1. Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science & Engineering Department, Mississippi State University * Now at Vital Images. Inc

2. Presentation Outline Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A

3. Nematic Liquid Crystals Unique state of matter Sensitivity

4. Liquid Crystals Physics Orientation measured via: scalar order parameter (S), director (n) and Q-tensor. UniaxialBiaxial

5. Biosensor Design Design a nematic liquid crystal (NLC)-based bio-sensor. 10-30nm Nanostructured substrate Receptor LC 10-30nm Nanostructured substrate Receptor pesticide Pesticide

6. Facilitate Bio-sensor Design Process Computational simulation (physics) Existing analysis methods Partial views Cutting-plane Isosurface Extend and provide both correct and deeper How does Visualization beneift design process?

7. Challenges Explore unstructured grid Identify defect cores Visualize and present relevant information

8. Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A

9. Structural Defects – Disclination Very high director (n) gradient. Defects influence physical properties. +1/2 defect -1/2 defect Axial +1 defect +1 +2

10. Defect Detection Methods – Physical Sciences Toyoki and Zapotockys 2D approach. Hobdell and Windel extended 2D to 3D. Fukuda et al. demonstrated benefit of adaptive grids Unstructured motivation [M. Zapotocky et al. Feb95] [J. Hobdell et al. 97]

11. Defect Detection Methods – Scientific Visualization Sparavigna et al. Oriented LIC and streamlines [A. Sparavigna et al. Oct99 ] Slavin et al. used Streamlines Streamtubes Ellipsoids Smooth field [V. Slavin et al. Oct04]

12. Existing Approaches: Contour map, Isosurface and Streamlines Tools used: Ensight and TecPLot Existing investigation methods

13. Existing Approach: S-based Analysis Cut-plane S-mapped to color Isosurface S-based Surface boundary Validation method for our algorithm Tool used: FieldView

14. Validation Approach Defect detection algorithm validation using Well understood models Comparative analysis Insights from experts

15. Presentation Outline Introduction and Motivation Defect Detection Background Algorithm Case Studies Conclusion Future Goals Q&A

16. Defect Core Identification in Unstructured Grid! Problems? Approaches not extensible Defect classification fails!!

17. NNP Sorted Spiral Winding Existing approaches on regular structure Regularity fails in unstructured space

18. Ordered Winding Nearest-neighbor-path traversals: random (left) and ordered (right) Nearest Neighbor - preserve spatial orientation

19. Defect Detection Algorithm – Visual Flow DATA GRID (HDF5) Nearest Neighbor Sorted listVisualization of Defect Nodes Pre-processDetectVisualize

20. Defect Detection Algorithm - Details

21. Visualization Defect structures and orientation vectors Colored nodes and arrow Analyte (impurity) outline visualized as a mesh

22. Presentation Outline Introduction and Motivation Defect Detection Background Algorithm Case studies Conclusion Future Goals Q&A

23. Effectiveness and Validation Verification using comparative analysis Based on three Case studies Regular structured backward compatibility Unstructured temporal detecting changing features Unstructured complex model real-life application

24. I: Defect Detection: Structured Model

25. II: Defect Detection: Temporal Time Line 1000015000 2000030000

26. II: Defect Detection: Temporal Time Line 1000015000 2000030000

27. II: Defect Detection: Temporal Time Line 1000015000 2000030000

28. II: Defect Detection: Temporal Time Line 1000015000 2000030000

29. III: Defect Detection: Immunoglobulin G Model (IgG) Complex bio- molecular model Existing method - isosurfacing Our method

30. Summary So Far Semi-automatic defect detection feasible On structured and un-structured grids Easier to navigate – reduces analysis time Provides correct and deeper insight

31. Surface mesh (red) and isosurface (blue) Existing Techniques: S-, b-parameter, Isosurface Biaxial plot Scalar order plot Cut-plane (mesh)

32. Comparison of Existing and Proposed Technique Defect nodes identified with our algorithm (new) Surface mesh (red) and isosurface (blue) (existing)

33. Future Work Integration with Q-tensor based analysis and NLCGlyph [Jankun-Kelly 06 – very soon]. Further user study based validation. Defect classification scheme for unstructured grids.

34. Acknowledgement Collaborators Dr. Rajendran Mohanraj, Huang Li Faculty and Staff of the Computer Science & Engineering Department, MSU National Science Foundation EPSCoR program via award #0132618. Vital Images, Inc.

35. Thank You!!