Time-frequency analysis of electrostatic discharge signal based on wavelet transform

Cong Cheng, Fangming Ruan*, Di Deng, Jia Li, Ming Su, David Pommerenke

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Electrostatic discharge signal is a non-stationary signal whose frequency is varied with time. Time-frequency analysis is able to reveal the more useful information hidden in the ESD signal. In this letter, we propose a time-frequency analysis approach using the wavelet transform. Based on the Morlet wavelet, this paper analyzes the actual ESD signal and obtains its time-frequency characteristic. 2-D and 3-D ESD data are showed in this paper. The result shown that high frequency component of ESD can reach 0.6GHz. In addition, the energy of the measured signal is mainly concentrated in the range of 100 to 200 MHz. The high-frequency component attenuations rapidly and the low-frequency duration is relatively long. It can provide some new idea for extraction or signal denoising.

Originalspracheenglisch
TitelProceedings of 2018 12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018
Herausgeber (Verlag)IEEE Computer Society
Seiten35-38
Seitenumfang4
ISBN (elektronisch)9781538660638
DOIs
PublikationsstatusVeröffentlicht - 2 Jul 2018
Extern publiziertJa
Veranstaltung12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018 - Xiamen, China
Dauer: 9 Nov 201811 Nov 2018

Publikationsreihe

NameProceedings of the International Conference on Anti-Counterfeiting, Security and Identification, ASID
Band2018-November
ISSN (Print)2163-5048
ISSN (elektronisch)2163-5056

Konferenz

Konferenz12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018
LandChina
OrtXiamen
Zeitraum9/11/1811/11/18

ASJC Scopus subject areas

  • !!Computer Graphics and Computer-Aided Design
  • !!Computer Science Applications
  • !!Computer Vision and Pattern Recognition
  • Software

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