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Agorlitm compare
- 这是压缩感知贪婪重构算法中的各种算法编写，以及曲线拟合，分别从稀疏度和测量次数与重构率之间的关系进行的曲线拟合(This is a compilation of algorithms for compressed sensing, greedy reconstruction algorithms, and curve fitting. The curves are fitted from the relationship between sparsity and the number of me
omp
- 基本的omp算法，里面包含稀疏度的设计和信号重构(The basic OMP algorithm contains the design of sparsity and the signal reconstruction)
Sparse image and signal processing
- 这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。 这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。 MATLAB和IDL代码伴随这些方法和应用程序重现。 实验并说明
信号盒维数和稀疏性的提取_matlab
- 信号复杂度特征的提取，主要实现盒维数和稀疏性的matlab代码实现(Extracting the feature of signal complexity and realizing the matlab code of box dimension and sparsity)
bcs-spl-1.5-1 (1).tar
- BCS-SPL将图像的基于块的压缩感测采样（BCS）与平滑的投影Landweber（SPL）迭代重建相结合。采样是通过逐块应用随机矩阵来驱动的，而重建则是预期的Landweber（PL）重建（也称为迭代硬阈值（IHT））的变体，其结合平滑操作（维纳滤波）减少块效应。实质上，除了PL所固有的稀疏性之外，这种滤波操作还能提供平滑性。(BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a sm
11
- 一篇较好的图像处理文章，Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity(Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity)
1604.03049
- Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid precoding is challenging, since the number of radio frequency (RF) chains is usually much smaller than that of antennas. To date, several channel estimation schemes have bee
samp
- SAMPmatlab程序与单次重构测试代码(Sparsity Adaptive MP)
L0smoothing
- 图像平滑，L0范数，稀疏，锐化，滤波 通过限制非零梯度的数量来实现最高对比度边缘，同时以全局方式实现平滑(Image Smoothing L0 sparsity highest-contrast edges by confining the number of nonzero gradients, while smoothing is achieved in a global manner)
基于SAMP算法OFDM系统信道估计11
- 本程序为基于压缩感知的信道估计算法，其用途在于利用信道的稀疏性来降低导频的使用，获得较好的信道估计精度(This program is a compressed sensing based channel estimation algorithm, which uses the sparsity of the channel to reduce the use of pilot and obtain better channel estimation accuracy.)
Fourier-k-m-reconstuction
- 在构造压缩感知测量矩阵中，用傅里叶矩阵实现稀疏度k和m与信噪比之间的关系(In the construction of compressed sensing measurement matrix, the relationship between sparsity k and m and signal-to-noise ratio is realized by Fourier matrix.)
顾伟三机九节点仿真程序
- 电力系统潮流计算体现在数学就上是多元非线性方程组的求解问题，求解的方法有很多种。牛顿—拉夫逊法是数学上解非线性方程式的有效方法，收敛性好、迭代次数少。将牛顿—拉夫逊法用于潮流计算是以导纳矩阵为基础的，由于利用了导纳矩阵的对称性、稀疏性及节点编号顺序优化等技巧，使牛顿—拉夫逊法解题规模和计算速度进一步提高，此算法至今仍是潮流计算中广泛采用的优秀算法。(Power flow calculation in power system is a problem of solving multivariat
BOMP
- 用于图像处理和信号DOA估计，是基于块稀疏的正交匹配追踪算法(This is an algorithm based on signal block sparsity, which can be used in image processing and signal DOA estimation)
新型大规模MIMO OFDM信道估计方法的研究
- 本篇论文研究的 FDD 模式下的 Massive MIMO OFDM 系统的信道估计方法，通过分析 MIMO OFDM 系统存在的空时共同稀疏性，应用结构化理论处理 Massive MIMO 信道。为了 减少导频的消耗，提出适合的估计方法(In this paper, the channel estimation method of Massive MIMO OFDM system in FDD mode is studied Space-time common sparsity in MI
Deterministic Message Passing Algorithm SCMA in NOMA 5G
- Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower complexity