On the generation of large passive macromodels for complex interconnect structures

This paper addresses some issues related to the passivity of interconnect macromodels computed from measured or simulated port responses. The generation of such macromodels is usually performed via suitable least squares fitting algorithms. When the number of ports and the dynamic order of the macromodel is large, the inclusion of passivity constraints in the fitting process is cumbersome and results in excessive computational and storage requirements. Therefore, we consider in this work a post-processing approach for passivity enforcement, aimed at the detection and compensation of passivity violations without compromising the model accuracy. Two complementary issues are addressed. First, we consider the enforcement of asymptotic passivity at high frequencies based on the perturbation of the direct coupling term in the transfer matrix. We show how potential problems may arise when off-band poles are present in the model. Second, the enforcement of uniform passivity throughout the entire frequency axis is performed via an iterative perturbation scheme on the purely imaginary eigenvalues of associated Hamiltonian matrices. A special formulation of this spectral perturbation using possibly large but sparse matrices allows the passivity compensation to be performed at a cost which scales only linearly with the order of the system. This formulation involves a restarted Arnoldi iteration combined with a complex frequency hopping algorithm for the selective computation of the imaginary eigenvalues to be perturbed. Some examples of interconnect models are used to illustrate the performance of the proposed techniques.     

Fast Passivity Verification and Enforcement via Reciprocal Systems for Interconnects With Large Order Macromodels

With the ever increasing signal speeds, signal integrity issues of high-speed VLSI designs are presenting increasingly difficult challenges for state-of-the-art modeling and simulation tools. Consequently, characterization and passive macromodeling of high-speed modules such as interconnects, vias, and packages based on tabulated data are becoming important. This paper presents a fast algorithm for passivity verification and enforcement of large order macromodels of scattering parameter based multiport subnetworks. Numerous examples tested on this algorithm demonstrate a significant speed-up compared to the existing algorithms in the literature     

Passivity enforcement via perturbation of Hamiltonian matrices

This paper presents a new technique for the passivity enforcement of linear time-invariant multiport systems in state-space form. This technique is based on a study of the spectral properties of related Hamiltonian matrices. The formulation is applicable in case the system input-output transfer function is in admittance, impedance, hybrid, or scattering form. A standard test for passivity is first performed by checking the existence of imaginary eigenvalues of the associated Hamiltonian matrix. In the presence of imaginary eigenvalues the system is not passive. In such a case, a new result based on first-order perturbation theory is presented for the precise characterization of the frequency bands where passivity violations occur. This characterization is then used for the design of an iterative perturbation scheme of the state matrices, aimed at the displacement of the imaginary eigenvalues of the Hamiltonian matrix. The result is an effective algorithm leading to the compensation of the passivity violations. This procedure is very efficient when the passivity violations are small, so that first-order perturbation is applicable. Several examples illustrate and validate the procedure.     

Passivity Verification in Delay-Based Macromodels of Multiconductor Electrical Interconnects

This paper presents a generalized theory of passivity verification in delay-based macromodels for multiconductor transmission line networks generated using the method of characteristics (MoCs). We demonstrate that the passivity in an MoC macro-model is equivalent to the nonnegative definiteness in the admittance matrices of two submodels. We then provide the necessary and sufficient conditions for each submodel to have a nonnegative definite admittance matrix. The presented theory develops an algebraic test to verify the passivity in MoC macromodels. Numerical results demonstrate the validity of the proposed theory.     

Global Passivity Enforcement Algorithm for Macromodels of Interconnect Subnetworks Characterized by Tabulated Data

With the continually increasing operating frequencies, complex high-speed interconnect and package modules require characterization based on measured/simulated data. Several algorithms were recently suggested for macromodeling such types of data to enable unified transient analysis in the presence of external network elements. One of the critical issues involved here is the passivity violations associated with the computed macromodel. To address this issue, a new passivity enforcement algorithm is presented in this paper. The proposed method adopts a global approach for passivity enforcement by ensuring that the passivity correction at a certain region does not introduce new passivity violations at other parts of the frequency spectrum. It also provides an error estimate for the response of the passivity corrected macromodel.     

Improved transparency in energy-based bilateral telemanipulation

In bilateral telemanipulation algorithms based on enforcing time-domain passivity, internal friction in the devices poses an additional energy drain. This can severely decrease the obtainable transparency of these algorithms when high amounts of friction are present in the slave device. Based on a model of the friction, the dissipated energy can be estimated and reclaimed inside the energy balance of the control algorithm. Extending the energy balance which is monitored, decreases the net passivity of the telemanipulation system enforced by the control algorithm, which usually enforces passivity of just the bilateral controller. Experimental results are provided that demonstrate the effectiveness of the proposed approach in increasing the obtainable transparency. As long as the physically dissipated energy is underestimated, the telemanipulation system as a whole will remain passive. Thus the guaranteed stability property of the time-domain passivity algorithm is maintained.     

Passivity and Passification of Uncertain Fuzzy Systems with Impulse

The passivity and feedback passification problems of fuzzy systems with parameter uncertainties and impulse are first presented in this paper. Based on the parallel distributed compensation （PDC） technique, some passivity and passification conditions are proposed in terms of linear matrix inequalities （LMIs）. Numerical examples are given to show the correctness and effectiveness of our theoretical results.     

An Adaptive Sampling Technique for Passivity Characterization and Enforcement of Large Interconnect Macromodels

This paper deals with the characterization and enforcement of passivity for linear lumped interconnect macromodels. An adaptive accuracy-controlled frequency sampling process is employed to identify a set of frequency bands where the macromodel is locally passive. These results are employed as a preliminary step, enabling the fast computation of imaginary eigenvalues of the Hamiltonian matrix associated to the macromodel. Then, iterative perturbation is employed to remove these eigenvalues from the imaginary axis and to achieve global passivity. The resulting scheme is highly optimized for macromodels having large dynamic order and with a sparse structure. Significant speedup factors up to two orders of magnitude are achieved with respect to a standard implementation of the same passivity compensation scheme based on a full eigensolver     

Passivity Enforcement With Relative Error Control

This paper introduces a new error control strategy in passivity enforcement schemes for linear lumped macromodels. We consider the general class of a posteriori passivity enforcement algorithms based on Hamiltonian matrix perturbation. Standard available formulations preserve the accuracy during passivity enforcement using special matrix norms associated to the controllability Gramian of the macromodel. This procedure leads to absolute error control. On the other hand, it is well known that relative error control in the macromodel is sometimes preferable, especially for structures that are characterized by small coupling coefficients or high dynamic range in their responses. Here, we present a frequency-weighting scheme leading to the definition of a modified Gramian that, when employed during passivity enforcement, effectively leads to relative error control. Several examples illustrate the reliability of the proposed technique.     

Temporal error concealment using motion field interpolation

An error concealment algorithm based on motion field interpolation is presented. For each pixel in a damaged block, the algorithm recovers a motion vector using bilinear interpolation of neighbouring motion vectors. This vector is then used to conceal the damaged pixel. Overlapped motion compensation is used to combine this algorithm with a boundary matching error concealment algorithm. Simulation results demonstrate the superior performance of the proposed algorithms     

基于特征线方法的无源传输线模型

基于特征线方法的传输线模型只能保证模型的因果性,但是不能保证模型的无源性。针对上述问题,该文提出了一种无源性补偿方法来实现传输线宏模型的无源性。该方法扩展了现有的用于集总模型的无源性补偿算法,以等式约束的二次规划方法为基础,采用拉格朗日乘数法进行优化。数值例子表明该方法在有限的仿真时间内产生了精确的无源宏模型。     

Protocol verification using database technology

A novel application of databases in communications networks, namely, protocol verification on a parallel database machine, is described. An approach to protocol verification that uses database algorithms executing on a commercially available, parallel architecture called a hypercube multicomputer is introduced. The goal is to achieve the high degree of computational parallelism necessary to explore rapidly the global state space of even very complex protocols, significantly reducing the time required to verify a protocol and allowing formal verification to be included as part of the process of protocol design. An overview is provided of the relational model of protocol verification used, and extensions to the model are described. The hypercube multicomputer and the algorithms for relational database operations designed to execute in that environment are then described. Estimates of the performance improvements achievable by parallel executing of verification algorithms in the proposed system are given     

基于遗传算法的RBF神经网络在涡流传感器非线性补偿中应用

为了解决涡流传感器的非线性问题,应用遗传算法(GA)训练径向基函数(RBF)神经网络(NN)实现其非线性补偿.介绍非线性补偿的原理和网络训练方法.从实测数据出发,建立了涡流传感器的非线性补偿模型.该方法能同时优化网络结构和参数,具有全局寻优能力,补偿精度高、鲁棒性好、网络训练速度快、能实现在线软补偿.实验结果表明,所采用的涡流传感器非线性补偿方法是有效的和可行的.补偿后,最大非线性误差在0.5%范围内,具有良好的线性.     

一种快速的混合参数宏模型无源性补偿方法

针对高速数字系统的混合参数宏建模,采用模态矢量拟合算法具有模型拟合精度高,易于电路仿真集成的优点,但是初始生成的宏模型可能会在局部频带内不满足系统无源性。该文提出一种基于特征值模态的混合参数宏模型无源性补偿方法。首先构建了包含宏模型特征对的无源性目标函数,利用特征对加权的形式来控制参数扰动带来的模型精度变化,然后结合相应的无源性约束条件,将无源性补偿过程等效为可以解析求解的最小二乘优化问题。该方法在补偿混合参数宏模型无源性的同时,兼顾模型在任意端接激励源条件下的应用要求。结合实例并与留数扰动、模态扰动等无源性补偿方法进行比较,结果表明该方法在精度或效率方面均具有一定的优势。     

点源目标质心探测中单个盲元线性插值补偿算法选取

自适应光学系统中应用哈特曼波前传感器对点源目标进行质心探测时,光电探测器上子孔径区域内存在的盲元会引入一定的附加质心探测误差,为了减小质心误差,需要选取合适的盲元补偿算法获得相对合理的补偿值。推导了哈特曼波前传感器子孔径内单个盲元补偿后造成的附加质心探测误差。利用仿真统计了各种利用盲元邻域正常像素进行线性插值的单个盲元补偿算法对点源目标质心探测误差的影响。提出了单个盲元邻域像素线性插值补偿算法选取的若干准则,指出选用盲元上下左右4邻域像素的均值进行补偿造成的平均质心误差最小,而选用盲元左右两个像素平均补偿算法可以在补偿误差较小的情况下简化运算。实验结果与仿真结果相符合。     

基于DSA型数字签名算法的批验证方案

Naccache等人首次提出了批数字签名验证方案,但是C.H.Lim,P.J.Lee成功地对它进行CP攻击。本文是基于L.HARN总结的18种ELGamal型签名算法,指出它们中能实现批验证方案的通用规则。总共有12种可以被改变成DSA型数字签名算法以用于批验证,它们可以抵抗接收者的攻击。     

Human Intention-Oriented Variable Admittance Control with Power Envelope Regulation in Physical Human-Robot Interaction

Human intention adaptation is usually realized by changing impedance/admittance parameters according to human interaction in physical human-robot interaction (pHRI). However, determining which admittance parameters need to be updated and how to update depends on the context of pHRI and system stability. This paper aims to provide a systematic study for all three admittance parameters considering human intention adaptation and system passivity for ensuring an intuitive and safe pHRI. For intuitiveness, a general human intention framework based on the robot's roles is summarized as a premise for designing human intention-oriented updating rules for admittance parameters. For the changing trends of these parameters (i.e., increasing or decreasing), only those impacting system passivity are studied. A power envelope regulation (PER) concept is then proposed to impose constraints on variable admittance parameters inferred from human intention for maintaining safe interaction. Our initial results suggest that the passivity condition is not a sufficient condition for system stability in pHRI, whereas preserving the power envelope inferred from the passivity condition can maintain passivity as well as avoid mismatch of admittance parameters arising from unpredicted human interaction. As a result, drastic changes in admittance controller dynamics, which usually result in instability, are restrained. The effectiveness of our approaches not only to maintain intuitive interaction but also to preserve passivity is validated via numerical simulation and experiments.     

优先度排序RBF神经网络在与文本无关说话人确认中的应用

该文介绍了优先度排序径向基函数(PORBF)神经网络的结构与算法,并提出了将其应用于与文本无关说话人确认时的训练算法、似然度的计算方法以及识别规则。为了增强PORBF网络的泛化能力,该文用压缩矢量构造抑制样本集,提出了顺序选取、最近邻选取和最远距离选取等3种选择抑制样本集中说话人的方法,并对PORBF神经元的输出进行了等比递减加权.在相同条件下的与文本无关说话人确认实验中,传统的矢量量化方法的等差错率可达10.56％,而基于PORBF网络的确认系统使用最近邻选择方法构造抑制样本集,其等差错率可达6.83％;性能提高很多。     

Passivity Analysis and Passification of Markovian Jump Systems

This paper is concerned with the problems of delay-dependent robust passivity analysis and robust passification for uncertain Markovian jump linear systems (MJLSs) with time-varying delay. The parameter uncertainties are time varying but norm bounded. For the robust passivity problem, the objective is to seek conditions such that the closed-loop system under the state-feedback controller with given gains is passive, irrespective of all admissible parameter uncertainties. For the robust passification problem, desired passification controllers will be designed which guarantee that the closed-loop MJLS is passive. By constructing a proper stochastic Lyapunov–Krasovskii function and employing the free-weighting matrix technique, delay-dependent passivity/passification performance conditions are formulated in terms of linear matrix inequalities. Finally, the effectiveness of the proposed approaches is demonstrated by a numerical example.     

Bias compensation based recursive least-squares identification algorithm for MISO systems

For multi-input single-output output-error systems, the least-squares (LS) estimates are biased. In order to obtain the unbiased estimates, we present a recursive LS identification algorithm based on a bias compensation technique. The basic idea is to eliminate the estimation bias by adding a correction term in the LS estimates, and further to derive a bias compensation based recursive LS algorithm. Finally, we test the proposed algorithms by simulation and show their effectiveness.