Abstract
Electromagnetic emissions were measured from several radio receivers to demonstrate the possibility of detecting and identifying these devices based on their unintended emissions. Radiated fields from the different radio receivers have unique characteristics that can be used to identify these devices by analyzing time-frequency plots of measured radiation. A neural network was also developed for automated device detection.
Recommended Citation
H. Weng et al., "Neural Network Detection and Identification of Electronic Devices based on their Unintended Emissions," Proceedings of the 2005 International Symposium on Electromagnetic Compatibility (2005, Chicago, IL), vol. 1, pp. 245 - 249, Institute of Electrical and Electronics Engineers (IEEE), Aug 2005.
The definitive version is available at https://doi.org/10.1109/ISEMC.2005.1513508
Meeting Name
2005 International Symposium on Electromagnetic Compatibility, EMC 2005 (2005: Aug. 8-12, Chicago, IL)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Automated Device Detection; Electromagnetic Emissions; Electromagnetic Waves; Electronic Devices; Neural Nets; Neural Network Detection; Radio Receivers; Time-Frequency Plots; Unintended Emissions; Cross-correlation; Detection; Neural network
International Standard Book Number (ISBN)
0-7803-9380-5
International Standard Serial Number (ISSN)
2158-110X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Aug 2005
