Distribución multicast de vídeo en directo sobre la red móvil LTE

En la última década, la telefonía móvil ha evolucionado a una extraordinaria velocidad, permitiéndonos acceder a funcionalidades características de los PC pero con la ventaja de poseer una movilidad total. Con la aparición de la tecnología Long Term Evolution (LTE), comúnmente conocida como 4G, se ha conseguido desarrollar un sistema que se ha mejorado notablemente las prestaciones proporcionando alta velocidad y eficiencia a los ya masivamente utilizados smartphones. Gracias a este exponencial incremento del ancho de banda disponible, los usuarios hoy en día no se conforman sólo con navegar por páginas Web, sino que cada vez muestran un mayor interés en poder explotar al máximo los recursos multimedia, dando lugar a servicios como el streaming de vídeo. De este modo, a raíz del proyecto LTExtreme centrado en el análisis y la propuesta de optimización para servicios de streaming multimedia multicast/unicast sobre la tecnología LTE, surge este trabajo en el cual se pretende extender dicho análisis a la multidifusión de vídeo en directo. El proyecto se basa en la implementación de la arquitectura propuesta por el organismo 3GPP para dar este servicio, considerándose como una solución eficiente en la que se combina el protocolo de transporte multicast FLUTE (File Delivery over Unidirectional Transport) con la tecnología DASH (Dynamic Adaptative Streaming over HTTP). La arquitectura se ha implementado mediante la creación y configuración de una maqueta de laboratorio gracias a la herramienta de virtualización Virtual Networks over linuX (VNX). Un escenario simplificado de la red móvil LTE junto con el servidor de contenidos y varios clientes móviles, pudiendo realizar simulaciones de una emisión de vídeo en directo, y a su vez analizar los resultados obtenidos, así como la calidad de servicio percibida. Concretamente, se realizará un análisis de los problemas asociados a los casos de uso tratados, tanto de la emisión de un único vídeo como una de duración infinita, asemejándose a lo que supondría la emisión de la programación televisiva para un determinado canal. Por último, se plantearán ideas surgidas a raíz de los resultados obtenidos de dichos estudios y que puedan tener futuro y ser aplicables al mundo real.

Aerodynamic database error filtering via high order singular value decomposition

Esta Tesis presenta un nuevo método para filtrar errores en bases de datos multidimensionales. Este método no precisa ninguna información a priori sobre la naturaleza de los errores. En concreto, los errrores no deben ser necesariamente pequeños, ni de distribución aleatoria ni tener media cero. El único requerimiento es que no estén correlados con la información limpia propia de la base de datos. Este nuevo método se basa en una extensión mejorada del método básico de reconstrucción de huecos (capaz de reconstruir la información que falta de una base de datos multidimensional en posiciones conocidas) inventado por Everson y Sirovich (1995). El método de reconstrucción de huecos mejorado ha evolucionado como un método de filtrado de errores de dos pasos: en primer lugar, (a) identifica las posiciones en la base de datos afectadas por los errores y después, (b) reconstruye la información en dichas posiciones tratando la información de éstas como información desconocida. El método resultante filtra errores O(1) de forma eficiente, tanto si son errores aleatorios como sistemáticos e incluso si su distribución en la base de datos está concentrada o esparcida por ella. Primero, se ilustra el funcionamiento delmétodo con una base de datosmodelo bidimensional, que resulta de la dicretización de una función transcendental. Posteriormente, se presentan algunos casos prácticos de aplicación del método a dos bases de datos tridimensionales aerodinámicas que contienen la distribución de presiones sobre un ala a varios ángulos de ataque. Estas bases de datos resultan de modelos numéricos calculados en CFD. ABSTRACT A method is presented to filter errors out in multidimensional databases. The method does not require any a priori information about the nature the errors. In particular, the errors need not to be small, neither random, nor exhibit zero mean. Instead, they are only required to be relatively uncorrelated to the clean information contained in the database. The method is based on an improved extension of a seminal iterative gappy reconstruction method (able to reconstruct lost information at known positions in the database) due to Everson and Sirovich (1995). The improved gappy reconstruction method is evolved as an error filtering method in two steps, since it is adapted to first (a) identify the error locations in the database and then (b) reconstruct the information in these locations by treating the associated data as gappy data. The resultingmethod filters out O(1) errors in an efficient fashion, both when these are random and when they are systematic, and also both when they concentrated and when they are spread along the database. The performance of the method is first illustrated using a two-dimensional toymodel database resulting fromdiscretizing a transcendental function and then tested on two CFD-calculated, three-dimensional aerodynamic databases containing the pressure coefficient on the surface of a wing for varying values of the angle of attack. A more general performance analysis of the method is presented with the intention of quantifying the randomness factor the method admits maintaining a correct performance and secondly, quantifying the size of error the method can detect. Lastly, some improvements of the method are proposed with their respective verification.

Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

To investigate the nature of plasticity in the adult visual system, perceptual learning was measured in a peripheral orientation discrimination task with systematically varying amounts of external (environmental) noise. The signal contrasts required to achieve threshold were reduced by a factor or two or more after training at all levels of external noise. The strong quantitative regularities revealed by this novel paradigm ruled out changes in multiplicative internal noise, changes in transducer nonlinearites, and simple attentional tradeoffs. Instead, the regularities specify the mechanisms of perceptual learning at the behavioral level as a combination of external noise exclusion and stimulus enhancement via additive internal noise reduction. The findings also constrain the neural architecture of perceptual learning. Plasticity in the weights between basic visual channels and decision is sufficient to account for perceptual learning without requiring the retuning of visual mechanisms.

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.

Power line interference filtering on surface electromyography based on the stationary wavelet packet transform

Power line interference is one of the main problems in surface electromyogram signals (EMG) analysis. In this work, a new method based on the stationary wavelet packet transform is proposed to estimate and remove this kind of noise from EMG data records. The performance has been quantitatively evaluated with synthetic noisy signals, obtaining good results independently from the signal to noise ratio (SNR). For the analyzed cases, the obtained results show that the correlation coefficient is around 0.99, the energy respecting to the pure EMG signal is 98–104%, the SNR is between 16.64 and 20.40 dB and the mean absolute error (MAE) is in the range of −69.02 and −65.31 dB. It has been also applied on 18 real EMG signals, evaluating the percentage of energy respecting to the noisy signals. The proposed method adjusts the reduction level to the amplitude of each harmonic present in the analyzed noisy signals (synthetic and real), reducing the harmonics with no alteration of the desired signal.

SeismicWaveTool: Continuous and discrete wavelet analysis and filtering for multichannel seismic data

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of multichannel seismic data. The considered time–frequency transforms include the continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform. The developed approaches provide a fast and precise time–frequency examination of the seismograms at different frequency bands. Moreover, filtering methods for noise, transients or even baseline removal, are implemented. The primary motivation is to support seismologists with a user-friendly and fast program for the wavelet analysis, providing practical and understandable results.

A Matrix PRNG with S-Box Output Filtering

We describe a modification to a previously published pseudorandom number generator improving security while maintaining high performance. The proposed generator is based on the powers of a word-packed block upper triangular matrix and it is designed to be fast and easy to implement in software since it mainly involves bitwise operations between machine registers and, in our tests, it presents excellent security and statistical characteristics. The modifications include a new, key-derived s-box based nonlinear output filter and improved seeding and extraction mechanisms. This output filter can also be applied to other generators.

On the decorrelation filtering of RL05 GRACE data for global applications

In autumn 2012, the new release 05 (RL05) of monthly geopotencial spherical harmonics Stokes coefficients (SC) from GRACE (Gravity Recovery and Climate Experiment) mission was published. This release reduces the noise in high degree and order SC, but they still need to be filtered. One of the most common filtering processing is the combination of decorrelation and Gaussian filters. Both of them are parameters dependent and must be tuned by the users. Previous studies have analyzed the parameters choice for the RL05 GRACE data for oceanic applications, and for RL04 data for global application. This study updates the latter for RL05 data extending the statistics analysis. The choice of the parameters of the decorrelation filter has been optimized to: (1) balance the noise reduction and the geophysical signal attenuation produced by the filtering process; (2) minimize the differences between GRACE and model-based data; (3) maximize the ratio of variability between continents and oceans. The Gaussian filter has been optimized following the latter criteria. Besides, an anisotropic filter, the fan filter, has been analyzed as an alternative to the Gauss filter, producing better statistics.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time–period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

Advanced filtering and prediction software for urban traffic control systems. Final report.

Transportation Department, Office of University Research, Washington, D.C.

Digital filtering using burst processing techniques /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Experimental results for logical filtering of digitized pictures /

"*This work was supported in part by the U.S. Atomic Energy Commission under Contract No. AT911-1)-1018."

Research in gum, dust and rust filtering.

cover-title

Aspects of optimum nonlinear filtering using nonlinear zero-memory elements.

"Cornell Aeronautical Laboratory internal research."