Cellular neural network virtual machine for graphics hardware with applications in image processing

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Cellular neural network virtual machine for graphics hardware with applications in image processing

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/6545

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dc.contributor.advisor DeSouza, Guilherme en_US
dc.contributor.author Dolan, Ryanne en_US
dc.date.accessioned 2010-03-12T21:06:42Z
dc.date.available 2010-03-12T21:06:42Z
dc.date.issued 2009 en_US
dc.date.submitted 2009 Spring en_US
dc.identifier.other DolanR-052009-T276 en_US
dc.identifier.uri http://hdl.handle.net/10355/6545
dc.description The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. en_US
dc.description Title from PDF of title page (University of Missouri--Columbia, viewed on November 13, 2009). en_US
dc.description Thesis advisor: Dr. Guilherme DeSouza. en_US
dc.description Includes bibliographical references. en_US
dc.description M.S. University of Missouri--Columbia 2009. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering. en_US
dc.description.abstract The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular computation using cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains universal and reasonably efficient. An image processing library with visualization software has been developed using the abstraction to showcase the flexibility and power of cellular computation on GPUs. A simple virtual machine and language is presented to manipulate images using the library for single-core, multi-core, and GPU backends. en_US
dc.format.extent viii, 63 pages en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2009 Freely available theses (MU) en_US
dc.subject GPU. en_US
dc.subject GPU en_US
dc.subject.lcsh Neural networks (Computer science) en_US
dc.subject.lcsh Image processing -- Digital techniques en_US
dc.subject.lcsh Virtual computer systems en_US
dc.title Cellular neural network virtual machine for graphics hardware with applications in image processing en_US
dc.type Thesis en_US
thesis.degree.discipline Electrical engineering en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name M.S. en_US
thesis.degree.level Masters en_US
dc.identifier.merlin .b72789773 en_US
dc.identifier.oclc 465468062 en_US
dc.relation.ispartofcommunity University of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2009 Theses


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