基于ARToolkit Plus的增强现实系统实现及框架研究

文章首先对基于ARToolkit Plus和WPF的增强现实系统实现的关键技术问题进行了描述,具体讨论了在MVC框架下如何将数据采集层与呈现层进行分划,在保证可用性的同时提供较强的扩展能力,并基于上述框架实现了一个应用于住房装修的实例。     

网络对抗训练混合虚拟化平台设计研究

讨论了虚拟化技术实现原理及其在网络对抗教学中的应用上,分析了各类虚拟化技术的优缺点和构建网络对抗训练环境存在的问题。提出了一种混合虚拟化平台设计,该平台利用Libvirt虚拟化的应用程序编程接口（API）调用两种虚拟化技术同时运行在同一物理主机上。并详细叙述了混合虚拟化平台的实现过程和方法,并在多个虚拟机内同时运行测试基准,分析其可扩展性。多种虚拟化技术在同一物理机上的混合,实现了复杂实验环境的创建,对构建大规模的网络实验平台具有一定的参考价值。     

基于GPS/DR的GPS滞后时间自适应检测算法

针对GPS输出信息的随机延迟特性,基于航位推算(DR)系统惯性器件的实时性与短时高精度,提出了航向差和速度自适应GPS滞后时间检测方法,在此基础上,对GPS和DR数据进行数据对准,提出了自适应GPS滞后时间处理算法。在GPS/DR实际系统中的应用结果表明,本算法能够准确检测出GPS的滞后时间,提高车辆的定位精度。     

基于OPC技术和现场总线的热轧钢带的电控系统网络平台构建

从网络构建、系统组态等方面介绍热轧钢带生产控制系统的网络平台构建过程和方法。     

A novel STAP method for the detection of fast air moving targets from high speed platform

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar.However,the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets.It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters.However,in the presence of serious Doppler ambiguity of fast air moving targets,distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets,and as a result the STAP performance will degrade.In this paper,a novel STAP method is proposed for the detection of fast air moving targets from high speed platform,which can deal with the range walk of both clutter and targets.Effectiveness of the new method is verified via simulation examples.     

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

浅析可信计算技术及其发展

首先介绍了可信计算技术的发展背景,然后对可信计算技术的体系结构进行了研究,最后列举了一些可信计算技术在实际中一些的应用。     

Negative effects of sufficiently small initial weights on back-propagation neural networks

In the training of feedforward neural networks,it is usually suggested that the initial weights should be small in magnitude in order to prevent premature saturation.The aim of this paper is to point out the other side of the story:In some cases,the gradient of the error functions is zero not only for infinitely large weights but also for zero weights.Slow convergence in the beginning of the training procedure is often the result of sufficiently small initial weights.Therefore,we suggest that,in these cases,the initial values of the weights should be neither too large,nor too small.For instance,a typical range of choices of the initial weights might be something like(0.4,0.1) ∪(0.1,0.4),rather than(0.1,0.1) as suggested by the usual strategy.Our theory that medium size weights should be used has also been extended to a few commonly used transfer functions and error functions.Numerical experiments are carried out to support our theoretical findings.