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Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.


Papers
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Journal ArticleDOI
TL;DR: The multiresolution decomposition provides a naturalest estimate of the random error in estimating a mean turbulent flux as mentioned in this paper, which is a simple and economic decomposition associated with cospectra that formally satisfy Reynolds averaging rules foreach averaging length.
Abstract: Geophysical variables are orthogonally decomposed by averaging timeseries using different averaging lengths, referred to as a (Haar)multiresolution decomposition. This simple and economic decomposition isassociated with cospectra that formally satisfy Reynolds averaging rules foreach averaging length. The multiresolution decomposition provides a naturalestimate of the random error in estimating a mean turbulent flux. The Fourierand multiresolution decompositions are compared using aircraft data fromBOREAS.

164 citations

Journal ArticleDOI
TL;DR: Both classification and location algorithms can be used as effective tools for real-time digital relaying purpose and are immune from effects of faults inception angle, impedance and distance.

162 citations

Journal ArticleDOI
TL;DR: The sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.
Abstract: A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.

159 citations

Journal ArticleDOI
TL;DR: In this article, a general solution of the orthogonality conditions up to J = 5 is presented, and a classification of the solutions according to the degree of symmetry of the associated scaling function is proposed.

159 citations

Journal ArticleDOI
TL;DR: In this article, the authors studied nonorthogonal but in a certain sense stable and even local decompositions of nested spaces and developed tools which are not necessarily confined to the translation invariant setting.

157 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202320
202252
202159
202070
201969
201879