搜索资源列表
oomp
- 基于子空间匹配追踪的信号稀疏逼近的算法——硕士论文
sparsedecom
- 利用匹配追踪对雷达信号进行稀疏分解，得到其稀疏表示形式-The use of matching pursuit for sparse decomposition of the radar signals, by their sparse representation
1
- 实现信号稀疏变换、观测矩阵设计、重构算法等一系列最新理论成果。-Achieve sparse signal transformation, observation matrix design, reconstruction algorithm and a series of recent theoretical results.
MP
- MP稀疏分解用于盲信号分离，图像压缩。去噪声等领域-MPsparse decomposition
CS_BP
- 基追踪稀疏重构算法matlab实现，包含信号稀疏过程和重构过程-Based tracking sparse reconstruction algorithm matlab realize, including the process and the sparse signal reconstruction process
yin
- 基因检测 合成语音信号，能够准确检测出输入信号的基频，并以稀疏矩阵的形式输出检测到的数值(Fundamental Frequency Component Detecting)
信号稀疏分解及压缩感知理论应用研究
- 信号稀疏分解及压缩感知理论应用研究，论文(Research on sparse signal decomposition and compressed sensing theory)
MP_decomposition
- 基于MP的稀疏分解算法，信号的去燥，重构，应用的是cabor原子(MP based sparse decomposition algorithm, signal drying, reconstruction, the application of Cabor atoms)
reconstruction
- 基于接收矩阵的稀疏重构，L1-SVD，阵列信号处理方面(Based on the sparse reconstruction of the received matrices, L1-SVD, array signal processing is presented)
sparse and redundant representation
- 稀疏与冗余表示-理论及其在信号与图像处理中的应用一书的源代码(Sparse and redundant representation -From theory to application in signal and image processing -- the source code of the Book)
MATLAB总结
- 信号稀疏表示的目的就是在给定的超完备字典中用尽可能少的原子来表示信号，可以获得信号更为简洁的表示方式，从而使我们更容易地获取信号中所蕴含的信息，更方便进一步对信号进行加工处理，如压缩、编码等。信号稀疏表示方向的研究热点主要集中在稀疏分解算法、超完备原子字典、和稀疏表示的应用等方面。(Signal sparse representation is to overcomplete dictionary given in as little as possible to represent atomi
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)
Shift_Invariant_Sparse_Coding
- Matlab编写的移不变稀疏分解程序 ,求得对应于不同源信号的基，可实现信号特征提取(The shift invariant sparse decomposition program written by Matlab can obtain the basis of the signal corresponding to the homologous signal, and the signal feature extraction can be realized.)
凸优化
- 以上代码为使用凸优化来求解稀疏信号的DOA(The above code uses convex optimization to solve DOA of sparse signals)
Desktop
- 以上代码为用OMP算法来求解稀疏信号的解，非常适合压缩感知入门(The above code is used OMP algorithm to solve sparse signal solution, very suitable for compressed sensing entry)
mmmv
- 压缩感知，多采样下稀疏信号的恢复 ，适合了解多采样模型(Compressed sensing, sparse signal recovery under multi - sampling)
contourlet_toolbox.tar
- 轮廓波变换的基本原理，对输入图像的进行轮廓波变换，并重构原始信号(The basic principle of contourlet transform, performing contourlet transform on the input image and reconstructing the original signal.)
omp可运行程序(m格式)
- 完成omp算法编程，其中加入了噪声及多种频率的信号完成了稀疏表征。(Programming of OMP algorithm with sparse representation)
一维信号的匹配追踪重建
- 主要用于一维信号的稀疏分解，适用于Gabor等多参数字典求取最佳原子(It is mainly used for sparse decomposition of one-dimensional signals. It is suitable for Gabor and other multi parameter dictionary to obtain the best atom.)
direction finding algorithm
- 利用信号本身的结构特征，通过附加不同的稀疏约束，该模型利用过完备字典进行信号分解，使其表示成字典中若干原子的线性组合，从而获得数据的精简表示。(By using the structural characteristics of the signal itself and adding different sparse constraints, the model decomposes the signal into linear combinations of atoms in the dic