Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Jamshidi, Mohammad | |
| dc.contributor.author | Roshani, Saeed | |
| dc.contributor.author | Talla, Jakub | |
| dc.contributor.author | Sharifi-Atashgah, Maryam S. | |
| dc.contributor.author | Roshani, Sobhan | |
| dc.contributor.author | Peroutka, Zdeněk | |
| dc.date.accessioned | 2022-03-07T11:00:24Z | - |
| dc.date.available | 2022-03-07T11:00:24Z | - |
| dc.date.issued | 2021 | |
| dc.identifier.citation | JAMSHIDI, 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: 978-1-66540-690-1 | cs |
| dc.identifier.isbn | 978-1-66540-690-1 | |
| dc.identifier.uri | http://hdl.handle.net/11025/47093 | |
| dc.format | 4 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON) | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © IEEE | en |
| dc.title | Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | 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. | en |
| dc.subject.translated | artificial intelligence | en |
| dc.subject.translated | cloud computing | en |
| dc.subject.translated | power amplifiers | en |
| dc.subject.translated | machine learning | en |
| dc.subject.translated | deep learning | en |
| dc.subject.translated | Microsoft Azure | en |
| dc.subject.translated | power electronics | en |
| dc.identifier.doi | 10.1109/UEMCON53757.2021.9666639 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43934861 | |
| dc.project.ID | EF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligence | cs |
| Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference Papers (KEV) Konferenční příspěvky / Conference papers (RICE) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| Jamshidi_A_Modified_Branch.pdf | 983,67 kB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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