Results 1 - 10 of 17,417

Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing

by Edward Ashford Lee, David G. Messerschmitt - IEEE TRANSACTIONS ON COMPUTERS , 1987
"... Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of pro ..."
Abstract - Cited by 598 (37 self) - Add to MetaCart
Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake

Singularity Detection And Processing With Wavelets

by Stephane Mallat, Wen Liang Hwang - IEEE Transactions on Information Theory , 1992
"... Most of a signal information is often found in irregular structures and transient phenomena. We review the mathematical characterization of singularities with Lipschitz exponents. The main theorems that estimate local Lipschitz exponents of functions, from the evolution across scales of their wavele ..."
Abstract - Cited by 595 (13 self) - Add to MetaCart
study separately. We show that the size of the oscillations can be measured from the wavelet transform local maxima. It has been shown that one and two-dimensional signals can be reconstructed from the local maxima of their wavelet transform [14]. As an application, we develop an algorithm that removes

Tensor Decompositions and Applications

by Tamara G. Kolda, Brett W. Bader - SIAM REVIEW , 2009
"... This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N -way array. Decompositions of higher-order tensors (i.e., N -way arrays with N ≥ 3) have applications in psychometrics, chemometrics, signal proce ..."
Abstract - Cited by 723 (18 self) - Add to MetaCart
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N -way array. Decompositions of higher-order tensors (i.e., N -way arrays with N ≥ 3) have applications in psychometrics, chemometrics, signal

A fast iterative shrinkage-thresholding algorithm with application to . . .

by Amir Beck, Marc Teboulle , 2009
"... We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast Iterat ..."
Abstract - Cited by 1058 (9 self) - Add to MetaCart
We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a

RADAR: an in-building RF-based user location and tracking system

by Paramvir Bahl, Venkata N. Padmanabhan , 2000
"... The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF) based system for locating and tracking users inside buildings. RADAR operates by recording and ..."
Abstract - Cited by 2036 (14 self) - Add to MetaCart
and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It employs techniques that combine empirical measurements with signal propagation modeling to enable location-aware services and applications. We present concrete experimental

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

by Michal Aharon, et al. , 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
Abstract - Cited by 935 (41 self) - Add to MetaCart
In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many

The University of Florida sparse matrix collection

by Timothy A. Davis - NA DIGEST , 1997
"... The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural enginee ..."
Abstract - Cited by 536 (17 self) - Add to MetaCart
The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural

Coupled hidden Markov models for complex action recognition

by Matthew Brand, Nuria Oliver, Alex Pentland , 1996
"... We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and ..."
Abstract - Cited by 501 (22 self) - Add to MetaCart
and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal---that it is a single process having a small number of states

Survey on Independent Component Analysis

by Aapo Hyvärinen - NEURAL COMPUTING SURVEYS , 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
Abstract - Cited by 2309 (104 self) - Add to MetaCart
A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation
Results 1 - 10 of 17,417