Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Chosen plaintext attack on JPEG image encryption with adaptive key and run consistency

A JPEG image encryption with the adaptive key and run consistency of MCUs is proposed. The chosen-plaintext attack (CPA) is given here on this encryption scheme. First, the adaptive key can be reproduced from the encrypted image, so that the plaintext images with the same adaptive key can be constructed. Second, the existence of run consistency of MCUs (RCM) between the original image and the encrypted image facilitates rapid estimation. In addition, the single swap for the runs of MCUs with RCM is designed for more accurate estimation. Detailed cryptanalytic results suggest that this encryption scheme can only be used to realize perceptual encryption but not to provide content protection for digital images. Furthermore, applications of the CPA to break other encryption schemes with RCM are presented.     

Electrophoresis simulations using Chebyshev pseudo-spectral method on a moving mesh

We present the implementation and demonstration of the Chebyshev pseudo-spectral method coupled with an adaptive mesh method for performing fast and highly accurate electrophoresis simulations. The Chebyshev pseudo-spectral method offers higher numerical accuracy than all other finite difference methods and is applicable for simulating all electrophoresis techniques in channels with open or closed boundaries. To improve the computational efficiency, we use a novel moving mesh scheme that clusters the grid points in the regions with poor numerical resolution. We demonstrate the application of the Chebyshev pseudo-spectral method on a moving mesh for simulating nonlinear electrophoretic processes through examples of isotachophoresis (ITP), isoelectric focusing (IEF), and electromigration-dispersion in capillary zone electrophoresis (CZE) at current densities as high as 1000 A/m. We also show the efficacy of our moving mesh method over existing methods that cluster the grid points in the regions with large concentration gradients. We have integrated the adaptive Chebyshev pseudo-spectral method in the open-source SPYCE simulator and verified its implementation with other electrophoresis simulators.     

Cross-layer modeling of adaptive wireless links for QoS support in heterogeneous wired-wireless networks

Future wired-wireless multimedia networks require diverse quality-of-service (QoS) support. To this end, it is essential to rely on QoS metrics pertinent to wireless links. In this paper, we develop a cross-layer model for adaptive wireless links, which enables derivation of the desired QoS metrics analytically from the typical wireless parameters across the hardware-radio layer, the physical layer and the data link layer. We illustrate the advantages of our model: generality, simplicity, scalability and backward compatibility. Finally, we outline its applications to power control, TCP, UDP and bandwidth scheduling in wireless networks. The work by Q. Liu and G. B. Giannakis are prepared through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The work by S. Zhou is supported by UConn Research Foundation internal grant 445157. Qingwen Liu (S’04) received the B.S. degree in electrical engineering and information science in 2001, from the University of Science and Technology of China (USTC). He received the M.S. degree in electrical engineering in 2003, from the University of Minnesota (UMN). He currently pursues his Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Minnesota (UMN). His research interests lie in the areas of communications, signal processing, and networking, with emphasis on cross-layer analysis and design, quality of service support for multimedia applications over wired-wireless networks, and resource allocation. Shengli Zhou (M’03) received the B.S. degree in 1995 and the M.Sc. degree in 1998, from the University of Science and Technology of China (USTC), both in electrical engineering and information science. He received his Ph.D. degree in electrical engineering from the University of Minnesota, 2002, and joined the Department of Electrical and Computer Engineering at the University of Connecticut, 2003. His research interests lie in the areas of communications and signal processing, including channel estimation and equalization, multi-user and multi-carrier communications, space time coding, adaptive modulation, and cross-layer designs. He serves as an associate editor for IEEE Transactions on Wireless Communications since Feb. 2005. G. B. Giannakis (Fellow’97) received his Diploma in Electrical Engineering from the National Technical University of Athens, Greece, 1981. From September 1982 to July 1986 he was with the University of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engineering, 1986. After lecturing for one year at USC, he joined the University of Virginia in 1987, where he became a professor of Electrical Engineering in 1997. Since 1999 he has been a professor with the Department of Electrical and Computer Engineering at the University of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications. His general interests span the areas of communications and signal processing, estimation and detection theory, time-series analysis, and system identification -- subjects on which he has published more than 200 journal papers, 350 conference papers and two edited books. Current research focuses on transmitter and receiver diversity techniques for single- and multi-user fading communication channels, complex-field and space-time coding, multicarrier, ultra-wide band wireless communication systems, cross-layer designs and sensor networks. G. B. Giannakis is the (co-) recipient of six paper awards from the IEEE Signal Processing (SP) and Communications Societies (1992, 1998, 2000, 2001, 2003, 2004). He also received the SP Society’s Technical Achievement Award in 2000. He served as Editor in Chief for the IEEE SP Letters, as Associate Editor for the IEEE Trans. on Signal Proc. and the IEEE SP Letters, as secretary of the SP Conference Board, as member of the SP Publications Board, as member and vice-chair of the Statistical Signal and Array Processing Technical Committee, as chair of the SP for Communications Technical Committee and as a member of the IEEE Fellows Election Committee. He has also served as a member of the IEEE-SP Society’s Board of Governors, the Editorial Board for the Proceedings of the IEEE and the steering committee of the IEEE Trans. on Wireless Communications.     

基于广义周期性的单通道多分量正弦调频信号分离和参数估计

针对噪声条件下的单通道多分量正弦调频(SFM)信号,该文提出一种信号分离和参数提取方法。利用正弦调频信号的广义周期性进行奇异值分解,以求出分量信号的调制频率;通过离散点搜索,估计出分量信号的调频(FM)初始相位、调制指数及载频,并对这些估计值利用信赖域算法进行优化,减小误差;利用内积计算,估计分量信号的幅度和初始相位。此外,还利用自相关矩阵特征值分解估计混合信号的信噪比(SNR),并根据信噪比确定停止分解的阈值。在仿真与分析中,针对具体的信号详细说明了该方法的各步骤,并在不同信噪比条件下分析了该方法的参数估计精确度。     

自适应跳频卫星通信系统的设计与实现

鉴于自适应跳频系统的良好抗干扰性能,提出了一种自适应跳频卫星通信系统的设计方案。在简要介绍自适应跳频技术基本原理的基础上,叙述了自适应跳频通信系统的结构和通信过程,重点讨论了自适应跳频卫星通信系统的设计模型、同步方式、频率集更新算法和抗干扰性能,最后,给出了自适应跳频卫星通信抗干扰终端设备的基本实现过程。     

相控阵机载雷达杂波抑制的时—空二维自适应滤波

本文提出了一种机载预警侧面相控阵雷达抑制地杂波的时-空二维自适应方法,该方法先对各列子阵的数据作多通道的多卜勒滤波,然后分别对相同多卜勒滤波器输出的数据作Capon自适应处理。理论分析和计算机模拟结果表明,本文方法的杂波抑制性能在天线阵元存在一定的随机幅度和相位误差情况下损失不大,对阵元失效也不敏感。     

基于模式间混淆关系自适应构建层次化分类器

为了解决层次化分类器的设计难点——子分类器的层属关系及其内部组成的确定,本文首先定义了模式间混淆关系,用于描述不同模式在判决域中的相互作用;进而提出了基于混淆矩阵度量模式间混淆关系的方法。设计并实现了多模式混淆关系分析机MPCRAM,将有师指派和无师自组两种常用的模式重组方法有机结合,遵循Fisher准则,自适应地产生层次化分类器结构。大量综合测试证实了该方法有效、实用,可显著提高分类器的识别性能和稳健性。     

On clutter dof for phased array airborne early warning radars

In recent years, considerable attention has been paid to adaptive clutter suppression for airborne early warning (AEW) radars. A necessary condition for efficient clutter suppression is that the number of adaptive degrees of freedom (DOF) is no less than that of clutter DOF. This paper analyzes the DOF of clutter for phased array AEW radars. The new results obtained here are helpful to design and adaptive system for clutter suppression.     

新的自适应盲噪声抑制在语音处理中的应用

传统的自适应滤波去噪的系统中需要一个参考信号,实际中一般事先并不知道噪声的特性,而且噪声也是时变的。因此提出一种新的基于盲噪声抑制方法,可降噪并跟踪噪声特性的变化,随时调节滤波器的系数,比传统的自适应噪声控制具有优势。