A method for enhancing eye movements data from eye-tracking devices

Eye movements play an important role in actual neuroscience and in the last twenty years, many eye-tracking devices have emerged with different methods and performance features. Generally, the highest quality ones with best performance in terms of accuracy and high framerates, are the most expensive apparatus and very often complicated to assembly. Also, they tend to work in fixed setups and it is hard to perform outdoor experiments, like driving a real car or walking long distances. The comfortable and cheaper ones are usually those having the poorest measuring characteristics, reaching maximum framerates below 250fps, yet with great advantages. These modern remote eye-tracking systems allow, in general, small head movements and the subject has not wear any kind of hardware. This feature is especially important when working with children or people with some kind of physical impairment. They are independent, small and one-piece hardware ready to plug into a mobile computer or laptop, making easy to set a large variety of experiments. In this work, we propose to use wavelet methods to improve real eye movements data, allowing the reconstruction of the signal at a higher resolution than the original one. Transformed data was upsampled and the new coefficients were obtained by interpolation using different techniques and looking for a minimum percentage error between the original and recovered signals. Then, treating the eyetracker data with low samplerate as a complete signal with periodic miss- ing parts or information and inspired in a method for restoring very damaged images, we present an approach to adapt one of the the algorithms for images to 1D signals. Mecánica Computacional Vol XXXV, págs. 2521-2532 (artículo completo) Martín I. Idiart, Ana E. Scarabino y Mario A. Storti (Eds.) La Plata, 7-10 Noviembre 2017 Copyright ©Publicado en: Mecánica Computacional vol. XXXV, no. 43Facultad de Ingenierí

Identifying Geographical Interdependency in Critical Infrastructure Systems Using Open Source Geospatial Data in Order to Model Restoration Strategies in the Aftermath of a Large-Scale Disaster

In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium- to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium- to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the ‘geographical interdependency’) of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center’s supply chain in the wake of an extreme event

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

Modeling supply chain interdependent critical infrastructure systems

While strategies for emergency response to large-scale disasters have been extensively studied, little has been done to map medium- to long-term strategies capable of restoring supply chain infrastructure systems and reconnecting such systems from a local urban area to national supply chain systems. This is, in part, because no comprehensive, data-driven model of supply chain networks exists. Without such models communities cannot re-establish the level of connectivity required for timely restoration of goods and services. This dissertation builds a model of supply chain interdependent critical infrastructure (SCICI) as a complex adaptive systems problem. It defines model elements, data needs/element, the interdependency of critical infrastructures, and suggests metrics for evaluating success. Previous studies do not consider the problem from a systematic view and therefore their solutions are piecemeal, rather than integrated with respect to both the model elements and geospatial data components. This dissertation details a methodology to understand the complexities of SCICI within a real urban framework (St. Louis, MO). Interdependencies between the infrastructures are mapped to evaluate resiliency and a framework for quantifying interdependence is proposed. In addition, this work details the identification, extraction and integration of the data necessary to model infrastructure systems --Abstract, page iv