管壁和血流多普勒信号的分离研究

根据超声多普勒血流信号和血管壁搏动信号的统计特性,提出了一种基于主元分析的非线性滤波方法。并用于计算机仿真的超声多普勒信号和实际采集的人体颈总动脉多普勒信号的处理,同时与传统的高通滤波器方法进行了比较。实验中得到了与实际更接近的平均流速估计,相对误差仅为5.62％,与滤波法的相对误差12.83％相比,准确性得到了一定的提高。结果表明:基于主元分析的非线性滤波方法能在较大的管壁血流功率比范围内滤除管壁搏动信号,同时保留低频血流信号成分,因而可用于超声多普勒系统中管壁和血流信号成分的分离,分离后的这些低频的血流成分信号能使以后的参数提取和信号分析工作具有更高的精度和可靠性。     

空间选择性降噪法提取血流超声多普勒信号

医学超声多普勒系统的高通滤波器在消除血管壁搏动带来的回波干扰时,也滤除了低速血流信息。为提取完整的血流超声多普勒信号,提出一种基于空间选择性降噪的方法。根据血管壁超声多普勒信号在小波尺度空间上的相关性,采用空间选择性降噪技术对管壁信号进行初步估计;然后用小波阈值滤波法消除管壁信号中残留的血流信号;最后从混合信号中减去管壁信号得到血流信号。对计算机仿真超声多普勒信号和人体颈总动脉超声多普勒信号应用本方法,实验结果表明:空间选择性降噪法能在较大的管壁／血流功率比范围内提取完整的血流信号,其平均相对误差比高通滤波器的结果降低了约45％。该方法有望成为超声多普勒系统中滤除管壁信号的一种有效方法。     

脉冲星信号的经验模态分解模态单元比例萎缩消噪算法

针对脉冲星信号的消噪问题, 提出了一种基于模态单元比例萎缩的经验模态分解(EMD)消噪方法. 利用经验模态分解将含噪脉冲星信号分解为一组内蕴模态函数(IMF), 将IMF中两个过零点间的部分定义为模态单元, 以模态单元为基本单位构造最优比例萎缩因子, 对IMF中的每个模态单元进行比例萎缩去噪, 进而建立基于模态单元比例萎缩的脉冲星信号滤波模型.对含噪脉冲星信号进行了消噪实验分析, 实验结果表明, 与小波硬阈值消噪法、比例萎缩小波消噪法和基于模态单元阈值的EMD消噪法相比, 该方法可以更有效地去除脉冲星信号中的噪声, 同时更好地保留了原信号中的有用细节信息. 关键词： 经验模态分解 脉冲星信号 模态单元比例萎缩 消噪     

噪声模态单元预判的经验模态分解脉冲星信号消噪

为了改善脉冲星辐射脉冲信号的消噪效果, 提出了一种基于噪声模态单元预判的经验模态分解(EMD) 消噪声方法. 该方法首先利用EMD将含噪辐射脉冲信号分解为一组内蕴模态函数(IMF), 根据IMF系数的统计特性采用局部均方误差准则进行噪声模态单元预判, 并将噪声模态单元置零; 然后对噪声模态单元预判处理后的IMF以模态单元为基本单位进行最优比例萎缩消噪, 从而达到抑制噪声、保留信号的目的. 实验结果表明: 与Sure Shrink小波阈值法、Bayes Shrink小波阈值法和EMD模态单元比例萎缩法相比, 基于噪声模态单元预判的EMD消噪方法可以更有效地去除脉冲辐射信号中的噪声, 同时更好地保留信号突变处的细节信息特征, 在信噪比、 均方误差、峰值相对误差、峰位误差和相位误差等方面都有一定程度的改善. 关键词： 脉冲星信号消噪 经验模态分解 噪声模态单元预判 局部均方误差     

基于经验模态分解和小波阈值的冲击信号去噪

冲击信号是非线性的并且容易受到噪声污染。为研究冲击信号去噪的问题,本文针对经验模态分解(Empirical Mode Decomposition,EMD)去噪和小波阈值去噪方法存在的不足,提出了基于EMD的小波阈值去噪方法。单纯的EMD去噪方法会在去除高频噪声的同时压制高频的有效信息。本文将EMD与小波阈值去噪相结合,利用连续均方误差准则确定含噪较多的高频固有模态函数(Intrinsic Mode Function, IMF),对高频IMF分量进行小波阈值去噪,以分离并保留这些分量中的有效信息,同时保持低频IMF分量不变。对模拟数据和实际冲击信号进行去噪处理,结果表明,基于EMD的小波阈值去噪方法的去噪效果优于单纯的EMD去噪方法和小波阈值去噪方法。     

基于EMD的单通道信源数估计方法

为了获取单通道接收信号的信源数目,针对普通信源数估计方法不能直接用于单通道接收信号的问题,提出了基于经验模态分解(empirical mode decomposition, EMD)的信源数估计方法。将单通道信号通过EMD处理,得到多个固有模态函数(intrinsic-mode function, IMF),据此构造数据协方差矩阵。对所构造的协方差矩阵进行特征值分解,采用基于信息论的AIC和MDL准则估计信源数。为进一步提高算法估计性能,引入对角加载技术对矩阵特征值进行平滑处理。仿真实验结果表面,本文提出的方法能够适用于单通道信源数估计,对角加载技术能够显著提高算法检测性能。     

基于EMD的拉曼光谱去噪方法研究

经验模态分解(EMD)方法是一个以信号极值特征尺度为度量的时空滤波过程,它充分保留了信号本身的非线性和非平稳特征,在信号的滤波和去噪中具有较大的优势。文章在介绍EMD分解方法的基础上,结合EMD的多尺度滤波特性,提出了一种新的拉曼光谱去噪方法——EMD阈值去噪法。该方法首先对含噪的拉曼光谱信号做EMD分解,得到各阶本征模态函数(IMF),然后对高频的IMF分量用阈值法进行处理,把经过阈值处理后的高频IMF分量与低频IMF分量叠加得到重构的信号,即去噪信号。通过处理对二甲苯的拉曼光谱信号,分析了在不同噪声水平上不同去噪方法的处理效果。实验结果表明EMD阈值去噪法有效地去除了噪声,较好地保留了光谱的细节信息,与小波阈值去噪方法相比较具有自适应的优势,在拉曼光谱去噪中有很好的应用前景。     

双向血流超声多普勒信号的小波降噪

利用小波框架结合软阈值的方法对表征双向血流的超声多普勒信号进行降噪。为了避免软阈值非线性处理对双向血流多普勒信号方向表征的影响,先对超声多普勒信号进行方向分离,然后对分离后的信号分别进行小波降噪,最后合成降噪后的双向血流超声多普勒信号。对计算机仿真的股动脉超声多普勒血流信号的降噪实验结果分析表明:本方法在不影响双向血流信息的前提下,提高了多普勒信号的信噪比、最大频率曲线和平均频率曲线的估计精度。仿真实验的结果证明了本方法可以用于双向血流超声多普勒信号的降噪。     

含管壁搏动的超声多普勒血流信号仿真

基于血流和血管壁的物理模型,提出了对包含血管壁搏动的超声多普勒血流信号进行计算机仿真的方法。实验中生成了不同采样容积下的超声多普勒仿真信号,通过计算仿真信号的平均频率曲线,并与给定值进行比较,证明了方法的有效性。该方法的提出,为进一步研究分离血流信号和管壁搏动信号的方法提供了方便有效的信号源。     

应用经验模态分解和独立成分分析解译岩矿光谱（英文）

岩矿光谱由多种矿物光谱混合而成,解译岩矿光谱能够得到岩矿的组分信息,且该方法具有快速、方便、不损坏样品的特点。经验模态分解(empirical mode decomposition,EMD)不能直接分离出混合信号中的源信号,独立成分分析(independent component analysis,ICA)要求混合信号数目不小于其所包括的源信号数目。将EMD和ICA两种方法相融合,首先用EMD分解混合信号得到本征模态函数(intrinsic mode function,IMF),再选择一定数目的IMF与混合信号一起组成ICA的输入数据矩阵,经过ICA运算可以获取单一混合信号中的源信号信息,克服了EMD和ICA两种方法各自的缺陷。研究表明,综合应用EMD和ICA方法可以获取单一混合信号中的源信号信息,混合信号中源信号含量越大,得到的源信号近似值越理想。参与ICA分离的IMF数目决定了分离得到的源信号近似值的数目,并且选择的IMF与混合信号相关系数越大,得到的源信号近似值越理想。运用该方法定量分析岩矿光谱,可以获取组成岩矿的矿物信息,比较适用于野外作业岩矿的快速分析鉴定及成分初步分析。     

时延匹配加权的舷外异常噪声源曲面投影定位方法

对现有的"滤波-时延估计-双曲面定位"的声源定位方法进行改进。结合经验模态分解消噪方法和广义平均幅度差函数时延估计方法,利用能量分布准则和频谱一致性准则进行源信号本征模态函数筛选和信号重构,提取多组分析信号求取时延,并利用时延匹配准则对真实时延值进行筛选和加权处理。考虑水听器贴近壳体布放,对壳体进行建模,并按照象限投影到平面再进行双曲线定位。分别进行模型系泊及航行实验,实验结果表明,改进后的定位方法有更高的定位精度,且减小了时延估计误差造成的定位精度影响。      

基于经验模式分解的拖曳式声纳拖船噪声抵消研究

拖曳式线列阵声纳的拖船噪声具有多途角扩展等特点，并且是一个非平稳过程，使得对该噪声的消除或抑制是一大难点。经验模式分解是一种用于分析非线性非平稳信号的新方法，该方法自适应地将嵌于数据内部的多个固有模式函数逐一分解开来。本文尝试利用经验模式分解方法分离出水听器接收信号中的拖船干扰噪声，从而达到消除干扰的目的。海上试验数据的处理结果充分验证了这种方法的可行性。     

A wavelet space based approach for Doppler ultrasound blood signals separation

In medical Doppler ultrasound systems, a high-pass filter which is usually employed to filter wall clutter components, will remove the information of the low velocity blood flow. To extract intact Doppler ultrasound blood signals, a novel approach is proposed based on the spatially selective noise filtration. The wall signals are firstly estimated by the spatially selective noise filtration from wavelet spatial correlation property. Then the wall clutters are exactly obtained by a wavelet threshold de-noising technique which eliminates the residual blood flow signals. Finally the intact blood flow signals are achieved by subtracting the wall signals from the mixed signals. This approach is applied to both computer simulated and in vivo carotid Doppler ultrasound signals. The experiment results show that the wavelet space based approach can exactly extract the blood flow signals, and achieve about 45% lower results in the mean absolute error than that of the high-pass filtering. This approach is expected to be an effective method to remove the wall clutters in Doppler ultrasound systems.     

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.     

Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.     

Doppler ultrasound signals simulation from vessels with various stenosis degrees

As a non-invasive method, the Doppler ultrasound technique is used to detect the vessel stenosis. To search for characteristics of Doppler ultrasound signals sensitive to the stenosis, a computer simulation approach is proposed in this paper to generate Doppler ultrasound signals from vessels with various stenosis degrees. The blood flow velocity distribution in a stenosed vessel is firstly calculated using the transient finite element method (FEM). Then the power spectral density of Doppler signals is estimated using the overall-distribution nonparametric estimation method. Finally Doppler signals are generated using the cosine-superposed method. The proposed approach is proved to be useful for simulating Doppler ultrasound signals from vessels with various stenosis degrees. It is also shown that characteristics of Doppler ultrasound signals may be used to estimate the vessel's stenosis degree.     

基于经验模态分解的高光谱遥感数据去噪方法

经验模态分解(EMD)是一种新的时频分析方法,经EMD分解后的各个固有模态函数(IMF)突出了原始信号的局部特征,从而可以区分噪声和有用信号。基于此,结合高光谱遥感数据的光谱变化特征,提出了一种基于经验模态分解的高光谱遥感数据去噪方法。通过对理论数据的实验表明,数据中的噪声无论是高斯分布还是均匀分布,数据经EMD分解后,噪声都主要集中在前几个特定的IMF,对相应的IMF进行滤波处理后并与其他IMF分量进行重构就可得到去噪信号,与小波去噪结果相比较,这种方法效果更好。最后把该去噪方法应用于野外实测的油膜高光谱数据去噪,实验结果表明,该方法能准确、有效地去除高光谱遥感数据的噪声。     

Application of Hilbert–Huang transform to laser Doppler velocimeter

A new method based on Hilbert–Huang transform is proposed to analyze the laser Doppler signal with a large acceleration. The Doppler signal is decomposed into several Intrinsic Mode Functions (IMFs) via empirical mode decomposition (EMD). And the Hilbert transform is used to compute the instantaneous frequency. The vehicle velocity parameter is estimated by taking linear fitting on the instantaneous frequency of the relevant IMF. The simulation results show that the HHT-based method is quite useful for the LDV that offers velocity parameter to the vehicle self-contained navigation system when the vehicle moves at a large acceleration.     

EEMD-ICA在功能性近红外光谱特征信号提取中的应用

近年来,功能性近红外光谱技术(fNIRS)广泛应用于神经影像学领域。为解决fNIRS特征信号提取中的信噪频谱混叠问题,依据近红外光谱脑功能成像信号非线性与非平稳特点,提出一种结合集合经验模态分解法和独立成分分析的多分辨率联合信号提取方法EEMD-ICA。在脑功能成像仪器平台上采集多通道多波长脑功能成像近红外光密度信号,先对该信号进行集合经验模态分解将其按频率成分分解为多层本征模态函数,之后将独立成分分析应用于目标频率分量函数进行自适应去噪,最后将处理后的分量累加、重构获得近红外光谱脑功能成像的特征信号。将Valsalva氏实验测试数据作为研究对象进行滤噪处理,与经验模态分解法和集合经验模态分解法对fNIRS特征信号的提取效果对比。对实测数据的处理结果进行信噪比和误差参数分析,结果表明,该方法能够有效解决去噪过程中丢失原始信号有用信息及由于信噪频谱混叠不能完整去除噪声的问题,信号处理效果理想,对比另外两种信号提取方法更为优化。     

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA(independent component analysis)and EMD(empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions(IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals.Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.