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

Design of polarization-encoded optical shadow-casting logic units using truth-table partitioning

1988 ◽  
Vol 66 (10) ◽  
pp. 841-843 ◽  
Author(s):  
Abdul Ahad S. Awwal ◽  
Mohammad A. Karim
Keyword(s):  
Optical Computing ◽  
Full Adder ◽  
Truth Table ◽  
Pixel Size ◽  
Design Algorithm ◽  
Computing Unit ◽  
Shadow Casting ◽  
Alternative Designs ◽  
Memory Efficient ◽  
Logic System

The input pixel size of a polarization-encoded optical shadow-casting logic system is reduced by means of truth-table partitioning. The proposed design algorithm is used to encode the inputs of a three-input, two-output binary full adder. A comparison with alternative designs proves that the technique leads to an improved memory-efficient optical computing unit.

Generalized Disturbance Observer Synthesis Using a Two-Stage Heuristic Algorithm

2017 ◽  
Author(s):  
Guofei Xiang ◽  
Jianbo Su
Keyword(s):  
Disturbance Observer ◽  
Filter Design ◽  
Mimo Systems ◽  
Solution Space ◽  
The Novel ◽  
Two Stage ◽  
Design Algorithm ◽  
Reduced Order ◽  
Kyp Lemma ◽  
Low Pass

Disturbance observer (DOB) based control has been widely applied in industries due to its easy usage but powerful disturbance rejection ability. However, the existence of innate structure constraint, namely the inverse of the nominal plant, prevents its implementation on more general class of systems, such as non-minimum phase plants, MIMO systems etc.. Furthermore, additional limitations exerted on Q-filter design, i.e., unity steady state gain and low-pass nature, which narrow down its solution space largely and prevent from achieving optimal performance even if it exists. In this paper, we present a novel DOB architecture, named generalized disturbance observer (G-DOB), with the help of nontraditional use of the celebrated Youla parametrization of two degree-of-freedom controller. Rigorous analyses show that the novel G-DOB not only inherits all the merits of the conventional one, but also alleviates the limitations stated before partially. By some appropriate system manipulation, the synthesis of Q-filter has been converted to the design of reduced-order controller. Thus, a heuristic two-stage algorithm has been developed with the help of Kalman-Yakubovich-Popov (KYP) lemma: firstly design a full information controller for the augmented system and then compute a reduced-order controller. Numerical examples are presented to demonstrate the effectiveness of the proposed G-DOB structure and design algorithm.

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.

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
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