多投影系统D-vision中双手6自由度力觉交互

D-vision系统(这里"D"有"Divide Screen"和"Duplex-Vision"双重含义)是一类基于PC机群的多投影虚拟现实系统(或简称多投影系统).给出D-vision系统中双手6自由度力觉交互的实现过程:在客户端协同控制两个力觉交互设备Spidar-G(Space Interface for Artificial Reality withGrip)实现双手协作交互,其次构造一个基于UDP的Socket类完成客户端和绘制服务器节点之间的通讯,传递跟踪球的位置、方向等信息;然后,通过分布绘制实现在大屏幕上无缝显示.最后实验结果表明:在D-vision系统中双手6自由度力觉交互是一种自然直观的人机交互方式.

基于D���定域和ITAE准则的主动队列管理算法

主动队列管理（active queue management，简称AQM）是网络拥塞控制的研究热点之一，其中的关键问题是如何设计反馈控制策略．提出一种新的基于D���定域和时间乘以误差绝对值乘积积分（integral of time-weighted absolute error,简称ITAE）性能准则的比例-积分-微分（proportional-integral-differential，简称PID���优化设计方法（简称DITAE-PID���，并用于AQM控制器的设计，控制闭环系统的理想动态性能．首先在复平面上设定一组理想的D���定域，然后以ITAE为目标函数，通过数值优化算法求出控制器的参数，使得闭环系统的所有特征根都在D���定域内，以降低排队延时，提高有效吞吐量．对比仿真实验结果表明孩算法能够预先探测和控制拥塞，有较好的鲁棒性，链路利用率更高，丢包率更小，平均队列长度更趋于期望值，同时，趋于期望队列长度的时间更短，其综合性能明显优于典型的随机早期探测（random early detection，简称RED���和比例-积分（proportional-integral，简称PI）算法．

Construction of 2-D directional filter bank by cascading checkerboard-shaped filter pair and CMFB

In this paper, we propose a new approach to construct a 2-dimensional (2-D) directional filter bank (DFB) by cascading a 2-D nonseparable checkerboard-shaped filter pair and 2-D separable cosine modulated filter bank (CMFB). Similar to diagonal subbands in 2-D separable wavelets, most of the subbands in 2-D separable CMFBs, tensor products of two 1-D CMFBs, are poor in directional selectivity due to the fact that the frequency supports of most of the subband filters are concentrated along two different directions. To improve the directional selectivity, we propose a new DFB to realize the subband decomposition. First, a checkerboard-shaped filter pair is used to decompose an input image into two images containing different directional information of the original image. Next, a 2-D separable CMFB is applied to each of the two images for directional decomposition. The new DFB is easy in design and has merits: low redundancy ratio and fine directional-frequency tiling. As its application, the BLS-GSM algorithm for image denoising is extended to use the new DFBs. Experimental results show that the proposed DFB achieves better denoising performance than the methods using other DFBs for images of abundant textures. (C) 2008 Elsevier B.V. All rights reserved.