About phase components of differential-phase LBIC from p-n junction transversal plane scanning

2010 ◽  
Author(s):  
L. N. Ilchenko ◽  
S. O. Kolenov ◽  
S. V. Litvinenko ◽  
P. V. Molochko
Keyword(s):  
Differential Phase ◽  
Transversal Plane ◽  
Plane Scanning

The role of differential phase contrast in electron microscopy

1978 ◽  
Vol 36 (1) ◽  
pp. 176-177
Author(s):  
E.M. Waddell ◽  
J.N. Chapman ◽  
R.P. Ferrier
Keyword(s):  
Phase Contrast ◽  
Practical Method ◽  
Position Vector ◽  
Differential Phase ◽  
Pure Phase ◽  
Differential Phase Contrast ◽  
The Difference ◽  
Image Position ◽  
First Moment

Dekkers and de Lang (1977) have discussed a practical method of realising differential phase contrast in a STEM. The method involves taking the difference signal from two semi-circular detectors placed symmetrically about the optic axis and subtending the same angle (2α) at the specimen as that of the cone of illumination. Such a system, or an obvious generalisation of it, namely a quadrant detector, has the characteristic of responding to the gradient of the phase of the specimen transmittance. In this paper we shall compare the performance of this type of system with that of a first moment detector (Waddell et al.1977).For a first moment detector the response function R(k) is of the form R(k) = ck where c is a constant, k is a position vector in the detector plane and the vector nature of R(k)indicates that two signals are produced. This type of system would produce an image signal given bywhere the specimen transmittance is given by a (r) exp (iϕ (r), r is a position vector in object space, ro the position of the probe, ⊛ represents a convolution integral and it has been assumed that we have a coherent probe, with a complex disturbance of the form b(r-ro) exp (iζ (r-ro)). Thus the image signal for a pure phase object imaged in a STEM using a first moment detector is b2 ⊛ ▽ø. Note that this puts no restrictions on the magnitude of the variation of the phase function, but does assume an infinite detector.

Resolution assessment from spectral signal-to-noise ratios

1987 ◽  
Vol 45 ◽  
pp. 746-747
Author(s):  
M. Unser ◽  
B.L. Trus ◽  
A.C. Steven
Keyword(s):  
Electron Microscopy ◽  
Limiting Factor ◽  
Biological Macromolecules ◽  
Signal To Noise ◽  
Differential Phase ◽  
Traditional Measure ◽  
Spectral Signal ◽  
Two Measures ◽  
Diffraction Patterns ◽  
Averaging Techniques

Since the resolution-limiting factor in electron microscopy of biological macromolecules is not instrumental, but is rather the preservation of structure, operational definitions of resolution have to be based on the mutual consistency of a set of like images. The traditional measure of resolution for crystalline specimens in terms of the extent of periodic reflections in their diffraction patterns is such a criterion. With the advent of correlation averaging techniques for lattice rectification and the analysis of non-crystalline specimens, a more general - and desirably, closely compatible - resolution criterion is needed. Two measures of resolution for correlation-averaged images have been described, namely the differential phase residual (DPR) and the Fourier ring correlation (FRC). However, the values that they give for resolution often differ substantially. Furthermore, neither method relates in a straightforward way to the long-standing resolution criterion for crystalline specimens.

Differential Phase Patterns of the LOFAR LBA Array Measured In Situ

2018 ◽  
Author(s):  
F. Paonessa ◽  
G. Virone ◽  
S. Matteoli ◽  
P. Bolli ◽  
G. Pupillo ◽  
...  
Keyword(s):  
Differential Phase

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Ultrasound differential phase contrast using backscattering and the memory effect

2021 ◽  
Vol 118 (12) ◽  
pp. 124103
Author(s):  
Timothy D. Weber ◽  
Nikunj Khetan ◽  
Ruohui Yang ◽  
Jerome Mertz
Keyword(s):  
Memory Effect ◽  
Phase Contrast ◽  
Differential Phase ◽  
Differential Phase Contrast
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