A Generalized Sampling Theorem for Frequency Localized Signals

A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies to various prefilters effecting a “soft” bandlimitation, (2) an approximate reconstruction from sample values rather than a perfect one is obt...

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Published inSampling theory in signal and image processing Vol. 8; no. 2; pp. 127 - 146
Main Author Hammerich, Edwin
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.05.2009
Sampling Publishing
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Online AccessGet full text
ISSN1530-6429
DOI10.1007/BF03549512

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Abstract A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies to various prefilters effecting a “soft” bandlimitation, (2) an approximate reconstruction from sample values rather than a perfect one is obtained (though the former might be “practically perfect” in many cases). For an arbitrary finite-energy signal the frequency localization is performed by a prefilter realizing a crosscorrelation with a function of prescribed properties. The range of the prefilter, the so-called localization space, is described in some detail. Regular sampling is applied and a reconstruction formula is given. For the reconstruction error a general error estimate is derived and connections between a critical sampling interval and notions of “soft bandwidth” for the prefilter are indicated. Examples based on the sinc-function, Gaussian functions, and B-splines are discussed.
AbstractList A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies to various prefilters effecting a "soft" bandlimitation, (2) an approximate reconstruction from sample values rather than a perfect one is obtained (though the former might be "practically perfect" in many cases). For an arbitrary finite-energy signal the frequency localization is performed by a prefilter realizing a crosscorrelation with a function of prescribed properties. The range of the prefilter, the so-called localization space, is described in some detail. Regular sampling is applied and a reconstruction formula is given. For the reconstruction error a general error estimate is derived and connections between a critical sampling interval and notions of "soft bandwidth" for the prefilter are indicated. Examples based on the sine-function, Gaussian functions, and B-splines are discussed. Key words and phrases : Frequency localization, reproducing kernel Hilbert space, interpolating function, error estimate, generalized Chebyshev inequality, critical sampling interval, generalized sampling theorem. 2000 AMS Mathematics Subject Classification--94A20,41A15
A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies to various prefilters effecting a “soft” bandlimitation, (2) an approximate reconstruction from sample values rather than a perfect one is obtained (though the former might be “practically perfect” in many cases). For an arbitrary finite-energy signal the frequency localization is performed by a prefilter realizing a crosscorrelation with a function of prescribed properties. The range of the prefilter, the so-called localization space, is described in some detail. Regular sampling is applied and a reconstruction formula is given. For the reconstruction error a general error estimate is derived and connections between a critical sampling interval and notions of “soft bandwidth” for the prefilter are indicated. Examples based on the sinc-function, Gaussian functions, and B-splines are discussed.
A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies to various prefilters effecting a "soft" bandlimitation, (2) an approximate reconstruction from sample values rather than a perfect one is obtained (though the former might be "practically perfect" in many cases). For an arbitrary finite-energy signal the frequency localization is performed by a prefilter realizing a crosscorrelation with a function of prescribed properties. The range of the prefilter, the so-called localization space, is described in some detail. Regular sampling is applied and a reconstruction formula is given. For the reconstruction error a general error estimate is derived and connections between a critical sampling interval and notions of "soft bandwidth" for the prefilter are indicated. Examples based on the sine-function, Gaussian functions, and B-splines are discussed.
Audience Academic
Author Hammerich, Edwin
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crossref_primary_10_1016_j_acha_2010_09_007
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Cites_doi 10.1002/sapm195938168
10.1142/S0219691305000981
10.1109/JRPROC.1949.232969
10.1007/s00041-003-0004-2
10.1007/978-0-8176-8224-8
10.1109/78.330352
10.1090/qam/15914
10.1016/0165-1684(92)90030-Z
10.1007/BF03549426
10.1109/18.57199
10.1109/79.799930
10.1080/01630569408816545
10.1109/5.843002
10.1109/18.119745
10.1109/PROC.1977.10771
10.1137/1034002
10.1007/978-1-4612-0133-5
10.1007/BF03549405
10.1109/81.948437
10.1007/978-3-662-09922-3
10.1007/BF02570568
10.1007/BF03549464
10.1007/978-1-4613-9757-1_7
10.1109/TSP.2006.873488
10.1006/acha.1998.0249
10.1007/978-1-4612-0003-1
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Issue 2
Keywords error estimate
generalized Chebyshev inequality
interpolating function
critical sampling interval
Frequency localization
reproducing kernel Hilbert space
generalized sampling theorem
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Snippet A generalized sampling theorem for frequency localized signals is presented. The generalization in the proposed model of sampling is two-fold: (1) It applies...
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StartPage 127
SubjectTerms Abstract Harmonic Analysis
Chebyshev approximation
Estimation theory
Hilbert space
Image and Speech Processing
Interpolation
Machine Learning
Mathematics
Methods
Properties
Sampling (Acoustical engineering)
Signal
Signal processing
Title A Generalized Sampling Theorem for Frequency Localized Signals
URI https://link.springer.com/article/10.1007/BF03549512
Volume 8
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