复FIR数字滤波器幅值约束Chebyshev设计

L.J.Karam和McClellan最早得到了有关复数域Chebyshev逼近的复交错点组定理,并提出了以此定理为基础的复Remez算法用于复FIR数字滤波器的Chebyshev设计.本文首先给出并证明了复交错点组定理在带不等式约束条件下的扩展定理,之后,根据此扩展定理中对最优解极值频率点特性的描述,提出了一种有效的算法来解决带幅值不等式约束的复FIR数字滤波器的Chebyshev设计问题.这一新算法中还结合了复Remez算法及赖晓平提出的迭代Remez算法,并且如果问题的解存在则保证收敛到此解.作者把上述算法做成了MATLAB语言程序,并进行了大量的实例设计实验,仿真结果表明此算法有效而可靠.     

带约束的复FIR数字滤波器Chebyshev设计算法

本文主要讨论了复FIR数字滤波器的频域不等式约束Chebyshev设计问题.作者首先把文献[1]中的复交错点组定理扩展到有不等式约束的情况,之后根据扩展定理中对最优解特性的描述,并结合复Remez算法[1]及赖晓平的迭代Remez算法[2][3],提出了一种有效的算法来解决频域带不等式约束的复FIR数字滤波器的Chebyshev设计问题.如果问题的解存在,则算法能保证收敛到最优解.作者用MATLAB语言对上述算法进行了实现并做了仿真分析.     

复算子■与二维静场

著名数学家Hadamard给我们提出一个奇怪的命题——实域中两个真理之间的最短路程是通过复域。据此,本文提出复算子和它的相应理论,把矢量理论中二维散度.与旋度×,积分理论中Gauss定理和Stokes定理融合于一个统一的整体。本文进一步研究了二维静电场与稳流磁场,使其支配的积分方程统一于复变函数论的留数定理,而线电荷与电流则是提供积分贡献的留数源。     

无耗网络的几个定理

本文总结了无耗互易网络的全匹配定理,提出了广义模对称定理。证明了除N=1和3而外,对任意端口网络均有可能达到全匹配。文中,给出了若干实例。     

一种高频周期信号的低频采样方法

在模拟信号转换为数字信号的过程中,对转换结果影响最大的是采样频率的设定,针对采样频率的设定受到Nyqu ist定理的限制,传统的模拟信号数字化系统可以处理的模拟信号频率较低的现状,文中主要从理论上推导和证明了一种突破Nyqu ist定理的限制方法,使用这种方法可以在模拟信号转换为数字信号的系统中,使用较低采样频率对高频率周期信号进行采样。从而能够使数字处理技术得到更加广泛的应用。     

应用FDTD/MP方法计算波导加载谐振器的复谐振频率

应用FDTD结合MP的方法计算了波导加载谐振器的谐振频率和品质因数。与FDTD/DFT方法比较,这种方法减少了对FDTD运行时间的需求,并且提高了数值精度。与FDTD/Prony方法比较,其优点是计算效率高,对噪声的敏感小。文中以计算实例说明了算法的有效性。     

电磁互易对称性和无耗对称性

本文深入探讨了两种电磁对称性:Lorentz互易对称性和Hermite无耗对称性.给出了它们在多端口网络系统中的表达形式.文中指出,非互易无耗网络的阻抗矩阵可以存在实部,即Z=R+jX,且RT=-R反对称,而网络型Foster定理则深刻的揭示了S参数的频率导数S(δ)S+/(δ)(jω)有广义惯性,不能过大.本文最后得到在辛意义下,电磁互易对称即辛内积正交,而二阶归一化电阻R矩阵正好是典型的辛矩阵.     

由传递函数分析光电成像系统的分辨率

光电成像系统的空间分辨是表征系统性能的一个最主要指标，通过分析光电成像系统中的传递函数，用特征频率法和传递函数对频率的积分方法分析了系统的空间分辨，并针对一组典型的光电成像系统参数进行了计算。结果为：系统在Nlyquist频率处显示图像的MIF=0．017；传递函数对频率积分法计算表明：系统实际的分辨率为18．6cycles/mm。分析过程表明：显示器是限制实时成像系统空间分辨的主要因素，其次是探测器的像元尺寸，再次是电子系统的带宽。同时表明，分辨率只有在系统中讨论才有意义，仅用Nyquist频率表示是欠妥的。     

基于SAWR的无源无线混合传感器研究

匹配网络是无源无线声表面波(SAWR)混合传感器的重要组成部分.该文提出了一种改进的低失配网络,即采用2个电抗元件将SAWR匹配成等效RLC串联谐振电路,再用第三个电抗元件灵活改变谐振频率.该方法可保持谐振点为最佳匹配状态,获得稳定的低失配和高品质因数.文中对双谐振器结构的混合传感器进行了研究,对应容性传感元1～10 pF的变化范围,谐振器的差频变化为147 kHz,驻波比为1～1.02,品质因数与谐振器原值基本一致.     

复接系统定时的数字提取技术及其性能

本文介绍了复接系统中数字定时提取电路的原理和结构,分析了数字定时提取电路输出定时的抖动特性,包括抖动幅度和抖动频率。同时,本文还分析了数字定时提取在数字复接系统中的应用,以及其对复接系统性能的影响,并给出实验结果     

Complex waves

Plotting the frequency versus the wave number provides a unifying way of permitting visualization of the linear dynamics of distributed systems. A series of simple examples is presented in order to illustrate the physical significance of ordinary and evanescent waves, absolute instability, waves on moving media, and convective instability or wave amplification. The examples give a rare opportunity to develop a complete physical and mathematical picture of the dynamics, since the presentation can serve as a guide to two films?produced by the Education Development Center for the National Committee on Electrical Engineering Films?in which the dispersion relation evolves by computer animation on the bottom half screen in synchronism with changes in the physical phenomenon in the top half screen.     

用自由空间法测试介质电磁参数

提出了一种测试介质材料微波复介电常数和复磁导率的新方法;建立了测试方法的物理模型;通过理论分析,获得了求解被测材料微波电磁参数的计算表达式;研制了测试装置和测试系统,讨论了用于测试系统的校准方法和提高测试准确度的时域门技术.在8～12GHz的频带范围内,对多种介质材料进行了实测,结果表明,本文提出的测试方法和研制的测试系统是正确可靠的,具有实用价值.     

Complex Low-Pass Filters

Zero-if transceivers suffer from the imbalance of the I and Q paths. By using a complex low-pass filter topology instead of a conventional pair of real low-pass filters, this imperfection can be reduced. Both analytical and numerical analysis show that the proposed technique is significantly more robust to circuit imperfections than the traditional architecture.     

Complex decision diagrams

Complex decision diagrams to represent integer-valued functions in the form of complex Hadamard transforms and spectra are introduced. With the distinctive and unique properties of the transform, the novel complex decision diagrams could be further simplified by reduction rules and a half-spectra theorem that will lead to a more compact representation     

Complex Derivative Filters

Steerable filters are a valuable tool for various low-level vision tasks. In this paper, we argue for the use of complex analysis in the context of 2-D steerable filters. In particular, we recommend the use of complex partial derivatives as a computational basis. Complex derivatives have a major advantage in comparison to real derivatives: they show a canonical rotation behavior, namely a rotation affects the derivative just by a multiplication with a complex unit number. So, the complex derivatives can be steered in a more elegant way and above that they are less expensive to compute. We present several analytical formulas for common and new filter kernels in terms of complex derivatives. Further we relate the complex derivatives of a Gaussian with the Gauss–Laguerre transform and show that the Gauss–Laguerre functions provide an optimal signal representation for local and smooth images. We discuss various finite difference schemes for the realization of the derivatives and use them in practice. In a first experiment, we use a newly introduced filter kernel for anisotropic blurring. The complex formalism offers an elegant way to locally adapt the shape and orientation of the kernel. Second, we use the proposed filters as matched filters to detect vessels in retinal images.      

Intermittent Complex Systems

Two models are given for a complex system; they depend on the demand of the system. In Model I, the system is stopped when there is no demand; in Model II, the system is always operable irrespective of the demand. Laplace transforms of the probabilities of the system states are obtained.     

Complex image theory-revisited

A quasi-historical sketch of complex image theory for low-frequency electromagnetic fields is presented from a personal perspective. Studies using complex image theory, on launching TM (transverse magnetic) modes into the Earth-ionosphere waveguide from a horizontal insulated cable, grounded at the end points, are described. Subsequent implementation and further development of the concept by other authors is summarized     

Complex Infomax: Convergence and Approximation of Infomax with Complex Nonlinearities

Independent component analysis (ICA) for separating complex-valued sources is needed for convolutive source-separation in the frequency domain, or for performing source separation on complex-valued data, such as functional magnetic resonance imaging or radar data. Previous complex Infomax approaches that use nonlinear functions in the updates have proposed using bounded (and hence non-analytic) nonlinearities. In this paper, we propose using an analytic (and hence unbounded) complex nonlinearity for Infomax for processing complex-valued sources. We show by simulation examples that using an analytic nonlinearity for processing complex data has a number of advantages. First, when compared to split-complex approaches (i.e., approaches that split the real and imaginary data into separate channels), the shape of the performance surface is improved resulting in better convergence characteristics. We also show that using an analytic complex-valued function for the nonlinearity is more effective in generating the higher order statistics required to establish independence when compared to complex nonlinear functions, i.e., functions that are ℂ → ℂ This work was supported in part by the National Science Foundation Career Award, NSF NCR-9703161 (to TA) and the National Institutes of Health 1 R01 EB 000840-01 (to VC). Vince Calhoun received a bachelor’s degree in Electrical Engineering from the University of Kansas, Lawrence, Kansas, in 1991, master’s degrees in Biomedical Engineering and Information Systems from Johns Hopkins University, Baltimore, in 1993 and 1996, respectively, and the Ph.D. degree in electrical engineering from the University of Maryland Baltimore County, Baltimore, in 2002. He worked as a Senior Research Engineer in Psychiatric Neuro-Imaging at Johns Hopkins from 1993 until 2002. He is currently the Director of the Medical Image Analysis Laboratory and an associate adjunct professor at Yale University. He is associate editor of the IEEE signal processing letters and on the editorial board for the Journal of Human Brain Mapping. Dr. Calhoun is a member of the IEEE, the American Scientific Affiliation, the Organization for Human Brain Mapping, and the International Society for Magnetic Resonance in Medicine. He has organized workshops for human brain mapping (HBM), the society of biological psychiatry (SOBP), and the international conference of independent component analysis and blind source separation (ICA). He is currently serving on the IEEE Machine Learning for Signal Processing (MLSP) Technical Committee and was the general chair for MLSP 2005 in Mystic, CT. He works primarily with magnetic resonance imaging (functional imaging, diffusion tensor imaging, and structural imaging) and electroencephalography (EEG) data and is the author of more than 70 refereed articles in journals and conference proceedings in the areas of image processing, data fusion, adaptive signal processing, neural networks, statistical signal processing, and pattern recognition. Tülay Adalı received the B.S. degree from Middle East Technical University, Ankara, Turkey, in 1987 and the M.S. and Ph.D. degrees from North Carolina State University, Raleigh, in 1988 and 1992 respectively, all in electrical engineering. In 1992, she joined the Department of Electrical Engineering at the University of Maryland Baltimore County, Baltimore, where she currently is a professor. She has worked in the organization of a number of international conference and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) and the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). She was the general co-chair for the NNSP workshops 2001-2003. She is the past chair and a current member of the IEEE Machine Learning for Signal Processing Technical Committee and is serving on the IEEE Signal Processing Society conference board. She is an associate editor for the IEEE Transactions on Signal Processing and the Journal of VLSI Signal Processing Systems. She has also guest-edited a number of special issues for the IEEE Transactions on Neural Networks and the VLSI Signal Processing Systems on biomedical, multimedia, and data mining applications of neural networks. She has authored or co-authored more than 175 refereed publications in the areas of statistical signal processing, neural computation, adaptive signal processing, biomedical data analysis, bioinformatics, and communications. Dr. Adalı is the recipient of a 1997 National Science Foundation CAREER Award.