Matching shape sequences in video with applications in human movement analysis

We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall's definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on dynamic time-warping. We suggest a modification of the dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication.     

Adaptive Harmonic Steady‐State Disturbance Rejection with Frequency Tracking

This paper is concerned with the rejection of sinusoidal disturbances of unknown frequency acting at the output of unknown plants. Disturbance rejection is based on an adaptive harmonic steady‐state (ADHSS) algorithm combined with a magnitude/phase locked‐loop (MPLL) frequency estimator. The harmonic steady‐state method assumes that the plant can be approximated by its steady‐state frequency response. For high‐order plants such as those encountered in active noise and vibration control (ANVC), this assumption greatly reduces the number of parameters and enables online estimation of the plant response using simple algorithms. The paper shows that when the MPLL is integrated with the ADHSS algorithm, the two components work together in such a way that the control input does not prevent frequency tracking by the MPLL, and so that the order of the ADHSS can be reduced. Thus, the addition of the MPLL allows disturbances of unknown frequency to be considered without significantly increasing the complexity of the original ADHSS. After analyzing the reduced‐order ADHSS in the ideal case, the equations describing the complete system are considered. The theory of averaging is used to gain insight into the steady‐state behavior of the algorithm. It is found that the system has a two‐dimensional equilibrium surface such that the disturbance is cancelled exactly. A subset of the surface is proved to be locally stable. Extensive active noise control experiments demonstrate the performance of the algorithm, even when disturbance and plant parameters are changing.     

一种基于序列零初相位调制的新型正弦信号频率测量方法

高准确度的正弦信号频率测量技术有广泛的应用,如应用于系统信号的同步处理,系统的谐波和系统的阻抗测量.但在低频正弦信号频率测量方面,现有的频率测量方法普遍存在准确度不高和抗谐波噪声干扰性不强等问题.文中提出了一种主要由序列零初相位调制等方法构成的新型正弦信号频率测量方法,分析了序列零初相位调制的原理.新方法避开了输入序列任意初相位的影响,同时调制序列携带了数值较大的信号序列全相位差信息,可实现准确度较高的低频正弦信号频率测量,此外新方法还具有较强的抗谐波噪声干扰特性,将新方法具体应用于电力系统正弦频率的测量,显示出明显的优越性.通过数学计算、仿真实验及物理实验结果验证了新型正弦信号频率测量方法的正确性和可靠性.     

SECANT-FUZZY LINEAR REGRESSION METHOD FOR HARMONIC COMPONENTS ESTIMATION IN A POWER SYSTEM

In order to avoid unnecessary damage of electrical equipments and installations,high quality power should be delivered to the end user and strict control on frequency should be made, Therefore, it is important to estimate the power system's harmonic components with higher accuracy. This paper presents a new approach for estimating harmonic component in a power system using secant - fuzzy linear regression method. In this approach the non - sinusoidal voltage or current waveform is written as I linear function. The coefficient of this function is assumed to be fuzzy number with a membership function that has center and spread value. The time dependent quantity is written as Taylor series with two different time dependent quantities. The objective is to use the sample obtained from the transmission line to find the power system harmonic components and frequencies. We used an experimental voltage signal from a sub power station as a numerical test.     

基于对偶观测器的谐波提取方法

提出一种基于对偶观测器的标准正弦信号提取方法.将受到谐波干扰的信号通过一种辅助滤波器得到与谐波频率相关的可测变量;然后利用对偶关系将受干扰信号分解为已知函数和受标准正弦信号驱动的待估项;进而构造观测器估计未知参数,直接对标准正弦信号进行重构.该方法可以将谐波信号表示为关于辅助变量的参数形式,直接建立频率与干扰谐波之间关系,实现标准信号与干扰谐波的分离.由于只需要估计受标准正弦信号驱动的未知参数,很大程度上可以降低计算复杂度.利用李雅普诺夫稳定性理论证明:当不存在有界噪声时,这种观测器可以渐近跟踪标准频率信号;在多源干扰环境下,观测器误差动态满足一致最终有界特性.仿真结果验证了该对偶观测结构的有效性.     

语音清浊音分类及浊音谐波提取算法--三阶累积量基于正弦语音模型的应用

在低信噪比和非平稳噪声干扰下,语音信号的清浊音检测是语音信号处理中的一个重要研究问题。论文基于语音正弦模型,提出了一种清浊音分类和浊音谐波提取算法。该方法在分析了语音的三阶累积量谱后,用子谐波-谐波方法取得基音,并计算出谐波参数和高低频能量比值。它利用谱包络估计器得到谱包络及尖峰信号,结合最小均方估计准则下的迭代算法计算语音谐波的信噪比;通过对上面各计算结果的综合评价得出语音帧的浊音度,从而得到语音清浊音的分类和浊音谐波数。仿真结果表明,该算法在复杂噪声背景下,能有效进行语音分类,准确得到浊音度。同时该算法还具有实时性好、语音参数分析精度高的特点。     

一种基于EEMD的新型谐波误差分析策略

针对电力系统中严重的谐波干扰问题,为提高其运行的安全有效性,去除谐波干扰并提高系统稳定性能成为研究热点。电网中常存在频率强烈波动或者白噪声强烈干扰的现象,虽说三谱线插值的FFT算法(基于Kaiser窗)的抗干扰能力很强,谐波参数估计也可以有较高的精准度,但是远不及使其与平均经验模态的总体水平来分解谐波的检测算法(EEMD)与直线度误差分析相结合。应用直线度误差来分析信号的谐波成分,有机结合了三谱线插值的FFT(基于Kaiser窗的)谐波检测分析法。通过对整体谐波分量的在线监测,不仅能够提高稳态信号的检测精度,还能提高系统的动态性能。本分析策略大限度地提高了计算精度以及系统稳定性。通过仿真分析得知,所述方法可以使系统具有较强的抑制频谱泄露能力,并能够显著降低栅栏效应的发生概率,进而使电力谐波信号的幅值误差、初相位误差和频率误差分别低于0.004 1%、0.002 4%和0.003 9%。     

调制中频生物电刺激的探讨与设计

由低频正弦调制的中频脉冲,不仅能克服中频脉冲治疗易被肌体所适应的缺点,而且还能使肌体所能承受的治疗阈值与治疗中的疼痛阈值明显分离。通过多方面分析得出,调制中频脉冲更适合作为治疗脉冲,而且较低频和中频脉冲有更好的治疗效果,同时综述了影响调制中频脉冲治疗效果的主要因素,如载波频率、调制方式、调制频率、调制幅度、调制波形等。最后设计了一个基于单片机的便携式调制中频电疗仪,经过周期性时间函数调幅的治疗机比原始的等幅输出脉冲的治疗机在克服肌体适应性方面有很大优势。     

小波变换在电力谐波分析中的应用

电力系统的谐波是影响电能质量的重要因素,谐波对电力系统和用电设备产生了严重危害和影响。文中应用小波变换分析电力系统的谐波,小波变换能描述频谱含量如何随着时间变化,同时在时间和频率上表示信号的能量和作用。与傅里叶变换对比,小波变换不仅可以知道哪些频率分量在信号中出现,而且可以知道这些频率分量在时域内是如何变化的,可以更精确地分析非平稳信号的谐波。     

基于流形调和变换的浅浮雕生成算法

针对利用3D网格生成浅浮雕不能使用精确的网格表示、浮雕细节保持特性依赖于规则采样高度域上分辨率的问题,提出一种基于3D网格频域分析的浅浮雕算法.从信号处理的角度出发,首先利用网格的几何微分性质和拓扑结构通过离散调和变换将网格的深度信号变换到频域;其次提出一种基于几何频率能量的频域划分策略,将频率分割为低频、高频和噪声,再对低频进行线性压缩,利用各项同性的扩散函数对高频进行自适应非线性压缩并过滤噪声,得到信号在频域上一组新的变换系数;最后利用调和逆变换将处理后的频域映射到信号的空域重建浮雕模型.实验结果表明,该算法可通过控制频谱的分割和扩散函数的参数实现浮雕模型细节的增强.     

一类非线性隔振器振动传递特性分析

非线性输出频率响应函数是由Volterra级数发展而来的一个新概念.对一类具有反对称阻尼特性的隔振器,通过该概念推导出了振动传递性与系统非线性参数之间的显式解析关系；进而系统地研究了非线性阻尼参数对隔振器的力传递性能和位移传递性能的影响.研究结果表明,虽然非线性隔振器在受正弦信号激励下会出现高次倍频分量,但对于其传递性...     

Local instantaneous frequency estimation of multi-component signals

We present a method for estimating the instantaneous frequency (IF) of multi-component signals. This method involves the calculation of a time–frequency energy density of the signal, then obtaining a local IF estimate from this joint density. Time–frequency energy density is calculated as a least squares optimal combination of multi-window Gabor based evolutionary spectra. The optimal weights are obtained by minimizing an error criterion that is the difference between a reference time–frequency distribution and the combination of evolutionary spectra. IF of the signal components is estimated from the final evolutionary spectrum at small time–frequency regions as the average of frequencies at each time. As such, local IF information of a multi-component signal can be estimated in the time–frequency plane.     

参数音频编码中的正弦波参数提取

研究了语音编码中基于倒谱的基音检测算法，并把它应用到参数音频编码中，提出了基于倒谱的正弦波参数提取法。本文根据倒谱的特性，结合音频信号的特征，利用前后帧的相关性和各次谐波的能量，有效保证了基频及其各次谐波在较大频率范围内的正确提取。另外结合谐波和单谱线提取，提高了算法效率。     

Optimizing Gabor Filter Design for Texture Edge Detection and Classification

An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.     

有源电力滤波器谐波检测的新型SRF-PLL设计研究

针对有源电力滤波器中基于同步坐标系的锁相环(synchronous reference frame PLL,SRF-PLL)在三相电网电压不平衡且有畸变时,负序基频分量和高频谐波电流对SRF-PLL的影响。文章采用在基于同步坐标系的锁相环中加入自适应陷波器(adaptive notch filter,ANF)和自适应滤波器(adaptive filter)的技术。利用自适应陷波器的两个相互正交的输出量抵消同频负序分量导致的2倍工频波动,并采用自适应滤波器的最小均方算法(Least Mean Square,LMS)滤除高频谐波分量,从而准确的提取基波电压的幅值与相位。文章以单独加入自适应滤波器或自适应陷波器作比较,通过仿真分析验证文中方法能更加准确的提取电网电压频率,正余弦函数曲线更加平滑,具有良好的动态性能和稳定性。     

小波多分辨率的谐波正弦语音建模

谐波正弦语音模型因固定帧长不能使每个谐波得到最佳分辨率,而分辨率决定着语音的建模效果。因此提出小波多分辨率的谐波正弦语音模型,将谐波语音信号通过小波变换分解成多分辨率子带信号,利用谐波正弦语音模型对这些子带信号独立建模,将建模后的各子带信号相加合成。仿真实验显示该模型的信号重构误差降低约两个数量级,通过PESQ软件测试得到的MOS分值约提高0.3。     

基于离散余弦变换基函数迭代的人脸图像识别

使用非线性混沌方法处理与识别图像的研究工作逐渐增多,已有文献给出了一种 将正弦函数作为辅助函数与图像构造动力系统,迭代生成混沌吸引子作为图像特征。为进一步 探究图像吸引子作为图像的特性,改进识别效果,使用离散余弦变换(DCT)基函数矩阵代替正 弦函数,迭代生成近似混沌吸引子,用于人脸识别。首先,研究分析了DCT 基函数矩阵的多样 性与振荡特性;然后利用DCT 基函数矩阵与图像矩阵构造迭代表达式,通过给出的迭代算法使 其产生吸引子,再对吸引子进行快速傅里叶变换,计算相关系数,识别人脸图像。对于Yalefaces 图像库,每幅图像都参加训练,识别率可以达到100%,当使用每组前5 幅图像训练提取特征, 识别率可以超过85%;对于CMU PIE 数据库,每幅图像都参加训练,识别率可以超过99%。 该吸引子方法可以作为一种图像底层特征提取方法,有待于进一步深入研究。     

基于Morlet复小波变换幅值和相位信息的间谐波检测方法

小波变换以其良好的时频局部化特性在电力系统中得到了广泛的应用,包括在谐波分析方面。但即使是时频窗面积最小的Morlet小波也存在频谱混叠现象,使得单纯利用小波变换系数的幅值无法对谐波准确检测。本文提出了基于Morlet复小波幅值和相位信息相结合的谐波、间谐波检测方法。首先利用a-S（a）曲线粗略确定包含被测信号信息的尺度范围,然后利用这些尺度上的小波变换系数的相位信息来实现谐波、间谐波频率的检测,最后根据检测出的频率确定特征尺度,进而确定被测信号的幅值。与FFT方法和传统的尺度-幅值法相比,该方法能够克服频谱泄漏现象,且能够区分频率相近的信号成分,提高了谐波、间谐波的检测精度。通过Matlab软件进行仿真,结果验证了该算法的正确性。     

利用傅里叶谐波分析法的时序数据周期迭代辨识算法

对时序数据进行周期辨识能有助于人们认识事物的本质。针对现有的时序数据其周期辨识算法存在着辨识精度低及计算成本高的问题,提出了一种具有基频迭代机制的周期辨识算法。该算法首先基于方差分析法从原始序列中析出其周期长度的整型估算值,然后,以任意小的频率间隔在估算值区间内进行傅里叶谐波的迭代拟合,最后,基于最小拟合残差和的准则来确定最优的周期成分。实验表明,该算法不仅具有良好的计算效能,而且还能精确地辨识出与序列样本长度无关的周期成分。