dc.contributor.author | Ζαφειράτος, Ευάγγελος![]() |
el |
dc.contributor.author | Zafeiratos, Evangelos![]() |
en |
dc.date.accessioned | 2015-08-31T12:50:29Z | |
dc.date.available | 2015-08-31T12:50:29Z | |
dc.date.issued | 2015-08-31 | |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/41137 | |
dc.identifier.uri | http://dx.doi.org/10.26240/heal.ntua.10322 | |
dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ | * |
dc.subject | Σενάρια συστήματος | el |
dc.subject | System Scenarios | en |
dc.subject | Δυναμική ανάθεση | el |
dc.subject | Νευρωνικά δίκτυα | el |
dc.subject | Εντοπισμός | el |
dc.subject | Σύνθεση | el |
dc.subject | Dynamic Scheduling | en |
dc.subject | Neural Networks | en |
dc.subject | Detection | en |
dc.subject | Fpga | en |
dc.subject | vhdl | en |
dc.title | Ανίχνευση δυναμικών σεναρίων συστήματος σε ασύρματες εφαρμογές με χρήση νευρωνικών δικτύων | el |
dc.title | Dynamic scenario detection in wireless apllications with the use of neural networks | en |
heal.type | bachelorThesis | |
heal.classification | Hardware | el |
heal.classificationURI | http://id.loc.gov/authorities/subjects/sh85058890 | |
heal.language | en | |
heal.access | free | |
heal.recordProvider | ntua | el |
heal.publicationDate | 2015-03-30 | |
heal.abstract | Artificial Neural Networks gain popularity in recent years, as modern processors evolve towards a parallel approach. Traditional, sequential, logic-based digital computing excels in many areas, but has been less successful for other types of problems. The development of artificial neural networks began approximately 60 years ago, motivated by a desire to try both to understand the brain and to emulate some of its strengths and is constantly gaining attention as modern Hardware platforms evolve and offer new promising capabilities for Neural Networks development. System Scenarios is also a developing field in science of Hardware which aims to convert the increasingly dynamic nature of embedded systems into an optimization opportunity instead of a potential problem. The use of system scenarios scheduling in modern devices allows us to exploit resources of the system in a sophisticated manner, since every different form of execution differs in terms of hardware requirements. Acknowledging the scenario to be executed, it is possible to modificate resources allocation and achieve greater performance. The goal of this diploma thesis is to provide a sufficient hardware/software co-design implementation which enables neural networks as the basic unit of a structure that detects Scenarios in real applications. The choice of neural networks was made because of their inherited parallelism and their ability to develop dynamic behavior. The implementation with Neural Networks is presented side by side with a straight – forward implementation in order to feature the advantages of each and highlight the differences. | el |
heal.advisorName | Σούντρης, Δημήτριος | el |
heal.committeeMemberName | Σούντρης, Δημήτριος | el |
heal.committeeMemberName | Πεκμεστζή, Κιαμάλ | el |
heal.committeeMemberName | Οικονομάκος, Γεώργιος | el |
heal.academicPublisher | Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών | el |
heal.academicPublisherID | ntua | |
heal.numberOfPages | 96 σ. | |
heal.fullTextAvailability | true |
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