Ανίχνευση δυναμικών σεναρίων συστήματος σε ασύρματες εφαρμογές με χρήση νευρωνικών δικτύων

Ζαφειράτος, Ευάγγελος; Zafeiratos, Evangelos

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