Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
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

1998


P.A. Sleigh, M. Berzins, P.H. Gaskell, N.G. Wright. “An Unstructured Finite Volume Algorithm for Predicting Flow in Rivers and Estuaries,” In Computers and Fluids, Vol. 27, No. 4, pp. 479--508. 1998.



P.P.J. Sloan, D.M. Weinstein, J.D. Brederson. “Importance Driven Texture Coordinate Optimization,” In Eurographics 98, Sep, 1998.



R. Stevens, H. Fuchs, A. van Dam, P. Hanrahan, C.R. Johnson, C. McMillan, P. Heermann, S. Louis, T. Defanti, D. Reed, E. Cohen. “Data and Visualization Corridors: Report on the 1998 DVC Workshop Series,” Note: DOE Report, September, 1998.

ABSTRACT

The Department of Energy and the National Science Foundation sponsored a series of workshops on data manipulation and visualization of large-scale scientific datasets. Three workshops were held in 1998, bringing together experts in high-performance computing, scientific visualization, emerging computer technologies, physics, chemistry, materials science, and engineering. These workshops were followed by two writing and review sessions, as well as numerous electronic collaborations, to synthesize the results. The results of these efforts are reported here. Across the government, mission agencies are charged with understanding scientific and engineering problems of unprecedented complexity. The DOE Accelerated Strategic Computing Initiative, for example, will soon be faced with the problem of understanding the enormous datasets created by teraops simulations, while NASA already has a severe problem in coping with the flood of data captured by earth observation satellites. Unfortunately, scientific visualization algorithms, and high-performance display hardware and software on which they depend, have not kept pace with the sheer size of emerging datasets, which threaten to overwhelm our ability to conduct research. Our capability to manipulate and explore large datasets is growing only slowly, while human cognitive and visual perception are an absolutely fixed resource. Thus, there is a pressing need for new methods of handling truly massive datasets, of exploring and visualizing them, and of communicating them over geographic distances. This report, written by representatives from academia, industry, national laboratories, and the government, is intended as a first step toward the timely creation of a comprehensive federal program in data manipulation and scientific visualization. There is, at this time, an exciting confluence of ideas on data handling, compression, telepresence, and scientific visualization. The combination of these new ideas, which we refer to as Da ta and Visualization Corridors (DVC), can raise scientific data understanding to new levels and will improve the way science is practiced



T. Tasdizen, L. Akarun, C. Ersoy. “Color Quantization with Genetic Algorithms,” In Signal Processing: Image Communication, Vol. 12, pp. 49--57. 1998.



N. Touheed, P. Selwood, P.K. Jimack, M. Berzins. “Parallel Dynamic Load-Balancing for the Solution of Transient CFD Problems Using Adaptive Tetrahedral Meshes,” In Parallel Computational Fluid Dynamics - Recent Developments and Advances Using Parallel Computers, Edited by D.R. Emerson and A. Ecer and J. Periaux and N. Satufoka and P. Fox, Elsevier Science, pp. 81--88. 1998.



D.C. Van Essen, H.A. Drury, S. Joshi, M. Miller. “Functional and Structural Mapping of Human Cerebral Cortex: Solutions are in the Surfaces,” In Proceedings of the National Academy of Sciences, Vol. 95, No. 3, Proceedings of the National Academy of Sciences, pp. 788--795. February, 1998.
DOI: 10.1073/pnas.95.3.788



J.A. Weiss, R.D. Rabbitt, A.E. Bowden. “Incorporation of Medical Image Data in Finite Element Models to Track Strain in Soft Tissues,” In Proc SPIE (Laser-Tissue Interaction IX), Vol. 3254, pp. 477--484. 1998.



D.M. Weinstein. “The Analytic 3-D Transform for the Least-Squared Fit of Three Pairs of Corresponding Points,” School of Computing Technical Report, No. UUCS-98-005, University of Utah, Salt Lake City, UT 1998.



R.T. Whitaker. “A Level-Set Approach to 3D Reconstruction From Range Data,” In International Journal of Computer Vision, Vol. 29, No. 3, pp. 203--231. 1998.


1997


I. Ahmad, M. Berzins. “An Algorithm for ODEs from Atmospheric Dispersion Problems,” In Applied Numerical Mathematics, Vol. 25, pp. 137--149. 1997.



O. Alter, Y. Yamamoto. “Reply to the Comment on 'Protective Measurement of the Wave Function of a Single Squeezed Harmonic Oscillator State',” In Physical Review A, Vol. 56, No. 1, pp. 1057--1059. July, 1997.
DOI: 10.1103/PhysRevA.56.1057



O. Alter, Y. Yamamoto. “Quantum Zeno Effect and the Impossibility of Determining the Quantum State of a Single System,” In Physical Review A, Vol. 55, No. 4, pp. R2499--R2502. April, 1997.
DOI: 10.1103/PhysRevA.55.R2499



C.L. Bajaj, V. Pascucci, D.R. Schikore. “The Contour Spectrum,” In Proceedings of the 8th Annual IEEE Conference on Visualization (VIS-97), Edited by Roni Yagel and Hans Hagen, IEEE Computer Society, pp. 167--175. November, 1997.



C. Bajaj, H.Y. Lee, R. Merkert, V. Pascucci. “NURBS based B-rep Models for Macromolecules and their Properties,” In Proceedings of the 4th Symposium on Solid Modeling and Applications, Edited by Christoph Hoffmann and Wim Bronsvort, ACM Press, New York pp. 217--228. May, 1997.
ISBN: 0-89791-946-7



C.L. Bajaj, V. Pascucci, D.R. Schikore. “Fast Isocontouring for Structured and Unstructured Meshes in Any Dimension,” In Late Breaking Hot Topics Proceedings of the 8th Annual IEEE Conference on Visualization (VIS-97), Edited by Amitabj Varshney and David S. Ebert, IEEE Computer Society, pp. 25--28. November, 1997.



D.M. Beazley. “Using SWIG to control, prototype, and debug C program with Python,” In 4th International Python Conference, 1997.



M. Berzins, S.V. Pennington, P.R. Pratt, J.M. Ware. “SPRINT2D Software for Convection Dominated PDEs,” In Modern Software Tools in Scientific Computing, Edited by E. Arge and A.M. Bruaset and H.P. Langtangen, Birkhauser Press, 1997.

ABSTRACT

SPRINT2D is a set of software tools for solving time-dependent partial differential equations in two space variables. The software uses unstructured triangular meshes and adaptive error control in both space and time. This chapter describes the software and shows how the adaptive techniques may be used to increase the reliability of the solution procedure for a challenging combustion problem. The recent construction of a problem solving environment (PSE) has partially automated the use of SPRINT2D. This PSE consists of tools such as a visual domain specification tool, which helps ease the input of complex geometries, and a visual problem specification tool. After describing these components an evaluation will be made of SPRINT2D and its associated PSE.



M. Berzins, P.J. Capon, P.K. Jimack. “On Spatial Adaptivity and Interpolation when Using the Method of Lines,” In Applied Numerical Mathematics: Transactions of IMACS, Vol. 26, No. 1--2, pp. 117--133. 1997.



G.E. Christensen, S.C. Joshi, M. Miller. “Volumetric Transformation of Brain Anatomy,” In IEEE Trans Med Imaging, Vol. 16, No. 6, pp. 864--877. December, 1997.



G.E. Christensen, S.C. Joshi, M.I. Miller. “Volumetric Transformation of Brain Anatomy,” In IEEE Transactions on Medical Imaging, Vol. 16, No. 6, pp. 864--877. December, 1997.