A two-step optical flow method for strain estimation in elastography: Simulation and phantom study

Optical flow (OF) method has been used in ultrasound elastography to estimate the strain distribution in tissues. However the bias of strain estimation by OF has previously been shown to be close to 20%. The objective in this paper is to improve the performance of OF-based strain estimation, a two-step OF method with a local warping technique is proposed in this paper. The local warping technique effectively decreases the decorrelation of the signals, and hence improves the performance of strain estimation. Simulations on both homogeneous and heterogeneous models with different strains are performed. Experiments on a heterogeneous tissue-mimicking phantom are also carried out. Simulation results of the homogeneous model show that the two-step OF method reduces the bias of strain estimation from 23.77% to 1.65%, and reduces the standard deviation of strain estimation from 2.9 × 10⁻³ to 0.47 × 10⁻³. Simulation results of the heterogeneous model shows that the signals-to-noise ratio (SNR_e) of strain estimation is improved by 2.1 and 5.3 dB in the inclusion and background, respectively, and the contrast-to-noise ratio (CNR_e) is improved by 6.8 dB. Finally, results of phantom experiments show that, by using the proposed method, the SNR_e is increased by 4.0 dB and 8.9 dB in the inclusion and background, respectively, while the CNR_e is increased by 13.1 dB. The proposed two-step OF method is thus demonstrated capable of improving the performance of strain estimation in OF-based elastography.     

多频带期望值最大声信号时延估计EI北大核心CSCD

通过分析复杂环境中不同频带声信号时延估计的差异,提出多频带期望值最大时延估计方法。为了使各频带之间无重叠,该方法采用独立分带划分声信号不同频带,然后计算各频带广义互相关函数,并对子带广义互相关函数建立最大似然模型,最后利用期望值最大算法将多维优化转为一维优化的迭代式,获得最优子带广义互相关函数,在此基础上估计声信号的时延信息。数据仿真和实际实验结果表明,多频带期望值最大化时延估计相较常规时延估计有效估计值的百分比提升了10%,并将最优频带互相关函数应用到该定位算法中,在网格间距为0.3 m时,得到的峰值区域汇聚更明显,定位效果更好。     

基于双传声器对的多声源二维定位跟踪算法

提出一种房间混响声场环境下的多声源二维定位跟踪算法。研究了基于盲源分离的时延估计,以及联合空间分布的多个传声器对的定位算法。用高斯似然函数解决在多源、多维情况下声源定位的时延匹对模糊问题,使之能够用双传声器对实现对多个声源的二维定位,结合粒子滤波算法实现对多个运动声源的跟踪。仿真实验验证了提出算法的有效性。      

Estimation of the Reliability of a Stress–Strength System from Poisson Half Logistic Distribution

This paper discussed the estimation of stress-strength reliability parameter R=P(Y<X) based on complete samples when the stress-strength are two independent Poisson half logistic random variables (PHLD). We have addressed the estimation of R in the general case and when the scale parameter is common. The classical and Bayesian estimation (BE) techniques of R are studied. The maximum likelihood estimator (MLE) and its asymptotic distributions are obtained; an approximate asymptotic confidence interval of R is computed using the asymptotic distribution. The non-parametric percentile bootstrap and student’s bootstrap confidence interval of R are discussed. The Bayes estimators of R are computed using a gamma prior and discussed under various loss functions such as the square error loss function (SEL), absolute error loss function (AEL), linear exponential error loss function (LINEX), generalized entropy error loss function (GEL) and maximum a posteriori (MAP). The Metropolis–Hastings algorithm is used to estimate the posterior distributions of the estimators of R. The highest posterior density (HPD) credible interval is constructed based on the SEL. Monte Carlo simulations are used to numerically analyze the performance of the MLE and Bayes estimators, the results were quite satisfactory based on their mean square error (MSE) and confidence interval. Finally, we used two real data studies to demonstrate the performance of the proposed estimation techniques in practice and to illustrate how PHLD is a good candidate in reliability studies.     

Blind estimation of number of motion multi-human targets in wireless pyroelectric infrared sensor networks

Due to the features of low energy consumption and flexible networking, nowadays the pyroelectric sensor has been applied widely in areas such as network instruction detection or human body target tracking recognition. Moreover, accurate estimation and judgment about the number of human targets moving in the networks is the foundation of tracking and recognition. This paper, under the condition of being lack of relevant prior knowledge, presents a novel method which selects the maximum likelihood function of the Bayesian network models as the independent criterion. In addition, the objective function is optimally solved by the Laplace estimation. The results of numerous experiments on both simulation and hardware experimental platforms are shown that this method has capability to blindly estimate the number of motion multiple human targets in wireless pyroelectric infrared sensor networks.     

Eigenfunction structure and scaling of two interacting particles in the one-dimensional Anderson model

The localization properties of eigenfunctions for two interacting particles in theone-dimensional Anderson model are studied for system sizes up to N = 5000 sitescorresponding to a Hilbert space of dimension ≈10⁷ using the Green function Arnoldi method. Theeigenfunction structure is illustrated in position, momentum and energy representation,the latter corresponding to an expansion in non-interacting product eigenfunctions.Different types of localization lengths are computed for parameter ranges in system size,disorder and interaction strengths inaccessible until now. We confirm that one-parameterscaling theory can be successfully applied provided that the condition of N being significantlylarger than the one-particle localization length L₁ is verified.The enhancement effect of the two-particle localization length L₂ behaving asL₂ ~ L₂¹ is clearly confirmed for a certain quite large intervalof optimal interactions strengths. Further new results for the interaction dependence in avery large interval, an energy value outside the band center, and different interactionranges are obtained.     

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。      

Outboard abnormal noise source localization method with curved surface projection based on time delay matching and weighting criterion

An improved localization method consisting of "filtering-time delay estimationhyperbolic localization" is proposed. Combining the empirical mode decomposition(EMD)and time delay estimation method based on generalized average magnitude difference function,the original signals are decomposed into intrinsic mode function(IMF) components. The energy distribution criterion and spectrum consistency criterion are used to select the IMFs, which can represent the physical characteristics of the source signal. Several sets of signals are applied to estimate the time delay, and then a vector matching criterion is proposed to select the correct time delay estimation. Considering the hydrophones location, a shell model is established and projected to a plane according to the quadrant before the hyperbolic localization. Results of mooring and sailing tests show that the proposed method improves the localization accuracy,and reduces the error caused by time delay estimation.     

Maximum likelihood estimator of operational modal analysis for linear time-varying structures in time–frequency domain

This paper investigates the problem of modal parameter estimation of time-varying structures under unknown excitation. A time–frequency-domain maximum likelihood estimator of modal parameters for linear time-varying structures is presented by adapting the frequency-domain maximum likelihood estimator to the time–frequency domain. The proposed estimator is parametric, that is, the linear time-varying structures are represented by a time-dependent common-denominator model. To adapt the existing frequency-domain estimator for time-invariant structures to the time–frequency methods for time-varying cases, an orthogonal polynomial and z-domain mapping hybrid basis function is presented, which has the advantageous numerical condition and with which it is convenient to calculate the modal parameters. A series of numerical examples have evaluated and illustrated the performance of the proposed maximum likelihood estimator, and a group of laboratory experiments has further validated the proposed estimator.     

A generalization of optimal estimation for the retrieval of atmospheric vertical profiles

The optimal estimation method for the retrieval of atmospheric vertical profiles uses a priori information made of a profile and its covariance matrix. The underlying assumption is that the a priori profile has an averaging kernel matrix equal to the identity. The method is herewith generalized to the case that the a priori profile has a different averaging kernel matrix. The averaging kernel matrix of the a priori profile is properly taken into account in the cost function and a rigorous and more general solution for the optimal estimation method is derived.     

A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.     

Electroinduced two-nucleon knockout and correlations in nuclei

A model to calculate cross sections for electroinduced two-nucleon emission from finite nuclei is presented. Short-range correlations in the wave functions and meson-exchange contributions to the photoabsorption process are implemented. Effects of the short-range correlations are studied with the aid of a perturbation expansion method with various choices of the Jastrow correlation function. The model is used to investigate the relative importance of the different reaction mechanisms contributing to the A(e,e′pn) and A(e,e′pp) process. Representative examples for the target nuclei ¹²C and ¹⁶O and for kinematical conditions accessible with contemporary high-duty cycle electron accelerators are presented. A procedure is outlined to calculate the two-nucleon knockout contribution to the semi-exclusive (e,e′p) cross section. Using this technique we investigate to what extent far semi-exclusive (e,e′p) reactions can be used to detect high-momentum components in the nuclear spectral function.     

Assessment of long-range correlation in animal behavior time series: The temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)

Detrended Fluctuation Analysis (DFA) is a method that has been frequently used to determine the presence of long-range correlations in human and animal behaviors. However, according to previous authors using statistical model systems, in order to correctly use DFA different aspects should be taken into account such as: (1) the establishment by hypothesis testing of the absence of short term correlation, (2) an accurate estimation of a straight line in the log–log plot of the fluctuation function, (3) the elimination of artificial crossovers in the fluctuation function, and (4) the length of the time series. Taking into consideration these factors, herein we evaluated the presence of long-range correlation in the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus  ). In our study, modeling the data with the general autoregressive integrated moving average (ARFIMA) model, we rejected the hypothesis of short-range correlations (d=0)$(d=0)$ in all cases. We also observed that DFA was able to distinguish between the artificial crossover observed in the temporal pattern of locomotion of Japanese quail and the crossovers in the correlation behavior observed in mosquito larvae locomotion. Although the test duration can slightly influence the parameter estimation, no qualitative differences were observed between different test durations.     

Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T₁-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T₁-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.     

The impact of ice particle roughness on the scattering phase matrix

The goal of this study is to explore the influence of ice particle habit (or shape) and surface roughness on the scattering phase matrix. As an example, reported here are the results for two wavelengths: 0.67 and 1.61 μm. For this effort, a database of single-scattering properties has been computed for a set of habits including hexagonal plates, hollow and solid columns, hollow and solid 3D bullet rosettes, droxtals, aggregates of solid columns, and aggregates of plates. The database provides properties for each of the habits at 101 wavelengths between 0.45 and 2.24 μm for smooth, moderately roughened, and severely roughened particles. At each wavelength, the scattering properties are provided at 233 discrete particle diameters ranging from 2 to 10,000 μm. A single particle size distribution from a very cold ice cloud sampled during the CRYSTAL-FACE field campaign (T_cld=–76 °C) is used to illustrate the influence of habit and roughness on the phase matrix. In all, four different habit mixtures are evaluated. The nonzero elements of the phase matrix are shown to be quite sensitive to the assumed habit, particularly in the case of ?P₁₂/P₁₁ that is associated with the degree of linear polarization of scattered radiation. Surface roughness is shown to smooth out maxima in the scattering phase function and in the other elements of the phase matrix, consistent with other studies. To compare with the theoretical simulations of the phase matrix for smooth and roughened particles, a full year of cloud-aerosol lidar with orthogonal polarization (CALIOP) data from 2008 is analyzed to provide global statistics on the values of P₁₁ and P₂₂/P₁₁ in the backscattering direction. In a comparison of two of the habit mixtures (one used for MODIS Collection 5 and another that incorporates new habits including hollow bullet rosettes and aggregates of plates) with the CALIOP data, the values for P₁₁ are higher regardless of the degree of particle surface roughness, and the values for P₂₂/P₁₁ are lower than those for CALIOP. Further investigation is warranted to better understand this discrepancy.     

Inverse problem for multivariate time series using dynamical latent variables

Factor analysis is a well known statistical method to describe the variability among observed variables in terms of a smaller number of unobserved latent variables called factors. While dealing with multivariate time series, the temporal correlation structure of data may be modeled by including correlations in latent factors, but a crucial choice is the covariance function to be implemented. We show that analyzing multivariate time series in terms of latent Gaussian processes, which are mutually independent but with each of them being characterized by exponentially decaying temporal correlations, leads to an efficient implementation of the expectation–maximization algorithm for the maximum likelihood estimation of parameters, due to the properties of block-tridiagonal matrices. The proposed approach solves an ambiguity known as the identifiability problem, which renders the solution of factor analysis determined only up to an orthogonal transformation. Samples with just two temporal points are sufficient for the parameter estimation: hence the proposed approach may be applied even in the absence of prior information about the correlation structure of latent variables by fitting the model to pairs of points with varying time delay. Our modeling allows one to make predictions of the future values of time series and we illustrate our method by applying it to an analysis of published gene expression data from cell culture HeLa.     

Performance of default risk model with barrier option framework and maximum likelihood estimation: Evidence from Taiwan

We investigate the performance of a default risk model based on the barrier option framework with maximum likelihood estimation. We provide empirical validation of the model by showing that implied default barriers are statistically significant for a sample of construction firms in Taiwan over the period 1994-2004. We find that our model dominates the commonly adopted models, Merton model, Z-score model and ZETA model. Moreover, we test the n-year-ahead prediction performance of the model and find evidence that the prediction accuracy of the model improves as the forecast horizon decreases. Finally, we assess the effect of estimated default risk on equity returns and find that default risk is able to explain equity returns and that default risk is a variable worth considering in asset-pricing tests, above and beyond size and book-to-market.     

Matched-field depth estimation for active sonar

This work concerns the problem of estimating the depth of a submerged scatterer in a shallow-water ocean by using an active sonar and a horizontal receiver array. As in passive matched-field processing (MFP) techniques, numerical modeling of multipath propagation is used to facilitate localization. However, unlike passive MFP methods where estimation of source range is critically dependent on relative modal phase modeling, in active sonar source range is approximately known from travel-time measurements. Thus the proposed matched-field depth estimation (MFDE) method does not require knowledge of the complex relative multipath amplitudes which also depend on the unknown scatterer characteristics. Depth localization is achieved by modeling depth-dependent relative delays and elevation angle spreads between multipaths. A maximum likelihood depth estimate is derived under the assumption that returns from a sequence of pings are uncorrelated and the scatterer is at constant depth. The Cramér-Rao lower bound on depth estimation mean-square-error is computed and compared with Monte Carlo simulation results for a typical range-dependent, shallow-water Mediterranean environment. Depth estimation performance to within 10% of the water column depth is predicted at signal-to-noise ratios of greater than 10 dB. Real data results are reported for depth estimation of an echo repeater to within 10-m accuracy in this same shallow water environment.     

Level statistics and localization: A study of the 1D Anderson model

The level statistics and the localization of a particle in a one-dimensional random potential are investigated numerically. First, we study the level spacing distributionP(S) as a function of the system lengthN and of the disorderw of the system. We show that in addition to the localized and delocalized regimes a third regime can be distinguished: For large disorder a resonance appears inP(S), which is caused by a local level repulsion effect. Second, we investigate the distribution of the localization lengths within the Anderson chain as a function ofN andw. Here, we identify the localization length with the rms spread of the wave functions and we show that this measure for the localization of the eigenstates is not a self-averaging quantity.     

Data assimilation using Ensemble Transform Kalman Filter (ETKF) in ROMS model for Indian Ocean

Study of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale.