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Mathematical Analysis of Random Noise-Conclusion
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This article is published in Bell System Technical Journal.The article was published on 1945-01-01 and is currently open access. It has received 807 citations till now.read more
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Geometric properties of the underside of sea ice
D. A. Rothrock,A. S. Thorndike +1 more
TL;DR: In this paper, it was found that the power spectral density functions of the profiles of the underside of sea ice are rough and disorderly, and that the spectra vary approximately as (wave number)−3 at high wave numbers.
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Mapping stochastic processes onto complex networks
A. H. Shirazi,G. Reza Jafari,G. Reza Jafari,J Davoudi,Joachim Peinke,M. Reza Rahimi Tabar,M. Reza Rahimi Tabar,Muhammad Sahimi +7 more
TL;DR: A method by which stochastic processes are mapped onto complex networks is introduced and it is demonstrated that the time series can be reconstructed with high precision by means of a simple random walk on their corresponding networks.
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Comparative Aspects of the Study of Ordinary Time Series and of Point Processes
TL;DR: In this paper, the authors discuss the cases in which the concepts and procedures of ordinary time series or point processes have direct analogs in the study of point processes or ordinary timeseries.
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Partially averaged field approach to cosmic ray diffusion
TL;DR: In this paper, the pitch angle diffusion coefficient is calculated for particles interacting with slab model magnetic turbulence, i.e., magnetic fluctuations linearly polarized transverse to a mean magnetic field, and the major effect of the nonlinear treatment in this illustration is the determination of D sub Mu Mu in the vicinity of 90 deg pitch angles where quasilinear theory breaks down.
Journal ArticleDOI
Maximum a posteriori estimation of diffusion tensor parameters using a Rician noise model: Why, how and but
TL;DR: A computational framework is presented where parameters pertaining to a spectral decomposition of the diffusion tensor are estimated using a Rician noise model, and it is demonstrated how the Fisher-information matrix can be used as a generic tool for designing optimal experiments.