基于分数间隔的四二阶归一化累积量盲均衡算法

针对目前分数间隔盲均衡算法存在的缺陷,提出了基于分数间隔的四二阶归一化累积量盲均衡算法.先对接收信号进行分数间隔采样,然后利用四二阶归一化累积量将盲均衡算法归结为无约束的极值问题,从而简化了算法,加快了收敛速度,降低了误码率.仿真验证了算法的有效性.     

OPAST子空间跟踪数据压缩半盲多用户检测

基于子空间方法的最小均方误差半盲多用户检测的计算核心是对信号子空间的特征值与特征向量的同时跟踪.仅跟踪计算信号子空间特征向量的子空间跟踪算法不能直接应用于这种检测方法.利用数据压缩技术,提出一种只需跟踪计算信号子空间正交规范基的自适应数据压缩半盲多用户检测.将著名的正交投影逼近子空间跟踪(OPAST)算法应用于这种数据压缩半盲多用户检测,发现OPAST算法具有自然的数据压缩结构,在几乎不增加运算量的情况下即可实现数据压缩半盲多用户检测.仿真实验表明:基于OPAST算法的数据压缩半盲多用户检测具有良好的检测性能.     

Winograd矩阵乘法算法用于任意阶矩阵时的一种新处理方法

摘要t矩阵乘法StraSsen算法及其变形winograd算法用分而治之的方法把矩阵乘法时间复杂性由传统的D(n。)改进到0(佗kg。n.但是对于奇数阶矩阵,在划分子矩阵时,要作特殊处理才能继续使用此算法.本文提出了一种非等阶“十”字架划分方法,可以最少化填零,最大化性能,使得奇数阶矩阵乘法的时间复杂性更加接近偶数阶矩阵乘法的效果.计算实例显示该方法是有效的.     

基于投影方法的约束独立成分分析

独立成分分析是解决盲源分离问题的一种有效工具，但ICA具有伸(dilation)与排序(permutation)的不确定性的本质特征。本利用一些约束条件，采用Lagrange乘子法并结合简单的投影方法，可以以特定的形式来进行独立成分的排序，并且可以在信号分离过程中规范化解混矩阵(demixing matrix)，能够系统地减轻ICA对于伸缩与排序的不确定性。仿真结果证实了算法的有效性。     

带初始奇异性的多项时间分数阶扩散方程一类全离散数值方法的误差分析

本文研究了带有初始奇异性的多项时间分数阶扩散方程的一种全离散数值方法.首先,基于L1公式在渐变网格下离散多项Caputo时间分数阶导数,构造了多项时间分数阶扩散方程的时间半离散格式,证明了时间格式通过选取合适的网格参数r,时间方向的误差可以达到最优的收敛阶2-α_1,其中α_1(0 α_11)为多项时间分数阶导数阶数的最大值.然后,空间采用谱方法进行离散,得到了全离散格式,证明了全离散格式的无条件稳定性和收敛性.为了降低计算量和储存量,对多项时间分数阶扩散方程又构造了时间方向的快速算法,同时证明了该格式的收敛性.数值算例验证了算法的有效性,显示了快速算法的高效性.     

一类基于低维框架张量积的高维信号传输算法

框架理论在图像处理、信号处理等方面有着非常重要的应用.在实际中,用框架进行信号传输的算法复杂度可达到立方阶.基于低维框架张量积提出了一类高维信号传输的算法,算法将复杂度降为平方阶.数值实例也表明了算法的有效性.     

基采用时频区域检测的雷达信号盲分选算法

为了解决欠定条件下密集雷达信号分选问题,提高雷达信号盲分选算法精度,提出了一种采用时频区域检测的雷达信号盲分选算法,首先利用短时傅立叶变换将混合信号映射至时频平面进行处理,然后通过聚类算法估计出混合矩阵,从而反解出源信号矩阵,进而估计出每一雷达源信号,能够在信号的时频平面投影相交且欠定的条件下,实现信号分选功能.仿真实验结果表明:提出的算法相比于传统的类MUSIC算法及其衍生的相关改进算法具有更高的分选精度和算法收敛性,且估计得到的源信号时域波形更优,体现了其有效性和优越性.     

基于模拟退火算法的盲均衡器设计

盲均衡技术是不需要训练序列的自适应均衡技术.该项技术可以消除由于通信信道失真造成的码间干扰.本文的目的是对盲均衡技术作一介绍,包括对广泛应用的最大归一化累积量判据的介绍和讨论,并在此基础上提出了基于模拟退火技术的设计方法,最后,对这种盲均衡器骑行了仿真,并得出了相应的结论.     

分数阶光滑函数线性和二次插值公式余项估计

本文在局部分数阶导数定义的基础上给出了高阶局部分数阶导数定义,并据此得到了一般形式的分数阶Taylor公式.用该公式给出了分数阶光滑函数线性和二次插值公式余项的表达式,并进一步导出了分段线性插值的收敛阶估计.针对分数阶导数临界阶计算困难的问题,本文利用线性插值余项设计了一种外推算法,能够比较准确地求出函数在某点的局部分数阶导数的临界阶.最后通过编写算法的Mathematica程序,验证了理论分析的正确性,并用实例说明了算法的有效性.     

一种基于Infomax的盲分离算法及其稳定性

本文讨论具有超高斯分布与亚高斯分布的混合源信号的分离问题 ,提出了一种有效的分离算法———拓广的Infomax算法 ,并分析了该算法的渐近稳定性 ,与原有的Infomax算法相比 ,本文所提算法具有更广泛的应用范围 .模拟实验验证了算法的有效性     

Blind source separation with nonlinear autocorrelation and non-Gaussianity

Blind source separation (BSS) is a problem that is often encountered in many applications, such as biomedical signal processing and analysis, speech and image processing, wireless telecommunication systems, data mining, sonar, radar enhancement, etc. One often solves the BSS problem by using the statistical properties of original sources, e.g., non-Gaussianity or time-structure information. Nevertheless, real-life mixtures are likely to contain both non-Gaussianity and time-structure information sources, rendering the algorithms using only one statistical property fail. In this paper, we address the BSS problem when source signals have non-Gaussianity and temporal structure with nonlinear autocorrelation. Based on the two statistical characteristics of sources, we develop an objective function. Maximizing the objective function, we propose a gradient ascent source separation algorithm. Furthermore, We give some mathematical properties for the algorithm. Computer simulations for sources with square temporal autocorrelation and non-Gaussianity illustrate the efficiency of the proposed approach.     

NMF versus ICA for blind source separation

Blind source separation (BSS) is a problem of recovering source signals from signal mixtures without or very limited information about the sources and the mixing process. From literatures, nonnegative matrix factorization (NMF) and independent component analysis (ICA) seem to be the mainstream techniques for solving the BSS problems. Even though the using of NMF and ICA for BSS is well studied, there is still a lack of works that compare the performances of these techniques. Moreover, the nonuniqueness property of NMF is rarely mentioned even though this property actually can make the reconstructed signals vary significantly, and thus introduces the difficulty on how to choose the representative reconstructions from several possible outcomes. In this paper, we compare the performances of NMF and ICA as BSS methods using some standard NMF and ICA algorithms, and point out the difficulty in choosing the representative reconstructions originated from the nonuniqueness property of NMF.     

Painless Reconstruction from Magnitudes of Frame Coefficients

The goal of this paper is to develop fast algorithms for signal reconstruction from magnitudes of frame coefficients. This problem is important to several areas of research in signal processing, especially speech recognition technology, as well as state tomography in quantum theory. We present linear reconstruction algorithms for tight frames associated with projective 2-designs in finite-dimensional real or complex Hilbert spaces. Examples of such frames are two-uniform frames and mutually unbiased bases, which include discrete chirps. The number of operations required for reconstruction with these frames grows at most as the cubic power of the dimension of the Hilbert space. Moreover, we present a very efficient algorithm which gives reconstruction on the order of d operations for a d-dimensional Hilbert space.     

城市道路交通信号实时控制的数学模型与算法研究

研究了交通信号的实时配时控制问题.建立了在已有交通设施条件下,控制信号具有线性约束的非线性实时配时系统优化模型,设计了与模型相适应的实时CLY系列算法.重点讨论了点控制问题,建立了相应的数学优化模型,设计了CLY-Point1算法求解.还对线控制问题和面控制问题,建立了多层优化控制模型,并设计CLY-Point2、CLY-Line和CLY-Area算法进行求解.数值模拟结果表明,CLY系列算法具有很强的实时性,车辆平均等待时间比固定配时减少了约20%.     

Multiple-Subarc Gradient-Restoration Algorithm, Part 1: Algorithm Structure

Rapid progresses in information and computer technology allow the development of more advanced optimal control algorithms dealing with real-world problems. In this paper, which is Part 1 of a two-part sequence, a multiple-subarc gradient-restoration algorithm (MSGRA) is developed. We note that the original version of the sequential gradient-restoration algorithm (SGRA) was developed by Miele et al. in single-subarc form (SSGRA) during the years 1968–86; it has been applied successfully to solve a large number of optimal control problems of atmospheric and space flight.MSGRA is an extension of SSGRA, the single-subarc gradient-restoration algorithm. The primary reason for MSGRA is to enhance the robustness of gradient-restoration algorithms and also to enlarge the field of applications. Indeed, MSGRA can be applied to optimal control problems involving multiple subsystems as well as discontinuities in the state and control variables at the interface between contiguous subsystems.Two features of MSGRA are increased automation and efficiency. The automation of MSGRA is enhanced via time normalization: the actual time domain is mapped into a normalized time domain such that the normalized time length of each subarc is 1. The efficiency of MSGRA is enhanced by using the method of particular solutions to solve the multipoint boundary-value problems associated with the gradient phase and the restoration phase of the algorithm.In a companion paper [Part 2 (Ref. 2)], MSGRA is applied to compute the optimal trajectory for a multistage launch vehicle design, specifically, a rocket-powered spacecraft ascending from the Earth surface to a low Earth orbit (LEO). Single-stage, double-stage, and triple-stage configurations are considered and compared.     

Inversion algorithm based on the generalized objective functional for compressed sensing

Owing to providing a novel insight for signal and image processing, compressed sensing (CS) has attracted increasing attention. The accuracy of the reconstruction algorithms plays an important role in real applications of the CS theory. In this paper, a generalized reconstruction model that simultaneously considers the inaccuracies on the measurement matrix and the measurement data is proposed for CS reconstruction. A generalized objective functional, which integrates the advantages of the least squares (LSs) estimation and the combinational M-estimation, is proposed. An iterative scheme that integrates the merits of the homotopy method and the artificial physics optimization (APO) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to evaluate the feasibility and effectiveness of the proposed algorithm. For the cases simulated in this paper, the reconstruction accuracy is improved, which indicates that the proposed algorithm is successful in solving CS inverse problems.     

Semi-stable distributions in free probability theory

Semi-stable distributions, in classical probability theory, are characterized as limiting distributions of subsequences of normalized partial sums of independent and identically distributed random variables. We establish the noncommutative counterpart of semi-stable distributions. We study the characterization of noncommutative semi-stability through free cumulant transform and develop the free semi-stability and domain of semi-stable attraction in free probability theory.     

A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.