An adaptive strain estimator for elastography

Elastography is based on the estimation of strain due to applied tissue compression. In conventional elastography, strain is computed from the gradient of the displacement estimates between gated pre- and postcompression echo signals. Gradient-based estimation methods are known to be susceptible to noise. In elastography, in addition to the electronic noise, a principal source of estimation error is the decorrelation of the echo signal as a result of tissue compression (decorrelation noise). Temporal stretching of postcompression signals previously was shown to reduce the decorrelation noise. In this paper, we introduce a novel estimator that uses the stretch factor itself as an estimator of the strain. It uses an iterative algorithm that adaptively maximises the correlation between the pre- and postcompression echo signals by appropriately stretching the latter. We investigate the performance of this adaptive strain estimator using simulated and experimental data. The estimator has exhibited a vastly superior performance compared with the conventional gradient-based estimator.     

Signal matching wavelet for ultrasonic flaw detection in high background noise

The wavelet transform (WT) is widely used in ultrasonic flaw detection (UFD) systems because of its property of multiresolution time-frequency analysis. Those traditional WT-based methods for UFD use a wavelet basis with limited types to match various echo signals (called wavelet matching signals), so it is difficult for those methods to achieve the optimal match between echo signal and wavelet basis. This results in limited detection ability in high background noise for those WT-based methods. In this paper, we propose a signal matching wavelet (SMW) method for UFD to solve this problem. Unlike traditional UFD systems, in the proposed SMW, the transmitted signal is designed to be a wavelet function for matching a wavelet basis. This makes it possible to obtain the optimal match between the echo signal and the wavelet basis. To achieve the optimal match from the aspect of energy, we derive three rules for designing the transmitted signal and selecting the wavelet basis. Further, the parameter selection in applying the proposed SMW method to a practical UFD system is analyzed. In addition, a low-rate discrete WT structure is designed to decrease the hardware cost, which facilitates the practical application of the proposed SMW. The simulation results show that the proposed SMW can efficiently detect flaws in high background noise even with SNR lower than -20 dB, outperforming the existing methods by 5 dB.     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究，重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造，把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测，利用小波变换的子带编码理论，通过在多个子带权值的自适应匹配，回复后拟合微弱信号。仿真结果表明，该方法可进一步改善信号的检测能力，在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

Enhancement of echo-signal correlation in elastography using temporal stretching

Echo-signal decorrelation due to tissue compression is a significant source of error in tissue displacement estimates obtained using crosscorrelation. Tissue displacement estimates are used to compute strain values for imaging the elasticity of biological soft tissues. The correlation coefficient between the pre- and post-compression echo rf signals reduces rapidly with signal decorrelation due to increased compression. Miniscule reductions in the value of the correlation coefficient can have a significant impact on the performance of the strain estimator as illustrated by the strain filter. Reducing the rate of signal decorrelation using temporal stretching (which improves the value of the correlation coefficient), significantly improves the performance of the strain filter. The reduction in the rate of signal decorrelation with the subsequent increase in the correlation coefficient using temporal stretching is discussed in this paper. Theoretical, simulation and experimental results quantify the enhancement in the value of the correlation coefficient attained with temporal stretching.     

基于小波变换的裂谱分析法

 为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于小波变换多分辨率分析的裂谱分析新方法．该方法在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用小波变换多分辨率分析能力将原始超声回波信号进行等Q子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的．实验结果表明,与传统裂谱分析方法相比,该方法提高了消噪性能的稳定性,增强了湮没晶粒(或其他散射体)散射中的缺陷回波信号能力．     

Comparison of Spectral and Wavelet Estimators of Transfer Function for Linear Systems

We compare spectral and wavelet estimators of the response amplitude operator (RAO) of a linear system, with various input signals and added noise scenarios. The comparison is based on a model of a heaving buoy wave energy device (HBWED), which oscillates vertically as a single mode of vibration linear system. HBWEDs and other single degree of freedom wave energy devices such as oscillating wave surge convertors (OWSC) are currently deployed in the ocean, making such devices important systems to both model and analyse in some detail. The results of the comparison relate to any linear system. It was found that the wavelet estimator of the RAO offers no advantage over the spectral estimators if both input and response time series data are noise free and long time series are available. If there is noise on only the response time series, only the wavelet estimator or the spectral estimator that uses the cross-spectrum of the input and response signals in the numerator should be used. For the case of noise on only the input time series, only the spectral estimator that uses the cross-spectrum in the denominator gives a sensible estimate of the RAO. If both the input and response signals are corrupted with noise, a modification to both the input and response spectrum estimates can provide a good estimator of the RAO. A combination of wavelet and spectral methods is introduced as an alternative RAO estimator. The conclusions apply for autoregressive emulators of sea surface elevation, impulse, and pseudorandom binary sequences (PRBS) inputs. However, a wavelet estimator is needed in the special case of a chirp input where the signal has a continuously varying frequency.     

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

基于提升小波变换的超声信号消噪技术

为了提高超声无损检测（UNDT）和无损评价（UNDE）中基础数据的信噪比（SNR）,提出了一种基于提升小波变换多分辨率分析的超声信号消噪新技术.在分析传统裂谱分析（SSP）方法原理及其局限性的基础上,通过采用提升小波变换多分辨率分析能力将原始超声回波信号进行子带分解,然后按照一定的信噪分离规则来消除噪声,达到提高信噪比的目的.实验结果表明,与传统裂谱分析方法相比,该方法增强了消噪性能的稳定性,提高了超声回波信号的信噪比.     

The detection of echoes from multilayer structures using thewavelet transform

A method for measuring unknown thicknesses of multilayer structures, based on echo detection by means of the wavelet transform (WT), is presented. A brief discussion of the theoretical considerations underlying the method is first given. This highlights the excellent performance shown by the WT as a powerful tool for the analysis of echoes in a noisy environment. A suitable operating procedure for validation of the method is then set up. To this end, tests on 1) simulated signals and 2) actual signals received from known thicknesses are carried out: the obtained results are finally given and discussed     

Compensation of Markov estimator errors in time-jittered samplingof nonmonotonic signals

The so-called Markov estimator is sometimes used to estimate signals from their time-jittered samples (1990). The estimates are unbiased for monotonic signals, but exhibit errors in regions of nonmonotonicity. A method of compensation is presented to reduce this error. It requires a knowledge of the PDF of the time jitter, and a proposed method for determining the PDF based on the generalized Markov estimator has been verified through simulations. The performance of the compensation approach is presented for four different nonmonotonic waveforms     

Short-time Fourier transform and wavelet transform with Fourier-domain processing

We discuss the semicontinuous short-time Fourier transform (STFT) and the semicontinual wavelet transform (WT) with Fourier-domain processing, which is suitable for optical implementation. We also systematically analyze the selection of the window functions, especially those based on the biorthogonality and the orthogonality constraints for perfect signal reconstruction. We show that one of the best substitutions for the Gaussian function in the Fourier domain is a squared sinusoid function that can form a biorthogonal window function in the time domain. The merit of a biorthogonal window is that it could simplify the inverse STFT and the inverse WT. A couple of optical architectures based on Fourier-domain processing for the STFT and the WT, by which real-time signal processing can be realized, are proposed.     

基于Lorenz混沌调频的水下正交波形集设计

针对网络水下航行器组联合探测情况下各网络节点间信号会互相干扰的问题,基于Lorenz混沌模型,提出一种混沌调频(Chaos Frequency Modulation,CFM)的正交波形集设计方法。首先建立基于Lorenz序列的CFM信号模型,对其进行相关特性分析和正交特性研究,包括CFM正交波形集的个数和波形长度对波形集正交性能的影响,提出对混沌序列进行加窗函数处理的CFM波形集正交性优化方法;然后构造网络水下航行器组分别发射线性调频(Linear Frequency Modulation,LFM)信号和CFM信号的回波模型,对比分析了匹配滤波器对回波信号的处理结果。仿真结果表明,该设计方法简便、灵活、高效,产生的CFM正交波形集性能良好,且相对于常规信号在水下信号检测方面具有明显的优势。     

Development and evaluation of a high-frequency ultrasound-based system for in vivo strain imaging of the skin

The elastic properties of skin are of great interest in dermatology because they are affected by many pathological conditions. In this paper, a technique for in vivo mechanical strain imaging of the skin based on high-frequency ultrasound (HFUS) is presented. Elastic skin properties are assessed applying suction to the skin surface with a stepwise increased vacuum and estimating the resulting displacements in a spatially resolved manner. Acquired radio frequency (RF) echo signals and their envelope are analyzed for this purpose. A computer-controlled vacuum system with a digital pressure control loop was developed for precise and reproducible deformation. In a first processing step, the skin surface is segmented. Local axial strains inside the skin are estimated from axial displacements, which are estimated from consecutive echo signal frames analyzing the phase of the complex cross correlation function of analytical echo signals. Furthermore, speckle tracking is applied to estimate axial and lateral displacements and to quantify axial and lateral strains. The correlation coefficient of windowed echo signals compensated for displacements are used as a measure to validate the estimated strains, which is essential to accomplish reliable in vivo measurements. Phantom experiments were performed to validate the proposed technique. Results of in vivo measurements are presented, showing the potential for mechanical strain imaging in the skin in vivo.     

Investigation of Errors in Estimates of the Frequency of Signals of Laser Doppler Anemometers Using Wavelet Analysis

A method of processing the signals of laser Doppler anemometers using wavelet transformation is considered. The errors of Doppler frequency estimation obtained from an analysis of the wavelet spectrum of the signal are calculated and the influence of a number of parameters on these errors is investigated. Recommendations for performing measurements are given.     

Neural classification of Lamb wave ultrasonic weld testing signalsusing wavelet coefficients

This paper presents an ultrasonic nondestructive weld testing method based on the wavelet transform (WT) of inspection signals and their classification by a neural network (NN). The use of Lamb waves generated by an electromagnetic acoustic transducer (EMAT) as a probe allows us to test metallic welds. In this work, the case of an aluminum weld is treated. The feature extraction is made by using a method of analysis based on the WT of the ultrasonic testing signals; a classification process of the features based on a neural classifier to interpret the results in terms of weld quality concludes the process. The aim of this complete process of analysis and classification of the testing ultrasonic signals is to lead to an automated system of weld or structure testing. Results of real-world ultrasonic Lamb wave signal analysis and classifications for an aluminum weld are presented; these demonstrate the feasibility and efficiency of the proposed method     

小波包能量谱和BP神经网络在波纹管压浆超声检测中的应用

针对小波分析在信号处理的局限性,将小波包分析和反向传播(Back Propagation,BP)神经网络相结合,提出一种基于小波包能量谱和BP神经网络的波纹管压浆超声检测方法。采用超声检测方法接收波纹管模型的回波信号,以小波包分解后各子频带的能量作为检测特征,当波纹管内部出现脱落时,检测特征会发生变化,最后将特征输入BP神经网络中进行分类识别。试验结果表明,该方法能够理想地实现波纹管内部缺陷的诊断,可为波纹管超声检测提供一定的技术支持。     

基于Shannon 复数小波的复合材料结构时间反转聚焦多损伤成像方法

研究了一种基于压电传感器阵列和主动Lamb 波的结构损伤成像方法,有助于克服Lamb 波在板结构中、特别是在复合材料板结构中存在的频散、多种模式及模式转换的现象给结构健康监测带来的困难。分析了结构多损伤散射信号的时间反转聚焦原理,在此基础上提出了一种基于Shannon 复数小波和时间反转聚焦的信号合成成像方法。该方法中,确定Lamb 波响应信号的到达时刻是信号能够准确聚焦的关键因素之一。提出了利用Shannon 复数小波变换计算Lamb 波响应信号到达时刻的方法。在碳纤维复合材料板结构上对整套信号合成成像方法进行了验证。研究结果表明,该方法能够有效地对同一个监测区域中的多个损伤进行成像定位。相对于30 cm ×30 cm 的监测区域,定位误差不超过2 cm。该方法有助于结构健康监测技术的工程应用。      

A fundamental limit on delay estimation using partially correlatedspeckle signals

Delay estimation is used in ultrasonic imaging to estimate blood or soft tissue motion, to measure echo arrival time differences for phase aberration correction, and to estimate displacement for tissue elasticity measurements. In each of these applications delay estimation is performed using speckle signals which are at least partially decorrelated relative to one another. Delay estimates which utilize such data are subject to large errors known as false peaks and smaller magnitude errors known as jitter. While false peaks can sometimes be removed through nonlinear processing, jitter errors place a fundamental limit on the performance of delay estimation techniques. The authors apply the Cramer-Rao Lower Bound to derive an analytical expression which predicts the magnitude of jitter errors incurred when estimating delays using radio frequency (RF) data from speckle targets. The analytical expression presented includes the effects of signal decorrelation due to physical processes, corruption by electronic noise, and a number of other factors. Simulation results are presented which show that the performance of the normalized cross correlation algorithm closely matches theoretical predictions. These results indicate that for poor signal to noise ratios (0 dB) a small improvement in signal to noise ratio can dramatically reduce jitter magnitude. At high signal to noise ratios (30 dB) small amounts of signal decorrelation can significantly increase the magnitude of jitter errors     

基于小波变换的反射波法基桩检测

鉴于反射波法在低应变检测中主要受较大长径比、桩侧土剪切模量及微小缺陷、桩顶非裸露自由等条件限制存在的局限性,而小波变换时频特性优良,适用于基桩检测信号中探测奇异成分,故通过建立三维轴对称有限元模型,对模拟信号进行小波变换后发现：对顶部裸露自由桩,小波变换可获取微小桩底或缺陷反射,可减小长径比、桩侧土剪切模量、缺陷面积的限制;通过平台激振,使平台在有效尺寸范围内实现对桩底的深度检测。     

小波变换在声发射信号特征参数检测中的应用

本文论述了小波变换在声发射信号的事件计数特版在数检测中应用的理论和方法,用小波变换来检测声发射信号的事件计数特征参数,其准确率与域值电平的取值在大小无关,从而可大大提高声发射信号特征参数检测的准确度,该方法可用于旋转机械（特别是大型汽轮发电机组）的碰磨故障和裂纹故障的检测。