scholarly journals Electromagnetic Multi–Gaussian Speckle

Optics ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 19-34
Author(s):  
Milo W. Hyde ◽  
Olga Korotkova

Generalizing our prior work on scalar multi-Gaussian (MG) distributed optical fields, we introduce the two-dimensional instantaneous electric-field vector whose components are jointly MG distributed. We then derive the single-point Stokes parameter probability density functions (PDFs) of MG-distributed light having an arbitrary degree and state of polarization. We show, in particular, that the intensity contrast of such a field can be tuned to values smaller or larger than unity. We validate our analysis by generating an example partially polarized MG field with a specified single-point polarization matrix using two different Monte Carlo simulation methods. We then compute the joint PDFs of the instantaneous field components and the Stokes parameter PDFs from the simulated MG fields, while comparing the results of both Monte Carlo methods to the corresponding theory. Lastly, we discuss the strengths, weaknesses, and applicability of both simulation methods in generating MG fields.

2006 ◽  
Vol 82 (3-4) ◽  
pp. 489-502 ◽  
Author(s):  
Antti Lauri ◽  
Joonas Merikanto ◽  
Evgeni Zapadinsky ◽  
Hanna Vehkamäki

2018 ◽  
Vol 46 (2) ◽  
pp. 902-912 ◽  
Author(s):  
Anthony J. Hardy ◽  
Maryam Bostani ◽  
Andrew M. Hernandez ◽  
Maria Zankl ◽  
Cynthia McCollough ◽  
...  

2014 ◽  
Vol 998-999 ◽  
pp. 806-813
Author(s):  
Jian Wang ◽  
Qing Xu

Realistic image synthesis technology is an important part in computer graphics. Monte Carlo based light simulation methods, such as Monte Carlo path tracing, can deal with complex lighting computations for complex scenes, in the field of realistic image synthesis. Unfortunately, if the samples taken for each pixel are not enough, the generated images have a lot of random noise. Adaptive sampling is attractive to reduce image noise. This paper proposes a new GH-distance based adaptive sampling algorithm. Experimental results show that the method can perform better than other similar ones.


2020 ◽  
Vol 5 (4) ◽  
pp. 64
Author(s):  
Themis Matsoukas

We formulate the statistics of the discrete multicomponent fragmentation event using a methodology borrowed from statistical mechanics. We generate the ensemble of all feasible distributions that can be formed when a single integer multicomponent mass is broken into fixed number of fragments and calculate the combinatorial multiplicity of all distributions in the set. We define random fragmentation by the condition that the probability of distribution be proportional to its multiplicity, and obtain the partition function and the mean distribution in closed form. We then introduce a functional that biases the probability of distribution to produce in a systematic manner fragment distributions that deviate to any arbitrary degree from the random case. We corroborate the results of the theory by Monte Carlo simulation, and demonstrate examples in which components in sieve cuts of the fragment distribution undergo preferential mixing or segregation relative to the parent particle.


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