Multiple signal classification for self-mixing flowmetry

Nikolic, Milan, Lim, Yah Leng, Bertling, Karl, Taimre, Thomas and Rakic, Aleksandar (2015) Multiple signal classification for self-mixing flowmetry. Applied Optics, 54 9: 2193-2198. doi:10.1364/AO.54.002193


Author Nikolic, Milan
Lim, Yah Leng
Bertling, Karl
Taimre, Thomas
Rakic, Aleksandar
Title Multiple signal classification for self-mixing flowmetry
Journal name Applied Optics   Check publisher's open access policy
ISSN 1559-128X
2155-3165
Publication date 2015-03-20
Year available 2015
Sub-type Article (original research)
DOI 10.1364/AO.54.002193
Open Access Status
Volume 54
Issue 9
Start page 2193
End page 2198
Total pages 6
Place of publication Washington, DC United States
Publisher Optical Society of America
Collection year 2016
Language eng
Abstract For the first time to our knowledge, we apply the multiple signal classification (MUSIC) algorithm to signals obtained from a self-mixing flow sensor. We find that MUSIC accurately extracts the fluid velocity and exhibits a markedly better signal-to-noise ratio (SNR) than the commonly used fast Fourier transform (FFT) method. We compare the performance of the MUSIC and FFT methods for three decades of scatterer concentration and fluid velocities from 0.5 to 50 mm/s. MUSIC provided better linearity than the FFT and was able to accurately function over a wider range of algorithm parameters. MUSIC exhibited excellent linearity and SNR even at low scatterer concentration, at which the FFT’s SNR decreased to impractical levels. This makes MUSIC a particularly attractive method for flow measurement systems with a low density of scatterers such as microfluidic and nanofluidic systems and blood flow in capillaries.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 11 Mar 2015, 10:22:14 EST by Karl Bertling on behalf of School of Information Technol and Elec Engineering