LSQR and ART algorithms are applied separately to calculate tomography for the underdetermined system of equation.
分别采用LSQR和ART算法对欠定方程组进行了层析成像计算。
The least fault principle may be employed to solve the underdetermined equations.
引入最少故障原理可以对亚定的故障方程组求解。
The two-step approach is often used to separate sources in underdetermined blind separation problem.
针对目前欠定盲分离问题中源数未知，采取“两步法”进行分离源信号。
However the neuroanatomical substrates of phonological store and the function of rehearsal in phonological loop remain underdetermined.
然而大脑中是否存在语音存储的特定部位及复述在语音回路中的作用还有很大的争论。
This paper discusses the recoverability of underdetermined blind source separation(BSS), based on a two-stage sparse representation approach.
基于一种两步稀疏表示的方法，利用随机框架讨论欠定盲源分离的恢复能力。
A method of the mixing matrix estimation in underdetermined source separation is proposed, which is based on the linear clustering of sparse component.
利用稀疏分量的直线聚类性，提出了欠定盲源分离中估计混合矩阵的一种方法。
This paper proposes a new method based on mixing matrix estimation for underdetermined blind speech separation, aiming at speech signals under weak sparseness.
针对语音信号的弱稀疏性，提出一种新的基于混合矩阵估计的欠定语音盲分离方法。
LSQR and ART algorithms are applied separately to calculate tomography for the determined system of equation, overdetermined system of equation and underdetermined system of equation.
采用弯曲射线追踪算法计算走时，分别用最小二乘QR分解算法与代数重建技术就恰定方程组、超定方程组与欠定方程组进行了成像计算。
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS).
非负矩阵分解（NMF）要求分解得到的左矩阵为列满秩，这限制了它在欠定盲分离（UBSS）中的应用。
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS).
非负矩阵分解（NMF）要求分解得到的左矩阵为列满秩，这限制了它在欠定盲分离（UBSS）中的应用。
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