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压缩传感理论的一个简单例子，首先对信号进行稀疏采样，然后利用MP算法对信号进行重建。,Compressed sensing theory of a simple example, first of all, the signal sparse sampling, and then use MP algorithm of signal reconstruction.
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多天线压缩宽带频谱感知在认知无线电，多级加权小波包分解在宽带频谱感知用在认知无线电上。-Compression of broadband multi-antenna spectrum sensing in cognitive radio, multi-level weighted wavelet packet used in the broadband spectrum sensing in cognitive radio on.
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压缩感知里面的稀疏重建问题，在原来MP的算法的基础上，提出了分块的思想，即将原子库分块后，测试算法的性能。-Compressive sensing is refered as the block sparse algorithm is described,that is based on the original MP algorithm,and the atom libruary is divided into blocks ,and the performance is testified.
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个人手机的关于压缩感知的入门教程，包括pdf，ppt以及个人整理的word文档，希望对大家有帮助！-Personal phone introductory tutorial on compressed sensing, including pdf, ppt, and personal finishing the word document, we want to help!
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演示Compressive Sensing的Matlab程序
信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) -Compressive Sensing of Matlab demonstration program to achieve signal compression sensor (orthogonal matching pursuit method Orthogonal Matching Pursuit)
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压缩传感，压缩感知，压缩采样，稀疏表达，稀疏表示，的入门介绍，自己精心搜集的ppt，pdf资料，助你轻松入门-compressive sening compressed sensing
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压缩传感理论仿真的一个实际例子，稀疏信号的恢复。-Compressed sensing theory of simulation of a practical example of sparse signal recovery.
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compressed sensing,图像压缩，用matlab仿真-compressed sensing, image compression, using matlab simulation
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压缩感知代码！小波分析理论与MATLAB-Compressed sensing code! Wavelet analysis theory and MATLAB
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压缩传感的经典文献，介绍了一种典型的重构算法-Compressed sensing classic literature, describes a typical reconstruction algorithm
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压缩感知论文综述，介绍压缩感知理论相关内容包括理论和应用方面的介绍-Including theoretical and applied aspects of the introduction of compressed sensing summary of the thesis, introduce compression perception theory
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基于贝叶斯压缩感知理论的相关介绍，压缩感知理论是近几年非常热门的研究内容-Introduction of compressed sensing theory based on Bayesian compressed sensing theory is very popular in recent years, the research content
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LFM宽带雷达信号的多通道盲压缩感知模型研究，提出了一种压缩感知技术新方法-Lfm compressive sensing
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
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Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent
unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of
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压缩感知经典重建算法，非局部低秩算法，实现低秩正则约束，优化压缩图片质量。(Compressive Sensing via Nonlocal Low-Rank Regularization)
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Bayesian compressive sensing implemented using matlab.
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Compressed Sensing of EEG
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压缩感知的例子。重建是用在稀疏域上求最小零范数的方法。首先把零范数凸松弛为一范数，然后变成线性规划问题，从而求得最优解。(An example of the compressive sensing. Its reconstruction is based on L0-norm minimization.)
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两步迭代收缩阈值算法用于实现压缩感知求解。(Two-Step Iterative Shrinkage/Thresholding(TwIST), used in compressive sensing image reconstruction.)
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