Convolutional Network Analysis of Optical Micrographs for Liquid Crystal Sensors

2020 ◽  
Vol 124 (28) ◽  
pp. 15152-15161 ◽  
Author(s):  
Alexander D. Smith ◽  
Nicholas Abbott ◽  
Victor M. Zavala
Keyword(s):  
Liquid Crystal ◽  
Network Analysis ◽  
Convolutional Network ◽  
Optical Micrographs

Macroprobe Mode for the Study of Segregation in Steels

1990 ◽  
Vol 48 (2) ◽  
pp. 260-261
Author(s):  
Auclair Gilles ◽  
Benoit Danièle
Keyword(s):  
Mechanical Properties ◽  
Spatial Distribution ◽  
High Performance ◽  
Volume Fraction ◽  
Sheet Steel ◽  
Acquisition Time ◽  
Chemical Information ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Optical Micrographs

During these last 10 years, high performance correction procedures have been developed for classical EPMA, and it is nowadays possible to obtain accurate quantitative analysis even for soft X-ray radiations. It is also possible to perform EPMA by adapting this accurate quantitative procedures to unusual applications such as the measurement of the segregation on wide areas in as-cast and sheet steel products.The main objection for analysis of segregation in steel by means of a line-scan mode is that it requires a very heavy sampling plan to make sure that the most significant points are analyzed. Moreover only local chemical information is obtained whereas mechanical properties are also dependant on the volume fraction and the spatial distribution of highly segregated zones. For these reasons we have chosen to systematically acquire X-ray calibrated mappings which give pictures similar to optical micrographs. Although mapping requires lengthy acquisition time there is a corresponding increase in the information given by image anlysis.

Freeze-fracture TEM of the helical smectic A* phase

1992 ◽  
Vol 50 (1) ◽  
pp. 278-279
Author(s):  
K.J. Ihn ◽  
R. Pindak ◽  
J. A. N. Zasadzinski
Keyword(s):  
Liquid Crystal ◽  
Grain Boundary ◽  
Grain Boundaries ◽  
Freeze Fracture ◽  
Smectic Phase ◽  
Layer Spacing ◽  
Screw Dislocations ◽  
Smectic A ◽  
Smectic Phases ◽  
Discrete Amount

A new liquid crystal (called the smectic-A* phase) that combines cholesteric twist and smectic layering was a surprise as smectic phases preclude twist distortions. However, the twist grain boundary (TGB) model of Renn and Lubensky predicted a defect-mediated smectic phase that incorporates cholesteric twist by a lattice of screw dislocations. The TGB model for the liquid crystal analog of the Abrikosov phase of superconductors consists of regularly spaced grain boundaries of screw dislocations, parallel to each other within the grain boundary, but rotated by a fixed angle with respect to adjacent grain boundaries. The dislocations divide the layers into blocks which rotate by a discrete amount, Δθ, given by the ratio of the layer spacing, d, to the distance between grain boundaries, lb; Δθ ≈ d/lb (Fig. 1).

Attractive mode force microscopy of chiral liquid crystal surfaces

1992 ◽  
Vol 50 (1) ◽  
pp. 268-269
Author(s):  
B.D. Terris ◽  
R. J. Twieg ◽  
C. Nguyen ◽  
G. Sigaud ◽  
H. T. Nguyen
Keyword(s):  
Liquid Crystal ◽  
Crystal Surfaces ◽  
Free Surfaces ◽  
Control Range ◽  
Force Microscopy ◽  
Liquid Crystal Films ◽  
Chiral Liquid Crystal ◽  
Crystal Films ◽  
Electrostatic Charging ◽  
And Shifts

We have used a force microscope in the attractive, or noncontact, mode to image a variety of surfaces. In this mode, the microscope tip is oscillated near its resonant frequency and shifts in this frequency due to changes in the surface-tip force gradient are detected. We have used this technique in a variety of applications to polymers, including electrostatic charging, phase separation of ionomer surfaces, and crazing of glassy films.Most recently, we have applied the force microscope to imaging the free surfaces of chiral liquid crystal films. The compounds used (Table 1) have been chosen for their polymorphic variety of fluid mesophases, all of which exist within the temperature control range of our force microscope.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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