Show simple item record

dc.contributor.authorGoras, Liviu
dc.date.accessioned2016-01-21T11:37:05Z
dc.date.available2016-01-21T11:37:05Z
dc.date.issued2009
dc.identifier.urihttp://10.11.10.50/xmlui/handle/123456789/3920
dc.descriptionThe Annals of "Dunarea de Jos" University of Galatien_US
dc.description.abstractAnalog Parallel Architectures like Cellular Neural Networks (CNN’s) have been thoroughly studied not only for their potential in high-speed image processing applications but also for their rich and exciting spatio-temporal dynamics. An interesting behavior such architectures can exhibit is spatio-temporal filtering and pattern formation, aspects that will be discussed in this work for a general structure consisting of linear cells locally and homogeneously connected within a specified neighborhood. The results are generalizations of those regarding Turing pattern formation in CNN’s. Using linear cells (or piecewise linear cells working in the central linear part of their characteristic) allows the use of the decoupling technique – a powerful technique that gives significant insight into the dynamics of the CNN. The roles of the cell structure as well as that of the connection template are discussed and models for the spatial modes dynamics are made as well.en_US
dc.language.isoenen_US
dc.publisher"Dunarea de Jos" University of Galaţien_US
dc.subjectanalog parallel architecturesen_US
dc.subjectCellular Neural Networksen_US
dc.subjectspatial filtersen_US
dc.titleSpatio-Temporal Dynamics in Celular Neural Networksen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record