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3370: James Mueller
No full textPhotograph of author Jim Mueller, professor in the Mayborn school of Journalism at UNT, posing with his book, "Shooting Arrows and Slinging Mud"
Data for "Mueller Matrix Extrapolations from Linear Partial Polarimetry"
No full textTo demonstrate our method for extrapolating full, depolarizing Mueller matrices from fewer than 16 measurements, we measured an ensemble of roughened plastic bricks using a Mueller imaging polarimeter and a linear Stokes camera. The Mueller matrix images from the Mueller imaging polarimeter and the images taken with four polarizer orientations from the linear Stokes camera are included.
For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to [email protected]</p
Near-Infrared Human Eye Mueller Matrix Images
No full textNear-infrared Mueller matrix images of 20 human subjects' eyes.
Images were taken using a dual-rotating-retarder Mueller polarimeter. The human subjects were illuminated with a diffuse near-infrared LED light source (945-955 nm) where 0.0315 W/m2 was incident on the subject’s face and/or eye. Mueller matrix images were reconstructed from 25 polarimetric measurements taken over the course of approximately 15 seconds. Due to the small, random unconscious movements of the eye, image alignment code was applied in post-processing.
For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to [email protected]</p
Analysis and Modeling of Depolarization Effects in Mueller Matrix Spectroscopic Ellipsometry Data
No full textAbstractIn this paper we present importance of depolarization effects modeling to fit spectroscopic Mueller matrix ellipsometry data. The relevant theoretical background based on Mueller matrix formalism is presented. The sample of SiO2 layer (approx. 1μm thick) on silicon substrate is used to demonstrate depolarization effects in obtained experimental data. In the first step the presence of interferences in the layer is used for modeling of depolarization effects caused by finite spectral resolution of the Mueller matrix ellipsometer. In the next step the depolarization caused by focusing of the probe light is analyzed and modeled. Both finite spectral resolution and beam focusing is a common issue in the optical characterization of samples with lateral dimensions smaller than (commonly used) collimated beam. Therefore to fit experimental data with model it is important to assume those depolarization effect into model
Determination of Anisotropic Crystal Optical Properties Using Mueller Matrix Spectroscopic Ellipsometry
No full textAbstractIn this paper the Mueller matrix ellipsometry in the spectral range from 0.73 to 6.4eV measured using dual rotating compensator ellipsometer RC2 (Woollam company) is applied to study anisotropic crystals. First we summarize the effects of optical anisotropy to Mueller matrix spectra. As an example of an uniaxial sample we have characterized a Rutile (TiO2) tetragonal crystal. The optical axis of the sample is parallel to its surface. The sample is characterized at variable angle of incidence and variable azimuthal rotation angle. The Mueller matrix spectra are fitted to the model based on Kramers-Kronig consistent Basis spline and obtained optical functions are compared with tabulated data and ab-initio models based on first-principle calculated electronic structure
Real Estate Rental Growth Rates at Different Points in the Physical Market Cycle
No full textReal estate markets go through both physical cycles (demand and supply) that affect rental growth rates and financial cycles (capital flows to real estate) that affect property Prices (Mueller, 1995). This study develops a rental growth rate hypothesis based on a market’s position in the physical (demand-supply) market cycle. Using data from fifty-four office and industrial markets in the United States over a thirty-year period, an aggregated national average rental growth rate was calculated for each point in the cycle. An ANOVA test for differences of means found that the national average rental growth rates at each point in the cycle were statistically different. The results show local demand and supply, which interact to affect occupancy, are major determinants in rental growth rates. This research should help investors move from using a single rental growth rate for multiple year forecasts, to using yearly cycle driven rental growth rate estimates in their discounted cash flow projections.
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