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NOISE PARAMETERS
"... that an onwafer diode noise source is a convenient noise reference to calibrate the receiver for measuring NPs of onwafer transistors directly. However, onwafer diode noise sources are highly mismatched devices and their reflection coefficient varies significantly between the hot and cold state ..."
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that an onwafer diode noise source is a convenient noise reference to calibrate the receiver for measuring NPs of onwafer transistors directly. However, onwafer diode noise sources are highly mismatched devices and their reflection coefficient varies significantly between the hot and cold
A densitybased algorithm for discovering clusters in large spatial databases with noise
, 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
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Cited by 1786 (70 self)
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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery
Estimation of Noise Parameters on Sonar Images
 In Signal and Image Processing, volume SPIE 2823
, 1996
"... We use the Markov Random Field (MRF) model in order to segment sonar images, i.e. to localize the sea bottom areas and the projected shadow areas corresponding to objects lying on seafloor. This model requires on one hand knowledges about the statistical distributions relative to the different zones ..."
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Cited by 14 (9 self)
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zones and on the other hand the estimation of the law parameters. The Kolmogorov criterion or the 2 criterion allow to estimate the distribution laws. The Estimation Maximization (EM) algorithm or the Stochastic Estimation Maximization (SEM) algorithm are used to determine the maximum likelihood
Analytical Noise Parameter Model of ShortChannel RF
, 2007
"... Abstract—In this paper, a simple and improved noise parameter model of RF MOSFETs is developed and verified. Based on the analytical model of channel thermal noise, closed form expressions for four noise parameters are developed from proposed equivalent small signal circuit. The modeling results sho ..."
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Abstract—In this paper, a simple and improved noise parameter model of RF MOSFETs is developed and verified. Based on the analytical model of channel thermal noise, closed form expressions for four noise parameters are developed from proposed equivalent small signal circuit. The modeling results
Accuracy improvements in microwave noise parameter measurements
 IEEE Trans. Microwave Theory Tech
, 1989
"... Abstract Factors contributing to microwave noise parameter measurement accuracy have been examined theoretically and experimentally. It is shown that for good accuracy the test source impedances need not be grouped around the impedance that produces minimum noise figure. System calibration and DU ..."
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Cited by 4 (0 self)
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Abstract Factors contributing to microwave noise parameter measurement accuracy have been examined theoretically and experimentally. It is shown that for good accuracy the test source impedances need not be grouped around the impedance that produces minimum noise figure. System calibration
VARIATION IN NOISE PARAMETER ESTIMATES FOR BACKGROUND NOISE CLASSIFICATION
"... ABSTRACT In current paper, authors try to investigate regarding variation in speech parameter estimates which can be used to classify environmental noise for grouping a large range of environmental noise into a reduced set of classes of noise with similar type of speech characteristic parameters. O ..."
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ABSTRACT In current paper, authors try to investigate regarding variation in speech parameter estimates which can be used to classify environmental noise for grouping a large range of environmental noise into a reduced set of classes of noise with similar type of speech characteristic parameters
Bayesian Interpolation
 NEURAL COMPUTATION
, 1991
"... Although Bayesian analysis has been in use since Laplace, the Bayesian method of modelcomparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and modelcomparison is demonstrated by studying the inference problem of interpolating noisy data. T ..."
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Cited by 728 (17 self)
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. `Occam's razor' is automatically embodied by this framework. The way in which Bayes infers the values of regularising constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 879 (14 self)
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In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n
Adapting to unknown smoothness via wavelet shrinkage
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1995
"... We attempt to recover a function of unknown smoothness from noisy, sampled data. We introduce a procedure, SureShrink, which suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: a threshold level is assigned to each dyadic resolution level by the princip ..."
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Cited by 1006 (18 self)
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We attempt to recover a function of unknown smoothness from noisy, sampled data. We introduce a procedure, SureShrink, which suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: a threshold level is assigned to each dyadic resolution level
Distinctive Image Features from ScaleInvariant Keypoints
, 2003
"... This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substa ..."
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Cited by 8955 (21 self)
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substantial range of affine distortion, addition of noise, change in 3D viewpoint, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also
Results 1  10
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