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Interpolation Techniques (interpolation + techniques)
Selected AbstractsNatural head motion synthesis driven by acoustic prosodic featuresCOMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 3-4 2005Carlos Busso Abstract Natural head motion is important to realistic facial animation and engaging human,computer interactions. In this paper, we present a novel data-driven approach to synthesize appropriate head motion by sampling from trained hidden markov models (HMMs). First, while an actress recited a corpus specifically designed to elicit various emotions, her 3D head motion was captured and further processed to construct a head motion database that included synchronized speech information. Then, an HMM for each discrete head motion representation (derived directly from data using vector quantization) was created by using acoustic prosodic features derived from speech. Finally, first-order Markov models and interpolation techniques were used to smooth the synthesized sequence. Our comparison experiments and novel synthesis results show that synthesized head motions follow the temporal dynamic behavior of real human subjects. Copyright © 2005 John Wiley & Sons, Ltd. [source] Geostatistical Analysis of RainfallGEOGRAPHICAL ANALYSIS, Issue 2 2010David I. F. Grimes Rainfall can be modeled as a spatially correlated random field superimposed on a background mean value; therefore, geostatistical methods are appropriate for the analysis of rain gauge data. Nevertheless, there are certain typical features of these data that must be taken into account to produce useful results, including the generally non-Gaussian mixed distribution, the inhomogeneity and low density of observations, and the temporal and spatial variability of spatial correlation patterns. Many studies show that rigorous geostatistical analysis performs better than other available interpolation techniques for rain gauge data. Important elements are the use of climatological variograms and the appropriate treatment of rainy and nonrainy areas. Benefits of geostatistical analysis for rainfall include ease of estimating areal averages, estimation of uncertainties, and the possibility of using secondary information (e.g., topography). Geostatistical analysis also facilitates the generation of ensembles of rainfall fields that are consistent with a given set of observations, allowing for a more realistic exploration of errors and their propagation in downstream models, such as those used for agricultural or hydrological forecasting. This article provides a review of geostatistical methods used for kriging, exemplified where appropriate by daily rain gauge data from Ethiopia. La precipitación puede ser modelada como un campo aleatorio correlacionado espacialmente sobrepuesto a un valor de fondo (background) medio. Dadas estas propiedades, resulta apropiado utilizar métodos geoestadísticos para el análisis de datos registrados con pluviómetros distribuidos en estaciones meteorológicas. Existen sin embargo, ciertas características de este tipo de datos que deben ser tomados en cuenta para producir resultados útiles:a) la distribución de datos tiende a ser mixta y no ser normal; b) las observaciones son heterogéneas y de escasa densidad espacial; y c) los patrones de correlación espacial son varían considerablemente en el tiempo y espacio. Numerosos estudios han demostrado ya que un análisis geoestadístico riguroso ofrece mejores resultados que las otras técnicas de interpolación disponibles para este tipo de datos. Cabe resaltar que en la aplicación de estas técnicas, el uso de variogramas climatológicos y el tratamiento apropiado de áreas lluviosas versus áreas no lluviosas son consideraciones importantes. El análisis geoestadístico de lluvias tiene además la ventaja de estimar promedios areales con facilidad, proporcionar una estimación espacial de la incertidumbre, y la posibilidad de incorporar información secundaria (ej. topografía) en el modelo. Asimismo, los métodos geoestadísticos también facilitan la generación de campos de lluvia que son consistentes con las observaciones. Esto hace posible exploraciones más realistas del error y la estimación de su propagación en modelos aplicados subsecuentemente, como por ejemplo en los modelos utilizados en predicción agrícola e hidrológica. Los autores reseñan los métodos geoestadísticos utilizados para krijeage o krijeado (kriging) mediante ejemplos de su uso apropiado con datos pluviométricos en Etiopia. [source] An interpolation-based local differential quadrature method to solve partial differential equations using irregularly distributed nodesINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 7 2008Hang Ma Abstract To circumvent the constraint in application of the conventional differential quadrature (DQ) method that the solution domain has to be a regular region, an interpolation-based local differential quadrature (LDQ) method is proposed in this paper. Instead of using regular nodes placed on mesh lines in the DQ method (DQM), irregularly distributed nodes are employed in the LDQ method. That is, any spatial derivative at a nodal point is approximated by a linear weighted sum of the functional values of irregularly distributed nodes in the local physical domain. The feature of the new approach lies in the fact that the weighting coefficients are determined by the quadrature rule over the irregularly distributed local supporting nodes with the aid of nodal interpolation techniques developed in the paper. Because of this distinctive feature, the LDQ method can be consistently applied to linear and nonlinear problems and is really a mesh-free method without the limitation in the solution domain of the conventional DQM. The effectiveness and efficiency of the method are validated by two simple numerical examples by solving boundary-value problems of a linear and a nonlinear partial differential equation. Copyright © 2007 John Wiley & Sons, Ltd. [source] Guidelines for assessing the suitability of spatial climate data setsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 6 2006Christopher Daly Abstract Spatial climate data are often key drivers of computer models and statistical analyses, which form the basis for scientific conclusions, management decisions, and other important outcomes. The recent availability of very high-resolution climate data sets raises important questions about the tendency to equate resolution with realism. This paper discusses the relationship between scale and spatial climate-forcing factors, and provides background and advice on assessing the suitability of data sets. Spatial climate patterns are most affected by terrain and water bodies, primarily through the direct effects of elevation, terrain-induced climate transitions, cold air drainage and inversions, and coastal effects. The importance of these factors is generally lowest at scales of 100 km and greater, and becomes greatest at less than 10 km. Except in densely populated regions of developed countries, typical station spacing is on the order of 100 km. Regions without major terrain features and which are at least 100 km from climatically important coastlines can be handled adequately by most interpolation techniques. Situations characterized by significant terrain features, but with no climatically important coastlines, no rain shadows, and a well-mixed atmosphere can be reasonably handled by methods that explicitly account for elevation effects. Regions having significant terrain features, and also significant coastal effects, rain shadows, or cold air drainage and inversions are best handled by sophisticated systems that are configured and evaluated by experienced climatologists. There is no one satisfactory method for quantitatively estimating errors in spatial climate data sets, because the field that is being estimated is unknown between data points. Perhaps the best overall way to assess errors is to use a combination of approaches, involve data that are as independent from those used in the analysis as possible, and use common sense in the interpretation of results. Data set developers are encouraged to conduct expert reviews of their draft data sets, which is probably the single most effective way to improve data set quality. Copyright © 2006 Royal Meteorological Society. [source] Tropospheric ducting phenomena over the Hellenic regionINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 4 2004Stergios A. Isaakidis Abstract The variation of the refractivity profiles of the troposphere and especially of the ducting effect, affects the radio wave propagation causing various phenomena such as refraction, fading and interference between radio-stations. In this work, the tropospheric ducting phenomena over the Hellenic region are studied using data from Helleniko and Thessaloniki Airports for the time period from 1991 to 1999. The data are analysed, corrected and enhanced using interpolation techniques and after a final statistical process the ducting conditions over the Hellenic region are summarized. Copyright © 2004 John Wiley & Sons, Ltd. [source] | ||||||||||