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https://hdl.handle.net/2445/180853
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Macià Bros, Ferran | - |
dc.contributor.author | Mas Giménez, Gerard de | - |
dc.date.accessioned | 2021-10-26T14:04:43Z | - |
dc.date.available | 2021-10-26T14:04:43Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.uri | https://hdl.handle.net/2445/180853 | - |
dc.description | Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2021, Tutor: Ferran Macià Bros | ca |
dc.description.abstract | Spin Torque Nano Oscillators are nanometric devices that turn polarized electrical current into magnetic oscillations via the spin transfer torque effect. These nanometric oscillators present non-linear behavior between amplitude and frequency resulting in synchronization with external perturbations or other oscillators. These properties, as well as the dependency of their characteristic oscillation with the current applied makes them very suitable candidates to be the physical hard-ware for artificial neural networks | ca |
dc.format.extent | 5 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) Mas, 2021 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Treballs Finals de Grau (TFG) - Física | - |
dc.subject.classification | Formació de patrons (Física) | cat |
dc.subject.classification | Oscil·ladors | cat |
dc.subject.classification | Treballs de fi de grau | cat |
dc.subject.other | Pattern formation (Physical sciences) | eng |
dc.subject.other | Oscillators | eng |
dc.subject.other | Bachelor's theses | eng |
dc.title | Pattern recognition with nanoscale oscillators | eng |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Treballs Finals de Grau (TFG) - Física |
Files in This Item:
File | Description | Size | Format | |
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DE MAS GIMENEZ GERARD_4253619_assignsubmission_file_TFG-DeMas-Giménez-Gerard.pdf | 676.37 kB | Adobe PDF | View/Open |
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