Wavelet Transforms and Their Applications:
Overview Historically, the concept of "ondelettes" or "wavelets" originated from the study of time-frequency signal analysis, wave propagation, and sampling theory. One of the main reasons for the discovery of wavelets and wavelet transforms is that the Fourier transform analysis...
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Format: | Elektronisch E-Book |
Sprache: | English |
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Boston, MA
Birkhäuser Boston
2002
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Online-Zugang: | FHI01 BTU01 URL des Erstveröffentlichers |
Zusammenfassung: | Overview Historically, the concept of "ondelettes" or "wavelets" originated from the study of time-frequency signal analysis, wave propagation, and sampling theory. One of the main reasons for the discovery of wavelets and wavelet transforms is that the Fourier transform analysis does not contain the local information of signals. So the Fourier transform cannot be used for analyzing signals in a joint time and frequency domain. In 1982, Jean MorIet, in collaboration with a group of French engineers, first introduced the idea of wavelets as a family of functions constructed by using translation and dilation of a single function, called the mother wavelet, for the analysis of nonstationary signals. However, this new concept can be viewed as the synthesis of various ideas originating from different disciplines including mathematics (Calder6n-Zygmund operators and Littlewood-Paley theory), physics (coherent states in quantum mechanics and the renormalization group), and engineering (quadratic mirror filters, sideband coding in signal processing, and pyramidal algorithms in image processing). Wavelet analysis is an exciting new method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, image processing, pattern recognition, computer graphics, the detection of aircraft and submarines, and improvement in CAT scans and other medical image technology. Wavelets allow complex information such as music, speech, images, and patterns to be decomposed into elementary forms, called the fundamental building blocks, at different positions and scales and subsequently reconstructed with high precision |
Beschreibung: | 1 Online-Ressource (XV, 565 p) |
ISBN: | 9781461200970 |
DOI: | 10.1007/978-1-4612-0097-0 |
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520 | |a Overview Historically, the concept of "ondelettes" or "wavelets" originated from the study of time-frequency signal analysis, wave propagation, and sampling theory. One of the main reasons for the discovery of wavelets and wavelet transforms is that the Fourier transform analysis does not contain the local information of signals. So the Fourier transform cannot be used for analyzing signals in a joint time and frequency domain. In 1982, Jean MorIet, in collaboration with a group of French engineers, first introduced the idea of wavelets as a family of functions constructed by using translation and dilation of a single function, called the mother wavelet, for the analysis of nonstationary signals. However, this new concept can be viewed as the synthesis of various ideas originating from different disciplines including mathematics (Calder6n-Zygmund operators and Littlewood-Paley theory), physics (coherent states in quantum mechanics and the renormalization group), and engineering (quadratic mirror filters, sideband coding in signal processing, and pyramidal algorithms in image processing). Wavelet analysis is an exciting new method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, image processing, pattern recognition, computer graphics, the detection of aircraft and submarines, and improvement in CAT scans and other medical image technology. Wavelets allow complex information such as music, speech, images, and patterns to be decomposed into elementary forms, called the fundamental building blocks, at different positions and scales and subsequently reconstructed with high precision | ||
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language | English |
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spelling | Debnath, Lokenath Verfasser aut Wavelet Transforms and Their Applications by Lokenath Debnath Boston, MA Birkhäuser Boston 2002 1 Online-Ressource (XV, 565 p) txt rdacontent c rdamedia cr rdacarrier Overview Historically, the concept of "ondelettes" or "wavelets" originated from the study of time-frequency signal analysis, wave propagation, and sampling theory. One of the main reasons for the discovery of wavelets and wavelet transforms is that the Fourier transform analysis does not contain the local information of signals. So the Fourier transform cannot be used for analyzing signals in a joint time and frequency domain. In 1982, Jean MorIet, in collaboration with a group of French engineers, first introduced the idea of wavelets as a family of functions constructed by using translation and dilation of a single function, called the mother wavelet, for the analysis of nonstationary signals. However, this new concept can be viewed as the synthesis of various ideas originating from different disciplines including mathematics (Calder6n-Zygmund operators and Littlewood-Paley theory), physics (coherent states in quantum mechanics and the renormalization group), and engineering (quadratic mirror filters, sideband coding in signal processing, and pyramidal algorithms in image processing). Wavelet analysis is an exciting new method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, image processing, pattern recognition, computer graphics, the detection of aircraft and submarines, and improvement in CAT scans and other medical image technology. Wavelets allow complex information such as music, speech, images, and patterns to be decomposed into elementary forms, called the fundamental building blocks, at different positions and scales and subsequently reconstructed with high precision Engineering Electrical Engineering Signal, Image and Speech Processing Functional Analysis Applications of Mathematics Functional analysis Applied mathematics Engineering mathematics Electrical engineering Wavelet (DE-588)4215427-3 gnd rswk-swf Wavelet (DE-588)4215427-3 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781461266105 https://doi.org/10.1007/978-1-4612-0097-0 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Debnath, Lokenath Wavelet Transforms and Their Applications Engineering Electrical Engineering Signal, Image and Speech Processing Functional Analysis Applications of Mathematics Functional analysis Applied mathematics Engineering mathematics Electrical engineering Wavelet (DE-588)4215427-3 gnd |
subject_GND | (DE-588)4215427-3 |
title | Wavelet Transforms and Their Applications |
title_auth | Wavelet Transforms and Their Applications |
title_exact_search | Wavelet Transforms and Their Applications |
title_full | Wavelet Transforms and Their Applications by Lokenath Debnath |
title_fullStr | Wavelet Transforms and Their Applications by Lokenath Debnath |
title_full_unstemmed | Wavelet Transforms and Their Applications by Lokenath Debnath |
title_short | Wavelet Transforms and Their Applications |
title_sort | wavelet transforms and their applications |
topic | Engineering Electrical Engineering Signal, Image and Speech Processing Functional Analysis Applications of Mathematics Functional analysis Applied mathematics Engineering mathematics Electrical engineering Wavelet (DE-588)4215427-3 gnd |
topic_facet | Engineering Electrical Engineering Signal, Image and Speech Processing Functional Analysis Applications of Mathematics Functional analysis Applied mathematics Engineering mathematics Electrical engineering Wavelet |
url | https://doi.org/10.1007/978-1-4612-0097-0 |
work_keys_str_mv | AT debnathlokenath wavelettransformsandtheirapplications |