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Jim X. Chen

Researcher at George Mason University

Publications -  126
Citations -  1895

Jim X. Chen is an academic researcher from George Mason University. The author has contributed to research in topics: Rendering (computer graphics) & Visualization. The author has an hindex of 19, co-authored 123 publications receiving 1758 citations. Previous affiliations of Jim X. Chen include Southwest Jiaotong University.

Papers
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Proceedings ArticleDOI

MUVEES: a PC-based multi-user virtual environment for learning

TL;DR: The design, implementation, and applications of MUVEES are discussed, including its structure, efficient approaches that achieve more realistic avatar behaviors, and pedagogical strategies that foster strong learning outcomes across a wide range of individual student characteristics.
Journal ArticleDOI

Advancing Interactive Visualization and Computational Steering

TL;DR: Two of the emerging visualization paradigms most useful to computational scientists are interactive vimalizatim and c m putational steering are presented, and an example of an interactive application for studying fluid flow is presented, showing how simulation and visualization can combine in real time for a better understanding of a phenomenon.
Journal ArticleDOI

Knee surgery assistance: patient model construction, motion simulation, and biomechanical visualization

TL;DR: A new system that integrates computer graphics, physics-based modeling, and interactive visualization to assist knee study and surgical operation and the results integrate 3-D construction, motion simulation, and biomechanical visualization into one system are presented.
Book

Foundations of 3D Graphics Programming: Using JOGL and Java3D

TL;DR: This revised edition of the successful, reader-friendly text covers all graphics basics and several advanced topics, as well as some basic concepts in Java programming for those who currently are C/C++ programmers.
Journal ArticleDOI

Data visualization: parallel coordinates and dimension reduction

TL;DR: This work introduces visualization methods for multidimensional data sets, including an effective dimension reduction method for the multivariate genetic algorithm data set.