<title>Artificial neural networks and Abelian harmonic analysis</title>
Author(s) -
Domingo Rodrı́guez,
Jairo Pertuz-Campo
Publication year - 1991
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.49801
Subject(s) - fast fourier transform , artificial neural network , computer science , digital signal processing , signal processing , discrete fourier transform (general) , reduction (mathematics) , fourier transform , harmonic , harmonic analysis , multidimensional signal processing , computer engineering , algorithm , theoretical computer science , artificial intelligence , electronic engineering , computational science , fourier analysis , engineering , computer hardware , fractional fourier transform , mathematics , acoustics , mathematical analysis , physics , geometry
This work deals with the use of artificial neural networks (ANN) for the digital processing of finite discrete time signals. The effort concentrates on the efficient replacement of fast Fourier transform (FFT) algorithms with ANN algorithms in certain engineering and scientific applications. The FFT algorithms are efficient methods of computing the discrete Fourier transform (DFT). The ubiquitous DFT is utilized in almost every digital signal processing application where harmonic analysis information is needed. Applications abound in areas such as audio acoustics, geophysics, biomedicine, telecommunications, astrophysics, etc. To identify more efficient methods to obtain a desired spectral information will result in a reduction in the computational effort required to implement these applications.
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