Non-Gaussian models for the statistics of scattered waves

1988 ◽  
Vol 37 (5) ◽  
pp. 471-529 ◽  
Author(s):  
E. Jakeman ◽  
R.J.A. Tough
Keyword(s):  
Gaussian Models ◽  
Non Gaussian

Poly-Gaussian models of a non-Gaussian randomly rough surface

2012 ◽  
Vol 57 (4) ◽  
pp. 524-533 ◽  
Author(s):  
M. Ya. Litvak ◽  
V. I. Malyugin
Keyword(s):  
Rough Surface ◽  
Gaussian Models ◽  
Non Gaussian ◽  
Randomly Rough Surface

scholarly journals Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2827 ◽  
Author(s):  
Danilo Pena ◽  
Carlos Lima ◽  
Matheus Dória ◽  
Luan Pena ◽  
Allan Martins ◽  
...  
Keyword(s):  
Impulsive Noise ◽  
Gaussian Mixture Models ◽  
Audio Signal ◽  
Poor Performance ◽  
Gaussian Mixture ◽  
Stable Model ◽  
Gaussian Models ◽  
Non Gaussian ◽  
Signal Processing Methods ◽  
Best Fit

In general, acoustic channels are not Gaussian distributed neither are second-order stationary. Considering them for signal processing methods designed for Gaussian assumptions is inadequate, consequently yielding in poor performance of such methods. This paper presents an analysis for audio signal corrupted by impulsive noise using non-Gaussian models. Audio samples are compared to the Gaussian, α -stable and Gaussian mixture models, evaluating the fitting by graphical and numerical methods. We discuss fitting properties as the window length and the overlap, finally concluding that the α -stable model has the best fit for all tested scenarios.

Non-Gaussian Models for Object Motion Analysis with Time-Lapse Fluorescence Microscopy Images

2021 ◽  
pp. 15-41
Author(s):  
Hanyi Yu ◽  
Sung Bo Yoon ◽  
Robert Kauffman ◽  
Jens Wrammert ◽  
Adam Marcus ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Motion Analysis ◽  
Time Lapse ◽  
Object Motion ◽  
Gaussian Models ◽  
Non Gaussian ◽  
Microscopy Images

Extremes of Nonlinear Vibration: Comparing Models Based on Moments, L-Moments, and Maximum Entropy

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Steven R. Winterstein ◽  
Cameron A. MacKenzie
Keyword(s):  
Maximum Entropy ◽  
Nonlinear Effects ◽  
Offshore Structures ◽  
Sufficient Information ◽  
Wave Loads ◽  
Gaussian Models ◽  
Maximum Entropy Models ◽  
Extreme Response ◽  
L Moments ◽  
Non Gaussian

Wind and wave loads on offshore structures show nonlinear effects, which require non-Gaussian statistical models. Here we critically review the behavior of various non-Gaussian models. We first survey moment-based models; in particular, the four-moment “Hermite” model, a cubic transformation often used in wind and wave applications. We then derive an “L-Hermite” model, an alternative cubic transformation calibrated by the response “L-moments” rather than its ordinary statistical moments. These L-moments have recently found increasing use, in part because they show less sensitivity to distribution tails than ordinary moments. We find here, however, that these L-moments may not convey sufficient information to accurately estimate extreme response statistics. Finally, we show that four-moment maximum entropy models, also applied in the literature, may be inappropriate to model broader-than-Gaussian cases (e.g., responses to wind and wave loads).

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