Cosine-modulated filterbanks based on extended Gaussian functions

A new family of cosine-modulated filterbanks based on functions called extended Gaussian functions (EGFs) is obtained. The design is particularly simple since it is mainly based on a closed-form expression. Nearly perfect reconstruction cosine-modulated filterbanks are obtained as well as guidelines to estimate the filterbank parameters. This analytical design method can be used to produce, with a controlled accuracy, filterbanks with practically no upper limitations in the number of coefficients and subbands. Furthermore, a slight modification of the prototype filter coefficients is sufficient to satisfy exactly the perfect reconstruction constraints. An analysis of the time-frequency localization of the discrete prototype filters also shows that under certain conditions, EGF prototypes are at less than 0.3% from the optimal upper bound     

一种基于正交匹配追踪的压缩感知信号检测算法

针对当前压缩感知信号检测算法没有充分利用稀疏系数幅值信息的不足,提出了一种新的检测算法。从正交匹配追踪算法切入,通过深入分析归一化残差的变化信息,提出归一化余差概念,建立了一种基于归一化残差和归一化余差二维判决的信号检测算法。仿真结果表明,算法的有效检测阈值区间随着信噪比的降低而不断减小,且在信噪比为-8 dB、压缩比为0.25时,该算法的检测概率仍能满足要求,具备较好的适应性。     

Optimal differentiation based on stochastic signal models

The problem of estimating the time derivative of a signal from sampled measurements is addressed. The measurements may be corrupted by colored noise. A key idea is to use stochastic models of the signal to be differentiated and of the measurement noise. Two approaches are suggested. The first is based on a continuous-time stochastic process as a model of the signal. The second uses a discrete-time ARMA model of the signal and a discrete-time approximation of the derivative operator. Digital differentiators are presented in a shift operator polynomial form. They minimize the mean-square estimation error, and are calculated from a linear polynomial equation and a polynomial spectral factorization. The three obstacles to perfect differentiation, namely a finite smoothing lag, measurement noise, and aliasing effects due to sampling, are discussed     

匹配追踪算法及其在MI-EEG的应用

介绍了采用随机时频函数词典的匹配追踪法（Matching Pursuit，MP）的基本原理，及其在运动想象脑电（Motor Imagination EEG，MI—EEG）分析中的应用。该算法可将信号分解成一系列时频原子函数的线性组合，并在每次分解前自适应地初始化时频原子的参数，它具有较高时-频分辨率和信号微观结构的参数化表示的优点。模拟信号分析表明MP算法可在强噪声背景下表达信号的基本时-频特征。在MI—EEG应用中，MP算法分析出脑电特定频率成分（如α波、β波）的一些规律，它们符合运动相关的mu节律的结论，且发生了事件相关EEG现象。     

基于分治试探的盲自适应匹配追踪重构算法

压缩感知是一种针对稀疏可压缩信号进行压缩采样的信号处理新方法,针对现有稀疏度探测方法中探测次数较多的问题,基于分治思想提出了盲稀疏度自适应匹配追踪（BSAMP）算法,首先分治试探信号稀疏度,使得其估计值快速逼近真实值,然后通过自适应分组并扩充信号支撑域的方法,快速筛选出有效支撑,并通过弱匹配剪枝得到重构信号。可以在信号稀疏度未知的情况下,快速估计出信号的稀疏度并精确重构出原信号。仿真实验表明：在相同条件下,该算法的重构时间比其他同类算法短,且重构概率也大于其他同类算法。     

基于原子分解理论的雷达欺骗式干扰信号特征提取

针对雷达信号多为非线性信号的特点以及原子分解在冗余字典集内对非线性信号能得到更优逼近效果的优点,提出将原子分解理论应用到雷达欺骗式干扰信号特征提取,为实现自动识别雷达欺骗式干扰奠定了基础.简要介绍了原子分解的基本原理及经典算法.根据欺骗式干扰信号受DRFM量化效应影响的特点,选取包含Heaviside字典的过完备字典集进行特征提取,找出了能表明目标回波信号与欺骗式干扰信号两者之间显著差异的特征参数.仿真实验验证了提取的特征参数的有效性.     

匹配追踪消噪在故障诊断中的应用

为了更有效地消除齿轮泵振动信号的噪声,引入匹配追踪消噪方法。该方法采用匹配追踪算法,通过迭代提取信号的相关结构,再利用提取的相关结构来估计信号的逼近,从而达到消噪的目的。在此基础上,采用中值滤波和线性回归算法来终止迭代过程。最后利用该方法对齿轮泵壳体的振动信号进行消噪处理。结果表明,该方法保留了信号的基本特征,有效地消除了大量的噪声。     

Design techniques for orthogonal Modulated filterbanks based on a compact representation

This paper presents a method to design perfect reconstruction orthogonal modulated filterbanks having a very large number of subbands. At first, using the angular parameterization of the underlying lattice structure, the problem is formulated as an unconstrained optimization problem. Then, it is shown for two different optimization criteria (the minimization of the out-of-band energy and the maximization of the time-frequency localization) that the optimized lattice coefficients can be interpolated in two different ways. A first characterization takes advantage of the fact that the angles related to successive polyphase components of the prototype filter have a smooth behavior. A new parameterization, named compact representation, is therefore introduced, which shows that a linear increase of the number of parameters with respect to the number of subbands can be avoided with practically no loss of accuracy. A second observation concerns the also smooth behavior of each angular parameter when considering an increasing number of subbands. The efficiency of the two approaches is illustrated by the design of filterbanks with a large number of subbands going from 32 up to 4096, including prototype filters up to a length of 32 768.     

基于光弹调制的原子磁强计中光信号检测方法

在无自旋交换弛豫原子磁强计中,需要检测极小的旋光角度。基于光弹调制器的偏振调制技术由于其较低的噪声和长时间的稳定性在各种检测方法中是优选的。但光弹调制器的输出信号里包含有大量噪声和高次谐波,严重影响了原子磁强计的性能。针对以上问题分析了基于光弹调制器的偏振调制技术的原理和待检测信号的特性,并提出一种基于双通道数字锁相放大器的原子磁强计微弱信号检测方法。该方法简化锁相放大算法,减小电路复杂度,并能准确地同时检测一次谐波和二次谐波的幅值。理论分析和仿真结果表明,该检测系统工作良好,可以准确地检测微弱信号,误差在0.1%以内。     

Iterative and recursive estimators for hidden Markoverrors-in-variables models

In this paper we propose maximum-likelihood (ML) estimation of errors in variables models with finite-state Markovian disturbances. Such models have applications in econometrics, speech processing, communication systems, and neurobiological signal processing. We derive the maximum likelihood (ML) model estimates using the expectation maximization (EM) algorithm. Then two recursive or “on-line” estimation schemes are derived for estimating such models. The first on-line algorithm is based on the EM algorithm and uses stochastic approximations to maximize the Kullback-Leibler (KL) information measure. The second on-line algorithm we propose is a gradient-based scheme and uses stochastic approximations to maximize the log likelihood     

Multicomponent AM-FM signal separation and demodulation with null space pursuit

The operator-based signal separation approach, which formulates signal separation as an optimization problem, uses an adaptive operator to separate a signal into additive subcomponents. Furthermore, it is possible to design different operators to fit different signal models. In this paper, we propose a new kind of differential operator to separate multicomponent AM-FM signals. We then use the estimated operators to calculate each sub-component’s envelope and instantaneous frequency. To demonstrate the efficacy of the proposed method, we compare the decomposition and AM-FM demodulation results of several signals, including real-life signals.     

基于投影寻踪分析的芯片硬件木马检测

提出一种利用芯片旁路泄漏信息的硬件木马无损检测方法,通过基于绝对信息散度指标的投影寻踪技术,将芯片运行过程中产生的高维旁路信号投影变换到低维子空间,在信息损失尽量小的前提下发现原始数据中的分布特征,从而实现芯片旁路信号特征提取与识别。针对示例性高级加密标准(AES-128)木马电路的检测实验表明,该技术可以有效分辨基准芯片与硬件木马测试芯片之间的旁路信号特征差异,实现硬件木马检测。     

基于自动消偏的原子陀螺微弱光信号检测方法

核磁共振原子陀螺凭借其高精度、小体积、低成本的特点在国内外受到广泛关注。在获取检测目标的诸多方法中,较为常用的方法是监测探测光极化面旋转角的平衡偏振法。由于小型化带来的尺寸效应明显,光学元件间存在不可避免的方位误差,导致微弱光信号产生偏置,制约了该信号的提取与放大。为提高检测性能,分析了激光偏振态的差分检测原理,提出了一种基于比例积分（P-I）反馈回路的自动消偏方法,设计了基于硅光二级管的低噪声、高增益前置放大电路。最后,结合核磁共振陀螺样机系统,给出了该方法的仿真分析和实验结果,验证了该方法的有效性。     

Internal models and recursive estimation for 2-D isotropic randomfields

Efficient recursive smoothing algorithms are developed for isotropic random fields that can be obtained by passing white noise through rational filters. The estimation problem is shown to be equivalent to a countably infinite set of 1-D separable two-point boundary value smoothing problems. The 1-D smoothing problems are solved using a Markovianization approach followed by a standard 1-D smoothing algorithm. The desired field estimate is then obtained as properly weighted sum of the 1-D smoothed estimates. The 1-D two-point boundary value problems are also shown to have the same asymptotic properties and yield a stable spectral factorization of the power spectrum of the isotropic random fields     

基于匹配追踪算法的电能质量研究

为了解决电能质量信号采集数据量大的问题,提出基于匹配追踪重构算法的压缩感知方法,并首次应用于电能质量信号压缩采样研究。文中通过采用不同的稀疏基和重构算法的方法,来提高原始电能质量信号重构效果。当采样数据空间稀疏基分别选取傅里叶变换基和小波变换基,重构算法分别采用正交匹配追踪(OMP)和压缩采样匹配追踪(CoSaMP)时,仿真结果表明,压缩采样比为20%时,两种重构算法的均方误差都低于3%,重构信噪比大于30dB,为电能质量信号压缩采样研究提供了一种新的思路。     

基于递归和SVM的特征选择方法研究

特征选择是模式识别领域的一个重要的研究方向,它可以提高分类的效率与效果。本文将递归特征排除算法与SVM决策树结合起来运用于特征选择,首先利用递归特征排除算法对所选择的特征进行初排序,然后依次将特征送入SVM决策树中进行优化评估,对数据中起显著作用的特征进行筛选,除去冗余和次要特征,得到特征子集。最后,通过对Linux主机和相关网络的27个入侵特征数据进行特征选择实验,实验结果表明,特征个数降至21个,而测试精度仍然能达到94%,从而证明本文所提出的递归和SVM相结合的方法是解决特征选择问题的一种有效方法。     

集成语音信号与表面肌电信号的情感自动识别模型

为了提高情感识别的正确率,针对单一语音信号特征和表面肌电信号特征存在的局限性,提出了一种集成语音信号特征和表面肌电信号特征的情感自动识别模型.首先对语音信号和表面肌电信号进行预处理,并分别提取相关的语音信号和表面肌电信号特征,然后采用支持向量机对语音信号和表面肌电信号特征进行学习,分别建立相应的情感分类器,得到相应的识别结果,最后将识别结果分别输入到支持向量机确定两种特征的权重系数,从而得到最终的情感识别结果.两个标准语情感数据库的仿真结果表明,相对于其它情感识别模型,本文模型大幅提高了情感识别的正确率,人机交互情感识别系统提供了一种新的研究工具.     

基于非高斯性和广义复杂度寻踪的盲源分离算法

现有的盲源分离算法往往利用信号某一方面的统计特性来分离信号,例如:利用信号的非高斯特性,或者利用信号的时序特性.在实际应用中,信号往往是具有这两种特性信号的混合,采用信号某一方面的特性往往不能够成功的分离出信号.当源信号具有非高斯性和非线性预测特性时,提出了基于非高斯性和广义复杂度寻踪的目标函数,最小化该目标函数,提出了一个梯度下降的盲源分离算法.计算机仿真表明了提出算法的有效性,和现有的盲源分离算法相比,提出算法具有更好的信号分离性能.     

Some considerations of signal/noise improvement using recursive and nonrecursive integrators

The letter compares the signal-to-noise improvement attainable with recursive and nonrecursive integrators as a function of the number of sample records available and the effective summing capability of the integrators. The conditions under which one or other integrator gives the better performance are established.     

基于递推最小二乘算法的小信号检测

受到强干扰影响的小信号通常难于有效检测。在分析递推最小二乘算法(RLS)原理及其几种改进形式的基础上,采用自适应方法将已检测出的大信号与原混叠信号对消,降低大信号对小信号的遮蔽作用,再进行小信号的检测。最后通过仿真证明,该方法能够在较小失真的情况下,有效检测出被大调幅信号干扰下的小调频信号;同时分别比较了各种算法的优劣,得出基于可变遗忘因子的RLS(VFF-RLS)算法不仅具有较快的收敛速度,而且收敛之后具有很好的平稳性能。