关于Semigroups of Linear Operators and Applications to Partial Differential Equations的两个注解

Introduction to Inverse Problems in Imaging

Dynamics of Linear Operators: An introduction to Read-type operators

We consider a thermoelastic theory where the heat conduction is described by the Moore–Gibson–Thompson equation. In fact, this equation can be obtained after the introduction of a relaxation parame...

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Moore–Gibson–Thompson thermoelasticity:

Abstract : The emerging discipline known as \"chaos theory\" is a relatively new field of study with a diverse range of applications (i.e., economics, biology, meteorology, etc.). Despite this, there is not as yet a universally accepted definition for \"chaos\" as it applies to general dynamical systems. Various approaches range from topological methods of a qualitative description; to physical notions of randomness, information, and entropy in ergodic theory; to the development of computational definitions and algorithms designed to obtain quantitative information. This thesis develops some of the current definitions and discusses several quantitative measures of chaos. It is intended to stimulate the interest of undergraduate and graduate students and is accessible to those with a knowledge of advanced calculus and ordinary differential equations. In covering chaos for continuous systems, it serves as a complement to the work done by Philip Beaver, which details chaotic dynamics for discrete systems.

Introduction of chaotic dynamical systems

The first and third authors are supported in part by MEC Project MTM2013-47093-P. The second author is partially supported by Project Anillo ACT1112. The authors are grateful to an anonymous referee for helpful comments that improved the presentation of the paper.

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Chaotic Behaviour of the Solutions of the Moore-Gibson- Thompson Equation

Parallel CT image reconstruction based on GPUs

Automatic evaluation of the image quality of a mammographic phantom.

Digital mammographic image processing often requires a previous application of filters to reduce the noise level of the image while preserving important details. This may improve the quality of digital mammographic images and contribute to an accurate diagnosis. In the literature, one can find a large amount of denoising techniques available for different kinds of images. We have adapted some of the existing denoising algorithms to mammographic images. We compare the effect of different denoising filters acting on digitized mammograms. The considered filters are: a local Wiener filter, a wavelet filter, a filter based on independent component analysis, and finally, a filter based on the diffusion equation. The noise reduction is measured by the mean squared error.

Comparing methods to denoise mammographic images

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections However, in practice, these methods are not widely used due to the high computational cost of their implementation Nowadays technology provides the possibility to reduce effectively this drawback It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data

F. Rodenas

Papers

Chaotic Behaviour of the Solutions of the Moore-Gibson- Thompson Equation

Parallel CT image reconstruction based on GPUs

Automatic evaluation of the image quality of a mammographic phantom.

Comparing methods to denoise mammographic images

CT Image Reconstruction Based on GPUs