scholarly journals Nonlinear diffusion models of detection

2008 ◽  
Vol 9 (S1) ◽  
Author(s):  
Daniel Martí ◽  
Anders Ledberg ◽  
Gustavo Deco
Keyword(s):  
Nonlinear Diffusion ◽  
Diffusion Models

scholarly journals Nonlinear diffusion models for gravitational wave turbulence

2019 ◽  
Vol 390 ◽  
pp. 84-88 ◽  
Author(s):  
Sébastien Galtier ◽  
Sergey V. Nazarenko ◽  
Éric Buchlin ◽  
Simon Thalabard
Keyword(s):  
Gravitational Wave ◽  
Nonlinear Diffusion ◽  
Diffusion Models ◽  
Wave Turbulence

scholarly journals Global well-posedness of advective Lotka–Volterra competition systems with nonlinear diffusion

2019 ◽  
Vol 150 (5) ◽  
pp. 2322-2348
Author(s):  
Qi Wang ◽  
Jingyue Yang ◽  
Feng Yu
Keyword(s):  
Global Existence ◽  
Elliptic System ◽  
Nonlinear Diffusion ◽  
Uniform Boundedness ◽  
Parabolic Systems ◽  
Growth Conditions ◽  
Diffusion Models ◽  
Well Posedness ◽  
Sensitivity Functions ◽  
Advection Diffusion

AbstractThis paper investigates the global well-posedness of a class of reaction–advection–diffusion models with nonlinear diffusion and Lotka–Volterra dynamics. We prove the existence and uniform boundedness of the global-in-time solutions to the fully parabolic systems under certain growth conditions on the diffusion and sensitivity functions. Global existence and uniform boundedness of the corresponding parabolic–elliptic system are also obtained. Our results suggest that attraction (positive taxis) inhibits blowups in Lotka–Volterra competition systems.

Nonlinear Diffusion Model for Fabric Image Denoising

2012 ◽  
Vol 627 ◽  
pp. 484-488
Author(s):  
Da Li Chen ◽  
Ding Yu Xue ◽  
Yang Quan Chen
Keyword(s):  
Image Denoising ◽  
Diffusion Model ◽  
Nonlinear Diffusion ◽  
Diffusion Models ◽  
Perceptual Comparison

In this paper, five nonlinear diffusion models for fabric image denoising are introduced. The advantages and drawbacks of these five models are described through describing their implementation methods. Quantitative and perceptual comparison experiments are given to verify the performance of these methods. Finally some valuable conclusions about denoising performance of these five models are present which is helpful for choosing and using these models in fabric image denoising.

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