多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了...多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了一种基于CS的MUSIC方法,简称CS-MUSIC,该方法针对不同的快拍数,建立二者之间的联系,构造出新的正交空间,获得尖锐的谱峰。理论分析和仿真结果表明,所提方法在不同快拍数条件下,具有较高的估计精度,克服了传统方法存在的缺陷,并且对噪声具有鲁棒性。展开更多
[Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data cl...[Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data clustering was regarded as high dimensional mixed data clustering. After preprocessing genetic data, the dimensions of the genetic data were reduced by principal component analysis, when genetic data presented Gaussian-like distribution. This distribution of genetic data could be clustered effectively through lossy data compression, which clustered the genes based on a simple clustering algorithm. This algorithm could achieve its best clustering result when the length of the codes of encoding clustered genes reached its minimum value. This algorithm and the traditional clustering algorithms were used to do the genetic data clustering of yeast and Arabidopsis, and the effectiveness of the algorithm was verified through genetic clustering internal evaluation and function evaluation. [Result] The clustering effect of the new algorithm in this study was superior to traditional clustering algorithms, and it also avoided the problems of subjective determination of clustering data and sensitiveness to initial clustering center. [Conclusion] This study provides a new clustering method for the genetic data clustering.展开更多
This contribution starts with the discussion on the classification of energy, and then the behaviors of various thermodynamic processes are analyzed, accompanying with the comparison of the adiabatic compression proce...This contribution starts with the discussion on the classification of energy, and then the behaviors of various thermodynamic processes are analyzed, accompanying with the comparison of the adiabatic compression process of an ideal gas and an elastic rod. All these analyses show that the internal energy of ideal gases exhibits the duality of thermal energy–mechanical energy, that is,the internal energy acts as the thermal energy during the isochoric process, while the internal energy acts as the mechanical energy during the isentropic process. Such behavior of the internal energy is quite different from other types of energy during the energy conversion process because the internal energy of ideal gases exhibits the duality of thermal energy–mechanical energy. Because of this duality, the internal energy of ideal gas is proposed to be refered to as thermodynamic energy rather than thermal energy as indicated in some literature, although it consists of kinetics of the microscopic random motion of particles and can be expressed as the function of temperature only.展开更多
This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data...This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity,three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis(PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification(MUSIC), the estimation of signal parameters via rotational invariance techniques(ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency.Simulation results indicate the proposed algorithm have a bright future in wireless communications.展开更多
文摘针对近场源参数估计计算复杂度大的问题,提出了一种基于对称阵列结构的快速估计算法。首先通过对称阵列结构构造多项式,通过求解多项式的根得到近场源的角度信息;在距离估计的时候,结合压缩多重信号分类算法(Compressed multiple signal classification,C-MUSIC)的思想,将菲涅尔区域分为若干个子区域,通过构造噪声子空间簇的交集,得到新的谱函数,将原来整个区域搜索变换成小区域搜索,可节省运算时间。通过仿真试验验证了算法的有效性,证明该算法的运算复杂度与传统估计算法相比得到了很大改善。
文摘多重信号分类(multiple signal classification,MUSIC)方法在少快拍数或者存在相干信源的情况下不能准确估计信号的波达方向,而压缩感知(compressive sensing,CS)方法在多快拍数或低信噪比情况下分辨性能不稳定,估计准确率受限。提出了一种基于CS的MUSIC方法,简称CS-MUSIC,该方法针对不同的快拍数,建立二者之间的联系,构造出新的正交空间,获得尖锐的谱峰。理论分析和仿真结果表明,所提方法在不同快拍数条件下,具有较高的估计精度,克服了传统方法存在的缺陷,并且对噪声具有鲁棒性。
文摘[Objective] This paper aimed to provide a new method for genetic data clustering by analyzing the clustering effect of genetic data clustering algorithm based on the minimum coding length. [Method] The genetic data clustering was regarded as high dimensional mixed data clustering. After preprocessing genetic data, the dimensions of the genetic data were reduced by principal component analysis, when genetic data presented Gaussian-like distribution. This distribution of genetic data could be clustered effectively through lossy data compression, which clustered the genes based on a simple clustering algorithm. This algorithm could achieve its best clustering result when the length of the codes of encoding clustered genes reached its minimum value. This algorithm and the traditional clustering algorithms were used to do the genetic data clustering of yeast and Arabidopsis, and the effectiveness of the algorithm was verified through genetic clustering internal evaluation and function evaluation. [Result] The clustering effect of the new algorithm in this study was superior to traditional clustering algorithms, and it also avoided the problems of subjective determination of clustering data and sensitiveness to initial clustering center. [Conclusion] This study provides a new clustering method for the genetic data clustering.
基金supported by the National Natural Science Foundation of China(51136001 and 51356001)Tsinghua University Initiative Scientific Research Program and Science Fund for Creative Research Groups(51321002)
文摘This contribution starts with the discussion on the classification of energy, and then the behaviors of various thermodynamic processes are analyzed, accompanying with the comparison of the adiabatic compression process of an ideal gas and an elastic rod. All these analyses show that the internal energy of ideal gases exhibits the duality of thermal energy–mechanical energy, that is,the internal energy acts as the thermal energy during the isochoric process, while the internal energy acts as the mechanical energy during the isentropic process. Such behavior of the internal energy is quite different from other types of energy during the energy conversion process because the internal energy of ideal gases exhibits the duality of thermal energy–mechanical energy. Because of this duality, the internal energy of ideal gas is proposed to be refered to as thermodynamic energy rather than thermal energy as indicated in some literature, although it consists of kinetics of the microscopic random motion of particles and can be expressed as the function of temperature only.
基金supported by the National Natural Science Foundation of China(6107116361271327+4 种基金61471191)the Fundamental Research Funds for the Central Universities(NP2015504)the Jiangsu Innovation Program for Graduate Education(KYLX 0277)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA)the Funding for Outstanding Doctoral Dissertation in NUAA(BCXJ14-08)
文摘This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity,three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis(PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification(MUSIC), the estimation of signal parameters via rotational invariance techniques(ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency.Simulation results indicate the proposed algorithm have a bright future in wireless communications.
