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CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
 California Institute of Technology, Pasadena
, 2008
"... Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery alg ..."
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Cited by 770 (13 self)
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with the sampling matrix. For compressible signals, the running time is just O(N log 2 N), where N is the length of the signal. 1.
Uncertainty principles and ideal atomic decomposition
 IEEE Transactions on Information Theory
, 2001
"... Suppose a discretetime signal S(t), 0 t<N, is a superposition of atoms taken from a combined time/frequency dictionary made of spike sequences 1ft = g and sinusoids expf2 iwt=N) = p N. Can one recover, from knowledge of S alone, the precise collection of atoms going to make up S? Because every d ..."
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Cited by 583 (20 self)
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is the number of frequency atoms, and N is the length of the discretetime signal.
Capacity of a Mobile MultipleAntenna Communication Link in Rayleigh Flat Fading
"... We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flatfading environment. The propagation coefficients between every pair of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence int ..."
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Cited by 495 (22 self)
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signals. We prove that there is no point in making the number of transmitter antennas greater than the length of the coherence interval: the capacity for M> Tis equal to the capacity for M = T. Capacity is achieved when the T M transmitted signal matrix is equal to the product of two statistically
Bayesian compressive sensing via belief propagation
 IEEE Trans. Signal Processing
, 2010
"... Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, subNyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can comple ..."
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Cited by 125 (19 self)
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also improve BP convergence by reducing the presence of loops in the graph. To decode a lengthN signal containing K large coefficients, our CSBP decoding algorithm uses O(K log(N)) measurements and O(N log 2 (N)) computation. Finally, sparse encoding matrices and the CSBP decoding algorithm can
On sparse reconstruction from Fourier and Gaussian measurements
 Communications on Pure and Applied Mathematics
, 2006
"... Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem ..."
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Cited by 262 (8 self)
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to a convex problem, and then solve it as a linear program. We show that there exists a set of frequencies Ω such that one can exactly reconstruct every rsparse signal f of length n from its frequencies in Ω, using the convex relaxation, and Ω has size k(r, n) = O(r log(n)·log 2 (r) log(r log n)) = O
Compressive sensing recovery of spike trains using a structured sparsity model
 in Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS
, 2009
"... Abstract—The theory of Compressive Sensing (CS) exploits a wellknown concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a lengthN signal x with sparsity level K, M = O(K log(N/K)) random linear projections of x are ..."
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Cited by 20 (13 self)
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Abstract—The theory of Compressive Sensing (CS) exploits a wellknown concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a lengthN signal x with sparsity level K, M = O(K log(N/K)) random linear projections of x
Compressive Sensing of Streams of Pulses
"... as an enticing alternative to the traditional process of signal acquisition. For a lengthN signal with sparsity K, merely M = O (K log N) ≪ N random linear projections (measurements) can be used for robust reconstruction in polynomial time. Sparsity is a powerful and simple signal model; yet, rich ..."
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Cited by 5 (2 self)
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as an enticing alternative to the traditional process of signal acquisition. For a lengthN signal with sparsity K, merely M = O (K log N) ≪ N random linear projections (measurements) can be used for robust reconstruction in polynomial time. Sparsity is a powerful and simple signal model; yet
A fast approximation algorithm for treesparse recovery
 In International Symposium on Information Theory (ISIT
, 2014
"... Abstract—Sparse signals whose nonzeros obey a treelike structure occur in a range of applications such as image modeling, genetic data analysis, and compressive sensing. An important problem encountered in recovering signals is that of optimal treeprojection, i.e., finding the closest treesparse ..."
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Cited by 3 (3 self)
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sparse signal for a given query signal. However, this problem can be computationally very demanding: for optimally projecting a lengthn signal onto a tree with sparsity k, the best existing algorithms incur a high runtime of O(nk). This can often be impractical. We suggest an alternative approach to tree
Prediction of signal peptides and signal anchors by a hidden Markov model
 Proc. Int. Conf. Intell. Syst. Mol. Biol
, 1998
"... A hidden Markov model of signal peptides has been developed. It contains submodels for the Nterminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our dat ..."
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Cited by 157 (10 self)
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A hidden Markov model of signal peptides has been developed. It contains submodels for the Nterminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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indicates the transposed complex conjugate, and ⌿ is the n C ϫ n C receiver noise matrix (see Appendix A), which describes the levels and correlation of noise in the receiver channels. Using the unfolding matrix, signal separation is performed by where the resulting vector v has length n P and lists
Results 1  10
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1,180