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dissert
- Sparse Signal Representation using Overlapping Frames-Sparse Signal Representation using Overl apping Frames
BCS
- 压缩传感是一个从2006年左右开始兴起的研究领域，它关注于如何采样信号，也就是信号的采样方式或者压缩方式。通过设计一种特殊的采样方案，可以使得采样频率降低为信号的“信息率”，而不是传统的奈奎斯特采样率，于是，实际的采样率可以大大低于奈奎斯特频率，却只损失很少的信息量，依然保持了充足的信息量足以恢复出采样前的原始信号。这个研究思想挑战了奈奎斯特频率的理论极限，会对整个信号处理领域产生极其深远的影响，同时，信号处理的许多应用领域也会随之发生根本性的发展和变化。 -Compressive sens
ChinhLa_thesis
- SIGNAL RECONSTRUCTIONS FROM LIMITED MEASUREMENTS USING SPARSE-TREE PRIORS
Sparse_Signal_Representation
- 介绍压缩传感理论中要用到的信号的稀疏表征原理-Introduced the theory of compressed sensing to use the sparse representation of signal theory
Demo_MWC
- 稀疏信号的压缩感知算法，将宽带稀疏信号通过压缩感知的方法压缩为窄带信号便于传输。-Sparse signal compressed sensing algorithm broadband sparse signals compressed by compressive sensing method for narrowband signals for transmission.
A-Bayesian-Approach
- In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth’s crust.We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution
Sparse-Coding-by-Elad
- Elad写的关于稀疏理论的书，内容丰富，适合初学稀疏理论的同学，不容错过额-a book about sparse representation of signal and its practice
system-identification
- 采用时频聚集性较好的线性调频信号作为线性时不变系统输入激励，采用Gabor字典作为过完备原子库。在利用传统系统辨识法之前先利用稀疏分解算法将输出信号进行去噪处理，显著提高系统辨识精度。 具体包括互谱算法，信号的Gabor稀疏分解的详细代码-Space can be a time for sparse decomposition to solve the problem of huge memory needed。This approach, combined with the rapid d
CompressSensing_4EnglishRef
- 压缩感知中四篇比较重要的英文参考文献，最近五年内发表的，涉及Sparse signal approximation,-Compression perception in four of the more important English Reference, published in the latest 5 years,involving Sparse signal approximation.
KSVD_Matlab_ToolBox
- 数字图像处理，K-SVD字典学习方法，信号的稀疏与冗余表示理论，图像压缩，图像去噪-Digital image processing, K-SVD dictionary learning methods, sparse and redundant signal representation theory, image compression, image denoising
CS
- 信号稀疏分解及压缩感知理论应用研究，理论基础-Signal sparse decomposition and compression perception theory applied research, theoretical basis
Short-duration-power_CS
- 根据压缩传感(Compressed Sensing，cs)N论，首次提出了短时电能质量扰动信号的压缩采样方法，该方法突破了奈奎斯特采样频率的限制，实现了低于奈奎斯特采样频率的低速率采样。文中对比分析了CS理论与传统采样理论，研究了cS短时电能质量信号压缩采样的实现方法，包括：测量矩阵的构建、稀疏基的选取和电能质量信号快速贝叶斯匹配追踪重构算法(FBMP)-Compressed sensing ( Compressed Sensing , cs ) N theory , first propose
Compression-perception-theory-
- 压缩感知理论及其研究进展,文综述了cs理论框架及关键技术问题，并着重介绍了信号稀疏变换、观测矩阵设计和重构算法三个方面的最新进展，是一篇综述。-Compression perception theory and research progress, cs paper reviews the theoretical framework and key technical issues and focuses on the latest developments signal sparse tran
signal-classification-by-sparse-representation.ra
- this tutorial is about signal classification using sparse representation.
infocom11-cheng
- In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first t
07389549
- Firstly, this study does research on the quantitative uation of the acoustic performance of the planar ultrasonic array sensors. Sparse signal decomposition theory is then applied to find three different directions of arrival (DOAs) of the signal
3130-sparse-representation-for-signal-classificat
- SPARSE REPRESENTATION CLASSIFIERS FOR SIGNAL REPRESENTATION
Compressive-Sensing-for-Signal-Ensembles
- 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
A-Robust-Algorithm-for-Joint-Sparse
- 脉冲噪声背景下的联合稀疏恢复方法， 在不同背景下给出了测试结果-presents a robust solution for joint sparse recovery (JSR) under impulsive noise. The unknown measurement noise is endowed with the Student-t distribution, then a novel Bayesian probabilistic model is proposed to
l1magic
- 实现压缩感知的稀疏信号恢复，采用L1范数约束最小化策略(Sparse signal recovery with compressed sensing, by using the L1 norm constraint minimization strategy)