Atmospheric trace gases profile retrievals using the nonlinear regularized total least squares method

We discuss the regularized total least squares (RTLS) method for the nonlinear overdetermined ill-posed problem of atmospheric trace gas retrieval. The RTLS method is used for the automatic determination of the regularization strength of the ill-conditioned matrix in an iterative process, and a mixed quadratic and cubic line search method is used for the nonlinear retrievals. Additional retrieval metrics, such as the model resolution matrix and the degrees of freedom in the retrieval, which characterize the vertical resolution of the retrievals, are also derived. Simulated retrievals as well as the retrieval from the data of a balloon-based spectroscopic measurement will be discussed. Retrieval results obtained using O₃ and CH₄ as test cases will be presented.     

Quantum algorithm for total least squares data fitting

The total least squares (TLS) method is widely used in data-fitting. Compared with the least squares fitting method, the TLS fitting takes into account not only observation errors, but also errors from the measurement matrix of the variables. In this work, the TLS problem is transformed to finding the ground state of a Hamiltonian matrix. We propose quantum algorithms for solving this problem based on quantum simulation of resonant transitions. Our algorithms can achieve at least polynomial speedup over the known classical algorithms.     

Fast multi-contrast MRI reconstruction

Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The joint reconstruction problem is formulated as minimizing a linear combination of three terms, corresponding to a least squares data fitting, joint total variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this problem, we decompose it into joint TV regularization and group sparsity subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework. Experiments demonstrate the efficiency and effectiveness of the proposed method compared to existing multi-contrast MRI methods.     

Noise and nonlinear estimation with optimal schemes in DTI

In general, the estimation of the diffusion properties for diffusion tensor experiments (DTI) is accomplished via least squares estimation (LSE). The technique requires applying the logarithm to the measurements, which causes bad propagation of errors. Moreover, the way noise is injected to the equations invalidates the least squares estimate as the best linear unbiased estimate. Nonlinear estimation (NE), despite its longer computation time, does not possess any of these problems. However, all of the conditions and optimization methods developed in the past are based on the coefficient matrix obtained in a LSE setup. In this article, NE for DTI is analyzed to demonstrate that any result obtained relatively easily in a linear algebra setup about the coefficient matrix can be applied to the more complicated NE framework. The data, obtained using non-optimal and optimized diffusion gradient schemes, are processed with NE. In comparison with LSE, the results show significant improvements, especially for the optimization criterion. However, NE does not resolve the existing conflicts and ambiguities displayed with LSE methods.     

Regularizing a vortex sheet near a separation point

Current methods for computing vortex sheet separation use a regularization parameter which is discontinuous from the body to the vortex sheet. We propose two methods for reducing the errors associated with the discontinuity and improving convergence with respect to the regularization parameter. The “velocity smoothing” method is the simpler of the two, and removes the discontinuity in regularization from one of the two equations where it occurs. The “tapered smoothing” method removes the discontinuity from both equations. In a model problem, both methods are found to converge much more rapidly (with exponents 3/2 and 2 versus 1/2 for the standard method) as the regularization parameter tends to zero. Unsteady algorithms are proposed for evolving the free sheet using the two methods, and are tested in a benchmark problem. Accuracy is significantly improved for similar computational expense.     

An image reconstruction algorithm based on Bayesian theorem for electrical resistance tomography

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for two-phase/multiphase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The iterative image reconstruction algorithm based on Bayesian theorem has been derived to solve the ERT inverse problem. It is taken into account, which is different to the existing ERT image reconstruction algorithms, including the prior probability of the permittivity distribution and the noise information in the measurement data. Both simulation and experimental data were carried out for typical two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the Bayesian iteration method and conventional methods. Results obtained indicate that the Bayesian iteration method improves the reconstructed image quality with the traditional linear back projection (LBP) and reweighted least squares (RLS). Therefore, the Bayesian iteration method is suitable for identification of online two-phase flow regimes.     

Image quality improvement in ultrasonic nondestructive testing by the maximum entropy method

We consider the possibility of solving the inverse scattering problem in the linear approximation (in the form of a convolution equation) by reducing it to a system of linear algebraic equations and minimizing the residual. Since the problem is an ill-posed one, the Tikhonov regularization proves useful. The possibility of using the entropy of the image estimate as a stabilizing functional is considered, which is the key idea of the maximum entropy method. The single-frequency and multifrequency versions of the method are realized. The advantage of the maximum entropy method over the conventional linear methods of solving the inverse scattering problem is shown. The superresolution and sidelobe suppression abilities of the maximum entropy method are demonstrated. The method is shown to be stable to measurement noise and multiplicative interference in the form of aperture decimation. Examples of the image reconstruction by the maximum entropy method from model and experimental data are presented.     

二维有耗色散介质的时域逆散射方法

为了重建二维有耗色散介质的电参数分布,基于Debye模型,应用泛函分析和变分法,提出一种时域逆散射新方法.该方法首先以最小二乘准则构造目标函数,将逆问题表示为约束最小化问题,接着应用罚函数法转化为无约束最小化问题,然后基于变分计算导出闭式的Lagrange函数关于特征参数的Fréchet导数,最后借助梯度算法和时域有限差分法迭代反演Debye模型参数.为了对抗噪声污染和逆问题的病态特性,采用了一阶Tikhonov正则化方法.数值应用中,利用Polak-Ribière-Polyak非线性共轭梯度法,对二维乳     

A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these objective functions, i.e., Hessian matrices. These theoretical connections provide new insights in designing efficient algorithms for NLS and constrained NLS (CNLS) estimation. Here, we propose novel algorithms of full Newton-type for the NLS and CNLS estimations, which are evaluated with Monte Carlo simulations and compared with the commonly used Levenberg-Marquardt method. The proposed methods have a lower percent of relative error in estimating the trace and lower reduced chi2 value than those of the Levenberg-Marquardt method. These results also demonstrate that the accuracy of an estimate, particularly in a nonlinear estimation problem, is greatly affected by the Hessian matrix. In other words, the accuracy of a nonlinear estimation is algorithm-dependent. Further, this study shows that the noise variance in diffusion weighted signals is orientation dependent when signal-to-noise ratio (SNR) is low (相似文献   

Regularization methods for near-field acoustical holography.

The reconstruction of the pressure and normal surface velocity provided by near-field acoustical holography (NAH) from pressure measurements made near a vibrating structure is a linear, ill-posed inverse problem due to the existence of strongly decaying, evanescentlike waves. Regularization provides a technique of overcoming the ill-posedness and generates a solution to the linear problem in an automated way. We present four robust methods for regularization; the standard Tikhonov procedure along with a novel improved version, Landweber iteration, and the conjugate gradient approach. Each of these approaches can be applied to all forms of interior or exterior NAH problems; planar, cylindrical, spherical, and conformal. We also study two parameter selection procedures, the Morozov discrepancy principle and the generalized cross validation, which are crucial to any regularization theory. In particular, we concentrate here on planar and cylindrical holography. These forms of NAH which rely on the discrete Fourier transform are important due to their popularity and to their tremendous computational speed. In order to use regularization theory for the separable geometry problems we reformulate the equations of planar, cylindrical, and spherical NAH into an eigenvalue problem. The resulting eigenvalues and eigenvectors couple easily to regularization theory, which can be incorporated into the NAH software with little sacrifice in computational speed. The resulting complete automation of the NAH algorithm for both separable and nonseparable geometries overcomes the last significant hurdle for NAH.     

德拜色散媒质的三维时域电磁逆散射技术

生物组织、土壤、水等媒质的电特性是频率相关的(称为色散媒质),常利用单极德拜(Debye)模型描述.为重建这一类媒质的色散特性,基于泛函分析和变分法,提出一种三维(3-D)时域电磁(EM)逆散射技术,主要流程为:①根据最小二乘准则,转化逆散射问题为约束最小化问题;②应用罚函数法,转化约束最小化问题为无约束最小化问题;③通过变分计算,解析导出梯度(Fréchet导数)表达式;④利用梯度法求解.此外,引入一阶吉洪诺夫(Tikhonov)正则化以应对逆问题的病态特性和噪声影响.数值应用中,将提出的目的 应用到一个简单的三维癌变乳房模型,借助PRP共轭梯度(CG)算法和时域有限差分(FDTD)法,仿真结果初步证实本文目的 的可行性、有效性和鲁棒性.     

Low frequency characteristic parameters measurement of vented-box loudspeaker system using total least squares method

I.IntroductiollThecomputeraideddesign(CAD)techniqueisanadvancedmethodforthedesignofvented-boxloudspeakersystemsI1l,whichisbasedontheThiele-Sma.llparametersofloudspeakersIa]andthefowfrequencycharpeteristicparametersofvelltedboxloudspeakersystems.TheThiele-Smallparametersofloudspeakersandsomeofthelowfrequellcycharacteristicparametersofvellted-boxloudspeakersystemshavebeendeterIIilnedbyfrequencyUomainmeth.dI3'4]foralongtime.Themaindisadwtagesofthismethodarecomplicatedandtimeconsuming.TheThi…     

基于小波变换的最小二乘相位解缠算法

最小二乘法是求解二维相位解缠问题最稳健的方法之一,其本质是在最小二乘意义下使缠绕相位的离散偏导数与解缠相位的偏导数整体偏差最小,并等效为可求解一大型的稀疏线性方程系统。由于系统矩阵结构的稀疏性,在采用迭代法求解时收敛速度非常慢。为了改善收敛特性,提出一种基于多分辨率表示的离散小波变换相位解缠算法。利用小波变换将原线性系统转化成具有较好收敛条件的等价新系统。仿真实验表明,该方法能够很好的恢复真实相位,其解缠效果优于Gauss-Seidel松弛迭代和多重网格法。     

Sampling the posterior: An approach to non-Gaussian data assimilation

The viewpoint taken in this paper is that data assimilation is fundamentally a statistical problem and that this problem should be cast in a Bayesian framework. In the absence of model error, the correct solution to the data assimilation problem is to find the posterior distribution implied by this Bayesian setting. Methods for dealing with data assimilation should then be judged by their ability to probe this distribution. In this paper we propose a range of techniques for probing the posterior distribution, based around the Langevin equation; and we compare these new techniques with existing methods.

When the underlying dynamics is deterministic, the posterior distribution is on the space of initial conditions leading to a sampling problem over this space. When the underlying dynamics is stochastic the posterior distribution is on the space of continuous time paths. By writing down a density, and conditioning on observations, it is possible to define a range of Markov Chain Monte Carlo (MCMC) methods which sample from the desired posterior distribution, and thereby solve the data assimilation problem. The basic building-blocks for the MCMC methods that we concentrate on in this paper are Langevin equations which are ergodic and whose invariant measures give the desired distribution; in the case of path space sampling these are stochastic partial differential equations (SPDEs).

Two examples are given to show how data assimilation can be formulated in a Bayesian fashion. The first is weather prediction, and the second is Lagrangian data assimilation for oceanic velocity fields. Furthermore the relationship between the Bayesian approach outlined here and the commonly used Kalman filter based techniques, prevalent in practice, is discussed. Two simple pedagogical examples are studied to illustrate the application of Bayesian sampling to data assimilation concretely. Finally a range of open mathematical and computational issues, arising from the Bayesian approach, are outlined.     

Spectral semi-blind deconvolution with least trimmed squares regularization

A spectral semi-blind deconvolution with least trimmed squares regularization (SBD-LTS) is proposed to improve spectral resolution. Firstly, the regularization term about the spectrum data is modeled as the form of least trimmed squares, which can help to preserve the peak details better. Then the regularization term about the PSF is modeled as L1-norm to enhance the stability of kernel estimation. The cost function of SBD-LTS is formulated and the numerical solution processes are deduced for deconvolving the spectra and estimating the PSF. The deconvolution results of simulated infrared spectra demonstrate that the proposed SBD-LTS can recover the spectrum effectively and estimate the PSF accurately, as well as has a merit on preserving the details, especially in the case of noise. The deconvolution result of experimental Raman spectrum indicates that SBD-LTS can resolve the spectrum and improve the resolution effectively.     

Local diffusion regularization method for optical tomography reconstruction by using robust statistics

We formulate a solution to the diffuse optical tomography (DOT) inverse problem as the minimization of an energy functional of the solution and the data. For the solution prior we introduce a local diffusion regularization potential with a threshold based on robust statistics (the Hubert function). We compare results on simulated data for the Hubert function and two other standard regularization functionals, Tikhonov and total variation.     

An iterative virtual projection method to improve the reconstruction performance for ill-posed emission tomographic problems

In order to improve the reconstruction performance for ill-posed emission tomographic problems with limited projections, a generalized interpolation method is proposed in this paper, in which the virtual lines of projection are fabricated from, but not linearly dependent on, the measured projections. The method is called the virtual projection(VP) method.Also, an iterative correction method for the integral lengths is proposed to reduce the error brought about by the virtual lines of projection. The combination of the two methods is called the iterative virtual projection(IVP) method. Based on a scheme of equilateral triangle plane meshes and a six asymmetrically arranged detection system, numerical simulations and experimental verification are conducted. Simulation results obtained by using a non-negative linear least squares method,without any other constraints or regularization, demonstrate that the VP method can gradually reduce the reconstruction error and converges to the desired one by fabricating additional effective projections. When the mean square deviation of normal error superimposed on the simulated measured projections is smaller than 0.03, i.e., the signal-to-noise ratio(SNR)for the measured projections is higher than 30.4, the IVP method can further reduce the reconstruction error reached by the VP method apparently. In addition, as the regularization matrix in the Tikhonov regularization method is updated by an iterative correction process similar to the IVP method presented in this paper, or the Tikhonov regularization method is used in the IVP method, good improvement is achieved.     

平面近场声全息中正则化参数的确定

近场声全息的逆向重建过程属于线性病态逆问题,必须进行正则化处理。本文对三种基于Tikhonov正则化的参数选择方法,即离差原理法、广义交叉验证法、L曲线法,在不同全息距离、声源频率和信噪比的条件下进行了比较,结果表明,它们在远距离及低噪声环境下难以获得合适的正则化参数。采用等效噪声方差的方法,对其中较为稳定的离差原理进行了改进,使其在较远全息距离及低噪声环境下仍能获得合适的正则化参数。相应的仿真实验表明,改进后的离差原理法在很宽的信噪比(＞6 dB)和较远的全息距离(~10 cm)均能稳定地找到合适的正则化参数。此外,由于该方法无须对全息声压进行平滑处理,其有效重建孔径和全息孔径相等。      

Application of the nonregularization method to solving the inverse problem of passive acoustic thermotomography on the wavelet basis

A comparative analysis of reconstruction quality is performed for the reconstruction of the temperature distribution under hyperthermia by the regularization and nonregularization methods, which are used for solving the systems of linear equations following from the statement of the ill-posed inverse problem of passive acoustic thermotomography. The basis functions are chosen to be wavelets, which allows a compact representation of the temperature peak under reconstruction. It is shown that, when deep-seated parts of tissue are heated, the nonregularization method gives a much smaller systematic error of temperature reconstruction at the focusing point. At the same time, the random error of reconstruction increases. The nonregularization method of reconstruction can be used in combination with regularization for monitoring hyperthermia procedures in oncology to obtain more detailed information on the in-depth temperature.