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Visualization and Networking Toolkits with Wavelets

Visualization and Networking Toolkits with Wavelets. Gordon Erlebacher Florida State University David A. Yuen University of Minnesota. Beyond Wavelets. E. Candes (Caltech) D. Donoho (Stanford University) Wavelets (point singularities) Curvelets (curve singularities)

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Visualization and Networking Toolkits with Wavelets

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  1. Visualization and Networking Toolkits with Wavelets Gordon Erlebacher Florida State University David A. Yuen University of Minnesota

  2. Beyond Wavelets • E. Candes (Caltech)D. Donoho (Stanford University) • Wavelets (point singularities) • Curvelets (curve singularities) • Surflets (surface singularities) • Beamlets (edge detection in images) • Early development: • Inefficient compared to wavelet transforms • Compare to wavelets 10 years ago ACES 2002, Maui, HW

  3. Original Orig + noise Wavelet Transform Curvelet Transform Curvelet Transform Based on ridgelets Donoho & Huo wavelet constant Multiscale Do & Vetterli 2001 ACES 2002, Maui, HW

  4. 900beamlets 256x256 = 65k pixels Beamletse.g., Edge Extraction Hierarchical beam basis ACES 2002, Maui, HW

  5. Fault extraction via beamlets Image from Regenauer & Yuen 2002 Ice ridges and grooveson Europa Shear zones on venus Feature extraction via wavelets San Andreas fault Microstructural image of mylonitc shear zone ACES 2002, Maui, HW

  6. Returning to wavelets … ACES 2002, Maui, HW

  7. ACES 2002, Maui, HW

  8. ACES 2002, Maui, HW

  9. Urgent Needs • 3D data compression • Better data representation • Methods for feature quantification • Efficient automatic feature extraction • Next two slides illustrate this using • 2D thermal convection at increasing Ra • 3D thermal convection at high Ra ACES 2002, Maui, HW

  10. Ra = 3×107 Ra = 3×108 Ra = 109 Ra = 1010 Temperature field, 2D grid: 3400x500 ACES 2002, Maui, HW

  11. ACES 2002, Maui, HW

  12. Wavelet-Based Toolkit • Visualization requires the ability to compute auxiliary variables • Given velocity, density, pressure, compute temperature transport • Compute the time-derivative of some variable • Variables must be computed on a time-dependent adaptive grid • Need to compute variables over • User-specified spatial region • User-specified scales • With a range of thresholds • Need to compute statistical quantities ACES 2002, Maui, HW

  13. Advanced VisualizationAmira: www.amiravis.com • General-purpose visualization and 3D reconstruction software • Ideally suited for 3D datasets: scalar and vector fields • Advanced volume visualization • Object-Oriented • Advanced manipulators • users can interact directly with the data • Extensible by the user with developer version • Flowchart-based • Harnesses hardware of commodity graphics cards ACES 2002, Maui, HW

  14. Wavelet ThresholdingModule development in Amira Wavelets: 1.2% of coefficients Flowchart GUI Full resolution ACES 2002, Maui, HW

  15. Wavelet ThresholdingFeature identification ACES 2002, Maui, HW

  16. Remote Visualization • Data could be computed, accumulated, stored, analyzed, and visualized at different locations • Data is stored in many databases around the world • Users collaborate • In the same location • At distributed locations • Need toolkits to simplify access, analysis, and visualization of the data in a transparent fashion!! ACES 2002, Maui, HW

  17. VisualizationServer VisualizationIpaq Frame Frame Wavelettransform Wavelettransform Encode Decode Video Streamingwith wavelets CORBA/SOAP GUIIpaq Color animations at 4 frames/sec on Ipaq (320 x 200) and 802.11b wireless network ACES 2002, Maui, HW

  18. ACES 2002, Maui, HW

  19. (slide provided by Fox) SERVICES (A) Community Contributed Services (research). (B) EarthScope Provided Services. EarthScope does not have to produce; can access existing (distributed) products. - Visualization Service: (commercial, open source) Needs: 3D, 4D, overlay, georeferenced. - Registration Service: different datasets into common reference system [e.g., GIS]. - Simple data mining tools:exist, new research mining tools will eventually become contributed as a standard service. - Data Aggregation Service: combine different datasets to form meta-sets. - Higher level Application Data Structure Service: (e.g., interpolation of Finite Element mesh). ACES 2002, Maui, HW

  20. Interactive Web QueryingAnother Grid Service • Data Maps • 3D data stored in various remote sites • Data can be queried for • Statistical information of primitive or derived variables (hook up wavelet calculator to this system) • User interface optimized for handheld devices ACES 2002, Maui, HW

  21. Map of data Histogram Two-way flow of information!! ACES 2002, Maui, HW

  22. Wireless SpeedsPresent and Near Future • Present: 802.11b • Range: 150 m • 10 Mbit/sec • 1st quarter 2002: 802.11a • Range: 150 m • 54 Mbit/sec • Not compatible with 802.11b • 3rd quarter 2002: 802.11g • Range: N/A • 54 Mbit/sec • Compatible with 802.11b!! ACES 2002, Maui, HW

  23. OQO: true mobile computing?Fall 2002 • Up to 1 GHz • Crusoe chip • 256 Mbytes memory • 10 Gbyte hard disk • Touchscreen • USB/Firewire • Windows XP • 4” screen ACES 2002, Maui, HW

  24. Conclusions • Size of datasets is exploding • Wavelets help to • Compress the data (1/100) • Visualize the data • Analyze the data • Communicate between centers • Wireless communication promises • Better access to field data • Ubiquitous access to data using pocket devices ACES 2002, Maui, HW

  25. The End ACES 2002, Maui, HW

  26. Beamlets • is to look at tracks (not cracks) and fault-like strtuctures produced in laboratory experiments . • There is a laboratory experiment done with glass recently to look for faults and tracks • which span from the micron to 3 cm range • the effective aspect-ratio is around 2x10**4 x 2x10**4 x 1 something you cannot do in numerical experiments so easily but beamlets would be a definite application. ACES 2002, Maui, HW

  27. ACES 2002, Maui, HW

  28. Beamlets • Objective: extract edges information from a noisy image • Edges are expressed as a series expansion in “beamlets” : • Issues: develop fast transforms to and from beamlet space ACES 2002, Maui, HW

  29. ANALYSIS FLOWS (KNOWLEDGE PATHS) Schematic of Slide Shown Earlier By Geoffry Fox (Monday afternoon, March 25). DATA STRUCTURE Flows Vary DATA SOURCE Raw data Raw data branches (Web) Service SERVICES EARTHSCOPE FRAMEWORK Data Mining, Imaging/ Analysis, Visualization iterations MIDDLE TIER USER Portal ACES 2002, Maui, HW

  30. DATA STRUCTURES *EarthScope has all Data Types: point matrix vector volume time series volume & time (4D) polygon/surface * Plus Higher Level Application Data Structure e.g., F.E. mesh, F.D. volume, Kirchhoff imaging volume ES/IT ACTION ITEM (Needs to be done fairly early): (A) Define EarthScope Data Structures. - Broad definitions common to all. - Foundation for an EarthScope Framework. (B) Define EarthScope Framework. - Provides commonality and communication between services. - Define up to the level of EarthScope observable data. - Build upon this basic definition to describe particular datasets (done by discipine). ACES 2002, Maui, HW

  31. Grid Services(Fox et al. 2002, Concurrency & Practice 2001) • Collaborative Portal • XML-based • Secure • Coupling of • Multi-scale numerical simulations / observational data • 4D space-time domain (visualization) • Data mining • Efficient I/O mechanisms • Computational Steering • Databases ACES 2002, Maui, HW

  32. Wireless Portal ACES 2002, Maui, HW

  33. Web Services G. Fox • Suscribe/Publish Model • Based on current standards • XML, XSL, schemas • Developed with Java • Room for alternate web-ready languages, i.e., Python • Peer to Peer structure • Offers wide range of services • Computation • Collaborative • Visualization ACES 2002, Maui, HW

  34. Grid Services(Fox et al. 2001) • Collaborative Portal • XML-based • Secure • Coupling of • Multi-scale numerical simulations / observational data • 4D space-time domain (visualization) • Data mining • Efficient I/O mechanisms • Computational Steering • Databases ACES 2002, Maui, HW

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