Time-domain BIE analysis of large three-dimensional electromagneticscattering problems

A time-domain boundary integral equation (BIE) solution of the magnetic field integral equation (MFIE) for large electromagnetic scattering problems is presented. It employs isoparametric curvilinear quadratic elements to model fields, geometry, and time dependence, eliminating staircasing problems. The approach is implicit, which seems to provide both stability and permits arbitrary local mesh refinement to model geometrically difficult regions without the significant cost penalty explicit methods suffer. Error dependence on discretization is investigated; accurate results are obtained with as few as five nodes per wavelength. The performance both on large scatterers and on low-radar cross section (RCS) scatterers is demonstrated, including the six wavelength “NASA almond,” two spheres, a thirteen wavelength missile, and a “high-Q” cavity     

一种新的无序量测处理算法

在中心式多传感器目标跟踪系统中,当从不同的传感器发送量测到处理中心时,会出现不同的时间延迟。这导致源自同样目标的量测会出现无序到达中心的现象,由此产生无序量测处理问题。该文受分布式/航迹融合理论中中心式估计的重构思想启发,通过组合前向预测与等价量测方法,提出一种新的处理无序量测的方法,该方法涉及到状态估计的去相关问题。最后通过理论分析和仿真试验表明:该算法对于一步延迟是最优的,且当过程噪声很小,系统航迹的更新速率相当高时,该算法表现出的性能下降是很小的。     

Subspace methods for the blind identification of multichannel FIRfilters

This paper addresses a problem arising in a context of digital communications. A digital source is transmitted through a continuous channel (the propagation medium), and several measurements are performed at the receiver, either by means of several sensors, or by oversampling the received signal compared to the emission rate. Given only these observations, the baseband equivalents of the corresponding channels have to be recovered. An orthogonality property between “signal” and “noise” subspaces is exploited to build some quadratic form whose minimization yields the desired estimates up to a scale factor. This is in the same spirit as recent works by Tong et al. (see Proc. 25th Asilomar Conf., p.856-860, 1991) but requires fewer computations. Numerical simulations demonstrate the performance of the proposed methods in a channel identification context     

Diagonal preconditioners for the EFIE using a wavelet basis

The electric field integral equation (EFIE) has found widespread use and in practice has been accepted as a stable method. However, mathematically, the solution of the EFIE is an “ill-posed” problem. In practical terms, as one uses more and more expansion and testing functions per wavelength, the condition number of the resulting moment-method matrix increases (without bound). This means that for high-sampling densities, iterative methods such as conjugate gradients converge more slowly. However, there is a way to change all this. The EFIE is considered using a wavelet basis for expansion and for testing functions. Then, the resulting matrix is multiplied on both sides by a diagonal matrix. This results in a well-conditioned matrix which behaves much like the matrix for the magnetic field integral equation (MFIE). Consequences for the stability and convergence rate of iterative methods are described     

An algebraic approach to the unequal-spaced channel-allocationproblem in WDM lightwave systems

To reduce four-wave-mixing crosstalk in high-capacity, long-haul, repeaterless, wavelength-division multiplexing (WDM) lightwave systems, the use of unequally spaced channels has been proposed. Instead of being solved by integer linear programming, the unequal-spaced channel-allocation problem is treated by constructing suitable optical orthogonal codes in optical code-division multiple-access (CDMA). An “algebraic” framework and three algorithms on finding the frequency locations of unequally spaced WDM channels are introduced, where the constructions are based on generating optical CDMA codewords with a predetermined pulse separation and “aperiodic” autocorrelation sidelobes no greater than one. The algorithms potentially provide a fast and simple alternative to solve the problem, besides the proposed computer-search method     

Omnidirectional pattern directivity in the absence of minor lobes:revisited

The article presents a simple, approximate formula for the directivity of an omnidirectional antenna without minor lobes. This problem has been treated before by Monser (1954), and the results have been cited by Levine and Monser (1961). In this earlier work, an approximate formula, showing antenna directivity as a function of the half-power beamwidth (HPBW), was determined by fitting a curve to data that had been obtained previously by numerically integrating across the half-power beamwidth spectrum: a process involving many integrations. Notwithstanding the evident good agreement between the “exact” and “curve-fitted” data in that instance, it is equally possible to return to the supporting sums and determine directly a very simple, alternative approximation to the defining integral and, hence, to the directivity of such antennas     

Experiments with simple neural networks for real-time control

We demonstrate the practical ability of neural networks (NNs) trained in a supervised mode to extract useful control “knowledge” from a large, high-dimensional empirical database, and then to deliver almost optimal control in “real time”. In particular, this paper describes experiments with NN-based controllers for allocating bandwidth capacity in a telecommunications network (SDH). This system was proposed in order to overcome a “real time” response constraint. Two basic architectures, each consisting of a combination of two methods, are evaluated: (1) a feedforward network-heuristic combination and (2) a feedforward network-recurrent network combination. These architectures are compared against a linear programming (LP) optimizer as a benchmark. This LP optimizer was also used as a teacher to label the data samples for the feedforward NN training algorithm. NN-based solutions are very accurate (~98% of optimal throughput) and, in contrast to the algorithmic approach, can be delivered in “real time”. It is found that while the “human” generated heuristics (greedy search optimization) fail to find a solution in approximately 30% of cases, the best NN fails only in 4.9% of cases. Moreover, it has been found that in spite of the very high dimensionality of the problem (55 inputs and 126 outputs), the solution can be delivered by surprisingly compact NNs, with as little as around 1000 synaptic weights. This proves that on this occasion the NNs were able to extract simple but powerful “heuristics” hidden in the complex sets of numerical data     

Blind source separation based on time-frequency signalrepresentations

Blind source separation consists of recovering a set of signals of which only instantaneous linear mixtures are observed. Thus far, this problem has been solved using statistical information available on the source signals. This paper introduces a new blind source separation approach exploiting the difference in the time-frequency (t-f) signatures of the sources to be separated. The approach is based on the diagonalization of a combined set of “spatial t-f distributions”. In contrast to existing techniques, the proposed approach allows the separation of Gaussian sources with identical spectral shape but with different t-f localization properties. The effects of spreading the noise power while localizing the source energy in the t-f domain amounts to increasing the robustness of the proposed approach with respect to noise and, hence, improved performance. Asymptotic performance analysis and numerical simulations are provided     

基于离散对数问题的两层分散式组密钥管理方案

该文基于多个解密密钥映射到同一加密密钥的公钥加密算法提出一个组密钥更新协议,结合LKH算法为特定源多播模型设计一个两层分散式组密钥管理方案。证明它具有后向保密性、高概率的前向保密性和抗串谋性。通过上层私钥的长寿性和密钥转换的方法来缓解子组管理者的性能瓶颈及共享组密钥方法中普遍存在的1影响n问题。分析表明,采用混合密码体制的新方案在一定程度上兼备了两类不同组密钥管理方法的优势。     

Corrections to “Optimal detection using bilineartime-frequency and time-scale representations”

Sayeed and Jones (IEEE Trans. Signal Processing, vol.43, p.2872-83, 1995) develop an optimal detection framework based on quadratic time-frequency and time-scale representations. The expressions for certain test statistics in that paper contain a factor of ½ that is not needed. Moreover, certain “MAP GLRT” detectors are proposed in Sayeed and Jones whose forms should be slightly different than those described there. The following section describes the appropriate changes that rectify the situation     

Convex Optimization Approaches to Robust ℒ1 Fixed-Order Filtering for Polytopic Systems with Multiple Delays

This paper investigates the robust ℒ₁ fixed-order filtering problem for continuous polytopic systems with multiple state delays. Attention is focused on the design of robust full-order and reduced-order filters that guarantee the filtering error system to be asymptotically stable and satisfy the worst case peak-to-peak gain of the filtering error system for all admissible uncertainties and time delays. In particular, we concentrate on the delay-dependent case, and the peak-to-peak performance criterion is first established for polytopic systems with multiple state delays. Two different convex optimization approaches are proposed to solve this problem. One is the parameter-dependent Lyapunov approach in which the filter is not only dependent on the parameters (residing in a polytope), but also the Lyapunov matrices are different over the entire polytope domain. The other is the quadratic stability approach which obtains an admissible filter in the quadratic framework. Computational algorithms in terms of linear matrix inequalities (LMIs) are provided. It is shown that the parameter-dependent Lyapunov approach turns out to be less conservative than the quadratic stability approach, but the quadratic stability approach is computationally less demanding. Two numerical examples are presented to illustrate the proposed theory. This work was partially supported by RGC HKU 7028/04P, the Natural Science Foundation of Heilongjiang Province under Grant F200504, and the Scientific and Technical Research Project of the Education Department of Heilongjiang Province under Grant 10551013.     

Orthogonal eigensubspace estimation using neural networks

We present a neural network (NN) approach for simultaneously estimating all or some of the orthogonal eigenvectors of a symmetric nonindefinite matrix corresponding to its repeated minimum (in magnitude) eigenvalue. This problem has its origin in the constrained minimization framework and has extensive applications in signal processing. We recast this problem into the NN framework by constructing an appropriate energy function which the NN minimizes. The NN is of feedback type with the neurons having sigmoidal activation function. The proposed approach is analyzed to characterize the nature of the minimizers:of the energy function. The main result is that “the matrix W* is a minimizer of the energy function if and only if the columns of W* are the orthogonal eigenvectors with a given norm corresponding to the smallest eigenvalue of the given matrix”. Further, all minimizers are global minimizers. Bounds on the integration time-step that is required to numerically solve the system of differential equations (which define the dynamics of the NN) have also been derived. Results of computer simulations are presented to support our analysis     

On solving first-kind integral equations using wavelets on abounded interval

The conventional method of moments (MoM), when applied directly to integral equations, leads to a dense matrix which often becomes computationally intractable. To overcome the difficulties, wavelet-bases have been used previously which lead to a sparse matrix. The authors refer to “MoM with wavelet bases” as “wavelet MoM”. There have been three different ways of applying the wavelet techniques to boundary integral equations: 1) wavelets on the entire real line which requires the boundary conditions to be enforced explicitly, 2) wavelet bases for the bounded interval obtained by periodizing the wavelets on the real line, and 3) “wavelet-like” basis functions. Furthermore, only orthonormal (ON) bases have been considered. The present authors propose the use of compactly supported semi-orthogonal (SO) spline wavelets specially constructed for the bounded interval in solving first-kind integral equations. They apply this technique to analyze a problem involving 2D EM scattering from metallic cylinders. It is shown that the number of unknowns in the case of wavelet MoM increases by m-1 as compared to conventional MoM, where m is the order of the spline function. Results for linear (m=2) and cubic (m=4) splines are presented along with their comparisons to conventional MoM results. It is observed that the use of cubic spline wavelets almost “diagonalizes” the matrix while maintaining less than 1.5% of relative normed error. The authors also present the explicit closed-form polynomial representation of the scaling functions and wavelets     

On the retention time distribution of dynamic random access memory(DRAM)

The retention time distribution of high-density dynamic random access memory (DRAM) has been investigated. The key issue for controlling the retention time distribution has been clarified and its model has been proposed for the first time. Trench capacitor cell with 0.6-μm ground rule was evaluated. It was found that the retention time distribution consists of “tail distribution” and “main distribution.” “tail distribution,” by which DRAM refresh characteristics are restricted, depends on the boron concentration of the memory cell region. As boron concentration of the memory cell region increases, “tail distribution” is enhanced. This enhancement is due to the increase of the junction leakage current from the storage node. For the purpose of accounting for the nature of “Tail Distribution,” the concept of thermionic field emission (TFE) current has been introduced. The high electric field at pn junction of the storage node enhances thermionic field emission from a deep level. The activation energy of the deep level is normally distributed among the memory cells, which leads to the normal distribution of log(retention time). Two methods for reducing “tail distribution” are proposed. One is to reduce the electric field of the depletion layer of the storage node. The other is to reduce the concentration of the deep level for TFE current     

A Robust Resilient Multi-Objective Delay-Dependent Tracker for Nonlinear Time-Delay Systems

In this paper, a multi-objective robust tracking problem for nonlinear time-invariant systems with known time delay in state vector and control input is discussed. An augmented integral error is included in the tracking performance index which eliminates the effect of deterministic constant disturbances in the tracking problem. Uncertainties are considered as a nonlinear additive term in the problem. The discretized complete Lyapunov–Krasovskii functional (LKF) is used to formulate the robust resilient tracker with general quadratic performance. This approach yields much less conservatism than the conventional simple LKF design methods, and still allows writing both LKF and LKF derivative conditions in the form of linear matrix inequalities. Two comparative examples are given to illustrate the effectiveness of the proposed design method.     

Combining the moment method with geometrical modelling by NURBSsurfaces and Bezier patches

Induced current distributions on conducting bodies of arbitrary shape modelled by NURBS (non uniform rational B-splines) surfaces are obtained by using a moment method approach to solve an electric field integral equation (EFIE). The NURBS surfaces are expanded in terms of Bezier patches by applying the Cox-de Boor transformation algorithm. This transformation is justified because Bezier patches are numerically more stable than NURBS surfaces. New basis functions have been developed which extend over pairs of Bezier patches. These basis functions can be considered as a generalization of “rooftop” functions. The method is applied to obtain RCS values of several objects modelled with NURBS surfaces. Good agreement with results from other methods is observed. The method is efficient and versatile because it uses geometrical modelling tools that are quite powerful     

On the realization of an analytic high-resolution EEG

The analytic solution of the harmonic downward continuation of the scalp potential field in an N-shell heterogeneous, but isotropic, spherical volume conductor model has been derived. The objective of this paper was to investigate the realization of a so-called “high-resolution electroencephalogram (EEG)”: by enhancing the poor spatial resolution of EEG recordings. To this end, the forward problem for a dipolar source arbitrarily located at the source point Q=Q(r_s,φ_s,ϑ_s) has been determined in a compact matrix notation. It is possible to transfer the potential field given on the outer surface of a spherically shaped volume conductor to an arbitrary inner surface (e.g., to the cortical surface) under consideration of the electrical and geometrical properties of the model. For the application of the proposed method to real-world problems, the coefficients of the series expansion describing the cortical potential distribution are determined by minimizing the squared curvature of the scalp potential field integrated over the scalp surface. Simulation results for distributed sources show that the proposed method is superior to the surface Laplacian method for interelectrode distances below 2.5 cm     

Evaluation of LEMP effects on complex wire structures located abovea perfectly conducting ground using electric field integral equation intime domain

In this paper, a novel approach for the implementation of scattering theory is proposed in order to evaluate the electromagnetic effects of lightning return stroke on complex wire structures (lightning EM pulse - LEMP). An electric field integral equation (EFIE) in the time domain is employed to describe the electromagnetic transients from lightning. The proposed approach is applied to a single-phase, three-phase as well as a Y-shaped transmission line located above a perfectly conducting ground. The simulation results reproduce accurately experimental data available in the literature. The proposed method provides new possibilities in estimation of lightning indirect effects on complex networks as an example “radial transmission systems tapped from main switching substations” are investigated     

The slowest descent method and its application to sequenceestimation

A new approach to sequence estimation is proposed and its performance is analyzed for a number of channels of practical interest. The proposed approach, termed the slowest descent method, comprises as a special case the zero-forcing equalizer for intersymbol interference channels and the decorrelator for the multiuser detection problem. The latter two methods quantize the unconstrained sequence that maximizes the likelihood function. The proposed method can be viewed as a generalization of these two methods in two ways. First, the unconstrained maximization is extended to nonquadratic log-likelihood functions; second, the decorrelator estimate can be “refined” by comparing its likelihood to a set of discrete-valued sequences along mutually orthogonal lines of the least decrease in the likelihood function. The gradient descent method for iterative computation of the line of least likelihood decrease (i.e., slowest likelihood descent) and its relationship to the expectation-maximization (EM) algorithm for unconstrained likelihood maximization is discussed. The slowest descent method is shown to provide a performance comparable to maximum-likelihood for a number of channels. These problems can be described by either quadratic or nonquadratic log-likelihood functions