NOTE: This item is not available outside the Texas A&M University network. Texas A&M affiliated users who are off campus can access the item through NetID and password authentication or by using TAMU VPN. Non-affiliated individuals should request a copy through their local library's interlibrary loan service.
An analog time-multiplexing cellular neural networks computer
dc.creator | Fong, Apollo Quan | |
dc.date.accessioned | 2012-06-07T15:39:03Z | |
dc.date.available | 2012-06-07T15:39:03Z | |
dc.date.created | 1995 | |
dc.date.issued | 1995 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-1995-THESIS-F66 | |
dc.description | Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item. | en |
dc.description | Includes bibliographical references. | en |
dc.description | Issued also on microfiche from Lange Micrographics. | en |
dc.description.abstract | The state of the art work in Cellular Neural Networks (CNN) has concentrated on VLSI implementations without really addressing the "systems level". While efficient implementations have been reported, no reports have been presented on the use of these implementations for processing large complex images. The work hereby presented introduces a strategy to process large images using small CNN arrays. The approach, time-multiplexing, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware processors and all the pixels in the image involved. This thesis presents a practical solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. The algorithm for implementing this approach is also presented, along with image processing results obtained from an actual laboratory discrete hardware prototype. | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | Texas A&M University | |
dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
dc.subject | electrical engineering. | en |
dc.subject | Major electrical engineering. | en |
dc.title | An analog time-multiplexing cellular neural networks computer | en |
dc.type | Thesis | en |
thesis.degree.discipline | electrical engineering | en |
thesis.degree.name | M.S. | en |
thesis.degree.level | Masters | en |
dc.type.genre | thesis | en |
dc.type.material | text | en |
dc.format.digitalOrigin | reformatted digital | en |
Files in this item
This item appears in the following Collection(s)
-
Digitized Theses and Dissertations (1922–2004)
Texas A&M University Theses and Dissertations (1922–2004)
Request Open Access
This item and its contents are restricted. If this is your thesis or dissertation, you can make it open-access. This will allow all visitors to view the contents of the thesis.