Multivariate Optical Computing for Biological Samples using a Digital Micromirror Device

2011 ◽  
Author(s):  
Zachary J. Smith ◽  
Sven Strombom ◽  
Sebastian Wachsmann-Hogiu
Keyword(s):  
Biological Samples ◽  
Optical Computing ◽  
Digital Micromirror Device ◽  
Multivariate Optical Computing

Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part I: Design and Theoretical Performance of Multivariate Optical Elements

2013 ◽  
Vol 67 (6) ◽  
pp. 620-629 ◽  
Author(s):  
Joseph A. Swanstrom ◽  
Laura S. Bruckman ◽  
Megan R. Pearl ◽  
Michael N. Simcock ◽  
Kathleen A. Donaldson ◽  
...  
Keyword(s):  
Optical Computing ◽  
Taxonomic Classification ◽  
Optical Elements ◽  
Multivariate Optical Computing ◽  
Theoretical Performance

scholarly journals A New Multivariate Optical Computing Microelement and Miniature Sensor for Spectroscopic Chemical Sensing in Harsh Environments: Design, Fabrication, and Testing

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 701 ◽  
Author(s):  
Christopher Jones ◽  
Bin Dai ◽  
Jimmy Price ◽  
Jian Li ◽  
Megan Pearl ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Chemical Constituents ◽  
Optical Computing ◽  
Compositional Analysis ◽  
Optical Element ◽  
Harsh Environments ◽  
Gas Reservoirs ◽  
Recent Developments ◽  
Miniature Sensor ◽  
Multivariate Optical Computing

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230°C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.

Novel filter design algorithm for multivariate optical computing

2001 ◽  
Author(s):  
Olusola O. Soyemi ◽  
Paul J. Gemperline ◽  
Lixia Zhang ◽  
DeLyle Eastwood ◽  
Hong Li ◽  
...  
Keyword(s):  
Filter Design ◽  
Optical Computing ◽  
Design Algorithm ◽  
Multivariate Optical Computing

Field applications of stand-off sensing using visible/NIR multivariate optical computing

2001 ◽  
Author(s):  
DeLyle Eastwood ◽  
Olusola O. Soyemi ◽  
Jeevanandra Karunamuni ◽  
Lixia Zhang ◽  
Hongli Li ◽  
...  
Keyword(s):  
Optical Computing ◽  
Multivariate Optical Computing

Characterization of ion-assisted induced absorption in A-Si thin-films used for multivariate optical computing

2015 ◽  
Author(s):  
Aditya B. Nayak ◽  
James M. Price ◽  
Bin Dai ◽  
David Perkins ◽  
Ding Ding Chen ◽  
...  
Keyword(s):  
Thin Films ◽  
Optical Computing ◽  
Induced Absorption ◽  
Multivariate Optical Computing

scholarly journals Precision in imaging multivariate optical computing

2007 ◽  
Vol 46 (7) ◽  
pp. 1066 ◽  
Author(s):  
Michael N. Simcock ◽  
Michael L. Myrick
Keyword(s):  
Optical Computing ◽  
Multivariate Optical Computing

Multivariate Optical Computing and Next-generation Spectrometer

2021 ◽  
Vol 49 (4) ◽  
pp. 593-601
Author(s):  
Chao-Shu DUAN ◽  
Wen-Sheng CAI ◽  
Xue-Guang SHAO
Keyword(s):  
Optical Computing ◽  
Next Generation ◽  
Multivariate Optical Computing
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