Examinando por Autor "Diaz, C."

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    Ítem
    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
  • No hay miniatura disponible
    Ítem
    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de Procesos
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
  • No hay miniatura disponible
    Ítem
    Adaptive architecture to support context-aware Collaborative Networked Virtual Surgical Simulators (CNVSS)
    (SPRINGER, 2014-01-01) Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Diaz, C.; Trefftz, H.; Quintero, L.; Acosta, D.; Srivastava, S.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session. © 2014 Springer International Publishing.
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    Ítem
    An immersive virtual reality training system for mechanical assembly
    (2011-01-01) Peniche, A.; Diaz, C.; Trefftz, H.; Paramo, G.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Given the growing evolution of technology, machinery and manufacturing techniques, conventional methodologies for training the workforce are not enough for the current needs. Therefore methodologies capable to accelerate the training process and able to train the trainee in a wide range of scenarios are claimed for the industrial sector. Virtual reality offers an alternative that has been successfully implemented in other industries, and virtual reality based training systems have numerous advantages over the conventional methodologies, making it a very good option. Based on that premise, this paper explores the implementation of an immersive training system for mechanical assembly based on virtual reality for improving the training process. This system was proved to be as effective as the conventional methodology.
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    Influence of preprocessing and segmentation on the complexity of the learning machines in medical imaging
    (Indian Society for Development and Environment Research) Paniagua, J.; Restrepo, D.; Ariza, L.; Jose Garcés E; Diaz, C.; Diana Serna Higuita; Wiston Arrazola; Sebastian Arango; Ramiro Vélez Koeppel; Miguel Mejia; Wayner Barrios; Jesus Vargas Bonilla; Quintero, Olga Lucia; Universidad EAFIT. Departamento de Geología; Ciencias del Mar
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    Influence of preprocessing and segmentation on the complexity of the learning machines in medical imaging
    (Indian Society for Development and Environment Research) Paniagua, J.; Restrepo, D.; Ariza, L.; Jose Garcés E; Diaz, C.; Diana Serna Higuita; Wiston Arrazola; Sebastian Arango; Ramiro Vélez Koeppel; Miguel Mejia; Wayner Barrios; Jesus Vargas Bonilla; Quintero, Olga Lucia; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
  • No hay miniatura disponible
    Ítem
    Influence of preprocessing and segmentation on the complexity of the learning machines in medical imaging
    (Indian Society for Development and Environment Research) Paniagua, J.; Restrepo, D.; Ariza, L.; Jose Garcés E; Diaz, C.; Diana Serna Higuita; Wiston Arrazola; Sebastian Arango; Ramiro Vélez Koeppel; Miguel Mejia; Wayner Barrios; Jesus Vargas Bonilla; Quintero, Olga Lucia; Paniagua, J.; Restrepo, D.; Ariza, L.; Jose Garcés E; Diaz, C.; Diana Serna Higuita; Wiston Arrazola; Sebastian Arango; Ramiro Vélez Koeppel; Miguel Mejia; Wayner Barrios; Jesus Vargas Bonilla; Quintero, Olga Lucia; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones