Design and Testing of a Multivariate Optical Element: The First Demonstration of Multivariate Optical Computing for Predictive Spectroscopy

2001 ◽  
Vol 73 (6) ◽  
pp. 1069-1079 ◽  
Author(s):  
O. Soyemi ◽  
D. Eastwood ◽  
L. Zhang ◽  
H. Li ◽  
J. Karunamuni ◽  
...  
Keyword(s):  
Optical Computing ◽  
Optical Element ◽  
Multivariate Optical Computing ◽  
Design And Testing

scholarly journals Design and Testing of a Multivariate Optical Element:  The First Demonstration of Multivariate Optical Computing for Predictive Spectroscopy

2001 ◽  
Vol 73 (17) ◽  
pp. 4393-4393 ◽  
Author(s):  
O. Soyemi ◽  
D. Eastwood ◽  
L. Zhang ◽  
H. Li ◽  
J. Karunamuni ◽  
...  
Keyword(s):  
Optical Computing ◽  
Optical Element ◽  
Multivariate Optical Computing ◽  
Design And Testing

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.

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

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

Nonlinear Optimization Algorithm for Multivariate Optical Element Design

2002 ◽  
Vol 56 (4) ◽  
pp. 477-487 ◽  
Author(s):  
Olusola O. Soyemi ◽  
Frederick G. Haibach ◽  
Paul J. Gemperline ◽  
Michael L. Myrick
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optical Computing ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optical Element ◽  
Optical Elements ◽  
Design Algorithm ◽  
Nonlinear Optimization Algorithm

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

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
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