,重点介绍了TAPI协议的层次化模型结构及其模块化设计方法,该方法可以对进一步研究和开发TAPI软电话应用产品提供借鉴。     

DCS系统在新型干法水泥生产线中的应用

介绍了在新型干法水泥生产线中全厂全线采用DCS系统进行设计应用,对企业降低费用、提高效率和生产率具有十分重大意义。     

数据分析及无损编码技术在电脑袜机上的应用

该文主要介绍了袜机数据的整合及对整合的效率进行了分析,有效的减小了数据所占用的位数;根据袜机数据的特点且存在大量的重复性字符,我们应用无损编码技术对袜机数据冗余部分进行压缩,通过比较几种无损编码技术的平均压缩率,得出了最适合袜机数据的压缩方式。     

基于工学结合的网络综合布线教学改革实践

该文阐述了网络综合布线在课程体系的作用,提出了工学结合的教学设计和教学方法,最后给出教学改革产生的教学效果。     

基于传统文化艺术资源整合基础上的山东区域特色动漫设计研究

山东区域的传统文化艺术资源主要包括:民间传说、寓言故事、文学作品、文物古迹、传统艺术等内容。     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.