Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Metasurface-integrated optofluidic sensing enabled by artificial vision intelligence for identifying liquid chemicals

dc.contributor.authorLi, Hongliang
dc.contributor.authorKim, Jin Tae
dc.contributor.authorKim, Jin-Soo
dc.contributor.authorChoi, Duk
dc.contributor.authorLee, Sang-Shin
dc.coverage.spatialSan Jose, California USA
dc.date.accessioned2024-06-26T05:46:53Z
dc.date.available2024-06-26T05:46:53Z
dc.date.created7 - 12 May 2023
dc.date.issued2023
dc.date.updated2024-01-14T07:15:49Z
dc.description.abstractThe currently used liquid identification techniques are costly, cumbersome, and spectrometer-reliant. In this study, a compact, accurate, and cost-effective method for identifying liquids was presented using visual intelligence algorithms and a metasurface-incorporated optofluidic device incorporated.
dc.description.sponsorshipThis study was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (2018R1A6A03025242), Ministry of Science and ICT (MIST) (2020R1A2C3007007), the research grant of Kwangwoon University in 2022, and Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (21YR2710, Nanophotonic vision intelligence for airborne virus detection).
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-1-957171-25-8
dc.identifier.urihttps://hdl.handle.net/1885/733713430
dc.language.isoen_AUen_AU
dc.publisherIEEE
dc.relation.ispartofseries2023 Conference on Lasers and ELectro-Optics Europe (CLEO Europe-EQEC)
dc.rights© Optica Publishing Group 2023
dc.sourceProceedings of the 2023 Conference on Lasers and ELectro-Optics Europe (CLEO Europe-EQEC)
dc.titleMetasurface-integrated optofluidic sensing enabled by artificial vision intelligence for identifying liquid chemicals
dc.typeConference paper
local.bibliographicCitation.lastpage2
local.bibliographicCitation.startpage1
local.contributor.affiliationLi, Hongliang, Kwangwoon University
local.contributor.affiliationKim, Jin Tae, Electronics and Telecommunications Research Institute
local.contributor.affiliationKim, Jin-Soo, Korea University
local.contributor.affiliationChoi, Duk, College of Science, ANU
local.contributor.affiliationLee, Sang-Shin, Kwangwoon University
local.contributor.authoruidChoi, Duk, u4219275
local.description.embargo2099-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor510203 - Nonlinear optics and spectroscopy
local.identifier.ariespublicationa383154xPUB44878
local.identifier.scopusID2-s2.0-85176354215
local.publisher.urlhttps://ieeexplore.ieee.org/document/10259576
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Metasurface-integrated_optofluidic_sensing_enabled_by_artificial_vision_intelligence_for_identifying_liquid_chemicals.pdf
Size:
610.21 KB
Format:
Adobe Portable Document Format