SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2003


P. Lorenzen, S. Joshi. “High-Dimensional Multi-modal Image Registration,” In Proceedings of the Workshop on Biomedical Image Registration, pp. 234--243. 2003.


A. Lu, C.J. Morris, J. Taylor, D.S. Ebert, C.D. Hansen, P. Rheingans, M. Hartner. “Illustrative Interactive Stipple Rendering,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 9, No. 2, pp. 127--138. April, 2003.


C. Lu, S. Pizer, S. Joshi. “A Markov Random Field Approach to Multi-scale Shape Analysis,” In Scale Space Methods in Computer Vision, Edited by LD Griffin and M Lillholm, pp. 416--431. 2003.


D. Lucor, D. Xiu, C.-H. Su, G.E. Karniadakis. “Predictability and Uncertainty in CFD,” In International Journal for Numerical Methods in Fluids, Vol. 43, No. 5, pp. 483--505. 2003.
DOI: 10.1002/fld.500

ABSTRACT

×
CFD has reached some degree of maturity today, but the new question is how to construct simulation error bars that reflect uncertainties of the physical problem, in addition to the usual numerical inaccuracies. We present a fast Polynomial Chaos algorithm to model the input uncertainty and its propagation in incompressible flow simulations. The stochastic input is represented spectrally by Wiener–Hermite functionals, and the governing equations are formulated by employing Galerkin projections. The resulted system is deterministic, and therefore existing solvers can be used in this new context of stochastic simulations. The algorithm is applied to a second-order oscillator and to a flow-structure interaction problems. Open issues and extensions to general random distributions are presented.

Keywords: computational fluid dynamics, polynomial chaos, Wiener–Hermite functionals, incompressible flows


N. Max, P. Williams, C.T. Silva, R. Cook. “Volume Rendering for Curvilinear and Unstructured Grids,” In Proceedings of Computer Graphics International, 2003, pp. 210--215. 2003.
ISBN: 0-7695-1946-6
ISSN: 1530-1052


C. Nuber, E.C. LaMar, V. Pascucci, B. Hamann, K.I. Joy. “Using Graphs for Fast Error Term Approximation of Time-varying Datasets,” In Proceedings of Eurographics VisSym'03 (Data Visualization 2003), Note: UCRL-JC-151965, Edited by Georges-Pierre Bonneau AND Stefanie Hahmann AND Charles D. Hansen, Association for Computing Machinery, New York, New York 2003.


A. Paoluzzi, V. Pascucci, M. Vicentino, C. Baldazzi, S. Protuesi. “Geometric Programming for Computer Aided Design,” John Wiley & Sons, June, 2003.


S.G. Parker. “C-SAFE Uses Linux HPCC in Fire Research,” In Syllabus, Technology for Higher Education, Vol. 16, No. 7, Feburary, 2003.


V. Pascucci, K. Cole-McLaughlin. “Parallel Computation of the Topology of Level Sets,” In Algorithmica, Vol. 38, No. 1, pp. 249--268. October, 2003.


V. Pascucci, D.E. Laney, R. Frank, G. Scorzelli, L. Linsen, B. Hamann, F. Gygi. “Real-Time Monitoring of Large Scientific Simulations,” In Proceedings of the 18-th annual ACM Symposium on Applied Computing, Melbourne, Florida, Note: UCRL-JC-151094, pp. 194--198. March, 2003.


V. Pascucci, R. J. Frank. “Hierarchical Indexing for Out-of-Core Access to Multi-Resolution Data,” In Mathematics and Visualization, Note: UCRL-JC-140581, Edited by G.Farin and B. Hamann and H. Hagen, Springer-Verlag, pp. 225--241. 2003.
DOI: 10.1007/978-3-642-55787-3_14


S.M. Pizer, G. Gerig, S. Joshi, S. Aylward. “Multiscale Medial Shape-Based Analysis of Image Objects,” In Proceedings of the IEEE, Vol. 91, No. 10, Edited by C. Roux and J.K. Udupa, IEEE, pp. 1670--1679. October, 2003.
DOI: 10.1109/jproc.2003.817876


S.M. Pizer, P.T. Fletcher, Y. Fridman, D.S. Fritsch, A.G. Gash, J.M. Glotzer, S. Joshi, A. Thall, G. Tracton, P. Yushkevich, E.L. Chaney. “Deformable M-Reps for 3D Medical Image Segmentation,” In International Journal of Computer Vision - Special UNC-MIDAG issue, Vol. 55, No. 2, Edited by O Faugeras and K Ikeuchi and J Ponce, Kluwer Academic, pp. 85--106. November-December, 2003.


S.M. Pizer, P.T. Fletcher, A. Thall, M. Styner, G. Gerig, S. Joshi. “Object Models in Multiscale Intrinsic Coordinates via M-reps,” In Image and Vision Computing, Special Issue on Generative Model-based Vision, Vol. 21, No. 1, pp. 5--15. January, 2003.


O. Portniaguine, C. Bonifasi, E. DiBella, R.T. Whitaker. “Inverse Methods for Reduced K-Space Acquisition,” In International Society for Magnetic Resonance in Medicine (ISMRM), Note: abstract., 2003.


S. Premoze, T. Tasdizen, J. Bigler, A.E. Lefohn, R. T. Whitaker. “Particle-Based Simulation of Fluids,” In Eurographics, pp. 401--410. 2003.


S.D. Ramsey, M. Bertram, C.D. Hansen. “Simplification of Arbitrary Polyhedral Meshes,” In IASTED Computer Graphics and Imaging (CGIM 2003), Honolulu, pp. 117--222. 2003.


M. Ramanath, J. Freire, J. Haritsa, P. Roy. “Searching for Efficient XML-to-Relational Mappings,” In Lecture Notes in Computer Science, Lecture Notes in Computer Science (LNCS), Vol. 2824, pp. 19--36. 2003.
ISBN: 978-3-540-20055-0
DOI: 10.1007/978-3-540-39429-7_2

ABSTRACT

We consider the problem of cost-based strategies to derive efficient relational configurations for XML applications that subscribe to an XML Schema. In particular, we propose a flexible framework for XML schema transformations and show how it can be used to design algorithms to search the space of equivalent relational configurations. We study the impact of the schema transformations and query workload on the search strategies for finding efficient XML-to-relational mappings. In addition, we propose several optimizations to speed up the search process. Our experiments indicate that a judicious choice of transformations and search strategies can lead to relational configurations of substantially higher quality than those recommended by previous approaches.


K. Riley, D. Ebert, C.D. Hansen, J. Levit. “Visually Accurate Multi-Field Weather Visualization,” In IEEE Visualization 2003, Seattle, pp. 279--286. 2003.


A.A. Samsonov, E.G. Kholmovski, C.R. Johnson. “Image Reconstruction from Sensitivity Encoded MRI Data Using Extrapolated Iterations of Parallel Projections Onto Convex Sets,” In SPIE Medical Imaging, Vol. 5032, pp. 1829--1838. 2003.