Search - Compressive Sampling

Search - Compressive Sampling - List

[Software Engineering] padm_unc DL : 0: 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
Date : 2025-12-30 Size : 1kb User : 杨俊锋
[Software Engineering] Compressive-sampling DL : 0: Emamnuel J. Candès∗ 写的关于压缩感知的文章，英文版-Compressive sampling write by Emamnuel J. Candès∗
Date : 2025-12-30 Size : 110kb User : 杨娟
[Software Engineering] ccs DL : 0: 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
Date : 2025-12-30 Size : 8kb User : lianglinlin
[Software Engineering] Compressive-Sensing-for-Signal-Ensembles DL : 0: 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 basis. The CS literature has focused almost exclusively on problems involving single signals in one or two dimensions. However, many important applications involve distributed networks or arrays of sensors. In other applications, the signal is inherently multidimensional and sensed progressively along a subset of its dimensions examples include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity models for signal ensembles that exploit both intra- and inter-signal correlation structures. Joint sparsity models enable a reduction in the total number of compressive mea-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 basis. The CS literature has focused almost exclusively on problems involving single signals in one or two dimensions. However, many important applications involve distributed networks or arrays of sensors. In other applications, the signal is inherently multidimensional and sensed progressively along a subset of its dimensions examples include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity models for signal ensembles that exploit both intra- and inter-signal correlation structures. Joint sparsity models enable a reduction in the total number of compressive mea
Date : 2025-12-30 Size : 6.24mb User : muhammedalijalil
[Software Engineering] Simple-sublinear-Fourier-sampling-master DL : 0: 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 original signal. The theory was put forth, to receive significant attention in the field of information theory, signal/image processing, medical imaging, pattern recognition, geological exploration, optical/radar imaging, wireless communications, and Technology Review named the 2007 top ten scientific and technological progress.
Date : 2025-12-30 Size : 14kb User : TELECOM