基于主元分析的超声多普勒栓子信号检测

提出基于主元分析的超声多普勒栓子信号检测方法,先从超声多普勒信号中提取出主元成分并获得相应的特征参数,再结合时域的特征参数对栓子、干扰噪声和正常血流信号进行分类。通过对300例仿真超声多普勒信号和168例临床采集的脑动脉超声多普勒血流信号进行分析,结果表明:主元分析方法对仿真信号和临床信号的最小误判率分别为2%和5%,其分类的准确率相比常规的时域检测方法有了较大提高。可见,基于主元分析的超声多普勒栓子检测方法具有较高的检测率,克服了常规的时域和频域检测方法中由于干扰噪声的影响和频谱分析中时频分辨率的矛盾导致的灵敏度不高的局限,有望用于栓子的临床自动检测。     

Principal component analysis of shear strain effects

Shear stresses are always present during quasi-static strain imaging, since tissue slippage occurs along the lateral and elevational directions during an axial deformation. Shear stress components along the axial deformation axes add to the axial deformation while perpendicular components introduce both lateral and elevational rigid motion and deformation artifacts into the estimated axial and lateral strain tensor images. A clear understanding of these artifacts introduced into the normal and shear strain tensor images with shear deformations is essential. In addition, signal processing techniques for improved depiction of the strain distribution is required. In this paper, we evaluate the impact of artifacts introduced due to lateral shear deformations on the normal strain tensors estimated by varying the lateral shear angle during an axial deformation. Shear strains are quantified using the lateral shear angle during the applied deformation. Simulation and experimental validation using uniformly elastic and single inclusion phantoms were performed. Variations in the elastographic signal-to-noise and contrast-to-noise ratios for axial deformations ranging from 0% to 5%, and for lateral deformations ranging from 0 to 5° were evaluated. Our results demonstrate that the first and second principal component strain images provide higher signal-to-noise ratios of 20 dB with simulations and 10 dB under experimental conditions and contrast-to-noise ratio levels that are at least 20 dB higher when compared to the axial and lateral strain tensor images, when only lateral shear deformations are applied. For small axial deformations, the lateral shear deformations significantly reduces strain image quality, however the first principal component provides about a 1–2 dB improvement over the axial strain tensor image. Lateral shear deformations also significantly increase the noise level in the axial and lateral strain tensor images with larger axial deformations. Improved elastographic signal and contrast-to-noise ratios in the first principal component strain image are always obtained for both simulation and experimental data when compared to the corresponding axial strain tensor images in the presence of both axial and lateral shear deformations.     

Principal component analysis algorithm in video compressed sensing

An algorithm of principal component analysis in video compressed sensing is proposed in the paper. Aiming at the compressed sensing problems of video sequences, the inter-frame correlation among the images is analyzed and the transform coefficients with lower value are removed according to the energy concentration characteristics of principal component analysis. Therefore, the sparse realization of video signals in the form of principal component analysis is accomplished and the possibility of the transformation being used in compressed sensing algorithm is verified. Finally, simulation results show that, with the comparison of the traditional algorithm based on wavelet transform, the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence, but also save the sampling resources. Moreover, it is more suitable for stream transmission of multimedia.     

Principal component and sampling analysis of color spectra

We studied the effect and relation between the optimal sampling interval and the optimal number of principal component analysis eigenvectors for color spectra. The effect of different type of light sources and illuminants are included in the study. Several reflectance spectra sets under different real light sources and standard illuminants were used as a test set. The needed sampling interval and number eigenvectors with different light sources and illuminants are found. The used bandwidth in measurements causes effect to the resulted optimal sampling interval, which is also shown.     

High-accuracy acoustic detection of nonclassical component of material nonlinearity

The aim is to assess the nonclassical component of material nonlinearity in several classes of materials with weak, intermediate, and high nonlinear properties. In this contribution, an optimized nonlinear resonant ultrasound spectroscopy (NRUS) measuring and data processing protocol applied to small samples is described. The protocol is used to overcome the effects of environmental condition changes that take place during an experiment, and that may mask the intrinsic nonlinearity. External temperature fluctuation is identified as a primary source of measurement contamination. For instance, a variation of 0.1?°C produced a frequency variation of 0.01%, which is similar to the expected nonlinear frequency shift for weakly nonlinear materials. In order to overcome environmental effects, the reference frequency measurements are repeated before each excitation level and then used to compute nonlinear parameters. Using this approach, relative resonant frequency shifts of 10(-5) can be measured, which is below the limit of 10(-4) often considered as the limit of NRUS sensitivity under common experimental conditions. Due to enhanced sensitivity resulting from the correction procedure applied in this work, nonclassical nonlinearity in materials that before have been assumed to only be classically nonlinear in past work (steel, brass, and aluminum) is reported.     

虚拟时间反转水声信号检测

针对目标辐射声信号特性未知的情况,在不增加额外水声换能器的条件下,研究提高水听器阵列对水声信号检测性能的方法。首先,按照经典二元检测问题处理方法分析了常规能量检测方法的性能,在此基础上,根据虚拟时间反转处理原理,对处理后的信号构建检测统计量,推导得到了虚拟时间反转检测方法的理论门限和检测性能的表达式,并通过与常规能量检测的对比分析了虚拟时间反转检测的处理增益。使用计算机仿真实验从声源频率、接收阵列阵元数、目标距离与深度等方面分析了两种方法的检测性能。结果表明,所提出的算法相对于常规能量检测算法在性能上有显著优势,且接收阵元数是对算法性能影响最为明显的因素。     

Principal component analysis of dual-luminophore pressure/temperature sensitive paints

Multi-luminophore pressure/temperature sensitive paints are investigated using principal component analysis of the spectral emission from the coatings. Two formulations are investigated. The first consists of Ru (4,7-diphenylphenanthroline) dichloride (Ruphen) and Coumarin-7 luminophores. The second coating contains Pt(II) meso-tetrakis (pentafluorophenyl) porphine (PtTFPP) and diethyloxadicarbocyanine iodide (DOCI). The principal component analysis revealed that the Ruphen/Coumarin-7 coating requires three fundamental spectra or modes to adequately model the coating emission characteristics. The PtTFPP/DOCI coating was modeled adequately with only two modes. Analysis of the PtTFFP/DOCI coating also revealed that a temperature independent calibration of the pressure sensing function could be developed. The requirement for a wind-off reference image was also eliminated.     

A finite element analysis of perforated component acoustic systems

A Lagrangian energy expression is presented that is suitable for finite element analysis of perforated component acoustic systems. This technique can be applied to systems with complex shaped boundaries and is not restricted to the limited geometry that inhibits the traditional one-dimensional techniques. The technique has been applied to automotive muffler component configurations and has been verified experimentally. Perforate flow effects have also been included.     

水声时变线谱建模与双稳态随机共振分析

建立了水声时变线谱的模型,将双稳态随机共振系统应用于水声时变线谱信号的检测,考查了随机共振系统对水声环境的适应能力,提出了设计水声线谱检测系统可以利用的外在参数。在信噪比变化、声压起伏、线谱漂移等情况下,仿真结果表明双稳态随机共振系统都能较好地工作。为水声领域新型线谱检测系统的设计提供了依据。     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

Nonlinear real-life signal detection with a supervised principal components analysis

A novel strategy named supervised principal components analysis for the detection of a target signal of interest embedded in an unknown noisy environment has been investigated. There are two channels in our detection scheme. Each channel consists of a nonlinear phase-space reconstructor (for embedding a data matrix using the received time series) and a principal components analyzer (for feature extraction), respectively. The output error time series, which results from the difference of both eigenvectors of the correlation data matrices from these two channels, is then analyzed using time-frequency tools, for example, frequency spectrum or Wigner-Ville distribution. Experimental results based on real-life electromagnetic data are presented to demonstrate the detection performance of our algorithm. It is found that weak signals hidden beneath the noise floor can be detected. Furthermore, the robustness of the detection performance clearly illustrated that signal frequencies can be extracted when the signal power is not too low.     

基于Hilbert变换及间歇混沌的水声微弱信号检测方法研究

利用Duffing振 子从混沌到间歇混沌的相变及其对策动力和待检测信号频差较小的周期信号的敏感性, 研究了强海洋背景噪声下微弱周期信号的检测. 通过构造混沌振子列的方法对频率未知信号进行扫频, 从而提取待检测信号的频率范围, 最后利用希尔伯特变换, 实现对间歇混沌的包络检测, 并计算出待检测信号的频率. 计算机仿真与实测水声信号处理结果表明, 利用基于希尔伯特变换的间歇混沌振子对水声微弱信号检测, 其检测信噪比比一般的间歇混沌振子提高了至少4.4 dB, 验证了所提方法的有效性.     

海洋声远距离传播测量数据中潮汐信号预报的计算机模拟

通过长期监测声波在海洋声道中远距离传播的时间,以对全球海水平均温度的变化进行观测时,对于潮汐的影响必须加以修正。本文讨论ATOC项目中潮汐信号预报的计算机模拟。基于潮汐信号的线性模型,根据在一段预定时间内每隔4小时测量一次所得到的数据,利用伪逆矩阵法可对潮汐所引起的传播时间变化进行预报。传播时间的这种变化是正压潮在整个声波传播路径上所产生的总效果。为了得到所要求的预报精度,必须能估计出包括长周期分潮在内的所有分潮,并分辨一些频率非常接近的谐波分潮。为此采样期不应短于18个月。在建立潮汐模型时,分潮频率是唯一需要用到的参数。由于所有天文分潮的频率都能十分精确地知道,而非天文成分对声波传播的影响很小,因此在线性模型中包括尽可能多的分潮即可有效地减小预报误差。量化是预报误差的主要来源之一。通过对多径信号的平均能有效地减小量化误差。     

Computer simulation of tidal signal prediction in long range ocean acoustic travel-time measurements

I.IntroductionLongdistancesoundtransmissionsareaffectedbynumerousoceanprocessessuchasin-ternalwavsl1'2]andtidesl31.Tidaleffectswerediscussedbyanumberofauthors[4'5'6].UndertheAToC(AbousticThermometryofOceanCllmate)project[7'8l,aseriesofstudieshasbeencarriedoutledbyCartwrightl9'1o].Helistedthreebasicwaysofremovingtidaleffects[9]:'(l)Usingadigitalfiltertoeliminatetidalcomponentsofknownfrequenciesillthetraveltimemeasurementsafterallthedataaregathered;(2)Estimatingtidalconstantsfromdataobtaine…     

Application of a signal recovery technique to acoustic emission analysis

The dependence of ringdown counting on signal peak height and trigger level, in acoustic emission during martensitic transformation, was determined experimentally using a signal recovery technique. The corresponding relation between the total ringdown counts and the trigger level, and consequently the system gain, was made. The results show a very sensitive dependence of the total ringdown counts on both trigger level and system gain. A method of obtaining the peak height distribution using both the above relations is described.     

血凝素蛋白及抗体相互作用的太赫兹光谱主成分分析

采用太赫兹时域光谱系统, 测量了7种不同浓度的血凝素蛋白及其与特异性抗体、无关抗体对照组反应的透射光谱, 采用光谱预处理及主成分分析法, 对多个太赫兹光谱参数进行分析. 结果显示, 主成分分析在数据降维的同时, 可以突出数据的主要变化趋势; 在原始变量相关性一致的条件下, 约化吸收截面与血凝素蛋白浓度之间表现出最强的相关性, 而介电损耗角正切值更适合于对血凝素蛋白-抗体复合物的聚类效果进行定性分析. 该研究表明主成分分析法对于太赫兹生物光谱的分析及进一步研究蛋白质的结构和功能具有重要的指导意义.     

Principal component analysis of non-uniformity in electrical characteristics of HgCdTe photodiode arrays

We present a method of analyzing the non-uniformity in electrical characteristics of HgCdTe photodiode arrays for infrared imaging applications. We have selected dynamic resistance–voltage (R–V) characteristics for analyzing electrical behavior of HgCdTe photodiodes because the dynamic resistance at a given operating voltage directly governs the imager performance and being derivative of current–voltage (I–V) characteristics, it has little impact of the constant shifts due to stray illumination during dark measurements, relaxing the stringent requirement of perfect dark conditions to some extent for performance analysis. We have demonstrated that by using statistical analysis such as correlation of the selected signatures and their principal component analysis, we can identify the root cause of the high non-uniformity among sensor pixels in the array. The method has been implemented using theoretical I–V model of MWIR HgCdTe photodiodes, but it is generic and may be implemented on any other types of diode arrays for theoretical or experimental analysis of their non-uniformity.     

用独立成份分析算法实现水声信号盲分离

独立成份分析算法是在研究信号盲分离过程中出现的一种新方法,本文试图将几种独立成份分析算法用于分离水声信号。分析和比较了5种算法的性能,并用仿真信号对算法进行了仿真,阐述了独立成份分析算法分离水声信号的不足。针对含噪声模型,提出了一种基于独立成份分析算法成功分离水声信号的方法。     

声速不均匀介质热声成像的声场仿真

为了精确表示声速不均匀介质热声成像的声场,提出了一种时域有限差分(FDTD)仿真方法。先用FDTD将热声成像的基本方程离散化,给出描述热声成像的离散差分方程;再对电磁波脉冲进行两次修正,以消除高频电磁波引入的计算误差。计算机仿真研究的结果表明:当介质声速不均匀时,FDTD仿真方法其准确度高于目前常用的飞行时间法。在仿真实验条件下,当介质的声速差异大于50%时,FDTD法的误差比飞行时间法小十倍以上。可见,FDTD方法是一种有效的热声成像声场仿真方法,可以为声速不均匀介质热声成像算法的研究提供理论基础。