The UA Campus Repository is experiencing systematic automated, high-volume traffic (bots). Temporary mitigation measures to address bot traffic have been put in place; however, this has resulted in restrictions on searching WITHIN collections or using sidebar filters WITHIN collections. You can still Browse by Title/Author/Year WITHIN collections. Also, you can still search at the top level of the repository (use the search box at the top of every page) and apply filters from that search level. Export of search results has also been restricted at this time. Please contact us at any time for assistance - email repository@u.library.arizona.edu.

Show simple item record

dc.contributor.authorCaprio, James R.
dc.contributor.authorWestin, Nancy
dc.contributor.authorEsposito, John
dc.date.accessioned2016-05-18T23:03:03Z
dc.date.available2016-05-18T23:03:03Z
dc.date.issued1978-11
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/609805
dc.descriptionInternational Telemetering Conference Proceedings / November 14-16, 1978 / Hyatt House Hotel, Los Angeles, Californiaen_US
dc.description.abstractThis paper treats the problem of optimal selection of data quantization levels for minimum error. No assumptions are made regarding the underlying statistics of the process to be quantized. A finite precursor sample of the data is analyzed to infer the underlying distribution. Selection of optimum quantization levels can then be related to the generation of an optimum histogram for the data record. The optimum histogram is obtained by a dynamic programming approach for both least mean square error and minimum Chebychev error criteria. Transmitted data can then be quantized according to levels specified by the histogram. The process can be repeated periodically either with a new data sample, if the underlying process is nonstationary, or performed on the accumulated record in the stationary case.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © International Foundation for Telemeteringen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleOptimum Quantization for Minimum Distortionen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentComptek Research, Inc.en
dc.contributor.departmentState University of N.Y. at Buffaloen
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-09-11T10:34:39Z
html.description.abstractThis paper treats the problem of optimal selection of data quantization levels for minimum error. No assumptions are made regarding the underlying statistics of the process to be quantized. A finite precursor sample of the data is analyzed to infer the underlying distribution. Selection of optimum quantization levels can then be related to the generation of an optimum histogram for the data record. The optimum histogram is obtained by a dynamic programming approach for both least mean square error and minimum Chebychev error criteria. Transmitted data can then be quantized according to levels specified by the histogram. The process can be repeated periodically either with a new data sample, if the underlying process is nonstationary, or performed on the accumulated record in the stationary case.


Files in this item

Thumbnail
Name:
ITC_1978_78-09-3.pdf
Size:
170.9Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record