Classification of Microwave Planar Filters by Deep Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F23%3A00558013" target="_blank" >RIV/60162694:G43__/23:00558013 - isvavai.cz</a>
Result on the web
<a href="https://www.radioeng.cz/papers/2022-1.htm" target="_blank" >https://www.radioeng.cz/papers/2022-1.htm</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/re.2022.0069" target="_blank" >10.13164/re.2022.0069</a>
Alternative languages
Result language
angličtina
Original language name
Classification of Microwave Planar Filters by Deep Learning
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
RADIOENGINEERING
ISSN
1210-2512
e-ISSN
1805-9600
Volume of the periodical
31
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
69-76
UT code for WoS article
000790989000009
EID of the result in the Scopus database
2-s2.0-85129945407