GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms.

artificial neural networks k-nearest neighbor algorithm optical sensor photodetection of reflected light from asphalt road conditions detection sensor road safety smart roads

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
09 Nov 2020
Historique:
received: 30 09 2020
revised: 29 10 2020
accepted: 05 11 2020
entrez: 13 11 2020
pubmed: 14 11 2020
medline: 14 11 2020
Statut: epublish

Résumé

One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO

Identifiants

pubmed: 33182467
pii: s20216395
doi: 10.3390/s20216395
pmc: PMC7665139
pii:
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : UEFISCDI
ID : M-ERA.NET PhotoNanoP Contract No. 33/2016

Déclaration de conflit d'intérêts

The authors declare no conflicts of interest.

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Auteurs

Catalin Palade (C)

National Institute of Materials Physics, 405A Atomistilor Street, 077125 Magurele, Romania.

Ionel Stavarache (I)

National Institute of Materials Physics, 405A Atomistilor Street, 077125 Magurele, Romania.

Toma Stoica (T)

National Institute of Materials Physics, 405A Atomistilor Street, 077125 Magurele, Romania.

Magdalena Lidia Ciurea (ML)

National Institute of Materials Physics, 405A Atomistilor Street, 077125 Magurele, Romania.
Academy of Romanian Scientists, 54 Splaiul Independentei, 050094 Bucharest, Romania.

Classifications MeSH