P-adaptive boundary elements for three-dimensional potential problems

This paper deals with the boundary element method (BEM) p-convergence approach applied to three-dimensional problems governed by Laplace's equation. The advantages derived from the boundary discretization and hierarchical interpolation functions are collated in order to minimize human effort in preparation of input data and improve numerical results.

Generating Adaptive Presentations of Hydrologic Behavior

This paper describes a knowledge-based approach for summarizing and presenting the behavior of hydrologic networks. This approach has been designed for visualizing data from sensors and simulations in the context of emergencies caused by floods. It follows a solution for event summarization that exploits physical properties of the dynamic system to automatically generate summaries of relevant data. The summarized information is presented using different modes such as text, 2D graphics and 3D animations on virtual terrains. The presentation is automatically generated using a hierarchical planner with abstract presentation fragments corresponding to discourse patterns, taking into account the characteristics of the user who receives the information and constraints imposed by the communication devices (mobile phone, computer, fax, etc.). An application following this approach has been developed for a national hydrologic information infrastructure of Spain.

Knowledge-based models for adaptive traffic management systems

This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems.

The p-Adaptive Biem Approach for Two-Dimensional Elasticity Analysis

This paper presents the development and application of the p-adaptive BIEM version in elastostatics. The basic concepts underlying the p-adaptive technique are summarized and discussed. Some Pascal pseudocodes which show the way how such a technique can be implemented easily in microcomputers are also provided. Both the applicability and the accuracy of the method proposed here are illustrated through a numerical example.

Fully adaptive gaussian mixture metropolis-hastings algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.

Development of AMREM: a Maxwell's equations solver with Adaptive Mesh Refinement

El principal objetivo de la presente tesis es el de desarrollar y probar un código capaz de resolver las ecuaciones de Maxwell en el dominio del tiempo con Malla Refinada Adaptativa (AMR por sus siglas en inglés). AMR es una técnica de cálculo basada en dividir el dominio físico del problema en distintas mallas rectangulares paralelas a las direcciones cartesianas. Cada una de las mallas tendrá distinta resolución y aquellas con mayor resolución se sitúan allí dónde las ondas electromagnéticas se propagan o interaccionan con los materiales, es decir, dónde mayor precisión es requerida. Como las ondas van desplazándose por todo el dominio, las mayas deberán seguirlas. El principal problema al utilizar esta metodología se puede encontrar en las fronteras internas, dónde las distintas mallas se unen. Ya que el método más corrientemente utilizado para resolver las ecuaciones de Maxwell es el de las diferencias finitas en el dominio del tiempo (FDTD por sus siglas en inglés) , el trabajo comenzó tratando de adaptar AMR a FDTD. Tras descubrirse que esta interacción resultaba en problemas de inestabilidades en las fronteras internas antes citadas, se decidió cambiar a un método basado en volúmenes finitos en el dominio del tiempo (FVTD por sus siglas en inglés). Este se basa en considerar la forma en ecuaciones de conservación de las ecuaciones de Maxwell y aplicar a su resolución un esquema de Godunov. Se ha probado que es clave para el correcto funcionamiento del código la elección de un limitador de flujo que proteja los extremos de la onda de la disipación típica de los métodos de este tipo. Otro problema clásico a la hora de resolver las ecuaciones de Maxwell es el de tratar con las condiciones de frontera física cuando se simulan dominios no acotados, es decir, dónde las ondas deben salir del sistema sin producir ninguna reflexión. Normalmente la solución es la de disponer una banda absorbente en las fronteras físicas. En AMREM se ha desarrollado un nuevo método basado en los campos característicos que con menor requisito de CPU funcina suficientemente bien incluso en los casos más desfaborables. El código ha sido contrastado con soluciones analíticas de diferentes problemas y también su velocidad ha sido comparada con la de Meep, uno de los programas más conocidos del ámbito. También algunas aplicaciones han sido simuladas con el fin de demostrar el amplio espectro de campos en los que AMREM puede funcionar como una útil herramienta.

Adaptive Pointing Theory (APT) Artificial Neural Network

The choice value and the testing process against the vigilance parameter, characteristic of ART Neural Network, are merged. Only, a single unique test is required to determine if a committed category node can represent the current input or not. Advantages of APT over ART are: 1-Avoid testing every committed category node before deciding to train a committed category node or a new node must be committed, 2-The vigilance parameter is fixed during training, and 3-The choice value parameter is eliminated.

Debonding detection of FRP strengthened concrete beams by using impedance measurements and an ensemble PSO adaptive spectral model

An impedance-based midspan debonding identification method for RC beams strengthened with FRP strips is presented in this paper using piezoelectric ceramic (PZT) sensor?actuators. To reach this purpose, firstly, a two-dimensional electromechanical impedance model is proposed to predict the electrical admittance of the PZT transducer bonded to the FRP strips of an RC beam. Considering the impedance is measured in high frequencies, a spectral element model of the bonded-PZT?FRP strengthened beam is developed. This model, in conjunction with experimental measurements of PZT transducers, is used to present an updating methodology to quantitatively detect interfacial debonding of these kinds of structures. To improve the performance and accuracy of the detection algorithm in a challenging problem such as ours, the structural health monitoring approach is solved with an ensemble process based on particle of swarm. An adaptive mesh scheme has also been developed to increase the reliability in locating the area in which debonding initiates. Predictions carried out with experimental results have showed the effectiveness and potential of the proposed method to detect prematurely at its earliest stages a critical failure mode such as that due to midspan debonding of the FRP strip.

Subjective quality assessment of an adaptive video streaming model

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients’ behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

Independent doubly Adaptive Rejection Metropolis Sampling

Adaptive Rejection Metropolis Sampling (ARMS) is a wellknown MCMC scheme for generating samples from onedimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete adaptation of the proposal in some cases), speeding up the convergence of the chain to the target. Numerical results show that IA2RMS outperforms the standard ARMS, providing a correlation among samples close to zero.

An adaptive population importance sampler

Monte Carlo (MC) methods are widely used in signal processing, machine learning and communications for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this work, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples generated. The cloud of proposals is adapted by learning from a subset of previously generated samples, in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error and robustness to initialization.

Quality of experience and HTTP adaptive streaming: a review of subjective studies

HTTP adaptive streaming technology has become widely spread in multimedia services because of its ability to provide adaptation to characteristics of various viewing devices and dynamic network conditions. There are various studies targeting the optimization of adaptation strategy. However, in order to provide an optimal viewing experience to the end-user, it is crucial to get knowledge about the Quality of Experience (QoE) of different adaptation schemes. This paper overviews the state of the art concerning subjective evaluation of adaptive streaming QoE and highlights the challenges and open research questions related to QoE assessment.

Crowdsourcing based subjective quality assessment of adaptive video streaming

In order to cater for user's quality of experience (QoE) requirements, HTTP adaptive streaming (HAS) based solutions of video services have become popular recently. User QoE feedback can be instrumental in improving the capabilities of such services. Perceptual quality experiments that involve humans are considered to be the most valid method of the assessment of QoE. Besides lab-based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative method. This paper presents insights into a study that investigates perceptual preferences of various adaptive video streaming scenarios through crowdsourcing based subjective quality assessment.

Subjective quality study of adaptive streaming of monoscopic and stereoscopic video

Nowadays, HTTP adaptive streaming (HAS) has become a reliable distribution technology offering significant advantages in terms of both user perceived Quality of Experience (QoE) and resource utilization for content and network service providers. By trading-off the video quality, HAS is able to adapt to the available bandwidth and display requirements so that it can deliver the video content to a variety of devices over the Internet. However, until now there is not enough knowledge of how the adaptation techniques affect the end user's visual experience. Therefore, this paper presents a comparative analysis of different bitrate adaptation strategies in adaptive streaming of monoscopic and stereoscopic video. This has been done through a subjective experiment of testing the end-user response to the video quality variations, considering the visual comfort issue. The experimental outcomes have made a good insight into the factors that can influence on the QoE of different adaptation strategies.

Effect of content characteristics on quality of experience of adaptive streaming

The growing popularity of adaptive streaming-based video delivery nowadays has raised the interest about the user's perception when experiencing quality adaptation. The impact of the video content characteristics on user's perceptual quality has already become evident. The aim of this study is to investigate the influence of this factor on the quality of experience of adaptive streaming scenarios. Our results show that the perceptual quality of adaptation strategies applied on videos with high spatial and low temporal amount of activity is significantly lower compared to the other content types.