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Cited by 295

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Fast Convolution using fermat number transforms with applications to digital filtering

Agarwal, R., Burrus, C.·1974·IEEE Transactions on Acoustics, Speech, and Signal Processing

Abstract

The structure of transforms having the convolution property is developed. A particular transform is proposed that is defined on a finite ring of integers with arithmetic carried out modulo Fermat numbers. This Fermat number transform (FNT) is ideally suited to digital computation, requiring on the order ofN \log Nadditions, subtractions and bit shifts, but no multiplications. In addition to being efficient, the Fermat number transform implementation of convolution is exact, i.e., there is no roundoff error. There is a restriction on sequence length imposed by word length but multi-dimensional techniques are discussed which overcome this limitation. Results of an implementation on the IBM 370/155 are presented and compared with the fast Fourier transform (FFT) showing a substantial improvement in efficiency and accuracy.

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Crossref · 10.1109/tassp.1974.1162555

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Metadata

Authors
Agarwal, R., & Burrus, C.
Venue
IEEE Transactions on Acoustics, Speech, and Signal Processing
Published
1974-04
Volume
22
Issue
2
Pages
87-97
ISSN
0096-3518 (ISSN-L)
OpenAlex
W2113994308
Cited by
295
References
9
FWCI
15.76
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fast Convolution using fermat number transforms with applications to digital filtering. https://app.cochat.ai/papers/10.1109/tassp.1974.1162555
The paper
Agarwal, R., & Burrus, C. (1974). Fast Convolution using fermat number transforms with applications to digital filtering. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/tassp.1974.1162555

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.