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New Properties For Discrete, Bilinear TimeFrequency Distributions
 in Proc. of the IEEE Int. Symp. on TimeFrequency and TimeScale Analysis
, 1996
"... The most straightforward discrete implementation of bilinear timefrequency distributions (TFDs) are aliased in frequency, so as a result many different discrete TFDs have been developed. The traditional viewpoint in constructing discrete TFDs is that they should be samples of continuous TFDs. This ..."
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Cited by 6 (3 self)
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the appropriate properties is also presented for each of the three types of discrete distributions. One of these is the class of aliasfree generalized discrete time timefrequency distributions (AFGDTFDs). It can be shown that the three classes of discrete TFDs presented here include every time and frequency
Matching pursuits with timefrequency dictionaries
 IEEE Transactions on Signal Processing
, 1993
"... AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures t ..."
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Cited by 1654 (13 self)
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to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive timefrequency transform. We derive a signal energy distribution in the timefrequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A
AliasFree Shadow Maps
, 2004
"... In this paper we abandon the regular structure of shadow maps. Instead, we transform the visible pixels P(x,y,z) from screen space to the image plane of a light source P ′ (x ′,y ′,z ′). The (x ′,y ′ ) are then used as sampling points when the geometry is rasterized into the shadow map. This elimina ..."
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Cited by 50 (2 self)
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In this paper we abandon the regular structure of shadow maps. Instead, we transform the visible pixels P(x,y,z) from screen space to the image plane of a light source P ′ (x ′,y ′,z ′). The (x ′,y ′ ) are then used as sampling points when the geometry is rasterized into the shadow map. This eliminates the resolution issues that have plagued shadow maps for decades, e.g., jagged shadow boundaries. Incorrect selfshadowing is also greatly reduced, and semitransparent shadow casters and receivers can be supported. A hierarchical software implementation is outlined.
DiscreteTime, DiscreteFrequency TimeFrequency Representations
 in Proc. of the IEEE Int. Conf. on Acoust., Speech, and Signal Processing
, 1995
"... A discretetime, discretefrequency Wigner distribution is derived using a grouptheoretic approach. It is based upon a study of the Heisenberg group generated by the integers mod N , which represents the group of discretetime and discretefrequency shifts. The resulting Wigner distribution satisfi ..."
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Cited by 22 (3 self)
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A discretetime, discretefrequency Wigner distribution is derived using a grouptheoretic approach. It is based upon a study of the Heisenberg group generated by the integers mod N , which represents the group of discretetime and discretefrequency shifts. The resulting Wigner distribution
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 588 (19 self)
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discretetime signal can be represented as a superposition of spikes alone, or as a superposition of sinusoids alone, there is no unique way of writing S as a sum of spikes and sinusoids in general. We prove that if S is representable as a highly sparse superposition of atoms from this time/frequency
Powerlaw distributions in empirical data
 ISSN 00361445. doi: 10.1137/ 070710111. URL http://dx.doi.org/10.1137/070710111
, 2009
"... Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the t ..."
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Cited by 589 (7 self)
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Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur
A survey of generalpurpose computation on graphics hardware
, 2007
"... The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware acompelling platform for computationally demanding tasks in awide variety of application domains. In this report, we describe, summarize, and analyze the l ..."
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Cited by 545 (18 self)
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the latest research in mapping generalpurpose computation to graphics hardware. We begin with the technical motivations that underlie generalpurpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim
Multipoint quantitativetrait linkage analysis in general pedigrees
 Am. J. Hum. Genet
, 1998
"... Multipoint linkage analysis of quantitativetrait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variancecomponent linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint i ..."
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Cited by 549 (56 self)
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Multipoint linkage analysis of quantitativetrait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variancecomponent linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint
Estimating Continuous Distributions in Bayesian Classifiers
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality ..."
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Cited by 489 (2 self)
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When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon
Results 1  10
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