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Compressive Sensing Framework for Speech Signal Synthesis Using a Hybrid Dictionary
"... Abstract—Compressive sensing (CS) is a promising focus in signal processing field, which offers a novel view of simultaneous compression and sampling. In this framework a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal is ..."
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Abstract—Compressive sensing (CS) is a promising focus in signal processing field, which offers a novel view of simultaneous compression and sampling. In this framework a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal
UCSWN: An Unbiased Compressive Sensing Framework for Weighted Networks
"... Abstract—In this paper, we propose a novel framework called UCSWN in the context of compressive sensing to efficiently recover sparse vectors representing the properties of the links from weighted networks with n nodes. Motivated by network inference, we study the problem of recovering sparse link ..."
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Abstract—In this paper, we propose a novel framework called UCSWN in the context of compressive sensing to efficiently recover sparse vectors representing the properties of the links from weighted networks with n nodes. Motivated by network inference, we study the problem of recovering sparse link
Compressive sensing
 IEEE Signal Processing Mag
, 2007
"... The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too m ..."
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Cited by 687 (65 self)
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will learn about a new technique that tackles these issues using compressive sensing [1, 2]. We will replace the conventional sampling and reconstruction operations with a more general linear measurement scheme coupled with an optimization in order to acquire certain kinds of signals at a rate significantly
Compressed sensing
 IEEE Trans. Inf. Theory
, 2006
"... We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal numbe ..."
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Cited by 3600 (24 self)
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We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal
SparseRI: A Compressed Sensing Framework for Aperture Synthesis Imaging in Radio Astronomy
"... In radio interferometry, information about a small region of the sky is obtained in the form of samples in the Fourier transform domain of the desired image. Since this sampling is usually incomplete, the missing information has to be reconstructed using additional assumptions about the image. The e ..."
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Cited by 9 (4 self)
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. The emerging field of compressed sensing (CS) provides a promising new approach to this type of problem which is based on the supposed sparsity of natural images in some transform domain. We present a versatile CSbased image reconstruction framework called SparseRI, an interesting alternative to the clean
Compressive sampling
, 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
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Cited by 1427 (15 self)
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resolution of the image, i.e. the number of pixels in the image. This paper surveys an emerging theory which goes by the name of “compressive sampling” or “compressed sensing,” and which says that this conventional wisdom is inaccurate. Perhaps surprisingly, it is possible to reconstruct images or signals
Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization
, 2007
"... The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative ..."
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Cited by 568 (23 self)
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with overwhelming probability, provided the codimension of the subspace is sufficiently large. The techniques used in our analysis have strong parallels in the compressed sensing framework. We discuss how affine rank minimization generalizes this preexisting concept and outline a dictionary relating concepts from
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE Journal of Selected Topics in Signal Processing
, 2007
"... Abstract—Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined wi ..."
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Cited by 524 (15 self)
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with a sparsenessinducing (ℓ1) regularization term.Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution, and compressed sensing are a few wellknown examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound
Automatic Word Sense Discrimination
 Journal of Computational Linguistics
, 1998
"... This paper presents contextgroup discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a highdimensional, realvalued space in which closen ..."
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Cited by 530 (1 self)
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This paper presents contextgroup discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a highdimensional, realvalued space in which
Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems
, 1992
"... Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern objectoriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of design ..."
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Cited by 569 (90 self)
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Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern objectoriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, simulation, hardware and software design, parallel computing, and generating realtime prototypes. To accommodate this breadth, Ptolemy must support a plethora of widelydiffering design styles. The core of Ptolemy is a set of objectoriented class definitions that makes few assumptions about the system to be modeled; rather, standard interfaces are provided for generic objects and more specialized, applicationspecific objects are derived from these. A basic abstraction in Ptolemy is the Domain, which realizes a computational model appropriate for a particular type of subsystem. Current e...
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