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 in | Sampling theory in signal and image processing Vol. 8; no. 2; pp. 127 - 146 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.05.2009
Sampling Publishing |
Subjects | |
Online Access | Get full text |
ISSN | 1530-6429 |
DOI | 10.1007/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. |
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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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CitedBy_id | crossref_primary_10_1016_j_acha_2017_07_003 crossref_primary_10_1016_j_acha_2010_09_007 crossref_primary_10_1007_BF03549522 |
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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Keywords | error estimate generalized Chebyshev inequality interpolating function critical sampling interval Frequency localization reproducing kernel Hilbert space generalized sampling theorem 41A15 94A20 |
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Mag. doi: 10.1109/79.799930 – volume: 341 start-page: 787 year: 1994 ident: BF03549512_CR4 publication-title: Trans. Amer. Math. Soc. – start-page: 219 volume-title: Advanced topics in Shannon sampling and interpolation theory year: 1993 ident: BF03549512_CR19 doi: 10.1007/978-1-4613-9757-1_7 – volume: 9 start-page: 77 year: 2003 ident: BF03549512_CR9 publication-title: J. Fourier Anal. Appl. doi: 10.1007/s00041-003-0004-2 – volume: 34 start-page: 40 year: 1992 ident: BF03549512_CR6 publication-title: SIAM Review doi: 10.1137/1034002 |
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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 |
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