Results 1  10
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701
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 483 (2 self)
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This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination
Complete discrete 2D Gabor transforms by neural networks for image analysis and compression
, 1988
"... A threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a comp ..."
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Cited by 478 (8 self)
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because t e elementary expansion functions are not orthogonal. One orthogonking approach developed for 1D signals by Bastiaans [8], based on biorthonormal expansions, is restricted by constraints on the conjoint sampling rates and invariance of the windowing function, as well as by the fact
Algorithms for simultaneous sparse approximation. Part II: Convex relaxation
, 2004
"... Abstract. A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals th ..."
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Cited by 366 (5 self)
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Abstract. A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals
Elementary and global aspects of calcium signalling
 J. Physiol
, 1997
"... Calcium is a ubiquitous second messenger used to regulate a wide range of cellular processes. This role in signalling has to be conducted against the rigid homeostatic mechanisms that ensure that the resting level of Ca2+ is kept low (i.e. between 20 and 100 nmol l−1) in order to avoid the cytotoxic ..."
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Cited by 114 (5 self)
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Calcium is a ubiquitous second messenger used to regulate a wide range of cellular processes. This role in signalling has to be conducted against the rigid homeostatic mechanisms that ensure that the resting level of Ca2+ is kept low (i.e. between 20 and 100 nmol l−1) in order to avoid
Orthographic processing in visual word recognition: a multiple readout model, Psychol
 518–565. Edwards et al. / Cognitive Brain Research 24 (2005) 648–662 661
, 1996
"... A model of orthographic processing is described that postulates readout from different information dimensions, determined by variable response criteria set on these dimensions. Performance in a perceptual identification task is simulated as the percentage of trials on which a noisy criterion set on ..."
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Cited by 266 (34 self)
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recognition. When skilled readers move their gaze across lines of printed text in order to make sense of letter sequences and spaces, it is very likely that for each word an elementary set of operations is repeated in the brain. These operations compute a form representation of the physical signal, match
On sparse representations in arbitrary redundant bases
 IEEE Trans. Inf. Th
, 2004
"... Abstract—The purpose of this contribution is to generalize some recent results on sparse representations of signals in redundant bases. The question that is considered is the following: given a matrix of dimension ( ) with and a vector = , find a sufficient condition for to have a unique sparsest re ..."
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Cited by 253 (0 self)
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Abstract—The purpose of this contribution is to generalize some recent results on sparse representations of signals in redundant bases. The question that is considered is the following: given a matrix of dimension ( ) with and a vector = , find a sufficient condition for to have a unique sparsest
Computational methods for sparse solution of linear inverse problems
, 2009
"... The goal of sparse approximation problems is to represent a target signal approximately as a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, ..."
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Cited by 167 (0 self)
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The goal of sparse approximation problems is to represent a target signal approximately as a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues
SAMOS: an Active ObjectOriented Database System
, 1992
"... events are not detected by SAMOS, but users/applications have to notify the system about their occurrence by issuing an explicit raise operation. 2.2 Composite events The kinds of primitive events described above correspond to elementary occurrences and are not adequate for handling events that occu ..."
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Cited by 220 (9 self)
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events are not detected by SAMOS, but users/applications have to notify the system about their occurrence by issuing an explicit raise operation. 2.2 Composite events The kinds of primitive events described above correspond to elementary occurrences and are not adequate for handling events
The Convex Geometry of Linear Inverse Problems
, 2010
"... In applications throughout science and engineering one is often faced with the challenge of solving an illposed inverse problem, where the number of available measurements is smaller than the dimension of the model to be estimated. However in many practical situations of interest, models are constr ..."
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Cited by 189 (20 self)
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. The class of simple models considered are those formed as the sum of a few atoms from some (possibly infinite) elementary atomic set; examples include wellstudied cases such as sparse vectors (e.g., signal processing, statistics) and lowrank matrices (e.g., control, statistics), as well as several others
Just relax: Convex programming methods for subset selection and sparse approximation
, 2004
"... Subset selection and sparse approximation problems request a good approximation of an input signal using a linear combination of elementary signals, yet they stipulate that the approximation may only involve a few of the elementary signals. This class of problems arises throughout electrical enginee ..."
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Cited by 103 (5 self)
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Subset selection and sparse approximation problems request a good approximation of an input signal using a linear combination of elementary signals, yet they stipulate that the approximation may only involve a few of the elementary signals. This class of problems arises throughout electrical
Results 1  10
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701