Desarrollo y evaluación de modelos marginales y de evolución temporal para el tráfico agregado de redes IP

En las redes de comunicaciones actuales existe una gran discrepancia entre las características del tráfico observadas y las predichas por los modelos de tráfico clásicos como el de Poisson, históricamente utilizados. En concreto, se observa un cierto grado de impulsividad tanto en la tasa binaria como en la cantidad de paquetes recibidos por unidad de tiempo, así como ciertas características de autosimilitud en el tráfico de red que estos modelos no son capaces de reflejar. Ante estas evidencias, en los últimos años han surgido multitud de propuestas de modelos de tráfico de red más avanzados que los tradicionales, aunque aún no existe consenso acerca de la superioridad de alguno de ellos. En este Trabajo Fin de Máster se desarrolla y evalúa la validez de un modelo de tráfico basado Vuelos de Lévy Truncados Suavemente (STLF) para el tráfico agregado en redes IP.Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione

Software solutions for two computationally intensive problems: reconstruction of dynamic MR and handling of alpha-stable distributions

En esta Tesis Doctoral se abordan dos problemas de interés computacionalmente muy intensivos, a saber, la reconstrucción de imagen por resonancia magnética (IRM) dinámica a partir de datos altamente sub-muestreados y la consecución eficiente del cálculo numérico implicado en el manejo de distribuciones alfa-estables para el modelado estadístico. Sobre el primer problema, la IRM está a menudo limitada por los largos tiempos requeridos obtener las imágenes deseadas. Una forma de reducir este tiempo es introducir cierto conocimiento a priori o modelo acerca de la estructura de las imágenes de interés durante el proceso de reconstrucción. Uno de los principales objetivos de esta Tesis es proporcionar un mejor modelo que los disponibles actualmente para la reconstrucción de imágenes de resonancia magnética cine del corazón que incorpore el movimiento de las estructuras a examen en su descripción. Sobre el segundo problema, la falta de fórmulas cerradas para el cálculo de las funciones de distribución y de densidad de probabilidad de distribuciones alfa-estables supone una importante limitación en su aplicación. En esta Tesis Doctoral se proporcionan herramientas necesarias para su cálculo numérico así como para la estimación de sus parámetros con alta precisión y rendimiento.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

libstable: Fast, Parallel, and High-Precision Computation of α-Stable Distributions in R, C/C++, and MATLAB

α-stable distributions are a family of well-known probability distributions. However, the lack of closed analytical expressions hinders their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or to estimate their parameters, but available solutions either do not reach sufficient precision on their evaluations or are excessively slow for practical purposes. Moreover, they do not take full advantage of the parallel processing capabilities of current multi-core machines. Other solutions work only on a subset of the α-stable parameter space. In this paper we present an R package and a C/C++ library with a MATLAB front-end that permit parallelized, fast and high precision evaluation of density, distribution and quantile functions, as well as random variable generation and parameter estimation of α-stable distributions in their whole parameter space. The described library can be easily integrated into third party developments

Libstable: Fast, Parallel and High-Precision Computation of -Stable Distributions in C/C++ and MATLAB

-stable distributions are a wide family of probability distributions used in many elds where probabilistic approaches are taken. However, the lack of closed analytical expressions is a major drawback for their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or estimate their parameters, but available solutions either do not reach su cient precision on their evaluations or are too slow for several practical purposes. Moreover, they do not take full advantage of the parallel processing capabilities of current multi-core machines. Other solutions work only on a subset of the -stable parameter space. In this paper we present a C/C++ library and a MATLAB front-end that allows fully parallelized, fast and high precision evaluation of density, distribution and quantile functions (PDF, CDF and CDF1 respectively), random variable generation and parameter estimation of -stable distributions in their whole parameter space. The library provided can be easily integrated on third party developments

Multi-Oriented Windowed Harmonic Phase Reconstruction for Robust Cardiac Strain Imaging

The purpose of this work is to develop a method for direct estimation of the cardiac strain tensor by extending the harmonic phase reconstruction on tagged magnetic resonance images to obtain more precise and robust measurements. The extension relies on the reconstruction of the local phase of the image by means of the windowed Fourier transform and the acquisition of an overdetermined set of stripe orientations in order to avoid the phase interferences from structures outside the myocardium and the instabilities arising from the application of a gradient operator. Results have shown that increasing the number of acquired orientations provides a signi cant improvement in the reproducibility of the strain measurements and that the acquisition of an extended set of orientations also improves the reproducibility when compared with acquiring repeated samples from a smaller set of orientations. Additionally, biases in local phase estimation when using the original harmonic phase formulation are greatly diminished by the one here proposed. The ideas here presented allow the design of new methods for motion sensitive magnetic resonance imaging, which could simultaneously improve the resolution, robustness and accuracy of motion estimates

Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruc- tion of Breath-Hold Cardiac Cine MRI

Purpose: Compressed sensing methods with motion estimation and compensation techniques have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion from reconstructed images, especially at high acceleration factors. This work introduces a robust groupwise non-rigid motion estimation technique applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static sequence with highly sparse representation in temporally transformed domains. Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. Conclusions: Reconstruction methods based on groupwise registration show higher quality recon- structions for cardiac cine images than the pairwise counterparts tested

Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images

The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns while providing information about the reliability of the classification. Specifically, each CCT scan is divided into cubic patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the pneumonia patterns regardless of their size or location. Decisions of each individual patch are then combined into a global one according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would result in a huge computational cost) evidence the applicability of our proposal in a real-world environment.Comment: 15 pages, 9 figure

Fast 4D elastic group-wise image registration. Convolutional interpolation revisited

Background and Objective:This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. Methods:Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. Results:The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation. Conclusions:Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.MEC-TEC2017-82408-