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mimo功率优化
- MIMO系统的功率优化问题，通过SVD分解和注水算法，可以实现不同发送天线上的功率分配。-MIMO system power optimization problem, and through the decomposition of water injection SVD algorithm can achieve different this antenna on the power distribution.
MIMO-WATERFILLING
- MIMO注水原理的简单运用,有助于理解SVD分解,注水算法,不同发送天线上如何进行功率分配-MIMO injection principle of a simple application helps to understand SVD decomposition algorithm injection, Send different antennas on how power distribution
SVD
- 自己编写的奇异值分解程序，希望与大家交流一下
cusvd_by_ZhangShu
- 在cuda上进行奇异值分解，采用Hestens 方法，与cpu相比加速4-5倍-svd in cuda
main
- 用C语言实现矩阵的SVD分解算法实例 很有用-Using C language SVD matrix decomposition algorithm is useful examples
svd_rar
- 奇异值分解/广义逆矩阵的svd算法代码,在解线性方程时用得上-svd siglar value decomposition
svd
- 奇异值分解算法的m代码，实现了数字水印技术的嵌入及提取过程，效果不错。-Singular value decomposition algorithm m code, the realization of the digital watermark embedding and extraction technology process, good results.
svd
- 矩阵的奇异值分解，程序编写比较公正，可读性强-The singular value decomposition, programming fair, readable
SVD
- 实现矩阵的奇异分解,详细的描述了SVD的实现过程。-the come true of svd
pca-cPP-svd-algorithm
- 主成分分析的c++实现使用奇异值分解svd算法-Principal Component Analysis based on svd algorithm ，used c++
svd
- MIMO预编码中的SVD分解，主要衡量数据为BER性能-SVD decomposition of MIMO precoding
MIMO-space-time-channel-modeling-
- mimo功率优化MIMO系统的功率优化问题，通过SVD分解和注水算法-mimo power optimization the MIMO system power optimization problem, SVD decomposition and water-filling algorithm
svd
- 用于协同过滤推荐算法，svd矩阵分解算法 C++实现-Matrix Factorization , performs well on the large, sparse, and very imbalanced Netflix dataset
ridgereg
- 岭回归函数，基于SVD分解的岭回归函数，已经证明了是非的有用和强大- center input and output, so we can estimate w0 separately since we don t want to shrink w0
SVD-EOF
- 基于matlab,采用SVD方法，对数据进行EOF分解，求得基函数-Based on matlab, using SVD method, the data EOF decomposition, obtained basis functions
svd
- svd奇异值举证分解，c++实现举证的svd分解-svd singular value decomposition of proof, c++ achieve
matlab-SVD
- 用matlab实现奇异值分解的算法,奇异值分解是LSA的基础，要理解LSA必须了解SVD.-Using matlab to achieve the singular value decomposition algorithm, singular value decomposition is the basis of the LSA, LSA must know to understand the SVD.
svd
- 对一个信号进行奇异值分解，根据差分谱最大值点，重构信号，达到降噪的目的。-Signal for a singular value decomposition, according to the differential spectrum maximum point, the reconstructed signal to achieve noise reduction purposes.
svd of matrix
- 可以对矩阵进行奇异值分解，有助于研究矩阵的正交性质，简单易懂(Singular value decomposition of the matrix can be helpful to study the orthogonal nature of the matrix, easy to understand)
L1 SVD
- 利用压缩感知实现波达方向估计，运用奇异值分解对接收信号进行降维，再利用L1范数进行估计(The DOA estimation is realized by compressed sensing, and the singular value decomposition is used to reduce the received signal, and then the L1 norm is used to estimate the DOA)