Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6396
Title: Combined neural network model to compute wavelet coefficients
Authors: Güler, İnan
Übeyli, Derya Elif
Keywords: combined neural network model
wavelet coefficients
discrete wavelet transform
Doppler signals
Issue Date: 2006
Publisher: Blackwell Publishing
Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.
URI: https://doi.org/10.1111/j.1468-0394.2006.00331.x
https://hdl.handle.net/20.500.11851/6396
ISSN: 0266-4720
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

2
checked on Sep 24, 2022

Page view(s)

2
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.