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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • 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