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http://dx.doi.org/10.1109/SAMP...
Conference object . 2015 . Peer-reviewed
Data sources: SNSF P3 Database
https://doi.org/10.1109/sampta...
Article . 2015 . Peer-reviewed
Data sources: Crossref
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Generalized poisson summation formula for tempered distributions

Authors: Ha Q. Nguyen; Michael Unser;

Generalized poisson summation formula for tempered distributions

Abstract

The Poisson summation formula (PSF), which relates the sampling of an analog signal with the periodization of its Fourier transform, plays a key role in the classical sampling theory. In its current forms, the formula is only applicable to a limited class of signals in L 1 . However, this assumption on the signals is too strict for many applications in signal processing that require sampling of non-decaying signals. In this paper we generalize the PSF for functions living in weighted Sobolev spaces that do not impose any decay on the functions. The only requirement is that the signal to be sampled and its weak derivatives up to order 1/2+ e for arbitrarily small e > 0, grow slower than a polynomial in the L 2 sense. The generalized PSF will be interpreted in the language of distributions.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average