FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor.

PM Particulate Matter atmospheric dust low-cost sensor mobile sensing ubiquitous computing

Journal

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

Informations de publication

Date de publication:
12 Feb 2019
Historique:
received: 07 01 2019
revised: 06 02 2019
accepted: 07 02 2019
entrez: 15 2 2019
pubmed: 15 2 2019
medline: 15 2 2019
Statut: epublish

Résumé

Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose

Identifiants

pubmed: 30759877
pii: s19030749
doi: 10.3390/s19030749
pmc: PMC6387415
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Matthias Budde (M)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. budde@teco.edu.

Simon Leiner (S)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. leiner@teco.edu.

Marcel Köpke (M)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. koepke@teco.edu.

Johannes Riesterer (J)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. riesterer@teco.edu.

Till Riedel (T)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. riedel@teco.edu.

Michael Beigl (M)

Karlsruhe Institute of Technology (KIT), TECO/Pervasive Computing Systems, Karlsruhe 76131, Germany. beigl@teco.edu.

Classifications MeSH