应用时频分析方法辨识时变系统的模态参数

应用Gabor展开及Hilbert变换进行时变线性系统模态参数的辨识。利用非平稳信号的Gabor变换将结构响应信号表示在时频面上，并通过Gabor展开重构分离模态分量，建立每一阶模态的时变线性模型。对单模态时变线性模型应用Hilbert变换来辨识随时间变化的模态频率和阻尼。通过对刚度和阻尼慢变的两自由度系统模态参数的仿真辨识验证辨识方法的有效性。仿真结果表明：本文方法为时变线性系统的模态参数辨识提供了一条新的途径。     

A call admission and rate control scheme for multimedia support over IEEE 802.11 wireless LANs

Quality of service (QoS) support for multimedia services in the IEEE 802.11 wireless LAN is an important issue for such WLANs to become a viable wireless access to the Internet. In this paper, we endeavor to propose a practical scheme to achieve this goal without changing the channel access mechanism. To this end, a novel call admission and rate control (CARC) scheme is proposed. The key idea of this scheme is to regulate the arriving traffic of the WLAN such that the network can work at an optimal point. We first show that the channel busyness ratio is a good indicator of the network status in the sense that it is easy to obtain and can accurately and timely represent channel utilization. Then we propose two algorithms based on the channel busyness ratio. The call admission control algorithm is used to regulate the admission of real-time or streaming traffic and the rate control algorithm to control the transmission rate of best effort traffic. As a result, the real-time or streaming traffic is supported with statistical QoS guarantees and the best effort traffic can fully utilize the residual channel capacity left by the real-time and streaming traffic. In addition, the rate control algorithm itself provides a solution that could be used above the media access mechanism to approach the maximal theoretical channel utilization. A comprehensive simulation study in ns-2 has verified the performance of our proposed CARC scheme, showing that the original 802.11 DCF protocol can statically support strict QoS requirements, such as those required by voice over IP or streaming video, and at the same time, achieve a high channel utilization. Hongqiang Zhai received the B.E. and M.E. degrees in electrical engineering from Tsinghua University, Beijing, China, in July 1999 and January 2002 respectively. He worked as a research intern in Bell Labs Research China from June 2001 to December 2001, and in Microsoft Research Asia from January 2002 to July 2002. Currently he is pursuing the PhD degree in the Department of Electrical and Computer Engineering, University of Florida. He is a student member of IEEE. Xiang Chen received the B.E. and M.E. degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 1997 and 2000, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Florida, Gainesville, in 2005. He is currently a Senior Research Engineer at Motorola Labs, Arlington Heights, IL. His research interests include resource management, medium access control, and quality of service (QoS) in wireless networks. He is a Member of Tau Beta Pi and a student member of IEEE. Yuguang Fang received a Ph.D degree in Systems and Control Engineering from Case Western Reserve University in January 1994, and a Ph.D degree in Electrical Engineering from Boston University in May 1997. From June 1997 to July 1998, he was a Visiting Assistant Professor in Department of Electrical Engineering at the University of Texas at Dallas. From July 1998 to May 2000, he was an Assistant Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology. In May 2000, he joined the Department of Electrical and Computer Engineering at University of Florida where he got the early promotion with tenure in August 2003 and has been an Associate Professor since then. He has published over one hundred (100) papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He is currently serving as an Editor for many journals including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and ACM Wireless Networks. He is also actively participating in conference organization such as the Program Vice-Chair for IEEE INFOCOM’2005, Program Co-Chair for the Global Internet and Next Generation Networks Symposium in IEEE Globecom’2004 and the Program Vice Chair for 2000 IEEE Wireless Communications and Networking Conference (WCNC’2000).     

热管传热性能对小型热管换热器散热效能影响

在零重力辅助下,小热管热阻是小型热管换热器散热效能的重要影响因素之一。基于集总参数法对于换热器系统数值计算的高效性思想,建立了热管、热管换热器的集总参数模型。对同一热管换热器选用不同传热性能的热管,在不同传热温差和流体质量流速下,就其散热效能进行了数值计算。计算结果表明,在零重力辅助下,热管换热器管内热阻存在一临界值Rcri。当热阻RHP≤Rcri时,管外热阻起主导作用,热管换热器的散热效能εh几乎不随热管管内热阻的值而变化。     

基于Agilent-VEE平台的测试系统设计与应用

美国安捷伦公司(原惠普公司)的Agilent-VEE，NI公司的Lab VIEW和Lab Windows／CVI开发软件等是国际上公认的优秀的虚拟仪器开发平台软件，人们对以应用后两者开发测试系统比较了解，但对VEE在测试系统中的应用少有介绍。本文介绍基于Agilent-VEE平台和GPIB总线的实用测试系统的组成、特点及仪器控制方式。     

一种高性能的新结构IGBT

提出了一种低功率损耗的新结构IGBT.该新结构的创新点在于其复合耐压层结构,该耐压层包括深扩散形成的n型缓冲层和硼注入形成的透明背发射区两部分.虽然在正常工作条件下,该新结构IGBT工作于穿通状态,但器件仍具有非穿通IGBT(NPT-IGBT)的优良特性.该新结构IGBT具有比NPT-IGBT更薄的芯片厚度,从而可以获得更好的通态压降和关断功耗之间的折衷.实验结果表明:与NPT-IGBT相比较,新结构IGBT的功率损耗降低了40%.     

基站智能天线多波束合成技术

采用数字波束形成的技术，给出了基站智能天线多波束合成的有效算法。对圆环阵分布的基站天线多波束方向图进行了综合，得到了空间隔离度很好的多个波束，并将副瓣控制在30dB以下，结果令人满意。该方法可用于任意单元构成的不均匀阵列天线，还可用于波束展宽及扇形波束的合成。     

南通节制闸闸门自动控制系统设计

以南通节制闸为应用背景，介绍了液压启闭闸门自动控制系统的设计方法，给出了系统的三级网络结构及相应的操作控制功能。     

全球宽带电话市场分析

近两年．IP电话又叫网络电话的发展受到全球的关注。随着宽带接入的发展，基于宽带接入提供的网络电话具有了更大的生命力。从宽带电话的经营模式出发，探究这类业务的出现时现有话音业务市场的影响，进而分析这类业务的发展前景。     

汽车内燃机油的现状及发展趋势

从汽车内燃机润滑油的性能和质量与设计制造技术。环保，节能的关系出发，论述了国内外汽车内燃机润滑油性能和质量的现状和发展趋势，提出了提高我国汽车内燃机润滑油性能和质量的思考。     

Object segmentation and labeling by learning from examples

We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only "elementary nodes" representing homogeneous low-level regions. The "learning" phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template(s). These combinations are labeled as "object nodes" in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images.