Design and Implementation of a Cellular Neural Network Based Oscillator Circuit

dc.contributor.authorTander, Baran
dc.contributor.authorÖzmen, Atilla
dc.contributor.authorÖzçelep, Yasin
dc.date.accessioned2021-02-19T17:24:07Z
dc.date.available2021-02-19T17:24:07Z
dc.date.issued2009
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a novel inductorless oscillator circuit with negative feedbacks, based on a simple version of a "Cellular Neural Network" (CNN) called "CNN with an Opposite Sign Template" (CNN-OST) is designed and implemented. The system is capable of generating quasi-sine oscillations with tuneable amplitude and frequency which can't be provided at the same time in the conventional oscillator circuits.en_US
dc.identifier.citation1
dc.identifier.endpage39en_US
dc.identifier.isbn978-960-474-139-7
dc.identifier.scopusqualityN/A
dc.identifier.startpage34en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3958
dc.identifier.wosWOS:000276789200004en_US
dc.identifier.wosqualityN/A
dc.institutionauthorTander, Baranen_US
dc.institutionauthorÖzmen, Atillaen_US
dc.institutionauthorÖzçelep, Yasinen_US
dc.language.isoenen_US
dc.publisherWorld Scientific and Engineering Acad and Socen_US
dc.relation.journalProceedings of the 8th Wseas International Conference on Circuits, Systems, Electronics, Control & Signal Processing (CSECS'09)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOscillator Circuitsen_US
dc.subjectCellular Neural Networksen_US
dc.subjectOperational Amplifiersen_US
dc.subjectCurve and Surface Fittingen_US
dc.titleDesign and Implementation of a Cellular Neural Network Based Oscillator Circuiten_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublication75e36d40-1e6e-401f-b656-5894d3bd22e9
relation.isAuthorOfPublicationcf8f9e05-3f89-4ab6-af78-d0937210fb77
relation.isAuthorOfPublication.latestForDiscovery75e36d40-1e6e-401f-b656-5894d3bd22e9

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