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Proceedings Paper

Optical display for radar sensing
Author(s): Harold Szu; Charles Hsu; Jefferson Willey; Joseph Landa; Minder Hsieh; Louis V. Larsen; Alan T. Krzywicki; Binh Q. Tran; Philip Hoekstra; John T. Dillard; Keith A. Krapels; Michael Wardlaw; Kai-Dee Chu
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Paper Abstract

Boltzmann headstone S = kB Log W turns out to be the Rosette stone for Greek physics translation optical display of the microwave sensing hieroglyphics. The LHS is the molecular entropy S measuring the degree of uniformity scattering off the sensing cross sections. The RHS is the inverse relationship (equation) predicting the Planck radiation spectral distribution parameterized by the Kelvin temperature T. Use is made of the conservation energy law of the heat capacity of Reservoir (RV) change T Δ S = -ΔE equals to the internal energy change of black box (bb) subsystem. Moreover, an irreversible thermodynamics Δ S > 0 for collision mixing toward totally larger uniformity of heat death, asserted by Boltzmann, that derived the so-called Maxwell-Boltzmann canonical probability. Given the zero boundary condition black box, Planck solved a discrete standing wave eigenstates (equation). Together with the canonical partition function (equation) an average ensemble average of all possible internal energy yielded the celebrated Planck radiation spectral (equation) where the density of states (equation). In summary, given the multispectral sensing data (equation), we applied Lagrange Constraint Neural Network (LCNN) to solve the Blind Sources Separation (BSS) for a set of equivalent bb target temperatures. From the measurements of specific value, slopes and shapes we can fit a set of Kelvin temperatures T’s for each bb targets. As a result, we could apply the analytical continuation for each entropy sources along the temperature-unique Planck spectral curves always toward the RGB color temperature display for any sensing probing frequency.

Paper Details

Date Published: 3 June 2015
PDF: 14 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960G (3 June 2015); doi: 10.1117/12.2176082
Show Author Affiliations
Harold Szu, The Catholic Univ. of America (United States)
Charles Hsu, The Catholic Univ. of America (United States)
Jefferson Willey, The Catholic Univ. of America (United States)
Joseph Landa, The Catholic Univ. of America (United States)
BriarTek, Inc. (United States)
Minder Hsieh, The Catholic Univ. of America (United States)
Louis V. Larsen, The Catholic Univ. of America (United States)
Alan T. Krzywicki, The Catholic Univ. of America (United States)
Binh Q. Tran, The Catholic Univ. of America (United States)
Philip Hoekstra, The Catholic Univ. of America (United States)
Therma-Scan, Inc. (United States)
John T. Dillard, Naval Postgraduate School (United States)
Keith A. Krapels, The Univ. of Memphis (United States)
Michael Wardlaw, Office of Naval Research (United States)
Kai-Dee Chu, U.S. Dept. of Homeland Security (United States)


Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)

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