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Emamnuel J. Candès∗ 写的关于压缩感知的文章，英文版-Compressive sampling write by
Emamnuel J. Candès∗
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求解l1优化问题，在压缩传感中有应用，概算发可以求解三个模型：BP，BPDN和约束版本-Solves L1 problems arsing from Compressive sensing, compressive sampling and sparse optimization
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在这个简短的论述中,我们提供一些基于这一新理论的关键性数学见解,并解释了一些压缩采样和其他领域,如统计学、信息论、编码理论以及理论性的计算机科学之间的交互。-In this short survey, we provide some of the key mathematical insights underlying this new
theory, and explain some of the interactions between compressive sampling and
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Compressive sampling
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缩感知（Compressed sensing），也被称为压缩采样(Compressive sampling)或稀疏采样(Sparse sampling)，是一种寻找欠定线性系统的稀疏解的技术。-about compress sensing
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An introduction to compressive sampling(Emmanuel Candès )
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An introduction to compressive sampling(Emmanuel Candès )
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Compressive Sampling(Emmanuel Candès)
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Imaging via compressive sampling(Justin Romberg)
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经典的香农采样定理认为,为了不失真地恢复模拟信号,采样频率应该不小于奈奎斯特频率(即模拟信号
频谱中的最高频率)的两倍.但是其中除了利用到信号是有限带宽的假设外,没利用任何的其它先验信息.采集到
的数据存在很大程度的冗余.Donoho等人提出的压缩感知方法(Compressed Sensing或Compressive Sampling,
CS)充分运用了大部分信号在预知的一组基上可以稀疏表示这一先验信息,利用随机投影实现了在远低于奈奎斯
特频率的采样频率下对压缩数据的直接采集.该
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compressive sensing using OMP
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详细介绍压缩采样技术的原理和帮助学习压缩采样新技术-Details compressive sampling techniques and principles to help learn new techniques compressed samples
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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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e: Compressed Sensing (Compressive Sensing (CS), known as Compressed Sensing, Compressed Sampling). The theory states: compressible signal can be much lower than the Nyquist criterion for sampling data, and still be able to accurately recover the ori
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