Recursive cellular nonlinear neural networks for ultra-low noise digital arithmetic

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dc.contributor.advisorJullien, Graham
dc.contributor.advisorHaslett, James W.
dc.contributor.authorYebjoah, Jonathan Johnson
dc.date.accessioned2005-08-16T17:34:57Z
dc.date.available2005-08-16T17:34:57Z
dc.date.issued2004
dc.descriptionBibliography: p. 63-66en
dc.descriptionSome pages are in colour.en
dc.format.extentxiv, 113 leaves : ill. ; 30 cm.en
dc.identifier.citationYebjoah, J. J. (2004). Recursive cellular nonlinear neural networks for ultra-low noise digital arithmetic (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/14808en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/14808
dc.identifier.isbn0494037369en
dc.identifier.lccAC1 .T484 2004 Y43en
dc.identifier.urihttp://hdl.handle.net/1880/42154
dc.language.isoeng
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleRecursive cellular nonlinear neural networks for ultra-low noise digital arithmetic
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
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