Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43963862" target="_blank" >RIV/49777513:23220/21:43963862 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9666639" target="_blank" >https://ieeexplore.ieee.org/document/9666639</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/UEMCON53757.2021.9666639" target="_blank" >10.1109/UEMCON53757.2021.9666639</a>

Result language
angličtina
Original language name
Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers
Original language description
Designing power amplifiers based on the demanded power and frequency is one of the challenging processes of circuits design in electrical engineering. This is best understood when it comes to thermal noises and other unwanted agents. This is why the application of cloud-based methods can be beneficial to save time and money for designing such complex systems. In this paper, several machine learning (ML) approaches have been used to design a class E amplifier. In this regard, the proposed methods, which are implemented via Microsoft Azure, are used to model and predict the circuit element values of the class E amplifier. In order to reach a reliable design, some important unwanted factors such as nonlinear parasitic elements of the transistor are considered. The results demonstrated that not only can the proposed could-based techniques estimate such elements accurately, but also working with such tools are really easy.
Czech name
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Czech description
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Project
<a href="/en/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Electrical Engineering Technologies with High-Level of Embedded Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)
ISBN
978-1-66540-690-1
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
0041-0044
Publisher name
IEEE
Place of publication
Piscaway
Event location
virtual, New York, USA
Event date
Dec 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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