1,720,968 research outputs found
Classification de l'état mental humain par programmation génétique sur des signaux EEG
No full textLos avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en inglés Brain-Computer Interface) se han incrementado en años recientes,principalmente porque ha evolucionado el nivel de convergencia detécnicas multidisciplinarias. La electroencefalografía (EEG), una teécnica degrabación de señales cerebrales estudiado en esta tesis, permite la construcciónde BCIs, sin embargo las señales son complejas para procesar, lo cual requieremetodologías que extraigan patrones de forma eficiente. Esta tesis explora dostópicos principales: primero, se propone un sistema para el reconocimientode convulsiones de epilepsia usando una combinacion de metodos de procesamientode señales para la extracción eficiente de rasgos; segundo, explora eluso de un algoritmo meta-heurístico, Programación Genética (GP, por sus siglasen inglés Genetic Programming), como una alternativa en el diseño de BCIs.Sin embargo, existen temas sin resolver in GP que esta tesis explora: ¿existe unametodologia de búsqueda en GP más eficiente?; ¿cual es una representaciónapropiada dependiendo del problema a estudiar?; ¿cual son los operadores debúsqueda más adecuados?. De esta forma, se presenta un estudio a fondo conla introducción de un GP memetico aplicado a problemas de regresión. Despues,se extiende adaptandolo a problemas de clasificación. Los resultados sonpositivos; GP se beneficia fuertemente de la combinación de una metodologiageneral de busqueda y una local (LS, por sus siglas en inglés Local Search). Losultimos dos cuestionamientos se estudian simultáneamente en el desarrollo deun sistema de reconocimiento para estados mentales usando EEG. Se proponeuna versión de GP (+FEGP) que evoluciona modelos de extracción de rasgosusando operadores especializados de busqueda, representación de individuosy función de aptitud. Los resultados muestran que esta combinación permiteuna exactitud de clasificación que aporta en el estado-del-arte para la tarea particulardel reconocimiento de estados mentales.The advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level ofconvergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in thisthesis, allows the construction of BCIs, however the signals are rathercomplex to process, which requires methodologies that efficiently extractpatterns from them. This thesis explores two directions: first, a systemis proposed for the epilepsy seizures recognition using a combinationof signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely GeneticProgramming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is therea more efficient search methodology in the exploration by GP?; what isa proper representation depending on the studied problem?; which arethe most adequate search operators?. For the first topic, a thoroughlystudy is presented by introducing a memetic GP applied to regressionproblems. Then, it is extended by adapting it to classification problems.The results are positive; GP is greatly benefited from the combinationof a general and a Local Search (LS) methodology. The last two topicsare studied simultaneously in the development of a recognition systemfor mental states using EEG. A GP version (+FEGP) is proposed thatevolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show thatthe combination of these reaches a state-of-the-art accuracy for the particulartask of mental states recognition
Two groups of patented devices for carpal tunnel syndrome rehabilitation: comparative study between traditional and additive manufacturing
No full textThis chapter presents a comparative study of two rehabilitation devices groups for people with carpal tunnel syndrome that were registered in different intel lectual property offices. The objective was to compare the rehabilitation devices
produced using traditional technology and 3D additive manufacturing, to show the advantages and disadvantages of both production processes. The methodology used in this research was structured into three stages. In the first stage, a systematic techno logical research of existing patents was developed using the search engines PatBase and Google Patent. The second stage describes the main characteristics and function ality of these devices. The last stage consisted of developing a comparative matrix of the properties and characteristics considered to define the relevance of each device. As a result, nine rehabilitation devices were selected describing and comparing their main characteristics. This research can serve as a reference for future studies related to carpal tunnel syndrome.Depto. de Sociología AplicadaFac. de Ciencias Políticas y SociologíaTRUEpu
Human mental states classification using EEG by means of Genetic Programming
No full textThe advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level ofconvergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in thisthesis, allows the construction of BCIs, however the signals are rathercomplex to process, which requires methodologies that efficiently extractpatterns from them. This thesis explores two directions: first, a systemis proposed for the epilepsy seizures recognition using a combinationof signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely GeneticProgramming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is therea more efficient search methodology in the exploration by GP?; what isa proper representation depending on the studied problem?; which arethe most adequate search operators?. For the first topic, a thoroughlystudy is presented by introducing a memetic GP applied to regressionproblems. Then, it is extended by adapting it to classification problems.The results are positive; GP is greatly benefited from the combinationof a general and a Local Search (LS) methodology. The last two topicsare studied simultaneously in the development of a recognition systemfor mental states using EEG. A GP version (+FEGP) is proposed thatevolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show thatthe combination of these reaches a state-of-the-art accuracy for the particulartask of mental states recognition.Los avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en inglés Brain-Computer Interface) se han incrementado en años recientes,principalmente porque ha evolucionado el nivel de convergencia detécnicas multidisciplinarias. La electroencefalografía (EEG), una teécnica degrabación de señales cerebrales estudiado en esta tesis, permite la construcciónde BCIs, sin embargo las señales son complejas para procesar, lo cual requieremetodologías que extraigan patrones de forma eficiente. Esta tesis explora dostópicos principales: primero, se propone un sistema para el reconocimientode convulsiones de epilepsia usando una combinacion de metodos de procesamientode señales para la extracción eficiente de rasgos; segundo, explora eluso de un algoritmo meta-heurístico, Programación Genética (GP, por sus siglasen inglés Genetic Programming), como una alternativa en el diseño de BCIs.Sin embargo, existen temas sin resolver in GP que esta tesis explora: ¿existe unametodologia de búsqueda en GP más eficiente?; ¿cual es una representaciónapropiada dependiendo del problema a estudiar?; ¿cual son los operadores debúsqueda más adecuados?. De esta forma, se presenta un estudio a fondo conla introducción de un GP memetico aplicado a problemas de regresión. Despues,se extiende adaptandolo a problemas de clasificación. Los resultados sonpositivos; GP se beneficia fuertemente de la combinación de una metodologiageneral de busqueda y una local (LS, por sus siglas en inglés Local Search). Losultimos dos cuestionamientos se estudian simultáneamente en el desarrollo deun sistema de reconocimiento para estados mentales usando EEG. Se proponeuna versión de GP (+FEGP) que evoluciona modelos de extracción de rasgosusando operadores especializados de busqueda, representación de individuosy función de aptitud. Los resultados muestran que esta combinación permiteuna exactitud de clasificación que aporta en el estado-del-arte para la tarea particulardel reconocimiento de estados mentales
Human mental states classification using EEG by means of Genetic Programming
No full textThe advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level ofconvergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in thisthesis, allows the construction of BCIs, however the signals are rathercomplex to process, which requires methodologies that efficiently extractpatterns from them. This thesis explores two directions: first, a systemis proposed for the epilepsy seizures recognition using a combinationof signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely GeneticProgramming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is therea more efficient search methodology in the exploration by GP?; what isa proper representation depending on the studied problem?; which arethe most adequate search operators?. For the first topic, a thoroughlystudy is presented by introducing a memetic GP applied to regressionproblems. Then, it is extended by adapting it to classification problems.The results are positive; GP is greatly benefited from the combinationof a general and a Local Search (LS) methodology. The last two topicsare studied simultaneously in the development of a recognition systemfor mental states using EEG. A GP version (+FEGP) is proposed thatevolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show thatthe combination of these reaches a state-of-the-art accuracy for the particulartask of mental states recognition.Los avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en inglés Brain-Computer Interface) se han incrementado en años recientes,principalmente porque ha evolucionado el nivel de convergencia detécnicas multidisciplinarias. La electroencefalografía (EEG), una teécnica degrabación de señales cerebrales estudiado en esta tesis, permite la construcciónde BCIs, sin embargo las señales son complejas para procesar, lo cual requieremetodologías que extraigan patrones de forma eficiente. Esta tesis explora dostópicos principales: primero, se propone un sistema para el reconocimientode convulsiones de epilepsia usando una combinacion de metodos de procesamientode señales para la extracción eficiente de rasgos; segundo, explora eluso de un algoritmo meta-heurístico, Programación Genética (GP, por sus siglasen inglés Genetic Programming), como una alternativa en el diseño de BCIs.Sin embargo, existen temas sin resolver in GP que esta tesis explora: ¿existe unametodologia de búsqueda en GP más eficiente?; ¿cual es una representaciónapropiada dependiendo del problema a estudiar?; ¿cual son los operadores debúsqueda más adecuados?. De esta forma, se presenta un estudio a fondo conla introducción de un GP memetico aplicado a problemas de regresión. Despues,se extiende adaptandolo a problemas de clasificación. Los resultados sonpositivos; GP se beneficia fuertemente de la combinación de una metodologiageneral de busqueda y una local (LS, por sus siglas en inglés Local Search). Losultimos dos cuestionamientos se estudian simultáneamente en el desarrollo deun sistema de reconocimiento para estados mentales usando EEG. Se proponeuna versión de GP (+FEGP) que evoluciona modelos de extracción de rasgosusando operadores especializados de busqueda, representación de individuosy función de aptitud. Los resultados muestran que esta combinación permiteuna exactitud de clasificación que aporta en el estado-del-arte para la tarea particulardel reconocimiento de estados mentales
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Classification de l'état mental humain par programmation génétique sur des signaux EEG
Get PDFLos avances en el desarrollo de Interfaces Cerebro-Computadora (BCI, porsus siglas en inglés Brain-Computer Interface) se han incrementado en años recientes,principalmente porque ha evolucionado el nivel de convergencia detécnicas multidisciplinarias. La electroencefalografía (EEG), una teécnica degrabación de señales cerebrales estudiado en esta tesis, permite la construcciónde BCIs, sin embargo las señales son complejas para procesar, lo cual requieremetodologías que extraigan patrones de forma eficiente. Esta tesis explora dostópicos principales: primero, se propone un sistema para el reconocimientode convulsiones de epilepsia usando una combinacion de metodos de procesamientode señales para la extracción eficiente de rasgos; segundo, explora eluso de un algoritmo meta-heurístico, Programación Genética (GP, por sus siglasen inglés Genetic Programming), como una alternativa en el diseño de BCIs.Sin embargo, existen temas sin resolver in GP que esta tesis explora: ¿existe unametodologia de búsqueda en GP más eficiente?; ¿cual es una representaciónapropiada dependiendo del problema a estudiar?; ¿cual son los operadores debúsqueda más adecuados?. De esta forma, se presenta un estudio a fondo conla introducción de un GP memetico aplicado a problemas de regresión. Despues,se extiende adaptandolo a problemas de clasificación. Los resultados sonpositivos; GP se beneficia fuertemente de la combinación de una metodologiageneral de busqueda y una local (LS, por sus siglas en inglés Local Search). Losultimos dos cuestionamientos se estudian simultáneamente en el desarrollo deun sistema de reconocimiento para estados mentales usando EEG. Se proponeuna versión de GP (+FEGP) que evoluciona modelos de extracción de rasgosusando operadores especializados de busqueda, representación de individuosy función de aptitud. Los resultados muestran que esta combinación permiteuna exactitud de clasificación que aporta en el estado-del-arte para la tarea particulardel reconocimiento de estados mentales.The advances in the development of Brain-Computer Interfaces(BCI) have been increasing in recent years, mostly because the level ofconvergence from multi-disciplinary techniques has evolved. The electroencephalography(EEG), a brain recording method studied in thisthesis, allows the construction of BCIs, however the signals are rathercomplex to process, which requires methodologies that efficiently extractpatterns from them. This thesis explores two directions: first, a systemis proposed for the epilepsy seizures recognition using a combinationof signal processing methods for an efficient feature extraction; second,it explores the usage of a meta-heuristic algorithm, namely GeneticProgramming (GP), as an alternative in the design of BCIs. Nonetheless,there is currently open-issues in GP that this thesis also explores: is therea more efficient search methodology in the exploration by GP?; what isa proper representation depending on the studied problem?; which arethe most adequate search operators?. For the first topic, a thoroughlystudy is presented by introducing a memetic GP applied to regressionproblems. Then, it is extended by adapting it to classification problems.The results are positive; GP is greatly benefited from the combinationof a general and a Local Search (LS) methodology. The last two topicsare studied simultaneously in the development of a recognition systemfor mental states using EEG. A GP version (+FEGP) is proposed thatevolves feature extraction models by using specialized search operators,individuals representation and fitness function. The results show thatthe combination of these reaches a state-of-the-art accuracy for the particulartask of mental states recognition
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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