Complex field reconstruction using gradient and intensity measurements from a Shack-Hartmann wave front sensor

2002 ◽  
Author(s):  
Troy A. Rhoadarmer ◽  
Jeffrey D. Barchers ◽  
Earl J. Spillar
Keyword(s):  
Wave Front ◽  
Title Complex ◽  
Complex Field ◽  
Field Reconstruction ◽  
Intensity Measurements

scholarly journals Self-coherent complex field reconstruction with in-phase and quadrature delay detection without a direct-detection branch

2012 ◽  
Vol 20 (14) ◽  
pp. 15452 ◽  
Author(s):  
Igor Tselniker ◽  
Moshe Nazarathy ◽  
Shalva-Ben Ezra ◽  
Jingshi Li ◽  
Juerg Leuthold
Keyword(s):  
Direct Detection ◽  
Complex Field ◽  
Field Reconstruction ◽  
Delay Detection

3D wavebeam field reconstruction from intensity measurements in a few cross sections

1995 ◽  
Vol 115 (5-6) ◽  
pp. 449-452 ◽  
Author(s):  
A.V Chirkov ◽  
G.G Denisov ◽  
N.L Aleksandrov
Keyword(s):  
Cross Sections ◽  
Field Reconstruction ◽  
Intensity Measurements

Wave-front phase estimation from Fourier intensity measurements

1989 ◽  
Vol 6 (7) ◽  
pp. 1020 ◽  
Author(s):  
J. N. Cederquist ◽  
J. R. Fienup ◽  
C. C. Wackerman ◽  
S. R. Robinson ◽  
D. Kryskowski
Keyword(s):  
Wave Front ◽  
Phase Estimation ◽  
Intensity Measurements

Thickness and composition determinations from T.E.M. specimens using both x-ray and backscattered electron data

1984 ◽  
Vol 42 ◽  
pp. 576-577
Author(s):  
M.D. Ball ◽  
H. Lagace ◽  
M.C. Thornton
Keyword(s):  
Electron Microscope ◽  
Atomic Number ◽  
Transmission Electron Microscope ◽  
Convenient Method ◽  
Flow Chart ◽  
X Ray ◽  
Intensity Measurements ◽  
Transmission Electron ◽  
Electron Intensity ◽  
Backscattered Electron

The backscattered electron coefficient η for transmission electron microscope specimens depends on both the atomic number Z and the thickness t. Hence for specimens of known atomic number, the thickness can be determined from backscattered electron coefficient measurements. This work describes a simple and convenient method of estimating the thickness and the corrected composition of areas of uncertain atomic number by combining x-ray microanalysis and backscattered electron intensity measurements.The method is best described in terms of the flow chart shown In Figure 1. Having selected a feature of interest, x-ray microanalysis data is recorded and used to estimate the composition. At this stage thickness corrections for absorption and fluorescence are not performed.

Identifying Patterns in Complex Field Data

2017 ◽  
Vol 225 (3) ◽  
pp. 268-284 ◽  
Author(s):  
Andrew J. White ◽  
Dieter Kleinböhl ◽  
Thomas Lang ◽  
Alfons O. Hamm ◽  
Alexander L. Gerlach ◽  
...  
Keyword(s):  
Heart Rate ◽  
Panic Disorder ◽  
Latent Class ◽  
Treatment Protocol ◽  
Situational Factors ◽  
Self Report ◽  
Complex Field ◽  
Cluster Assignment ◽  
Experimental Conditions ◽  
Conventional Analysis

Abstract. Ambulatory assessment methods are well suited to examine how patients with panic disorder and agoraphobia (PD/A) undertake situational exposure. But under complex field conditions of a complex treatment protocol, the variability of data can be so high that conventional analytic approaches based on group averages inadequately describe individual variability. To understand how fear responses change throughout exposure, we aimed to demonstrate the incremental value of sorting HR responses (an index of fear) prior to applying averaging procedures. As part of their panic treatment, 85 patients with PD/A completed a total of 233 bus exposure exercises. Heart rate (HR), global positioning system (GPS) location, and self-report data were collected. Patients were randomized to one of two active treatment conditions (standard exposure or fear-augmented exposure) and completed multiple exposures in four consecutive exposure sessions. We used latent class cluster analysis (CA) to cluster heart rate (HR) responses collected at the start of bus exposure exercises (5 min long, centered on bus boarding). Intra-individual patterns of assignment across exposure repetitions were examined to explore the relative influence of individual and situational factors on HR responses. The association between response types and panic disorder symptoms was determined by examining how clusters were related to self-reported anxiety, concordance between HR and self-report measures, and bodily symptom tolerance. These analyses were contrasted with a conventional analysis based on averages across experimental conditions. HR responses were sorted according to form and level criteria and yielded nine clusters, seven of which were interpretable. Cluster assignment was not stable across sessions or treatment condition. Clusters characterized by a low absolute HR level that slowly decayed corresponded with low self-reported anxiety and greater self-rated tolerance of bodily symptoms. Inconsistent individual factors influenced HR responses less than situational factors. Applying clustering can help to extend the conventional analysis of highly variable data collected in the field. We discuss the merits of this approach and reasons for the non-stereotypical pattern of cluster assignment across exposures.

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