基于超声RF信号熵分析的声空化时空监测方法

高强度聚焦超声(HIFU)治疗过程中剧烈的空化效应可能损伤靶区周围健康组织,因此,亟需开发可对生物组织内部声空化效应进行高精度时空定量监测的新型技术手段,方能确保临床安全和有效.相对于传统的商用超声灰度值信号,超声射频(RF)信号可以更好地保留声波散射信号更多的细节信息.而信息熵作为非基于数学函数模型的统计参数,可以表征由声空化效应引发的组织内部散射体无序度演变状态.因此,本文提出了一种基于超声RF信号熵分析的声空化实时监测成像系统,在此基础上实时评估HIFU引发的超声空化区域时空演化行为.首先,通过改制后的B超系统获取凝胶生物仿体内部由HIFU引发的空化泡群产生的散射回波原始RF信号,利用二维均值滤波方法抑制HIFU强声束对声空化监测成像回波信号的干扰后,通过数据标准化处理扩展RF信号的动态变化范围,再基于滑动窗信息熵分析重建熵值图像,经过二值化处理后即可实现对HIFU作用下组织内部声空化区域的时空监测.实验结果表明,相比于传统B超灰度成像法,基于RF信号熵分析的声空化监测成像算法可以更灵敏且精确地确定空化发生的起始时间和空间位置,有助于更好地保障HIFU临床治疗的安全性和有效性.本...     

多尺度多变量模糊熵分析

多尺度多变量样本熵评价同步多通道数据的多变量复杂度, 是非线性动态相互关系的一种反映, 但其统计稳定性差, 且不适用于非线性非平稳信号. 研究利用模糊隶属度函数代替模式相似判断的硬阈值准则, 并分析模糊隶属度函数形式的影响; 研究利用多变量经验模态分解算法进行多尺度化, 并对比其处理效果. 仿真试验表明, 模糊隶属度函数的引入可以有效提高算法的统计稳定性, 所构造的物理模糊隶属度函数的性能最为显著; 基于多变量经验模态分解算法的多尺度化过程可更有效地捕获信号的不同尺度成分, 从而更敏感地区分具有不同复杂度的信号. 对临床试验数据的分析支持以上结论, 且结果提示随着年龄增加或心脏疾病的发生, 心率变异性和心脏舒张间期变异性的多变量复杂度以不同的方式降低: 年龄增加会使低尺度熵值降低, 表示近程相关性的丢失; 而心脏疾病会同时影响各个尺度的熵值, 即同时丢失了近程和长时相关性. 该结论可用于指导心血管疾病的无创预警研究. 关键词： 多变量复杂度 多尺度多变量模糊熵 物理模糊隶属度函数 多变量经验模态分解     

混沌干扰中基于同步挤压小波变换的谐波信号提取方法

针对混沌干扰背景下多个谐波信号的提取问题, 提出了一种基于同步挤压小波变换(SST)的谐波信号抽取方法. 首先利用SST将混沌信号和谐波信号组成的混合信号分解为不同的内蕴模态类函数, 然后利用Hilbert变换对分离出的内蕴模态类函数进行频率识别, 从中分离出各谐波信号. 以Duffing混沌背景为例, 对混沌干扰下多谐波信号的提取进行了实验分析. 实验结果表明: 对于不同频率间隔的多个谐波分量, 本文方法的提取结果都具有较高的精度, 而且所提方法对高斯白噪声的干扰具有较好的鲁棒性, 综合提取效果优于经典的经验模态分解方法.     

油水两相流水包油流型多尺度排列熵分析

采用多尺度排列熵算法研究了垂直油水两相流水包油流型的多尺度动力学特性.首先,在内径为20 mm的垂直管道内采集了油水两相流水包油流型电导传感器波动信号,然后计算了不同流动工况下电导波动信号的多尺度排列熵值.研究发现多尺度排列熵率与均值可定量刻画水包油流型动力学复杂性;此外,提出了通过增量时间序列累积量与多尺度排列熵率联合分布识别三种不同水包油流型的新途径.     

水下目标声散射信号的时频域盲抽取

目标声散射机理及其散射特性为识别目标的物理依据.针对水下目标声散射成分在时-频域存在相互混叠干扰,造成目标弹性声散射特征不稳定的问题,提出一种适合在欠定问题下分离目标声散射成分的时频域盲抽取方法.研究声散射成分的时频特征差异,构造目标回波单源自项的空间时频分布矩阵,通过对其进行特征值分解抽取相应的声散射成分,建立描述目标声散射物理特性的信号模型.抽取出的目标各弹性波分量与以表面环绕波产生理论计算结果相符.仿真与消声水池实验数据处理结果表明,该算法可以分离出目标回波的各个声散射成分,提高了分离信号的输出信噪比,为水下目标识别提供稳定和可靠的特征.     

水下目标弹性声散射信号分离

水下目标弹性声散射与其他声散射成分在时域和频域上均存在混叠, 现有信号处理方法受分辨力限制无法在混叠状态下识别目标弹性声散射特征. 针对这个问题, 提出了一种目标弹性声散射信号分离方法. 以目标回波亮点模型为基础, 分析了线性调频信号入射时目标声散射成分的信号特性, 提出了一种目标声散射成分向单频信号的映射方法, 并理论推导出了目标声散射结构与映射结果之间的线性对应关系, 实现了通过窄带滤波分离出目标弹性声散射成分. 仿真与消声水池实验数据处理结果表明, 该方法基本可以完全分离出目标回波信号中的弹性声散射成分, 分离出的弹性声散射具有与理论一致的信号特征, 验证了该分离方法的有效性.     

脉冲激光探测水下目标回波特性分析研究

海洋表层是水下生物以及水下目标的主要活动区域,对海洋表层中的目标进行高精度的探测是一个十分重要的课题。本文建立了半解析蒙特卡洛激光辐射传输仿真模型,能够对机载海洋雷达回波信号过程进行全链路仿真,并且讨论了不同形状(包括：平面形、圆锥形、球形和类球形)目标的最大回波辐射强度随着水下深度分布。基于此,本文分析了脉冲激光雷达回波过程中不同目标在相同场景下考虑的截止散射阶次对回波信号的能量占比贡献。最后,分别从脉冲激光回波半峰全宽(FHWM)以及海水后向散射信噪比(SNR)两个方面分析了不同形状的水下目标识别过程中海水的后向散射对脉冲激光回波的回波半宽增宽效应以及不同深度下的海水后向散射信噪比。     

基于自适应多尺度时频熵的遥测振动信号异常检测方法

针对遥测振动信号频域成份复杂、非平稳非线性和强噪声特性,提出一种基于自适应多尺度时频熵的遥测振动信号异常检测方法;首先对采集到的遥测振动信号进行零漂修正和趋势项消除,然后采用自适应分解方法对信号进行多尺度分解,得到若干分量,利用相关系数剔除虚假分量;接下来用筛选出的分量作时频分布,对时频分布进行多层多尺度划分,计算相应尺度频段内信号的分形维数,依据分形维数的大小自适应地确定各频段的时频划分尺度;最后计算时频平面的自适应多尺度时频熵,通过时频熵的变化情况对遥测振动信号进行异常检测;实测数据验证了该方法的有效性。     

多角度光散射颗粒的粒径解析和属性识别

通过提取光散射信号中颗粒粒径和属性的非线性特征向量,利用广义神经网络(GRNN)同时解析颗粒粒径和识别属性。采用经验模态分解(EMD)方法分解颗粒物的光散射信号,提取三维能量分布,计算3种相同粒径不同属性颗粒的样本熵,发现样本熵能够反映颗粒的属性;为了消除粒径和属性对散射的影响,对散射信号进行Hilbert变换,提取时频域特征,与样本熵结合组成高维特征集,通过局部线性嵌入(LLE)算法将特征集归为6个特征向量,作为广义神经网络的输入层,解析粒径和识别属性;采用粒径为0.11μm的二氧化硅颗粒、2μm和4μm的聚苯乙烯小球进行实验,结果表明,粒径解析和属性识别的正确率均在90%以上。     

基于超声射频信号的支持向量机双参量B线识别方法*

肺超声中的特殊征象B线对于临床诊断肺水肿等肺部疾病有重要意义,但诊断结果依赖于医生的主观判断,为了客观、自动地识别B线,提高诊断准确率,本文提出了一种基于超声回波射频信号的肺脏超声特殊征象B线识别方法。本文首先选取了射频信号的排列熵、信息熵、峰度、偏度、能量作为特征参数,利用独立样本t检验和单参数贝叶斯分类的方法检验超声射频数据中B线以及非B线所对应射频数据的各个参量的差异性以及各参数与B线识别的相关性。然后将不同的双参量组合输入非线性支持向量机（SVM）中进行分类,比较各个组合的分类效果。结果显示信息熵与排列熵参数组合基于射频信号的分类效果最好,分类灵敏度为90.521%,特异性为98.106%,准确率为96.328%,AUC等于0.95。在引入后处理算法后,B线识别效果有进一步提升,得到分类平均灵敏度为95.23%,平均特异性为97.22%,平均准确率为96.88%。研究结果表明基于射频数据的SVM双参量B线识别方法对辅助临床诊断具有重要价值,信息熵和排列熵的组合可以有效的对特殊征象B线进行高精度识别。     

Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment

In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.     

Fault Recognition of Rolling Bearings Based on Parameter Optimized Multi-Scale Permutation Entropy and Gath-Geva

To extract fault features of rolling bearing vibration signals precisely, a fault diagnosis method based on parameter optimized multi-scale permutation entropy (MPE) and Gath-Geva (GG) clustering is proposed. The method can select the important parameters of MPE method adaptively, overcome the disadvantages of fixed MPE parameters and greatly improve the accuracy of fault identification. Firstly, aiming at the problem of parameter determination and considering the interaction among parameters comprehensively of MPE, taking skewness of MPE as fitness function, the time series length and embedding dimension were optimized respectively by particle swarm optimization (PSO) algorithm. Then the fault features of rolling bearing were extracted by parameter optimized MPE and the standard clustering centers is obtained with GG clustering. Finally, the samples are clustered with the Euclid nearness degree to obtain recognition rate. The validity of the parameter optimization is proved by calculating the partition coefficient and average fuzzy entropy. Compared with unoptimized MPE, the propose method has a higher fault recognition rate.     

New Techniques and Designs of Focusing Piezoelectric Transducers for Ultrasonic Diagnostics and Therapy

New techniques of forming high intensity focused ultrasound (HIFU) fields using dynamic focusing and harmonic multifrequency excitation are developed for ultrasonic diagnostics and therapy. New designs of HIFU transducers based on high-performance composite materials are developed and studied. Finite-element and finite-difference simulations of HIFU transducers and processes of ultrasonic wave propagation in biological tissues are performed. The parameters of piezoceramic materials, piezoelements, and the acoustic fields of focusing ultrasonic transducers are measured. Experiments are performed on biological tissues ex vivo that confirm the efficiency, selectivity, and safety of the developed HIFU transducers and techniques of forming acoustic fields.     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).     

Tissue structure study through ultrasonic forward scattering

A procedure is demonstrated for characterization of biological tissues at small scattering angles. The power spectra of ultrasonic pulses transmitted through excised tissue samples were measured and compared to the spectra of signals transmitted through a water path. The specimens were examined in two spatial-frequency bands by acquiring data at scattering angles of 10 degrees and 20 degrees using 2.25 MHz transducers. Peaks in the measured power spectra are interpreted using two signal models. The medium is modelled either as a periodic structure producing a single spectral peak, or by two discrete targets producing a periodic modulation of the spectrum. The periodic structure model appears to be the more promising method for interpretation of forward-scattered signals. Data acquired from hyperplastic spleen and atheromatous aorta specimens both exhibited increases in pulse-tissue interaction at low spatial frequencies compared to normal specimens of those tissues. This observation is tentatively linked to increases in the size or separation of distributed scattering structures resulting from those pathologies.     

Reflectivity function reconstruction using adaptive lattice deconvolution technique

Deconvolution of ultrasonic echo signals improves resolution and quality of ultrasonic images. The problem of reconstructing the reflectivity of a biological tissue is examined by adaptive lattice deconvolution of the echo ultrasound signals. The simulation of the signal formation process in an ultrasonic-echo scan line in noisy conditions is estimated. The reflectivity of a biological tissue is estimated as cross-correlation coefficients between forward and backward predication errors in each stage of the adaptive lattice filter.     

基于深度学习的材料超声回波衰减预测方法

材料超声回波衰减是评价材料均匀一致性的常用方法, 针对具有复杂结构的航空发动机盘件难以进行材料底面超声回波衰减评价的问题, 本文提出了利用超声背散射波信号直接预测底面回波衰减的方法。采用10MHz聚焦探头进行超声背散射波数据的采集, 利用深度学习技术构建和训练模型,建立了基于深度学习的材料底面回波衰减预测方法, 同时讨论了采用不同信号形式的超声波信号分类识别模型的准确率差异。研究发现：基于深度学习技术可实现通过超声背散射波预测材料的底面回波衰减, 预测结果和实际底面回波衰减试验结果具有良好的一致性。     

Reflectivity function reconstruction using the frequency deconvolution technique

Deconvolution of ultrasonic echo signals improves resolution and quality of ultrasonic images. A frequency deconvolution algorithm depends on the Fast Fourier transform is proposed for ultrasonic data. The stability of the algorithm and the influence of the truncation effect on the deconvoluted results were investigated with respect to the duration time of reflectivity function reconstruction and the signal to noise ratio. Reliability of the separation of reconstructing the reflectivity of a biological tissue is estimated by frequency deconvolution of the echo ultrasound signals.     

Analysis of microstructural alterations of normal and pathological breast tissue in vivo using the AR cepstrum

The mean scatterer spacing is considered to be an important parameter for describing ultrasonic scattering and characterization of biological tissue. Autoregressive models are widely used in parametric techniques for spectral estimation. In this paper, we describe the results of a careful examination of the mean scatterer spacing parameter in normal and pathological breast tissues in vivo using the autoregressive cepstrum. Our experimental results carried out at 4.5 MHz using weakly focused pulse-echo single element transducer show that the mean scatterer spacing in normal breast tissues in vivo is 1.25+/-0.21 mm whereas in several pathological breast tissues, it is between 0.82+/-0.10 and 1.09+/-0.07 mm. These results indicate good correlation with microstructure of breast tissue characterization, and hence the AR cepstrum holds promise that it could be used as an effective method for signal analysis of ultrasonic scattering and characterization of breast tissues scatterers.     

Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, discrete and wavelet packet de-noising are evaluated by means of signal-to-noise ratio enhancement. Measured and simulated ultrasonic signals are used to verify the proposed de-noising methods. For comparison, we use signal-to-noise ratio enhancement related to fault echo amplitudes and filtering efficiency specific for ultrasonic signals. The best results in our setup were achieved with the wavelet packet de-noising method.