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

Show simple item record
dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorRoshani, Saeed
dc.contributor.authorTalla, Jakub
dc.contributor.authorSharifi-Atashgah, Maryam S.
dc.contributor.authorRoshani, Sobhan
dc.contributor.authorPeroutka, Zdeněk
dc.date.accessioned2022-03-07T11:00:24Z
dc.date.available2022-03-07T11:00:24Z
dc.date.issued2021
dc.description.abstract-translatedDesigning 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.en
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationJAMSHIDI, M. ROSHANI, S. TALLA, J. SHARIFI-ATASHGAH, MS. ROSHANI, S. PEROUTKA, Z. Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers. In Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON). Piscaway: IEEE, 2021. s. 0041-0044. ISBN: 978-1-66540-690-1cs
dc.identifier.doi10.1109/UEMCON53757.2021.9666639
dc.identifier.isbn978-1-66540-690-1
dc.identifier.obd43934861
dc.identifier.urihttp://hdl.handle.net/11025/47093
dc.language.isoenen
dc.project.IDEF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligencecs
dc.publisherIEEEen
dc.relation.ispartofseriesProceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.rights.accessrestrictedAccessen
dc.subject.translatedartificial intelligenceen
dc.subject.translatedcloud computingen
dc.subject.translatedpower amplifiersen
dc.subject.translatedmachine learningen
dc.subject.translateddeep learningen
dc.subject.translatedMicrosoft Azureen
dc.subject.translatedpower electronicsen
dc.titleCloud-based machine learning techniques implemented by microsoft azure for designing power amplifiersen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

Files

Original bundle
Showing 1 - 1 out of 1 results
No Thumbnail Available
Name:
Jamshidi_A_Modified_Branch.pdf
Size:
983.67 KB
Format:
Adobe Portable Document Format
Download

Collections

OBD
 Conference Papers (KEV)
 Conference papers (RICE)