Abstract
The application of three-component stacked artificial neural networks (ANN) for discrimination of dielectric cylinders of different diameters by their radio images is considered. Neural networks include two sparse autoencoders and the softmax unit. Neural networks are not tied to the frequency range, unlike many well-known methods based on the resonant properties of objects, and they are a powerful tool for object recognition. Radio images were obtained using the method of auxiliary sources (MAS) for cylinders with the radius of 15 to 35 mm. The possibility of successful recognition was confirmed for the case of the diameter deviation of 1 mm and the presence of additive Gaussian noise with SNR of up to 5 dB.
Original language | English |
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Title of host publication | 2023 IEEE 28th International Seminar/Workshop - Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED 2023 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 133-137 |
Number of pages | 5 |
ISBN (Electronic) | 9798350315332 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Event | 28th IEEE International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED 2023 - Tbilisi, Georgia Duration: 11 Sept 2023 → 13 Sept 2023 |
Publication series
Name | Proceedings of International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED |
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Volume | 2023-September |
ISSN (Print) | 2165-3585 |
ISSN (Electronic) | 2165-3593 |
Conference
Conference | 28th IEEE International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED 2023 |
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Country/Territory | Georgia |
City | Tbilisi |
Period | 11/09/23 → 13/09/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- artificial neural networks
- autoencoder
- dielectric cylinders
- discrimination of targets
- method of auxiliary sources (MAS)
- radio images
- softmax