Augmented Reality (AR) offers the potential to integrate physical, digital and social learning experiences in hybrid learning environments and thereby to achieve learning gains, higher motivation or improved interacti...
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Augmented Reality (AR) offers the potential to integrate physical, digital and social learning experiences in hybrid learning environments and thereby to achieve learning gains, higher motivation or improved interaction and collaboration. Moreover, by means of AR, theory- or calculus-based learning and experimental exploration in the lab can be brought closer together. Here we present a data-driven AR enhancement of experiments in an electricity and magnetism lab course, where measurement data such as actual current and voltage, are transmitted to a head-mounted semi-transparent display (HMD). In addition, preliminary evaluation results are presented. An improved technical understanding of the subject and especially more dense interrelations between individual learners conceptions are observed.
This methodological note identifies and describes a data-driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to per...
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This methodological note identifies and describes a data-driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to pertinent structure-related ISCN research questions in the global electronics industry. Our visualization approach can be used to reveal and understand important clusters, patterns, trends, and outliers of ISCN not necessarily identified with traditional methods. The broader aim of this note is to demonstrate the complementary value of emerging visual analytic approaches in managerial decision-making contexts and describe how actionable insights can be achieved.
In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with th...
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ISBN:
(纸本)9783540725831
In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with the preoperative MRI, fMRI, and DT-MRI data. We describe our distributed implementation of a non-rigid image registration method which can be executed over the Grid. Previously, non-rigid registration algorithms which use landmark tracking across the entire brain volume were considered not practical because of the high computational demands. The IGNS-GSE, for the first time ever in clinical practice, alleviated this restriction. We show that we can compute and present enhanced MR images to neurosurgeons during the tumor resection within minutes after IMRI acquisition. For the last 12 months this software system is used routinely (on average once a month) for clinical studies at Brigham and Women's Hospital in Boston, MA. Based on the analysis of the registration results, we also present future directions which will take advantage of the vast resources of the Grid to improve the accuracy of the method in places of the brain where precision is critical for the neurosurgeons.
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