Tissue characterization using the continuous wavelet transform. Part II: Application on breast RF data

For pt.I see ibid., vol.48, no.2, p.356-63 (2001). In the first part of this work (Georgiou and Cohen 2000), a wavelet-based decomposition algorithm of the RF echo into its coherent and diffuse components was introduced. In this paper, the proposed algorithm is used to estimate structural parameters of the breast tissue such as the number and energy of coherent scatterers, the energy of the diffuse scatterers, and the correlation between them. Based on these individual parameters, breast tissue characterization is performed. The database used consists of 155 breast scans from 42 patients. The results are presented in terms of empirical receiver operating characteristics (ROC) curves. The results of this study are discussed in relation to the tissue microstructure. Individual estimated parameters are able to differentiate reliably between normal and fibroadenoma or fibrocystic or cancerous tissue (area under the ROC A_z>0.93). Also, the differentiation between malignant and benign (normal, fibrocystic, and fibroadenoma) tissue was possible (A_z>0.89)     

Classification of simulated hyperplastic stages in the breast ducts based on ultrasound RF echo

Visual inspection of ultrasound is diagnostically limited for characterizing breast tissue, in particular when it comes to visually detecting hyperplasia that forms in the ducts at its early formation (at submillimeter resolution) stages. It can, of course, be seen using biopsies. But this will not be done unless the areas have been flagged using noninvasive modalities. The aim of this paper is to draw to the attention of the medical community (albeit through simulations) that the continuous wavelet transform decomposition (CWTD) that was proven in vivo for tissue characterization before has the potential to flag out simulated hyperplasia data at submillimeter resolutions. And it might be an excellent candidate for detecting in vivo hyperplastic changes in the breast. To the best of our knowledge, this is the first attempt at studying the potential of detecting cell growth in breast ducts using ultrasound. The stochastic decomposition model (the CWTD) of the RF echo with its coherent and diffuse components, yields image parameters that correlate closely with the structural parameters of the (simulated) hyperplastic stages of the breast tissue. The discrimination power of the various parameters is studied under a host of conditions, such as varying resolution, depth, and coherent to diffuse energy ratio (CDR) values using a point-scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. These are shown to be useful for detecting the various types of simulated hyperplastic data. Careful analysis shows that three parameters, in particular the number of coherent scatterers, the Rayleigh scattering degree, and the energy of the diffuse scatterers, are most sensitive to variations in the hyperplastic simulated data. And they show very high ability to discriminate between various stages of simulated hyperplasia, even in cases of low resolution and low CDR values. Using the area under the receiver operating characteristics (ROC) curve (A(z)) as the performance metric, values of A(z) > 0.942 are obtained when discriminating between stages for resolution 0.948 for different duct densities.     

Modeling ultrasound imaging as a linear, shift-variant system

Wo solve the equation that governs acoustic wave propagation in an inhomogeneous medium to show that the radio-frequency (RF) ultrasound signal can he expressed as the result of filtering the scatterer field with a point-spread function. We extend the analysis to make the link between the RF ultrasound signal and the representation of ultrasound scatterers as vectors with small magnitude and random phase in the complex plane. Others have previously performed parts of this analysis. The contribution of the present paper is to provide a single, coherent treatment emphasizing the assumptions that have to be made and the physical consequences of the models derived. This leads to insights into the interaction of monopole and dipole scattering, useful techniques for simulating and analyzing speckle statistics in the complex plane and a new expression for the normalized covariance of the analytic RF ultrasound signal in terms of the complex envelope of the point-spread function.     

基于协方差矩阵拟合的宽带无网格波达角估计

宽带波达角(Direction of Arrival,DOA)估计是声呐系统阵列信号处理中一个重要的研究方向。文章提出了一种基于相干子空间的改进稀疏与参数方法(Coherent Signal-subspace based Modified Sparse and Parameter Approach,C-MSPA),以实现高精度和高空间方位分辨能力的宽带DOA估计。算法利用聚焦矩阵将各子带上的采样协方差矩阵投影至聚焦频率上。完成聚焦后,文章基于频率选择的范德蒙分解理论对协方差矩阵拟合准则进行改进,使重构的协方差矩阵中包含的DOA信息严格限制在聚焦区域内,最终对重构的协方差矩阵进行范德蒙分解,得到DOA估计值。所提出的算法无需选取正则参数,同时避免了基不匹配问题。仿真和湖上实测数据分析结果表明,所提出的方法实现了高空间方位分辨能力且提高了DOA估计精度。     

EMD-Based Signal Filtering

In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.     

一种改进HVD信号特征提取方法及应用研究

希尔伯特振动分解(HVD)广泛应用于风电机组、齿轮箱等旋转机械的故障诊断,然而,它有2个亟待解决的问题：一是算法的参数需要经验设置或人工试定;二是如何避免模态混叠选择敏感的本征模态函数分量。针对上述2个问题,提出一种优化的HVD改进算法,有效解决了希尔伯特振动分解的参数设置和模态混叠问题。首先用粒子群优化算法(PSO)对HVD算法的2个参数进行优化。其次,提出了一种新的评估指标—最大包络峰度均值作为PSO优化算法的目标函数,并提出采用最大包络峰度自适应地选择敏感的IMF分量。最后,对选定的重构信号进行平方包络谱分析并提取故障特征频率,以识别风电机组设备故障类型。通过模拟信号、实验信号和风电机组应用实例分析,验证了所提改进HVD方法的有效性。     

基于参数优化变分模态分解的滚动轴承故障特征提取方法

在滚动轴承早期故障阶段,代表轴承故障特征的冲击成分容易被较强的背景噪声淹没,针对这一问题提出相关峭度(Correlated Kurtosis,CK)优化变分模态分解(Variational Mode Decomposition,VMD)的滚动轴承故障特征提取方法.针对变分模态分解方法参数不确定问题,提出利用以相关峭度为...     

WOLD decomposition of the backscatter echo in ultrasound images of soft tissue organs

Deals with a method of detecting and estimating the scatterer spacing between the regularly spaced resolvable coherent scatterers in tissue. Scatterer spacing has been successfully used in classifying tissue structure, in differentiating between normal and cirrhotic liver, and in detecting diffuse liver disease. This paper presents a WOLD decomposition of the radio frequency (RF) field into its diffused and coherent components from which maximum likelihood estimates (MLE) or minimum mean square error (MMSE) estimates of the scattering spacing are easily computed. The MLE are efficient and for relatively long record are unbiased. They result in accurate estimates in low signal-to-noise (SNR) ratios. Unfortunately, they require nonlinear minimization and knowledge of the probability density associated with the RF backscatter echo. The MMSE estimates, on the other hand, are computationally simple, yield unique closed form solutions, do not require a-priori knowledge of the probability distribution function of the backscatter echo, and result in accurate estimates in low SNR ratios. This paper also presents an unbiased decision rule to detect whether or not an RF echo exhibits any specular scattering relative to the wavelength of the interrogating ultrasonic pulse. The approach has been tried on simulations as well as on in-vivo scans of liver data, and appears to perform well.     

基于改进自适应变分模态分解的滚动轴承微弱故障诊断

滚动轴承早期故障信息微弱,且混有大量背景噪声,难以提取其故障特征。提出了一种改进的自适应变分模态分解(AVMD)与Teager能量谱的微弱故障诊断方法。将最小平均包络熵(MMEE)作为目标函数,自动搜寻影响参数最佳值,确保变分模态分解(VMD)实现最优分解,并提出加权峭度指标(WK)用于选择有效模态分量进行信号重构,对重构信号进行Teager能量谱分析,从而识别故障特征频率。对轴承微弱故障振动信号的研究表明,所提方法改进了传统VMD算法分解精度受参数影响较大,导致信号出现过分解或欠分解的问题;与集合经验模态分解和局部均值分解算法相比所提方法具有更强的噪声鲁棒性和故障信息提取能力。     

双光束叠加与差异模的光学图像加密算法

目的为了解决当前基于干涉原理的光学图像加密算法因存在轮廓显现导致其安全性不高的问题,提出双光束相干叠加与差异模矢量分解的图像加密算法。方法基于Gyrator变换,将明文变成一个Gyrator频域的复杂函数;随后引入矢量分解方法,将Gyrator频域复杂函数进行差异分解,输出幅度与相位不均等的2个矢量成分;利用2个相位掩码对矢量成分进行调制,将其从频域变为空域,将其相位部分视为私密,而幅度部分视为最终加密密文。结果实验显示,与当前基于模均等分解的图像加密技术相比,所提算法具有更高的保密性与敏感性,有效消除了轮廓显现问题。结论所提算法能够确保图像信息在网络中的安全传输,有效抵御外部攻击,在包装印刷与防伪二维码等领域具有一定的应用价值。     

Unsupervised segmentation of RF echo into regions with different scattering characteristics

Recent experimental results verify that the probability distribution function of the diffuse component of the RF echo depends primarily on the concentration of the diffuse scatterers in the resolution cell. In this paper we apply these results to develop an unsupervised segmentation scheme that partitions an RF A-scan or B-scan image into statistically homogeneous regions that reflect the underlying scattering characteristics. The proposed segmentation scheme is based on a nonparametric homogeneity test that compares two regions of interest (ROI) for possible merging utilizing information about both the coherent and the diffuse component of the RF echo. For the coherent component, homogeneity is defined in terms of the estimated average spacing of each ROI. For the diffuse component, we use the nonparametric Kolmogorov-Smirnov (K-S) homogeneity statistical test that compares two empirical distributions associated with any two ROIs. This test can be used to obtain a segmentation into regions with different scattering characteristics regardless of the nature of the scattering conditions (e.g., Rayleigh regions with different scatterer concentration, different non-Rayleigh regions, or different coherent scattering regions). Finer segmentation can be obtained by learning the distributions associated with the various homogeneous regions obtained from the coarse segmenter. The proposed segmentation scheme is applied on simulated RF scans with different scatterer concentration per resolution cell, on phantom data which mimic tissue, and on liver scans. The results demonstrate the effectiveness of the segmentation algorithm even in cases of subtle differences in the scattering characteristics of each region (for example, diffuse component with scatterer density of 16 and 32 scatterers per resolution cell).     

Convergence of backward-error-propagation learning in photorefractive crystals

We analytically determine that the backward-error-propagation learning algorithm has a well-defined region of convergence in neural learning-parameter space for two classes of photorefractive-based optical neural-network architectures. The first class uses electric-field amplitude encoding of signals and weights in a fully coherent system, whereas the second class uses intensity encoding of signals and weights in an incoherent/coherent system. Under typical assumptions on the grating formation in photorefractive materials used in adaptive optical interconnections, we compute weight updates for both classes of architectures. Using these weight updates, we derive a set of conditions that are sufficient for such a network to operate within the region of convergence. The results are verified empirically by simulations of the xor sample problem. The computed weight updates for both classes of architectures contain two neural learning parameters: a learning-rate coefficient and a weight-decay coefficient. We show that these learning parameters are directly related to two important design parameters: system gain and exposure energy. The system gain determines the ratio of the learning-rate parameter to decay-rate parameter, and the exposure energy determines the size of the decay-rate parameter. We conclude that convergence is guaranteed (assuming no spurious local minima in the error function) by using a sufficiently high gain and a sufficiently low exposure energy per weight update.     

基于EEMD-ICA的脑电去噪算法研究

脑电信号(EEG)是脑神经细胞的电生理活动在大脑皮层的反映,但采集到的脑电信号一般都含有大量噪声.为了保留有效信息同时消除尽可能多的噪声,提出通过构造虚拟通道将集合经验模态分解与独立成分分析相结合的脑电信号去噪方法.首先,对脑电信号进行EEMD分解得到固有模态函数(IMF)分量,根据相关性准则筛选含噪声成分多的IMF分...     

Robust determination of the anisotropic polarizability of nanoparticles using coherent confocal microscopy

A coherent confocal microscope is proposed as a means to fully characterize the elastic scattering properties of a nanoparticle as a function of wavelength. Using a high numerical aperture lens, two-dimensional scanning, and a simple vector-beam shaper, the rank-2 polarizability tensor is estimated from a single confocal image. A method for computationally efficient data processing is described, and numerical simulations show that this algorithm is robust to noise and uncertainty in the focal plane position. The proposed method is a generalization of techniques that provide an estimate of a limited set of scattering parameters, such as a single orientation angle for rodlike particles. The measurement of the polarizability obviates the need for a priori assumptions about the nanoparticle.     

完全互补小波噪声辅助集总经验模态分解

经验模态分解(EMD)是一种自适应非线性非平稳数据处理方法。噪声辅助的EMD方法能克服EMD方法在处理间歇信号时出现的"模态混叠"现象。在这些噪声辅助方法中,互补集总经验模态分解(CEEMD)和完全噪声辅助噪声集总经验模态分解(CEEMDAN)恢复了EMD分解的完整性。在现有分析方法上提出了完全互补小波噪声辅助集总经验模态分解(CCWEEMDAN)算法。该算法能用更小的集总数、更少的迭代次数和极小的计算消耗获得更好的光谱分离效果和数目较少的筛选模态。     

Power Measurement in DVB-T Systems: On the Suitability of Parametric Spectral Estimation in DSP-Based Meters

Performance assessment and large-scale monitoring of terrestrial digital video broadcasting (DVB-T) transmission apparatuses are currently a pressing need due to the rapid growth of related broadcasting networks in many countries. Power measurements, in particular, play a very important role since RF and IF signal power, RF channel power, RF and IF power spectrum, noise (or unwanted) power, and power efficiency are relevant parameters to be measured as accurately as possible. Although modern spectrum analyzers and high-performance vector signal analyzers exhibit satisfying accuracy and repeatability, their cost, weight, and size make them unsuitable for the purpose. This paper is focused on the design and realization of a digital signal processing-based meter for power measurement in DVB-T systems that is capable of granting good accuracy, satisfying repeatability, reduced measurement time, and cost effectiveness. This paper deals with the digital signal processing algorithm to be implemented in the meter, paying attention to parametric power spectral density (PSD) estimators for their reduced memory requirement and potentially limited computational burden. In particular, a parametric estimation algorithm that is capable of assuring a fast measurement rate is implemented and made operative. The simulation and emulation stages are properly designed to regulate the most relevant parameters of the adopted PSD estimators according to the specific features of the signals involved; a number of experiments on actual DVB-T signals are conducted, the results of which are compared to those provided by competitive measurement solutions.      

基于改进HHT和SVM的滚动轴承故障状态识别

针对滚动轴承故障信号特征难以提取与故障诊断效率较低问题,引入集合经验模态分解(EEMD)对Hilbert-Huang变换(HHT)进行改进,将改进的HHT结合拉普拉斯得分(Laplacian score,LS)进行轴承故障特征提取,并利用遗传算法(GA)优化支持向量机(SVM)分类参数,将其应用于滚动轴承振动信号故障状...     

基于EEMD的振动信号自适应降噪方法

摘 要：应用集合经验模式分解（Ensemble empirical mode decomposition ,EEMD）能有效抑制模态混叠的特性,根据白噪声经经验模式分解（Empirical mode decomposition, EMD）后其固有模式函数（intrinsic mode functions ,IMF）分量的能量密度与其平均周期的乘积为一常量这一特点设计了自动选择IMF分量重构信号的算法,提出了基于EEMD的振动信号自适应降噪方法。对仿真信号和滚动轴承振动信号的降噪结果表明了该降噪方法的可行性和有效性。     

改进的HHT方法及其在旋转机械故障诊断中的应用

针对希尔伯特-黄变换(Hilbert-Huang Transform,HHT)方法中存在的模态混叠和虚假固有模态函数(Intrinsic Mode Function,IMF)问题,提出一种基于总体包络均值经验模态分解(Ensemble Envelop Mean Empirical Mode Decomposition,EEMEMD)和虚假模态函数剔除算法相结合的改进HHT方法。该方法利用EEMEMD可准确反映加噪后信号的自身变化,一定程度上中和残留在各模态分量间的噪声,获得无模式混淆的较纯净的IMF分量。同时,通过基于归一化能量熵值的虚假模态函数剔除算法可有效剔除噪声干扰成分和迭代误差分量,从而提高信号特征提取的准确性。通过仿真分析和转子不对中故障诊断的工程实例表明,改进HHT方法能够较好地抑制模态混叠问题并有效剔除同故障无相关的虚假IMF,实现对旋转机械故障的有效诊断。     

基于VMD-PNN的砂轮钝化声发射检测

在磨削加工过程中,加工刀具即砂轮会发生钝化现象,砂轮表面磨损影响加工精度和工件质量,需要及时检测并修整.磨粒的塑性变形、破碎、断裂等会产生声发射信号,能够作为精确识别砂轮钝化状态的依据,且不易被噪声干扰,因此提出一种基于变分模态分解(Variational Mode Decomposition,VMD)和概率神经网络(...