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GETSEL: Gallery entropy for template selection on large datasets

Author:
De Marsico, Maria
,
Riccio, Daniel
,
Vazquez, Heydi Mendez
,
Calana, Yenisel Plasencia
Publisher:
IEEE
Year
: 2014
DOI: 10.1109/DRC.2014.6872389
URI: http://libsearch.um.ac.ir:80/fum/handle/fum/1073772
Keyword(s): electrochemical devices,n ionic conductivity,n low-power electronics,n memristors,n palladium compounds,n PdH<,sub>,x<,/sub>,n electronic current,n fast moving electron,n ionic motion,n low-power fully ionic two terminal proton conducting device,n memristive-based device,n neuromorphic computing,n slow moving ions,n synaptic-like reversible short-term depression,n Chemicals,n Contacts,n Educational institutions,n Hydrogen,n Neurotran
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    GETSEL: Gallery entropy for template selection on large datasets

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contributor authorDe Marsico, Maria
contributor authorRiccio, Daniel
contributor authorVazquez, Heydi Mendez
contributor authorCalana, Yenisel Plasencia
date accessioned2020-03-12T22:10:00Z
date available2020-03-12T22:10:00Z
date issued2014
identifier other6996289.pdf
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/1073772?locale-attribute=en
formatgeneral
languageEnglish
publisherIEEE
titleGETSEL: Gallery entropy for template selection on large datasets
typeConference Paper
contenttypeMetadata Only
identifier padid8209896
subject keywordselectrochemical devices
subject keywordsn ionic conductivity
subject keywordsn low-power electronics
subject keywordsn memristors
subject keywordsn palladium compounds
subject keywordsn PdH<
subject keywordssub>
subject keywordsx<
subject keywords/sub>
subject keywordsn electronic current
subject keywordsn fast moving electron
subject keywordsn ionic motion
subject keywordsn low-power fully ionic two terminal proton conducting device
subject keywordsn memristive-based device
subject keywordsn neuromorphic computing
subject keywordsn slow moving ions
subject keywordsn synaptic-like reversible short-term depression
subject keywordsn Chemicals
subject keywordsn Contacts
subject keywordsn Educational institutions
subject keywordsn Hydrogen
subject keywordsn Neurotran
identifier doi10.1109/DRC.2014.6872389
journal titleiometrics (IJCB), 2014 IEEE International Joint Conference on
filesize397776
citations0
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