搜索结果: 1-14 共查到“数学 Compressed sensing”相关记录14条 . 查询时间(0.312 秒)
Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing
Compressed sensing random sensing matrix the space coupling space coupling
2015/8/21
We study the compressed sensing reconstruction problem for a broad class of random, banddiagonal sensing matrices. This construction is inspired by the idea of spatial coupling in coding theory. As de...
Graphical Models Concepts in Compressed Sensing
Creative graphics model transfer the algorithm the compressed sensing the analysis of high-dimensional lasso risk limits
2015/8/20
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed se...
A Probabilistic and RIPless Theory of Compressed Sensing
Compressed sensing `1 minimization the LASSO the Dantzig selector (weak) restricted isometries random matrices sparse regression operator Bernstein inequalities Gross’ golfing scheme
2015/6/17
This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probability distribut...
We present a mathematical connection between channel coding and compressed sensing.
Relations between $\beta$ and $\delta$ for QP and LP in Compressed Sensing Computations
Relations $\beta$ and $\delta$ QP LP Compressed Sensing Computations
2011/2/25
In many compressed sensing applications, linear programming (LP) has been used to reconstruct
a sparse signal.
Orthogonal symmetric Toeplitz matrices for compressed sensing: Statistical isometry property
Orthogonal symmetric Toeplitz matrices compressed sensing Statistical isometry property
2011/3/1
Recently, the statistical restricted isometry property (RIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing.
Compressed Sensing for Feedback Reduction in MIMO Broadcast Channels
Compressed Feedback Reduction MIMO Broadcast Channels
2011/2/28
We propose a generalized feedback model and compressive sensing based opportunistic feedback
schemes for feedback resource reduction in MIMO Broadcast Channels under the assumption that both uplink a...
Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data
compressed-sensing sparse map-making Herschel/PACS data
2011/1/4
The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited ba...
Graphical Models Concepts in Compressed Sensing
Graphical Models Concepts Compressed Sensing
2010/11/23
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed s...
In this paper we consider the compressed sensing-based encryption and proposed the conditions in which the perfect secrecy is obtained. We prove when the Restricted Isometery Property (RIP) is hold an...
A probabilistic and RIPless theory of compressed sensing
A probabilistic and RIPless theory compressed sensing
2010/11/22
This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probability distribut...
Deterministic Compressed Sensing Matrices from Multiplicative Character Sequences
Sensing Matrices Multiplicative Character Sequences
2010/11/18
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a $K \times N$ measurement matrix for compressed sensing is determinist...
Spatially regularized compressed sensing of diffusion MRI data
Spatially regularized compressed diffusion MRI data
2010/12/3
Despite the relative recency of its inception, the theory of compres-sive sampling (aka compressed sensing) (CS) has already revolutionized multiple areas of applied sciences, a particularly important...
A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery
compressed sensing best-first search A* search matching pursuit sparse representations sparse signal
2010/11/29
Compressed sensing is a recently developing area which is interested in reconstruction of sparse signals acquired in reduced dimensions. Acquiring the data with a small number of samples makes the rec...