Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2005


 M.D. Meyer, H. Pfister, C.D. Hansen, C.R. Johnson. “Image-Based Volume Rendering with Opacity Light Fields,” SCI Institute Technical Report, No. UUSCI-2005-002, University of Utah, 2005.


M.D. Meyer, P. Georgel, R.T. Whitaker. “Robust Particle Systems for Curvature Dependent Sampling of Implicit Surfaces,” In In Proceedings of the International Conference on Shape Modeling and Applications (SMI), pp. 124--133. June, 2005.


S.M. Moore, R.E. Debski, B. Ellis, P.J. McMahon, J.A. Weiss. “All Three Regions of the Inferior Glenohumeral Ligament Contribute to Anterior Stability,” In Proceedings, 51st Annual Orthopaedic Research Society Meeting, Vol. 30, pp. 804. 2005.


V. Natarajan, V. Pascucci. “Volumetric Data Analysis Using Morse-Smale Complexes,” In Proceedings of the International Conference Shape Modeling and Applications, Edited by A.G. Belyaev and A.A. Pasko and M. Spagnuolo, IEEE Computer Society, pp. 320--325. June, 2005.


R. Palmer, S. Barrus, Y. Yang, G. Gopalakrishnan, R.M. Kirby. “Gauss: A Framework for Verifying Scientific Computing Software,” In Proceeding of the Software Model Checking Workshop, Edinburgh, Scotland, July, 2005.


S.M. Pizer, P.T. Fletcher, S. Joshi, A.G. Gash, J. Stough, A. Thall, G. Tracton, E. Chaney. “A Method and Software for Segmentation of Anatomic Object Ensembles by Deformable M-Reps,” In Med Phys, Vol. 32, No. 5, pp. 1335--1345. May, 2005.


S.M. Pizer, J.Y. Jeong, C. Lu, K. Muller, S. Joshi. “Estimating the Statistics of Multi-Object Anatomic Geometry Using Inter-Object Relationships,” In Proceedings of International Workshop on Deep Structure, Singularities and Computer Vision (DSSCV), Edited by O.F. Olsen and L. Florack and A Kuijper, pp. 60--71. June, 2005.


M. Ramanath, L. Zhang, J. Freire, J. Haritsa. “IMAX: Incremental Maintenance of Schema-Based XML Statistics,” In International Conference on Data Engineering (ICDE), IEEE Computer Society, Los Alamitos, CA, USA pp. 273--284. 2005.
ISSN: 1084-4627


D. Reed, R. Bajcsy, J.M. Griffiths, J. Dongarra, C.R. Johnson. “Computational Science: Ensuring America's Competitiveness,” Note: President's Information Technology Advisory Committee (PITAC), June, 2005.


F. Sachse, M. Cole, R.M. Kirby, X. Tricoche, C.R. Johnson. “Advanced Modeling and Visualization of Cardiothoracic Electrical Fields,” In Proceedings of 13th Medicine Meets Virtual Reality (MMVR13), 2005.


A.A. Samsonov, E.V.R. DiBella, P. Kellman, E.G. Kholmovski, C.R. Johnson. “Adaptive k-t BLAST/k-t SENSE for Accelerating Cardiac Perfusion MRI,” In Proceedings of the Society for Cardiovascular Magnetic Resonance (SCMR) 8th Annual Scientific Sessions, pp. 277--278. 2005.


A.A. Samsonov, E.G. Khölmovski, C.R. Johnson. “POCS-Enhanced Parallel MRI Correction of MR Image Artifacts,” In Proc. Intl. Soc. Mag. Reson., Note: Abstract, pp. 690. 2005.


G. Scheuermann, X. Tricoche. “Topological Methods for Flow Visualization,” In The Visualization Handbook, Edited by C.D. Hansen and C.R. Johnson, Elsevier, pp. 341--356. 2005.
ISBN: 0-12-387582-X


C. Scheidegger, S. Fleishman, C.T. Silva.. “Triangulating Point-Set Surfaces With Bounded Error,” In Proceedings of the Third Eurographics/ACM Symposium on Geometry Processing, Eurographics Association, pp. 63--72. 2005.
ISBN: 3-905673-24-X


C. Scheidegger, J. Comba, R. Cunha.. “Practical CFD Simulations on the GPU Using SMAC,” In Computer Graphics Forum, Vol. 24, No. 4, pp. 715--728. 2005.


Y. Serinagaoglu, D.H. Brooks, R.S. MacLeod. “Bayesian Solutions and Performance Analysis in Bioelectric Inverse Problems,” In IEEE Trans Biomed. Eng., Vol. 52, No. 6, pp. 1009--1020. June, 2005.


Y.T. Shiu, J.A. Weiss, J.B. Hoying, M.N. Iwamoto, I.S. Joung, C.T. Quam. “The Role of Mechanical Stresses in Angiogenesis,” In CRC Crit. Rev. Biomed. Eng., Vol. 33, No. 5, pp. 431--510. 2005.


C.T. Silva, J.L.D. Comba, S.P. Callahan, F.F. Bernardon. “A Survey of GPU-Based Volume Rendering of Unstructured Grids,” In Revista de Informatica Teorica e Aplicada, Vol. 12, No. 2, pp. 9--29. 2005.
ISSN: 01034308


S. Sirisup, G.E. Karniadakis, D. Xiu, I.G. Kevrekidis. “Equation-free/Galerkin-free POD-assisted Computation of Incompressible Flows,” In Journal of Computational Physics, Vol. 207, No. 2, pp. 568--587. 2005.
DOI: 10.1016/j.jcp.2005.01.024

ABSTRACT

We present a Galerkin-free, proper orthogonal decomposition (POD)-assisted computational methodology for numerical simulations of the long-term dynamics of the incompressible Navier–Stokes equations. The approach is based on the \"equation-free\" framework: we use short, appropriate initialized bursts of full direct numerical simulations (DNS) of the Navier–Stokes equations to observe, estimate, and accelerate, through \"projective integration\", the evolution of the flow dynamics. The main assumption is that the long-term dynamics of the flow lie on a low-dimensional, attracting, and invariant manifold, which can be parametrized, not necessarily spanned, by a few POD basis functions. We start with a discussion of the consistency and accuracy of the approach, and then illustrate it through numerical examples: two-dimensional periodic and quasi-periodic flows past a circular cylinder. We demonstrate that the approach can successfully resolve complex flow dynamics at a reduced computational cost and that it can capture the long-term asymptotic state of the flow in cases where traditional Galerkin-POD models fail. The approach trades the overhead involved in developing POD-Galerkin and POD-nonlinear Galerkin codes, for the repeated (yet short, and on demand) use of an existing full DNS simulator. Moreover, since in this approach the POD modes are used to observe rather than span the true system dynamics, the computation is much less sensitive than POD-Galerkin to values of the system parameters (e.g., the Reynolds number) and the particular simulation data ensemble used to obtain the POD basis functions.


 J.G. Stinstra, D.M. Weinstein, B. Hopenfeld, C.S. Henriquez, R.S. MacLeod. “Software Challenges in the New Field of Integrated Cardiac Models,” In Proceedings of The Joint Meeting of The 5th International Conference on Bioelectromagnetism and The 5th International Symposium on Noninvasive Functional Source Imaging within the Human Brain and Heart, Vol. 7, pp. 195--198. 2005.