The optimal cost expansion of finite controls finite states Markov chains with weak and strong interactions

2005 ◽  
pp. 322-337 ◽  
Author(s):  
F. Delebecque ◽  
J. P. Quadrat
Keyword(s):  
Markov Chains ◽  
Strong Interactions ◽  
Optimal Cost ◽  
Finite States

scholarly journals A methodology for stochastic analysis of share prices as Markov chains with finite states

SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 657 ◽  
Author(s):  
Felix Mettle ◽  
Enoch Quaye ◽  
Ravenhill Laryea
Keyword(s):  
Markov Chains ◽  
Stochastic Analysis ◽  
Share Prices ◽  
Finite States

200kv superconducting cryo electron microscope

1987 ◽  
Vol 45 ◽  
pp. 642-643
Author(s):  
M. Iwatsuki ◽  
Y. Kokubo ◽  
Y. Harada ◽  
J. Lehman
Keyword(s):  
Electron Microscope ◽  
Structure Analysis ◽  
Organic Materials ◽  
Strong Interactions ◽  
Specimen Preparation ◽  
Great Effort ◽  
Electron Microscopic ◽  
Crystal Fields ◽  
Special Skill ◽  
Long Time

In recent years, the electron microscope has been significantly improved in resolution and we can obtain routinely atomic-level high resolution images without any special skill. With this improvement, the structure analysis of organic materials has become one of the interesting targets in the biological and polymer crystal fields.Up to now, X-ray structure analysis has been mainly used for such materials. With this method, however, great effort and a long time are required for specimen preparation because of the need for larger crystals. This method can analyze average crystal structure but is insufficient for interpreting it on the atomic or molecular level. The electron microscopic method for organic materials has not only the advantage of specimen preparation but also the capability of providing various information from extremely small specimen regions, using strong interactions between electrons and the substance. On the other hand, however, this strong interaction has a big disadvantage in high radiation damage.

New form of Transmission Cross Coefficient for High-Resolution Imaging

1990 ◽  
Vol 48 (1) ◽  
pp. 60-61
Author(s):  
Kazuo Ishizuka
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Transmission Function ◽  
Incident Beam ◽  
Optical Microscope ◽  
Strong Interactions ◽  
Small Range ◽  
Specimen Thickness ◽  
Beam Direction ◽  
Diffracted Waves

It is well known that taking into account spacial and temporal coherency of illumination as well as the wave aberration is important to interpret an image of a high-resolution electron microscope (HREM). This occues, because coherency of incident electrons restricts transmission of image information. Due to its large spherical and chromatic aberrations, the electron microscope requires higher coherency than the optical microscope. On an application of HREM for a strong scattering object, we have to estimate the contribution of the interference between the diffracted waves on an image formation. The contribution of each pair of diffracted waves may be properly represented by the transmission cross coefficients (TCC) between these waves. In this report, we will show an improved form of the TCC including second order derivatives, and compare it with the first order TCC.In the electron microscope the specimen is illuminated by quasi monochromatic electrons having a small range of illumination directions. Thus, the image intensity for each energy and each incident direction should be summed to give an intensity to be observed. However, this is a time consuming process, if the ranges of incident energy and/or illumination direction are large. To avoid this difficulty, we can use the TCC by assuming that a transmission function of the specimen does not depend on the incident beam direction. This is not always true, because dynamical scattering is important owing to strong interactions of electrons with the specimen. However, in the case of HREM, both the specimen thickness and the illumination angle should be small. Therefore we may neglect the dependency of the transmission function on the incident beam direction.

Markov models—Markov chains

2019 ◽  
Vol 16 (8) ◽  
pp. 663-664 ◽  
Author(s):  
Jasleen K. Grewal ◽  
Martin Krzywinski ◽  
Naomi Altman
Keyword(s):  
Markov Chains ◽  
Markov Models
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