scholarly journals When Can Non-Gaussian Density Fields Produce a Gaussian Sachs-Wolfe Effect?

1995 ◽  
Vol 446 ◽  
pp. 44 ◽  
Author(s):  
Robert J. Scherrer ◽  
Robert K. Schaefer
Keyword(s):  
Gaussian Density ◽  
Non Gaussian

scholarly journals Modelling with star-shaped distributions

2020 ◽  
Vol 8 (1) ◽  
pp. 45-69
Author(s):  
Eckhard Liebscher ◽  
Wolf-Dieter Richter
Keyword(s):  
Probabilistic Models ◽  
Arbitrary Dimension ◽  
Probability Density Functions ◽  
Density Functions ◽  
Wide Range ◽  
Gaussian Density ◽  
Spherical Distributions ◽  
Data Points ◽  
Non Gaussian ◽  
General Method

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.

M-Ary Alpha-Stable Noise Modulation in Spread-Spectrum Communication

2015 ◽  
Vol 14 (03) ◽  
pp. 1550022 ◽  
Author(s):  
Mehmet Emre Cek
Keyword(s):  
Gaussian Noise ◽  
Spread Spectrum ◽  
Additive White Gaussian Noise ◽  
Characteristic Exponent ◽  
Random Signal ◽  
Spread Spectrum Communication ◽  
Order Moment ◽  
Gaussian Density ◽  
Shift Keying ◽  
Non Gaussian

In this paper, a spread-spectrum communication system based on a random carrier is proposed which transmits M-ary information. The random signal is considered as a single realization of a random process taken from prescribed symmetric α-stable (SαS) distribution that carries digital M-ary information to be transmitted. Considering the noise model in the channel as additive white Gaussian noise (AWGN), the transmitter sends the information carrying random signal from non-Gaussian density. Alpha-stable distribution is used to encode the M-ary message. Inspired by the chaos shift keying techniques, the proposed method is called M-ary symmetric alpha-stable differential shift keying (M-ary SαS-DSK). The main purpose of preferring non-Gaussian noise instead of conventional pseudo-noise (PN) sequence is to overcome the drawback of self-repeating noise-like sequences which are detectable due to the periodic behavior of the autocorrelation function of PN sequences. Having infinite second order moment in α-stable random carrier offers secrecy of the information due to the non-constant autocorrelation behavior. The bit error rate (BER) performance of the proposed method is illustrated by Monte Carlo simulations with respect to various characteristic exponent values and different data length.

Non-Gaussian Density Processes Arising from Non-Poisson Systems of Independent Brownian Motions

1998 ◽  
Vol 35 (01) ◽  
pp. 213-220
Author(s):  
Raisa E. Feldman ◽  
Srikanth K. Iyer
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Stochastic Partial Differential Equation ◽  
Particle System ◽  
Initial Distribution ◽  
Martingale Measure ◽  
Partial Differential ◽  
Gaussian Density ◽  
Non Gaussian ◽  
Density Process

The Brownian density process is a Gaussian distribution-valued process. It can be defined either as a limit of a functional over a Poisson system of independent Brownian particles or as a solution of a stochastic partial differential equation with respect to Gaussian martingale measure. We show that, with an appropriate change in the initial distribution of the infinite particle system, the limiting density process is non-Gaussian and it solves a stochastic partial differential equation where the initial measure and the driving measure are non-Gaussian, possibly having infinite second moment.

Response of a Tethered Aerostat to Simulated Turbulence

2004 ◽  
Author(s):  
Keith A. Stanney ◽  
Christopher D. Rahn
Keyword(s):  
Atmospheric Turbulence ◽  
Initial Conditions ◽  
Probability Distributions ◽  
Decomposition Theorem ◽  
Worst Case ◽  
Gaussian Density ◽  
Time Histories ◽  
Wind Input ◽  
Non Gaussian ◽  
Proper Orthogonal

Aerostats are lighter-than-air vehicles tethered to the ground by a cable and used for broadcasting, communications, surveillance, and drug interdiction. The dynamic response of tethered aerostats subject to extreme atmospheric turbulence often dictates survivability. This paper develops a theoretical model that predicts the planar response of a tethered aerostat subject to atmospheric turbulence and simulates the response to 1000 simulated hurricane scale turbulent time histories. The aerostat dynamic model assumes the aerostat hull to be a rigid body with nonlinear fluid loading, instantaneous weathervaning for planar response, and a continuous tether. Galerkin’s method discretizes the coupled aerostat and tether partial differential equations to produce a nonlinear initial value problem that is integrated numerically given initial conditions and wind inputs. The proper orthogonal decomposition theorem generates, based on Hurricane Georges wind data, turbulent time histories that possess the sequential behavior of actual turbulence, are spectrally accurate, and have non-Gaussian density functions. The generated turbulent time histories are simulated to predict the aerostat response to severe turbulence. The resulting probability distributions for the aerostat position, pitch angle, and confluence point tension predict the aerostat behavior in high gust environments. The results uncover a worst case wind input consisting of a two-pulse vertical gust.

Non-gaussian density perturbations in inflationary cosmologies

1987 ◽  
Vol 197 (1-2) ◽  
pp. 66-70 ◽  
Author(s):  
T.J. Allen ◽  
B. Grinstein ◽  
Mark B. Wise
Keyword(s):  
Gaussian Density ◽  
Density Perturbations ◽  
Non Gaussian
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