Examinando por Autor "Rueda, Antonio J."
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Ítem A formal framework for the representation of stack-based terrains(Taylor & Francis, 2018-05) Graciano-Segura, Alejandro; Rueda, Antonio J.; Feito, Francisco R.This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.Ítem A New Linguistic Description Approach for Time Series and its Application to Bed Restlessness Monitoring for Eldercare(IEEE, 2022-04) Martínez Cruz, Carmen; Rueda, Antonio J.; Popescu, Mihail; Keller, James M.Time series analysis has been an active area of research for years, with important applications in forecasting or discovery of hidden information such as patterns or anomalies in observed data. In recent years, the use of time series analysis techniques for the generation of descriptions and summaries in natural language of any variable, such as temperature, heart rate or CO2 emission has received increasing attention. Natural language has been recognized as more effective than traditional graphical representations of numerical data in many cases, in particular in situations where a large amount of data needs to be inspected or when the user lacks the necessary background and skills to interpret it. In this work, we describe a novel mechanism to generate linguistic descriptions of time series using natural language and fuzzy logic techniques. The proposed method generates quality summaries capturing the time series features that are relevant for a user in a particular application, and can be easily customized for different domains. This approach has been successfully applied to the generation of linguistic descriptions of bed restlessness data from residents at TigerPlace (Columbia, Missouri), which is used as a case study to illustrate the modeling process and show the quality of the descriptions obtained.Ítem QuadStack: An Efficient Representation and Direct Rendering of Layered Datasets(IEEE, 2021-09) Graciano, Alejandro; Rueda, Antonio J.; Pospísil, Adam; Bittner, Jirí; Benes, BedrichWe introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint.