Can you see me thinking (about my answers)? Using eye-tracking to illuminate developmental differences in monitoring and control skills and their relation to performance

This study focuses on relations between 7- and 9-year-old children’s and adults’ metacognitive monitoring and control processes. In addition to explicit confidence judgments (CJ), data for participants’ control behavior during learning and recall as well as implicit CJs were collected with an eye-tracking device (Tobii 1750). Results revealed developmental progression in both accuracy of implicit and explicit monitoring across age groups. In addition, efficiency of learning and recall strategies increases with age, as older participants allocate more fixation time to critical information and less time to peripheral or potentially interfering information. Correlational analyses, recall performance, metacognitive monitoring, and controlling indicate significant interrelations between all of these measures, with varying patterns of correlations within age groups. Results are discussed in regard to the intricate relationship between monitoring and recall and their relation to performance.

Cue Recognition and Integration - Eye Tracking Evidence of Processing Differences in Sentence Comprehension in Aphasia

PURPOSE: We aimed at further elucidating whether aphasic patients' difficulties in understanding non-canonical sentence structures, such as Passive or Object-Verb-Subject sentences, can be attributed to impaired morphosyntactic cue recognition, and to problems in integrating competing interpretations. METHODS: A sentence-picture matching task with canonical and non-canonical spoken sentences was performed using concurrent eye tracking. Accuracy, reaction time, and eye tracking data (fixations) of 50 healthy subjects and 12 aphasic patients were analysed. RESULTS: Patients showed increased error rates and reaction times, as well as delayed fixation preferences for target pictures in non-canonical sentences. Patients' fixation patterns differed from healthy controls and revealed deficits in recognizing and immediately integrating morphosyntactic cues. CONCLUSION: Our study corroborates the notion that difficulties in understanding syntactically complex sentences are attributable to a processing deficit encompassing delayed and therefore impaired recognition and integration of cues, as well as increased competition between interpretations.

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper.

Object-based image analysis for mapping Natura 2000 habitats.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos.

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos

Integración de tecnologías eye-tracking con vídeos interactivos para neurorrehabilitación cognitiva

La integración de las nuevas tecnologías en el proceso de rehabilitación permite la generación de terapias personalizadas, ubicuas y basadas en la evidencia. Tecnologías como el vídeo interactivo son propicias para el desarrollo de entornos virtuales en los que el paciente se ve inmerso dentro de actividades de la vida diaria en los que tiene que lograr un objetivo ecológico en un contexto seguro, controlado y adaptado a su perfil disfuncional. Dentro de este marco de rehabilitación la interacción visual paciente-entorno virtual se entiende como el mecanismo de comunicación principal, siendo además la atención visual un reflejo del estado cognitivo del paciente. El trabajo presentado en este artículo permite la integración de un sistema de eye-tracking con un entorno de neurorrehabilitación basado en vídeo interactivo. El objetivo último del sistema es la monitorización en tiempo real de la atención visual del usuario durante el proceso de neurorrehabilitación. Esta monitorización permite no sólo reproducir la ejecución de la actividad junto con el foco de la mirada, sino también detectar faltas de atención por parte del usuario, que permiten al vídeo interactivo reaccionar y adaptar la presentación de estímulos para ayudar a centrar su atención y así completar el objetivo de la actividad.

Evaluación de un entorno de rehabilitación cognitiva basado en tecnologías de Vídeo Interactivo y Eye-Tracking

El Daño Cerebral Adquirido (DCA) se ha convertido en una de las principales causas de discapacidad neurológica de las sociedades desarrolladas. La alteración de las funciones cognitivas como consecuencia del DCA, limita no sólo la calidad de vida del paciente sino también la de las persona de su entorno. Aunque la neurorrehabilitación permite recuperar algunas de las funciones alteradas aprovechando la naturaleza plástica del sistema nervioso, su práctica siguiendo procesos tradicionales no permiten en muchos casos ajustarse a las necesidades de cada individuo ni, en general, cubrir todos los aspectos necesarios que conviertan al proceso rehabilitador en un tratamiento realmente efectivo. La incorporación al proceso de rehabilitación de las nuevas tecnologías ha permitido aumentar la intensidad del tratamiento, personalizando y prolongándolo en el tiempo de forma sostenible. Los entornos virtuales (EV) apoyados en esta tendencia permiten reproducir Actividades de Vida Diaria (AVD) controladas que incrementan el valor ecológico de las terapias. Este Trabajo Fin de Grado aborda el uso pionero de la tecnología de Vídeo Interactivo (VI) para el desarrollo de dichos entornos en el campo de la rehabilitación cognitiva. En concreto, el objetivo del TFG es la evaluación de un EV de rehabilitación desarrollado mediante tecnología de VI e integrado con un sistema de Eye-Tracking, capaz de capturar y analizar la información referente al comportamiento visual del paciente. Para este fin, se realiza el diseño, implementación y evaluación de un estudio experimental que registre el comportamiento de diferentes sujetos ante dos modalidades de AVD.

World Map Showing the Voyages of the Vettor Pisani, 1882-1885 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Viaggio della R[a] Corvetta 'Vettor Pisani', negli anni 1882-1883-1884-1885 (comandante G. Palumbo). It was published by Stab. Lit. Bruno e Salomone in 1885. Scale [ca. 1:250,500]. World map showing the voyages of the Vettor Pisani. The image inside the map neatline is georeferenced to the surface of the earth and fit to a non-standard 'Mercator' projection with the central meridian at 160 degrees east. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, ship route with dates, territorial boundaries, shoreline features, and more. This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection and the Harvard University Library as part of the Open Collections Program at Harvard University project: Organizing Our World: Sponsored Exploration and Scientific Discovery in the Modern Age. Maps selected for the project correspond to various expeditions and represent a range of regions, originators, ground condition dates, scales, and purposes.

2000 University of Michigan Baseball Team

3rd Row: (13) Trainer Joel Pickerman, Student Trainer Jon Sweeney, Blake Rutkowski, Brock Koman, Tim Leveque, Bobby Wood, Rich Hill, Alex Coleman, Brad McCloskey, Jordan Cantalamessa, Jason Wuerfel, Student Trainer Channing Bennett.

2nd Row (14) Student Manager Jeff Singer, Bill LaRosa, Scott Tousa, Dan Dombos, Nick Alexander, Nate Wright, Kirk Taylor, Jeff Trzos, Phil Lobert, Nick Bellows, Joe Young, Jordan French, Mike Sokol, Student Manager Josh Taft.

Front Row: (13) Vince Pistilli, David Parrish, Rob Bobeda, Bryce Ralston, Bryan Cranson, Assistant Coach Chris Harrison, Head Coach Geoff Zahn, Assistant Coach Matt Hyde, Assistant Coach Andy Hood, Kevin Quinn, Jay Dines, Stephen Lenick, C.J. Ghannam.

2000 University of Michigan Football Team

2000 University of Michigan Men's Golf Team

Back Row: Kyle Kilcherman, Andy Matthews, Kevin Hinton, Brian Seipke, Nicholas Lossia, Scott Hayes, Ray Coyne

Front Row: coach Jim Carras, Andrew Chapman, Michael Harris, Mike, Affeldt, Asst. coach Ed Klum