Blind signal separation based on ME and statistical estimation

There are two major approaches for Blind Signal Separation (BSS) problem: Maximum Entropy (ME) and Minimum Mutual Information (MMI) algorithms. Based on the recursive architecture and the relationship between the ME and MMI algorithms, an Extended ME(EME) algorithm is proposed by using probability density function (pdf) estimation of the outputs to deduce the corresponding iterative formulas in BSS. Based on the simulation results, it can be concluded that the proposed algorithm has better performances than the traditional ME algorithm in convolute mixture BSS problems.     

高速MCM中互连线时域宏模型的递归算法

提出一种求解高速ＭＣＭ 中有耗互连线两端瞬态响应递归形式的时域宏模型,利用频域内的泰勒近似,通过逆拉氏变换得到一组时域内的递归公式,递归公式只涉及传输线两端的电流和电压,因而具有较高的计算效率,数值实验结果表明,本方法可以达到相当高的精度。     

基于回声状态网络的卫星信道在线盲均衡算法

针对非线性卫星信道,该文提出了两种基于回声状态网络(ESN)的在线盲均衡算法。利用ESN良好的非线性逼近能力,将发送信号的高阶统计量(HOS)代入ESN,结合常模算法(CMA)和多模算法(MMA)构造盲均衡的代价函数,并采用递归最小二乘(RLS)算法对ESN输出权值进行迭代寻优,实现了Volterra卫星信道下常模和多模信号的在线盲均衡。实验表明,该文算法可以有效降低非线性信道对发送信号产生的畸变,相较于传统的Volterra滤波方法,有更快的收敛速度和更低的均方误差值。     

PERFORMANCE ANALYSIS FOR ATM SWITCHING OF MIXED CONTINUOUS-BIT-RATE AND BURSTY TRAFFIC WITH SMOOTHING FUNCTION

Traditional packet switching networks have typically employed window-based congestion control schemes in order to regulate traffic flow. In ATM networks, the high speed of the communication links and the varied nature of the carried traffic make such schemes inappropriate. Therefore, simpler and more efficient schemes have to be proposed to improve the congestion control for ATM switching. This paper presents an exact performance analysis of ATM switching whose inputs consist of Continuous-Bit-Rate(CBR) and bursty traffic. The CBR traffic and bursty traffic are described by Bernoulli process and the Interrupted Bernoulli Process(IBP), respectively. Bursty traffic smoothing mechanism is analyzed. With the use of a recursive algorithm, the cell loss probability and the average delay for ATM switching of mixed CBR and bursty traffic are exactly calculated. Traffic smoothing could be implemented at a slower peak rate keeping the average rate constant or decreasing the average bursty length. Both numerical a     

分层介质球内场计算的一种改进算法

球体内部电场的获取是介质目标电磁散射特性计算中的关键技术问题.常用方法之一是基于Mie理论的递推算法,由于小宗量时Riccati-Bessel函数存在奇异,使得现有算法存在计算不够稳定的缺点.针对这一问题,提出了一种分层介质球内场获取的改进算法.该算法提出了新的内场展开系数递推关系,改进了现有的Riccati-Bessel函数递推式,解决了Riccati-Bessel函数的奇异性问题.同时,与FEKO 5.4的计算结果比对表明,该算法具有很高的精度,能够为复杂介质散射特性研究提供参考和对比.     

Finite-stage stochastic decision processes with recursive reward structure I: optimality equations and deterministic strategies

In this paper we examine an N-stage stochastic decision model with a recursive reward structure whose state and action spaces are standard Borel ones. The central results relate to the validity of the optimality equations and to the sufficiency of deterministic strategies. The results expand statements known for classical dynamic programming problems with additive total rewards to a wide class of recursive reward functions under certain monotonicity and continuity assumptions. The existence of an expected utility representation of the total rewards is generally not presupposed     

A nonsymmetric matrix with integer eigenvalues

A nonsymmetric N?×?N matrix with elements as certain simple functions of N distinct real or complex numbers r ₁, r ₂, …, r_N is presented. The matrix is special due to its eigenvalues???the consecutive integers 0,1,2, …, N?1. Theorems are given establishing explicit expressions of the right and left eigenvectors and formulas for recursive calculation of the right eigenvectors. A special case of the matrix has appeared in sampling theory where its right eigenvectors, if properly normalized, give the inclusion probabilities of the conditional Poisson sampling design.     

An Online Expectation–Maximization Algorithm for Changepoint Models

Changepoint models are widely used to model the heterogeneity of sequential data. We present a novel sequential Monte Carlo (SMC) online expectation–maximization (EM) algorithm for estimating the static parameters of such models. The SMC online EM algorithm has a cost per time which is linear in the number of particles and could be particularly important when the data is representable as a long sequence of observations, since it drastically reduces the computational requirements for implementation. We present an asymptotic analysis for the stability of the SMC estimates used in the online EM algorithm and demonstrate the performance of this scheme by using both simulated and real data originating from DNA analysis. The supplementary materials for the article are available online.     

基于集群的增量分布式RSOM聚类方法

对于海量和高维的大规模数据聚类问题,其数据个数以及模式种类通常处于一个动态增加的过程之中,为此进行增量、并行算法的设计,以提供更好的计算能力是十分必要的.注意到人脑增量学习的本质和RSOM(Recursive Self-Organizing Map)的层次化、分布式结构特点,本文研究了基于高性能集群并行计算环境的增量、分布式RSOM并行算法,并以视频图像特征集实例证实了算法的可行性.