在图像稀疏分解的基础上，对分解出的数据进行压缩。
Image sparse representation data got by sparse decomposition method are compressed by quantization and coding.
在第三章中，介绍了基于信号稀疏分解的频率估计方法。
In the third chapter, a measure of frequency estimation based on signal sparse decomposition is introduced.
最后将一种新的信号处理方法——稀疏分解应用于多导联心电信号的压缩。
Finally, the novel signal processing method - sparse decomposition is applied to our problem.
分析研究图像和噪声在稀疏分解中的不同特性，明确图像信息和噪声的区别。
Based on study of the different characteristic of image and noise in sparse decomposition, the difference between image and noise is identified.
分析研究图像和噪声在稀疏分解中的不同表现，明确图像信息和噪声的区别。
Based on study of the different behavior of image and noise in sparse decomposition, the difference between image and noise is identified.
为促进信号稀疏表示、稀疏分解的研究与应用，研究其快速算法是十分必要的。
Otherwise, signal sparse denotation and decomposition can not be practical, it can only remain in the searching phases.
针对目前冗余字典下信号稀疏分解常用算法计算复杂度高的问题，提出一种分组匹配追踪算法。
For the extremely high complexity of usual algorithms for sparse decomposition, a new group matching pursuit algorithm is presented based on a redundant dictionary with several orthonormal bases.
本文着重于语音信号的稀疏分解，解决因为信号的长度大而引起的计算量大、存储量大的问题。
This paper focuses on the sparse decomposition of audio signal, and resolves the problem of large storage and huge computational complexity which is caused by the long signal.
为了提高地震信号的稀疏分解速度，国内外学者已经提出了一些快速算法，蚁群算法就是其中的一种。
In order to improve the speed of sparse decomposition of seismic signal, scholars have proposed some fast algorithm, and the Ant Colony algorithm is one of them.
然后利用稀疏分解方法和BP（BasisPursuit）算法实现干扰抑制和有用信号的重构。
Secondly, the jamming suppression and the signal reconstruction are achieved with the sparse decomposition method and BP(Basis Pursuit)algorithm.
针对信号稀疏分解中过完备原子库存储量大的问题，本文提出了利用信号集合划分研究过完备原子库的新方法。
Aiming at the problem of the large over-complete dictionary storage, a new method is proposed based on signal set partitioning method.
文章首先将稀疏分解法等同于支撑向量回归(SVR)的一种形式，为稀疏分解法提供新的直观解释和求解方法。
First, algorithm equate the sparse_decomposition to a form of support_vector regression (SVR), for providing new interpretation and solution.
因此本论文将对蚁群算法和蒙特卡罗方法进行研究，在保证重建信号质量的前提下，提高地震信号稀疏分解的速度。
We will improve the speed of seismic signal sparse decomposition in the case of that the quality of the reconstructed signal is ensured.
说明：曲波变换是图像处理领域中稀疏表示最常用的一种字典，其中MCA分解模型中经常用到。
Bo transform the field of image processing is the most common kind of sparse representation dictionary MCA decomposition model which is often used.
以IEEE检验系统为例，通过数值试验将多波前算法与电力系统分析中常用的稀疏三角分解技术进行对比分析。
The performance of multifrontal method is evaluated and compared with direct sparse factorization method through load flow calculation and transient stability simulation using IEEE benchmark data.
对于一般的带状矩阵，详述对对称与非对称三对角化矩阵做QR分解后，Q矩阵与R矩阵的稀疏元素分布型态。
For a general banded matrix, discuss the sparsity pattern of the Q and R matrices from the QR decomposition of symmetric and non-symmetric tridiagonal matrices.
稀疏信号分解；阶比分析；齿轮；故障诊断；
Sparse signal decomposition Order analysis Gear Fault diagnosis;
提出了基于线调频基稀疏信号分解的包络阶次谱方法，并将其应用于变速齿轮箱故障诊断之中。
An envelope order spectrum based on multi-scale chirplet and sparse signal decomposition was proposed and applied to the fault diagnosis of gearboxes with rotating speed fluctuation.
本发明公开了一种基于多尺度线调频基稀疏信号分解的齿轮故障诊断方法。
The invention discloses a gear fault diagnosis method based on multiscale linear frequency modulation-based sparse signal decomposition.
然后将其与地震子波褶积，使其求解结果与实际地震数据的最小平方问题归结为求解一大型稀疏矩阵方程，并采用奇异位分解法求解。
The least square problem of the convolution result and real seismic data can be considered as the solution of a huge rarefactional matrix equation, which can be solved by singular value decomposition.
本文主要是论述稀疏非负矩阵分解算法在矿产资源定量预测中的应用研究。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
非负矩阵分解算法简单，易于实现，并且具有降维、收敛和稀疏等特性。
Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence.
非负矩阵分解算法简单，易于实现，并且具有降维、收敛和稀疏等特性。
Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence.
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