Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems

Bibliographic Details
Title: Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems
Authors: Dongbao Jia, Cunhua Li, Qun Liu, Qin Yu, Xiangsheng Meng, Zhaoman Zhong, Xinxin Ban, Nizhuan Wang
Source: Complexity, Vol 2021 (2021)
Publisher Information: Hindawi-Wiley, 2021.
Publication Year: 2021
Collection: LCC:Electronic computers. Computer science
Subject Terms: Electronic computers. Computer science, QA75.5-76.95
More Details: Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1076-2787
1099-0526
Relation: https://doaj.org/toc/1076-2787; https://doaj.org/toc/1099-0526
DOI: 10.1155/2021/6618833
Access URL: https://doaj.org/article/b8b03903859c446583f30abcf38e3422
Accession Number: edsdoj.b8b03903859c446583f30abcf38e3422
Database: Directory of Open Access Journals
More Details
ISSN:10762787
10990526
DOI:10.1155/2021/6618833
Published in:Complexity
Language:English