A weighted least squares method for circle fitting to frequency response data

Despite the recent development of powerful multi-degree of freedom curve fitting programs the advent of the microcomputer has meant that there is still scope for improvement of simpler single degree of freedom models. A weighted least squares curves fitting method, based on a well known conformal mapping, is described. This method is shown to have important theoretical advantages over some established methods.     

基于自适应加权约束最小二乘法的麦克风阵列稳健频率不变波束形成算法

为了解决麦克风阵列通道失配时波束形成算法的稳健性问题, 提出一种基于自适应加权约束最小二乘法的麦克风阵列稳健频率不变波束形成算法. 该算法在分析无通道失配和通道失配时阵列模型特点基础上, 深入研究了通道失配时约束最小二乘频率不变波束形成算法存在的问题及其产生的原因; 将麦克风特性的概率密度函数作为稳健因子加入到约束最小二乘频率不变波束形成算法后, 其频率不变性的稳健性得到了一定的提高, 但稳健性仍较差. 为了进一步提高约束最小二乘法频率不变波束形成算法的稳键性, 通过定义代价函数中控制频率不变性的动态加权系数来调节旁瓣频谱能量, 大大提高了频率不变波束形成算法的稳键性, 将频率不变的频带范围内同一到达角度上不同频率所形成的阵列响应的最大值与最小值之比定义为波动误差, 并作为比较本文算法与约束最小二乘稳健波束形成算法和minmax稳健波束形成算法在通道失配时频率不变性稳键性的评价指标. 算法实例验证结果表明, 在麦克风阵列通道失配时, 本文算法的波动误差最小、频率不变波束形成稳健性最好, 而且适用于任意结构的阵列.     

Simultaneous least squares fitter based on the Lagrange multiplier method

We developed a least squares fitter used for extracting expected physics parameters from the correlated experimental data in high energy physics. This fitter considers the correlations among the observables and handles the nonlinearity using linearization during the χ² minimization. This method can naturally be extended to the analysis with external inputs. By incorporating with Lagrange multipliers, the fitter includes constraints among the measured observables and the parameters of interest. We applied this fitter to the study of the m D⁰-D⁰ mixing parameters as the test-bed based on MC simulation. The test results show that the fitter gives unbiased estimators with correct uncertainties and the approach is credible.     

Modelling of chaotic systems based on modified weighted recurrent least squares support vector machines

Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function tocompensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.     

基于最小二乘法的均匀扩声技术研究

针对目前室外及超大型室内空间的扩声应用中声场分布不均匀的问题,本文提出了一种基于最小二乘法声场重建方法的扩声技术。该技术通过对目标声场的逼近来计算线性扬声器阵列各通道的输入参数,实现扩声区域内声压级的均匀分布,同时约束非扩声区域的声能量以获得较好指向性。本文通过仿真研究算法各参数的改变对控制结果的影响,探讨不同扩声区域和目标所对应参数的选取方法。仿真和实验比较了该算法以及未经控制的声场、相移法波束控制的效果,证明该方法可以获得更好的声场均匀度。     

Properties of integral least squares method

A new modification of the least squares method (LSM) is proposed. The main idea is to consider the fitting parameters β _i as independent random variables with a certain distribution density F(β₁, β₂, ..., β _k ; φ₁, ..., φ _m ), which depends on a set of m experimental points φ _j . Within this approach, the estimates of the parameters minimize squared deviations and are equivalent to means of the probability distribution = = ∫β _i F(β₁, β₂, ..., β _k ; φ₁, ..., φ _m )dβ₁ dβ₂...dβ _k . Original Russian Text ? I.D. Gorlachev, B.B. Knyazev, A. Kuketayev, F.M. Pen’kov, 2009, published in Izvestiya Rossiiskoi Akademii Nauk. Seriya Fizicheskaya, 2009, Vol. 73, No. 2, pp. 257–260.     

A weighted least squares algorithm for time-of-flight depth image denoising

In this paper, we present a new denoising method for the depth image of a time-of-flight (ToF) camera, based on weighted least squares (WLS) framework. The common method for ToF depth image denoising is to use bilateral filter. However, the ability of bilateral filter in edge preservation would be reduced while we attempt to smooth out larger spatial scale noise. In order to avoid this problem and preserve the edge information as much as possible, we introduce a new way to construct edge-preserving ToF depth image denoising based on WLS. We are to our knowledge the first to present a WLS-based method for ToF depth image denoising. Experimental results demonstrate that compared with bilateral filter, our proposed algorithm not only achieves better performance in edge preservation, but also improves the PSNR values of the denoised images by 0.5–2.6 dB.     

基于加权最小二乘估计的双基地声呐定位算法研究

依据T/R-R型双基地声呐的工作特点,提出了基于加权最小二乘(WLS)估计的双基地声呐定位算法,通过数值仿真,研究了不同基线长度下该算法的定位精度,给出了WLS算法定位精度的几何分布。结果表明,运用该算法对水下目标进行定位,比单纯基于波达时间(TOL)的定位方法,双基地系统可以获得更高的定位精度。     

An integration method for scanned multi-view range images (MRIs) based on local weighted least squares (LWLS) surface fitting

We present a novel integration method that can fuse registered partially overlapping multi-view range images (MRIs) into a single-layer, smooth and detailed point set surface. A maximum likelihood criterion is developed to detect overlapping points in MRIs. Subsequently, the detected overlapping points are shifted onto a series of piecewise smooth local weighted least squares (LWLS) surfaces to remove bad influence of scanning noises, outliers and large gaps/registration errors. The LWLS surface is fitted in background neighborhood which contains sufficient information to reconstruct local surface accurately. And the shifting operation is done in a concentric tiny neighborhood which contains corresponding overlapping points. Finally, a simple procedure is designed to identify and merge those corresponding overlapping points. The novel method has the advantages of robust to large gaps/registration errors, possessing least squares means and uniform density distribution. Furthermore, the novel method is efficient since only overlapping points are processed and the non-overlapping points are remained as they are. Several state of the art integration methods were employed for comparison study and the experimental results demonstrate the superiority of the novel method.     

Point pattern matching based on kernel partial least squares

Point pattern matching is an essential step in many image processing applications.This letter investigates the spectral approaches of point pattern matching,and presents a spectral feature matching algorithm based on kernel partial least squares(KPLS).Given the feature points of two images,we define position similarity matrices for the reference and sensed images,and extract the pattern vectors from the matrices using KPLS,which indicate the geometric distribution and the inner relationships of the feature points.Feature points matching are done using the bipartite graph matching method.Experiments conducted on both synthetic and real-world data demonstrate the robustness and invariance of the algorithm.     

基于加权最小二乘法的干涉图校准方法

针对现有干涉图校准方法的不足,提出了一种基于加权最小二乘法的干涉图校准方法。根据干涉图中标准件边缘与干涉条纹边缘之间的差异,用合适大小的掩模对标准件的边缘进行初步判定和提取,再利用加权最小二乘法对提取出的边缘进行精确定位。通过计算标准件中已知尺寸部分在干涉图中所占据的像素数目,求出每个像素代表的实际距离,完成校准。实验表明,该方法的计算耗时较霍夫变换法减少一半,定位精度小于1个像素,且适用于不同对比度的干涉图,具有很强的实用性。     

Robust GRAPPA reconstruction using sparse multi-kernel learning with least squares support vector regression

Accuracy of interpolation coefficients fitting to the auto-calibrating signal data is crucial for k-space-based parallel reconstruction. Both conventional generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction that utilizes linear interpolation function and nonlinear GRAPPA (NLGRAPPA) reconstruction with polynomial kernel function are sensitive to interpolation window and often cannot consistently produce good results for overall acceleration factors. In this study, sparse multi-kernel learning is conducted within the framework of least squares support vector regression to fit interpolation coefficients as well as to reconstruct images robustly under different subsampling patterns and coil datasets. The kernel combination weights and interpolation coefficients are adaptively determined by efficient semi-infinite linear programming techniques. Experimental results on phantom and in vivo data indicate that the proposed method can automatically achieve an optimized compromise between noise suppression and residual artifacts for various sampling schemes. Compared with NLGRAPPA, our method is significantly less sensitive to the interpolation window and kernel parameters.     

基于最小二乘支持向量机建模的混沌系统控制

提出了基于最小二乘支持向量机（LS-SVMs）建模的混沌系统控制方法.与前向神经网络相比，LS-SVMs的优点是其训练过程遵循结构风险最小化原则，不易发生过拟合现象；它通过解一组线性方程组可得到全局惟一的最优解；LS-SVMs的拓扑结构在训练结束时自动获得而不需要预先确定.该方法不需要被控混沌系统的解析模型，且当测量噪声存在情况下控制仍然有效.以一维和二维非线性映射为例进行数值仿真，表明该方法是有效和可行的. 关键词： 混沌控制 支持向量机 建模     

A method for seeking an appropriate model of complicated organic crystal structure based on two-dimensional least squares refinement

In crystals of complicated organic substances of all atoms having approximately the same weights most of the atoms overlap in projections and therefore it is impossible to verify the correctness of a proposed model of structure and to improve it by Fourier projections. As the three-dimensional Fourier syntheses are rather time-consuming, two-dimensional least squares refinement of coordinates and individual temperature factors is used.The described method is appropriate for selecting the best model of crystal structure from several which come into consideration.The paper was presented at 9. Diskussionstagung für Kristallkunde der Deutschen Mineralogischen Gesellschaft held in Bonn in April 1967.The author wishes to thank Dr. B. Sedláek for his continuous interest, Dr. D. Oenáková for kindly supplying her least squares program and D. Kotíková for careful preparation of the drawings.     

Iterative Wiener deconvolution based method for phase retrieval from noisy intensities

To retrieve the phase from the noisy measured intensities in the diffraction planes, an iterative Wiener deconvolution based method is proposed. With the same iterative scheme as the iterative angular spectrum method (IAS), the propagation of the optical wave function between the input plane and the diffraction planes is calculated by Wiener deconvolution in this method. The angular spectrum convolution kernel used in the iterative angular spectrum method is incorporated into the Wiener filter. The simulation experiments show that the proposed method can reduce the impact of the noise on the retrieved phase and performed better than the pre-denoising method. Furthermore, the proposed method exhibits great advantage compared to IAS for retrieving the complicated phase distribution from two measured intensities.     

Phase aberration compensation of digital holographic microscopy based on least squares surface fitting

Digital holographic microscopy allows the numerical reconstruction of the complex wavefront of samples, especially biological samples such as living cells. In digital holographic microscopy, a microscope objective is introduced to improve the transverse resolution of the sample; however a phase aberration in the object wavefront is also brought along, which will affect the phase distribution of the reconstructed image. We propose here a numerical method to compensate for the phase aberration of thin transparent objects with a single hologram. The least squares surface fitting with points number less than the matrix of the original hologram is performed on the unwrapped phase distribution to remove the unwanted wavefront curvature. The proposed method is demonstrated with the samples of the cicada wings and epidermal cells of garlic, and the experimental results are consistent with that of the double exposure method.     

Spline based least squares integration for two-dimensional shape or wavefront reconstruction

In this work, we present a novel method to handle two-dimensional shape or wavefront reconstruction from its slopes. The proposed integration method employs splines to fit the measured slope data with piecewise polynomials and uses the analytical polynomial functions to represent the height changes in a lateral spacing with the pre-determined spline coefficients. The linear least squares method is applied to estimate the height or wavefront as a final result. Numerical simulations verify that the proposed method has less algorithm errors than two other existing methods used for comparison. Especially at the boundaries, the proposed method has better performance. The noise influence is studied by adding white Gaussian noise to the slope data. Experimental data from phase measuring deflectometry are tested to demonstrate the feasibility of the new method in a practical measurement.     

最小二乘法线性拟合中参数的确定问题

指出一些文献在讲术最小二乘法线性拟合中参数确定时存在的问题。     

基于小波核偏最小二乘回归方法的混沌系统建模研究

基于核学习的强大非线性映射能力,结合用于回归建模的线性偏最小二乘(PLS)算法,提出一种小波核偏最小二乘(WKPLS)回归方法. 该方法基于支持向量机使用的经典核函数技巧,将输入映射到高维非线性的特征空间,在特征空间中,构造线性的PLS回归模型. PLS方法利用输入与输出变量之间的协方差信息提取潜在特征,而可允许的小波核函数具有近似正交以及适用于信号局部分析的特性. 因此,结合它们优点的WKPLS方法显示了更好的非线性建模性能. 将WKPLS方法应用在非线性混沌动力系统建模上,并与基于高斯核的核偏最小二乘 关键词： 小波核 偏最小二乘回归 混沌系统 建模