S
Santasri R. Bose-Pillai
Researcher at Air Force Institute of Technology
Publications - 47
Citations - 286
Santasri R. Bose-Pillai is an academic researcher from Air Force Institute of Technology. The author has contributed to research in topics: Turbulence & Scintillometer. The author has an hindex of 8, co-authored 41 publications receiving 221 citations. Previous affiliations of Santasri R. Bose-Pillai include Oak Ridge Institute for Science and Education.
Papers
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Journal ArticleDOI
Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis
Russell C. Hardie,Jonathan D. Power,Daniel A. LeMaster,Douglas R. Droege,Szymon Gladysz,Santasri R. Bose-Pillai +5 more
TL;DR: I think this tool can be used effectively to study optical anisoplanatic turbulence and to aid in the development of image restoration methods.
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Generation of vector partially coherent optical sources using phase-only spatial light modulators
TL;DR: In this article, the authors control both shape and coherence using liquid-crystal spatial light modulators, and can produce two different classes of PCBs using the same simple optical setup.
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Synthesis of non-uniformly correlated partially coherent sources using a deformable mirror
TL;DR: In this paper, the authors present a near real-time synthesis of a non-uniformly correlated partially coherent source using a low-actuator-count deformable mirror.
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Estimation of atmospheric turbulence using differential motion of extended features in time-lapse imagery
Santasri R. Bose-Pillai,Jack E. McCrae,Christopher A. Rice,Ryan A. Wood,Ryan A. Wood,Ryan A. Wood,Connor E. Murphy,Connor E. Murphy,Connor E. Murphy,Steven T. Fiorino +9 more
TL;DR: A method to estimate turbulence parameters, such as path weighted Cn2 and Fried’s coherence diameter r0 from turbulence-induced random, differential motion of extended features in the time-lapse imagery of a distant target is presented.
Journal ArticleDOI
Estimation of turbulence from time-lapse imagery
TL;DR: In this article, a modified version of this approach that creates larger patches by averaging several smaller patches together solves this noise issue, and the weighting functions for a number of different patch sizes can be combined through the Moore-Penrose pseudoinverse to create a weighting function that yields the least-squares optimal linear combination of measurements for the estimation of r0.