In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Fi...In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Firstly,the incoming color image is partitioned into non-overlapping blocks and each block is encoded using the W-plane method to get an initial common bitmap and quantization values.Then,the hill climbing algorithm is applied to optimize an initial common bitmap and generate a near-optimized common bitmap.Finally,the quantization values are recalculated by the near-optimized common bitmap and the considered color image is reconstructed block by block through the common bitmap and the new quantization values.Since the processing of each image block in SBBTC is independent and identical,parallel computing is applied to reduce the time consumption of this scheme.The simulation results show that the proposed scheme has better visual quality and time consumption than those of the reference SBBTC schemes.展开更多
基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作...基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作目标位姿估计精度与鲁棒性.该方法包括粗配准与精配准两个环节:在粗配准环节中,基于局部点云与模型点云的方向直方图特征(signature of histogram of orientation,SHOT)确定匹配对,利用TEASER算法求解初始位姿;在精配准环节中,可结合ICP算法优化位姿估计结果.北斗卫星仿真实验表明:在连续帧位姿估计中,噪声标准差为3倍点云分辨率时,基于TEASER的周期关键帧配准方法的平移误差小于3.33 cm,旋转误差小于2.18°;与传统ICP方法相比,平均平移误差与平均旋转误差均有所降低.这表明所提出的空间非合作目标位姿估计方法具有良好的精度和鲁棒性.展开更多
目前,传统雷达成像方法的发展日渐完善,但在前视成像场景下,雷达难以获取方位向上的多普勒信息,从而限制了其方位向分辨率。为了解决这一问题,国内提出了微波关联成像方法。微波关联成像方法利用关联成像原理进行雷达成像,无需利用目标...目前,传统雷达成像方法的发展日渐完善,但在前视成像场景下,雷达难以获取方位向上的多普勒信息,从而限制了其方位向分辨率。为了解决这一问题,国内提出了微波关联成像方法。微波关联成像方法利用关联成像原理进行雷达成像,无需利用目标的多普勒信息即可实现高分辨率成像。这一新型雷达成像方法突破了传统雷达成像方法中受限于雷达孔径的分辨率,具有极高的前视成像发展潜力。目前,国内外对微波关联成像的研究主要集中在产生随机波前、解决模型失配问题和研制超材料孔径等方面,但对关键的关联过程的优化主要集中在压缩感知和深度学习方面,而在伪逆算法方面的研究相对较少。因此,为了进一步完善微波关联成像体系,本文提出了一种新的针对伪逆算法优化的微波关联前视成像方法。本文结合截断奇异值分解(Truncated Singular Value Decomposition,TSVD)处理和吉洪诺夫正则化(Tikhonov)提出了奇异值分解和吉洪诺夫正则化的联合处理方法(TSVD-Tikhonov,TSVDT),通过TSVDT方法对时空随机辐射阵进行处理,然后进行压缩关联成像。同时,本文比较了广义交叉验证(Generalized Cross-Validation,GCV)和L曲线法,并证明了在微波关联成像方法中,利用GCV法选择截断参数的运算耗时更短且更稳定。最后,利用微波暗室实验验证了该方法在低信噪比条件下提高了成像的抗干扰能力,并且仍能保持较快的运算速度。展开更多
A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments...A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments（GMM） estimation and the maximum likelihood estimation（MLE） for the truncated multivariate normal distribution, the EM algorithm features in fast calculation and high accuracy which are shown in the simulation results. For the real data of the national college entrance exams（NCEE）, we estimate the distribution of the NCEE examinees＇ scores in Anhui, 2003, who were admitted to the university of science and technology of China（USTC）. Based on our analysis, we have also given the ratio truncated by the NCEE admission line of USTC in Anhui, 2003.展开更多
In a variety of modern applications there arises a need to tessellate the domain into representative regions,called Voronoi cells.A particular type of such tessellations,called centroidal Voronoi tessellations or CVTs...In a variety of modern applications there arises a need to tessellate the domain into representative regions,called Voronoi cells.A particular type of such tessellations,called centroidal Voronoi tessellations or CVTs,are in big demand due to their optimality properties important for many applications.The availability of fast and reliable algorithms for their construction is crucial for their successful use in practical settings.This paper introduces a new multigrid algorithm for constructing CVTs that is based on the MG/Opt algorithm that was originally designed to solve large nonlinear optimization problems.Uniform convergence of the new method and its speedup comparing to existing techniques are demonstrated for linear and nonlinear densities for several 1d and 2d problems,and O(k)complexity estimation is provided for a problem with k generators.展开更多
Sampling from a truncated multivariate normal distribution （TMVND） constitutes the core computational module in fitting many statistical and econometric models. We propose two efficient methods, an iterative data au...Sampling from a truncated multivariate normal distribution （TMVND） constitutes the core computational module in fitting many statistical and econometric models. We propose two efficient methods, an iterative data augmentation （DA） algorithm and a non-iterative inverse Bayes formulae （IBF） sampler, to simulate TMVND and generalize them to multivariate normal distributions with linear inequality constraints. By creating a Bayesian incomplete-data structure, the posterior step of the DA Mgorithm directly generates random vector draws as opposed to single element draws, resulting obvious computational advantage and easy coding with common statistical software packages such as S-PLUS, MATLAB and GAUSS. Furthermore, the DA provides a ready structure for implementing a fast EM algorithm to identify the mode of TMVND, which has many potential applications in statistical inference of constrained parameter problems. In addition, utilizing this mode as an intermediate result, the IBF sampling provides a novel alternative to Gibbs sampling and elimi- nares problems with convergence and possible slow convergence due to the high correlation between components of a TMVND. The DA algorithm is applied to a linear regression model with constrained parameters and is illustrated with a published data set. Numerical comparisons show that the proposed DA algorithm and IBF sampler are more efficient than the Gibbs sampler and the accept-reject algorithm.展开更多
Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to ...Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to the truncated L1 regularized linear regression problem and study the support recovery property.Moreover,a primal dual active set algorithm(PDAS)for variable estimation and selection is proposed.Coupled with continuation by a warm-start strategy leads to a primal dual active set with continuation algorithm(PDASC).Data-driven parameter selection rules such as cross validation,BIC or voting method can be applied to select a proper regularization parameter.The application of the proposed method is demonstrated by applying it to simulation data and a breast cancer gene expression data set(bcTCGA).展开更多
基金Supported by the National Natural Science Foundation of China(No.61402537)the Open Fund of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(No.HCIC201706)the Sichuan Science and Technology Programme(No.2018GZDZX0041)
文摘In order to generate an efficient common bitmap in single bitmap block truncation coding(SBBTC)of color images,an improved SBBTC scheme based on weighted plane(W-plane)method and hill climbing algorithm is proposed.Firstly,the incoming color image is partitioned into non-overlapping blocks and each block is encoded using the W-plane method to get an initial common bitmap and quantization values.Then,the hill climbing algorithm is applied to optimize an initial common bitmap and generate a near-optimized common bitmap.Finally,the quantization values are recalculated by the near-optimized common bitmap and the considered color image is reconstructed block by block through the common bitmap and the new quantization values.Since the processing of each image block in SBBTC is independent and identical,parallel computing is applied to reduce the time consumption of this scheme.The simulation results show that the proposed scheme has better visual quality and time consumption than those of the reference SBBTC schemes.
文摘基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作目标位姿估计精度与鲁棒性.该方法包括粗配准与精配准两个环节:在粗配准环节中,基于局部点云与模型点云的方向直方图特征(signature of histogram of orientation,SHOT)确定匹配对,利用TEASER算法求解初始位姿;在精配准环节中,可结合ICP算法优化位姿估计结果.北斗卫星仿真实验表明:在连续帧位姿估计中,噪声标准差为3倍点云分辨率时,基于TEASER的周期关键帧配准方法的平移误差小于3.33 cm,旋转误差小于2.18°;与传统ICP方法相比,平均平移误差与平均旋转误差均有所降低.这表明所提出的空间非合作目标位姿估计方法具有良好的精度和鲁棒性.
文摘目前,传统雷达成像方法的发展日渐完善,但在前视成像场景下,雷达难以获取方位向上的多普勒信息,从而限制了其方位向分辨率。为了解决这一问题,国内提出了微波关联成像方法。微波关联成像方法利用关联成像原理进行雷达成像,无需利用目标的多普勒信息即可实现高分辨率成像。这一新型雷达成像方法突破了传统雷达成像方法中受限于雷达孔径的分辨率,具有极高的前视成像发展潜力。目前,国内外对微波关联成像的研究主要集中在产生随机波前、解决模型失配问题和研制超材料孔径等方面,但对关键的关联过程的优化主要集中在压缩感知和深度学习方面,而在伪逆算法方面的研究相对较少。因此,为了进一步完善微波关联成像体系,本文提出了一种新的针对伪逆算法优化的微波关联前视成像方法。本文结合截断奇异值分解(Truncated Singular Value Decomposition,TSVD)处理和吉洪诺夫正则化(Tikhonov)提出了奇异值分解和吉洪诺夫正则化的联合处理方法(TSVD-Tikhonov,TSVDT),通过TSVDT方法对时空随机辐射阵进行处理,然后进行压缩关联成像。同时,本文比较了广义交叉验证(Generalized Cross-Validation,GCV)和L曲线法,并证明了在微波关联成像方法中,利用GCV法选择截断参数的运算耗时更短且更稳定。最后,利用微波暗室实验验证了该方法在低信噪比条件下提高了成像的抗干扰能力,并且仍能保持较快的运算速度。
基金Supported by the National Natural Science Foundation(Grant No.11571337,11271347,71172214)
文摘A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments（GMM） estimation and the maximum likelihood estimation（MLE） for the truncated multivariate normal distribution, the EM algorithm features in fast calculation and high accuracy which are shown in the simulation results. For the real data of the national college entrance exams（NCEE）, we estimate the distribution of the NCEE examinees＇ scores in Anhui, 2003, who were admitted to the university of science and technology of China（USTC）. Based on our analysis, we have also given the ratio truncated by the NCEE admission line of USTC in Anhui, 2003.
基金supported by the U.S.Department of Energy under Award DE-SC-0001691support from the ORAU Ralph E.Powe Junior Faculty Enhancement Award and from the National Science Foundation under the grants DMS-1056821 and DMS-0915013.
文摘In a variety of modern applications there arises a need to tessellate the domain into representative regions,called Voronoi cells.A particular type of such tessellations,called centroidal Voronoi tessellations or CVTs,are in big demand due to their optimality properties important for many applications.The availability of fast and reliable algorithms for their construction is crucial for their successful use in practical settings.This paper introduces a new multigrid algorithm for constructing CVTs that is based on the MG/Opt algorithm that was originally designed to solve large nonlinear optimization problems.Uniform convergence of the new method and its speedup comparing to existing techniques are demonstrated for linear and nonlinear densities for several 1d and 2d problems,and O(k)complexity estimation is provided for a problem with k generators.
基金Supported by the National Social Science Foundation of China (No. 09BTJ012)Scientific Research Fund ofHunan Provincial Education Department (No. 09c390)+1 种基金supported in part by a HKUSeed Funding Program for Basic Research (Project No. 2009-1115-9042)a grant from Hong Kong ResearchGrant Council-General Research Fund (Project No. HKU779210M)
文摘Sampling from a truncated multivariate normal distribution （TMVND） constitutes the core computational module in fitting many statistical and econometric models. We propose two efficient methods, an iterative data augmentation （DA） algorithm and a non-iterative inverse Bayes formulae （IBF） sampler, to simulate TMVND and generalize them to multivariate normal distributions with linear inequality constraints. By creating a Bayesian incomplete-data structure, the posterior step of the DA Mgorithm directly generates random vector draws as opposed to single element draws, resulting obvious computational advantage and easy coding with common statistical software packages such as S-PLUS, MATLAB and GAUSS. Furthermore, the DA provides a ready structure for implementing a fast EM algorithm to identify the mode of TMVND, which has many potential applications in statistical inference of constrained parameter problems. In addition, utilizing this mode as an intermediate result, the IBF sampling provides a novel alternative to Gibbs sampling and elimi- nares problems with convergence and possible slow convergence due to the high correlation between components of a TMVND. The DA algorithm is applied to a linear regression model with constrained parameters and is illustrated with a published data set. Numerical comparisons show that the proposed DA algorithm and IBF sampler are more efficient than the Gibbs sampler and the accept-reject algorithm.
文摘Truncated L1 regularization proposed by Fan in[5],is an approximation to the L0 regularization in high-dimensional sparse models.In this work,we prove the non-asymptotic error bound for the global optimal solution to the truncated L1 regularized linear regression problem and study the support recovery property.Moreover,a primal dual active set algorithm(PDAS)for variable estimation and selection is proposed.Coupled with continuation by a warm-start strategy leads to a primal dual active set with continuation algorithm(PDASC).Data-driven parameter selection rules such as cross validation,BIC or voting method can be applied to select a proper regularization parameter.The application of the proposed method is demonstrated by applying it to simulation data and a breast cancer gene expression data set(bcTCGA).
