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VESELÝ, J. OLIVOVÁ, J. GÖTTHANS, J. GÖTTHANS, T. RAIDA, Z.
Original Title
Classification of Microwave Planar Filters by Deep Learning
Type
journal article in Web of Science
Language
English
Original Abstract
Over the last few decades, deep learning has been considered to be powerful tool in the classification tasks, and has become popular in many applications due to its capabil-ity of processing huge amount of data. This paper presents approaches for image recognition. We have applied convolu-tional neural networks on microwave planar filters. The first task was filter topology classification, the second task was filter order estimation. For the task a dataset was generated. As presented in the results, the created and trained neural networks are very capable of solving the selected tasks.
Keywords
Convolutional neural network; deep learning; band pass filter; low pass shunt filter; low pass stepped filter; order of filter
Authors
VESELÝ, J.; OLIVOVÁ, J.; GÖTTHANS, J.; GÖTTHANS, T.; RAIDA, Z.
Released
1. 4. 2022
Publisher
Czech Technical University in Prague
Location
PRAHA
ISBN
1221-2512
Periodical
Radioengineering
Year of study
31
Number
1
State
Czech Republic
Pages from
69
Pages to
76
Pages count
8
URL
https://www.radioeng.cz/fulltexts/2022/22_01_0069_0076.pdf
BibTex
@article{BUT178117, author="Jiří {Veselý} and Jana {Olivová} and Jakub {Götthans} and Tomáš {Götthans} and Zbyněk {Raida}", title="Classification of Microwave Planar Filters by Deep Learning", journal="Radioengineering", year="2022", volume="31", number="1", pages="69--76", doi="10.13164/re.2022.0069", issn="1221-2512", url="https://www.radioeng.cz/fulltexts/2022/22_01_0069_0076.pdf" }