Discrete-Time Estimation of Second-Order Statistics of Generalized Almost-Cyclostationary Processes

In this paper, the problem of estimating second-order statistics of continuous-time generalized almost-cyclostationary (GACS) processes is addressed. GACS processes in the wide sense have autocorrelation function almost-periodic in time whose generalized Fourier series expansion has both frequencies and coefficients that depend on the lag shifts. Almost-cyclostationary (ACS) processes are obtained as a special case when the frequencies do not depend on the lag shifts. ACS processes filtered by Doppler channels and communications signals with time-varying parameters are further examples. It is shown that continuous-time GACS processes do not have a discrete-time counterpart. The discrete-time cyclic cross-correlogram of the discrete-time ACS processes obtained by uniformly sampling GACS processes is considered as estimator of samples of the continuous-time cyclic cross-correlation function. The asymptotic performance analysis is carried out by resorting to the hybrid cyclic cross-correlogram which is partially continuous-time and partially discrete-time. Its mean-square consistency and asymptotic complex Normality as the number of data-samples approaches infinity and the sampling period approaches zero are proved under mild conditions on the regularity of the Fourier series coefficients and the finite or practically finite memory of the processes expressed in terms of summability of cumulants. It is shown that the asymptotic properties of the hybrid cyclic cross-correlogram are coincident with those of the continuous-time cyclic cross-correlogram. Hence, discrete-time estimation does not give rise to any loss in asymptotic performance with respect to continuous-time estimation.     

A New Fast and Accurate Algorithm for the Computation of Microstrip Capacitances

A new algorithm is presented for the calculation of TEM parameters of microstrips, based on a lumped-circuit model of the microstrip transverse static field. The use of such an algorithm allows one to obtain either very high accuracies or very low computer times when ordinary accuracies are tolerated. Compared with the similar technique by Lennartsson, the analysis method presented here allows substantial reduction in computer time, storage, and errors.     

A Fast Algorithm for Computation of Electromagnetic Wave Propagation in Half-Space

A new frequency-domain algorithm, the planar Taylor expansion through the fast Fourier transform (FFT) method, has been developed to speed the computation of the Green's function related formulas in the half-space scenario for both the near-field (NF) and the far-field (FF). Two types of Taylor-FFT algorithms are presented in this paper: the spatial Taylor-FFT and the spectral Taylor-FFT. The former is for the computation of the NF and the latter is for the computation of the FF or the Fourier spectrum. The planar Taylor-FFT algorithm has a computational complexity of ${O(N^{2} log _{2} N^{2})}$ for an ${Ntimes N}$ computational grid, comparable to the multilevel fast multipole method (MLFMM). What's more important is that, the narrowband property of many electromagnetic fields allows the Taylor-FFT algorithm to use larger sampling spacing, which is limited by the transverse wave number. In addition, the algorithm is free of singularities. An accuracy of $-50~{rm dB}$ for the planar Taylor-FFT algorithm is easily obtained and an accuracy of $-80~{rm dB}$ is possible when the algorithm is optimized. The algorithm works particularly well for narrowband fields and quasi-planar geometries.      

A Novel Algorithm for Fast Detection of Network Failure

Fast failure detection is critical to traffic protection/restoration in a high-speed network. Generally, the way to detect failures is to periodically exchange keep-alive messages by layer 3 protocols. In traditional protocols like Open Shortest Path First (OSPF), the failure detection time is about tens of seconds, which makes the protection/restoration time too long to keep up with the increasing quality-of-service (QoS) demand. Although configuring the related parameters in OSPF can reduce the detection time to the order of milliseconds, it introduces notable issues of scalability and instability to the network. In this paper, a novel algorithm called Low Priority Hello Reduction (LPHR) is proposed to achieve faster failure detection while maintaining high network stability. This algorithm reduces the Hello messages sent when the signaling network is congested and maintains the adjacency by other messages. We compare it with previous schemes and our experimental results show that the algorithm we propose is superior in about an order of magnitude in reduction of both false failure alarms and queuing delay of other messages, especially at a light network load.     

一种新的运动搜索算法

根据序列图像中运动矢量的中心偏置分布特性和矢量间的时空相关性,设计了一种基于连续消除的正方形-菱形搜索策略。在此基础上,融合了搜索起点预测和自适应搜索等技术,提出了一种可预测搜索起点的自适应的基于连续消除的正方形-菱形搜索算法（PA-SEA-SDS）。实验表明,该算法以较小的代价取得了与全搜索算法相当的效果,在搜索次数和精度上均优于菱形和正方形-菱形搜索算法。     

A Novel Fast Recursive MMSE-SIC Detection Algorithm for V-BLAST Systems

A novel fast recursive minimum mean square error successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed. In this algorithm, the MMSE Alter matrices and the optimal detection order are successively computed from the previously obtained filter matrices according to simple recursive pseudoinverse formulas, so that the algorithmic complexity is reduced significantly, especially for the practical number of transmit/receive antennas.     

任意平面分层介质中正演计算的快速方法

在非均匀介质电磁散射、逆散射等领域中，正演计算速度直接影响着反演计算效率。快速高精度的正演计算方法有利于反演效率的改善。对于任意平面分层介质情形，传统高效的数值模式匹配法的正演计算效率并不很高。本文通过首先推得每一从向分层中场量的汉克尔变换表达，再用快速傅里叶变换来完成汉克尔变换计算，从而得到更高效的计算方法。它避免了数值本征模式的求解，特别适用于处理平面分层介质中纵向分层较多的情形。对于其它非均     

一种改进的快速分解后向投影 SAR 成像算法

与传统后向投影(Back-Projection, BP)算法相比,快速分解 BP(Fast Factorized BP, FFBP)算法可以大幅度提高 SAR 成像的处理效率,具有较强的工程价值.该文在现有 FFBP 算法的基础上,提出了改进 FFBP 算法,进一步简化处理流程并提高运算效率.改进 FFBP 算法的主要特点:(1)成像平面选取在- sinr q坐标系,代替现有 FFBP 算法的-r q坐标系,从而直接避免了对投影视角的反正弦计算;(2)采用2维多项式拟合的方法对投影斜距和视角进行计算,代替现有 FFBP 算法的逐点计算或1维多项式拟合,从而大大减小了运算量.最后,利用 X波段的 SAR 回波仿真数据对该文提出的改进 FFBP 算法进行验证.     

Estimation of Second-Order Cross-Moments of Generalized Almost-Cyclostationary Processes

In this paper, the problem of estimating second-order cross-moments of generalized almost-cyclostationary (GACS) processes is addressed. GACS processes have statistical functions that are almost-periodic functions of time whose (generalized) Fourier series expansions have both frequencies and coefficients that depend on the lag shifts of the processes. The class of such nonstationary processes includes the almost-cyclostationary (ACS) processes which are obtained as a special case when the frequencies do not depend on the lag shifts. ACS processes filtered by Doppler channels and communications signals with time-varying parameters are further examples. It is shown that the second-order cross-moment of two jointly GACS processes is completely characterized by the cyclic cross-correlation function. Moreover, it is proved that the cyclic cross-correlogram is an asymptotically normal, mean-square consistent, estimator of the cyclic cross-correlation function. Furthermore, it is shown that well-known consistency results for ACS processes can be obtained by specializing the results of this paper.     

Stability and Convergence Analysis of Transform-Domain LMS Adaptive Filters With Second-Order Autoregressive Process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.     

混合高斯自回归模型参数估计方法之ML-DC

混合高斯自回归模型可以对有色非高斯数据的概率密度和功率谱密度进行有效的拟合.而ML-DC算法则可解决这一模型的参数估计问题。描述了混合高斯自回归模型及其参数估计问题之后,分别导出了功率谱密度参数的最大似然估计和概率密度参数估计的动态簇算法,并由此组成了参数耦合估计的ML-DC算法。最后结合一组仿真实例对其估计性能进行了详细探讨,指出并解释了算法的适用范围。     

Distributed Beamforming for Relay Networks Based on Second-Order Statistics of the Channel State Information

In this paper, the problem of distributed beamforming is considered for a wireless network which consists of a transmitter, a receiver, and $r$ relay nodes. For such a network, assuming that the second-order statistics of the channel coefficients are available, we study two different beamforming design approaches. As the first approach, we design the beamformer through minimization of the total transmit power subject to the receiver quality of service constraint. We show that this approach yields a closed-form solution. In the second approach, the beamforming weights are obtained through maximizing the receiver signal-to-noise ratio (SNR) subject to two different types of power constraints, namely the total transmit power constraint and individual relay power constraints. We show that the total power constraint leads to a closed-form solution while the individual relay power constraints result in a quadratic programming optimization problem. The later optimization problem does not have a closed-form solution. However, it is shown that using semidefinite relaxation, this problem can be turned into a convex feasibility semidefinite programming (SDP), and therefore, can be efficiently solved using interior point methods. Furthermore, we develop a simplified, thus suboptimal, technique which is computationally more efficient than the SDP approach. In fact, the simplified algorithm provides the beamforming weight vector in a closed form. Our numerical examples show that as the uncertainty in the channel state information is increased, satisfying the quality of service constraint becomes harder, i.e., it takes more power to satisfy these constraints. Also our simulation results show that when compared to the SDP-based method, our simplified technique suffers a 2-dB loss in SNR for low to moderate values of transmit power.      

Design of a Cellular Architecture for Fast Computation of the Skeleton

This paper presents a new algorithm to extract the skeleton and its Euclidean distance values from a binary image. The extracted skeleton reconstructs the objects in the image exactly. The algorithm runs in O(n) time for an image of size n × n. It involves simple local neighborhood operations for each pixel and hence it is quite amenable to VLSI implementation in a cellular architecture. Results of simulation of the algorithm in a sequential computer are presented. Results of implementation of a VLSI design in Xilinx FPGA are also presented and they confirm the speed and suitability of our method for real-time applications.     

A Second-Order Algorithm for Solving Dynamic Cell Membrane Equations

This paper describes an extension of the so-called Rush-Larsen scheme, which is a widely used numerical method for solving dynamic models of cardiac cell electrophysiology. The proposed method applies a local linearization of nonlinear terms in combination with the analytical solution of linear ordinary differential equations to obtain a second-order accurate numerical scheme. We compare the error and computational load of the second-order scheme to the original Rush-Larsen method and a second-order Runge-Kutta (RK) method. The numerical results indicate that the new method outperforms the original Rush-Larsen scheme for all the test cases. The comparison with the RK solver reveals that the new method is more efficient for stiff problems.     

Two Techniques for Fast Computation of Constrained Shortest Paths

Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, MPLS path selection, ATM circuit routing, and traffic engineering. The problem is to find the cheapest path that satisfies certain constraints. In particular, finding the cheapest delay-constrained path is critical for real-time data flows such as voice/video calls. Because it is NP-complete, much research has been designing heuristic algorithms that solve the epsiv-approximation of the problem with an adjustable accuracy. A common approach is to discretize (i.e., scale and round) the link delay or link cost, which transforms the original problem to a simpler one solvable in polynomial time. The efficiency of the algorithms directly relates to the magnitude of the errors introduced during discretization. In this paper, we propose two techniques that reduce the discretization errors, which allows faster algorithms to be designed. Reducing the overhead of computing constrained shortest paths is practically important for the successful design of a high-throughput QoS router, which is limited at both processing power and memory space. Our simulations show that the new algorithms reduce the execution time by an order of magnitude on power-law topologies with 1000 nodes. The reduction in memory space is similar.     

实现相息图快速计算的一种方法

建立一张物点位置和它在相息图上单位振幅波前分布关系的表格,采用查表的方法求物点在相息图样点上的波前分布,加快了计算输出相息图的速度。在保留重构图象细节的前提下,该方法的运算速度比平方根准确计算的方法提高5倍以上。     

A Novel, Fast, Reliable Data Transmission Algorithm for Wireless Machine Health Monitoring

Vibration-based machine health monitoring is a well-known technique that is commonly used in machine fault diagnosis applications. In conjunction with existing wireless technologies, even machines that are operated in a hazardous or moveable environment can be monitored wirelessly. However, to facilitate a reliable machine health assessment, various sensory data, particularly the vibration signals, must be acquired at a high sampling rate, generating large packets of data. These requirements are impractical in a wireless data transmission context due to the usual low transmission rate, and lengthy transmission time. One possible solution is to compress the captured numerous sensory data before wireless transmission commences. This paper presents a novel data compression algorithm that combines Empirical Mode Decomposition (EMD) with Differential Pulse Code Modulation (DPCM). EMD is effective in decomposing and identifying any instantaneous changes in non-linear, and non-stationary signals that are caused by anomalous operation of machines. After the data have been decomposed and compressed by EMD, the DPCM is applied to further compress the data through the use of linear predictor and quantizer prior to wireless transmission. A pair of Bluetooth based wireless devices was tailor-made to host the EMD and DPCM for performing wireless communication. The effectiveness of the new algorithm has been verified using real machines, operating in a noisy environment. The results prove that the algorithm provides much faster wireless data transmission by significantly reducing the size of sensory data. Nevertheless, it maintains the integrity of sensory data with negligible reconstruction error at the receiving end of the wireless device.      

TSP问题的一种快速求解算法

针对现有遗传算法收敛速度慢和容易"早熟"两大缺陷,提出一种新的遗传算法求解TSP问题.该算法融入不同的启发式算法分别作为交叉和变异算子,并引入外部最优个体集,以改善局部搜索能力弱的情况和增加群体多样性.通过对该算法的测试,论证了其有效性.以此为基础,利用递归分治策略,将上述算法并行化.实验结果表明,该算法可以得到理想的解和收敛速度,具有一定的普适性.     

一种用于VLSI制造业的生产调度算法

介绍了一种用于VLSI制造业的生产调度算法,它包含一个策略部分和一个流程排序部分,二者协作实现实际工艺线的生产安排。该算法对于VLSI企业实现生产品种多样化,提高企业的管理水平和管理效率,具有重要意义及实用前景,是VLSI生产制造企业计算机集成制造系统中的核心软件之一。