Results 261 to 270 of about 1,002,666 (308)
Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering. [PDF]
BMC BioinformaticsWen F, Su Y, Liu D, Wang Y, Liu M.
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Energy-Efficient Sensing in Wireless Sensor Networks Using Compressed Sensing [PDF]
Sensors, 2014 Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission ...
Simon Dobson
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A Compressed Sensing Framework for Magnetic Resonance Fingerprinting [PDF]
SIAM Journal on Imaging Sciences, 2014 Inspired by the recently proposed magnetic resonance fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are a random pulse excitation sequence following the MRF technique, a
Yves Wiaux, Mike Davies
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Applied and Computational Harmonic Analysis, 2023 We introduce the broad subclass of algebraic compressed sensing problems, where structured signals are modeled either explicitly or implicitly via polynomials. This includes, for instance, low-rank matrix and tensor recovery. We employ powerful techniques from algebraic geometry to study well-posedness of sufficiently general compressed sensing ...
Nick Vannieuwenhoven +2 more
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Compressive sensing: To compress or not to compress
2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011In this paper, we consider the compressive sensing scheme from the information theory point of view and derive the lower bound of the probability of error for CS when length N of the information vector is large. The result has been shown that, for an i.i.d.
Davis Kirachaiwanich, Qilian Liang
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Compressed sensing of compressible signals
2017 IEEE International Symposium on Information Theory (ISIT), 2017A novel low-complexity robust-to-noise iterative algorithm named compression-based gradient descent (C-GD) algorithm is proposed. C-GD is a generic compressed sensing recovery algorithm, that at its core, employs compression codes, such as JPEG2000 and MPEG4.
Sajjad Beygi +3 more
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Demosaicking with compressive sensing
2012 20th Signal Processing and Communications Applications Conference (SIU), 2012Sparse signals can be recovered with less number of measurements compared to standard methods using Compressive Sensing (CS) theory. In digital cameras, color filter arrays (CFA) are used to sample each color band with less measurements than the normal. The color images are reconstructed using interpolation of measured pixel values.
Handan Ilbegi, Ali Cafer Gürbüz
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IEEE Transactions on Image Processing, 2012
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F. Duarte, Richard G. Baraniuk
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Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F. Duarte, Richard G. Baraniuk
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IEEE Transactions on Signal Processing, 2009
Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications
Shihao Ji 0001 +2 more
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Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications
Shihao Ji 0001 +2 more
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Decentralized compressive sensing
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010Motivated by sensor network applications, in this paper we study the problem of a decentralized network of J sensors, in which each sensor observes either all or some components of an underlying sparse signal ensemble. Sensors operate with no collaboration with each other or the fusion center.
Delaram Motamedvaziri +2 more
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