Visualization and Networking Toolkits with Wavelets

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Visualization and Networking Toolkits with
Wavelets

• Gordon Erlebacher
• Florida State University
• David A. Yuen
• University of Minnesota

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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

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Curvelet Transform
Based on ridgelets
Donoho Huo
wavelet
constant
Multiscale
Do Vetterli 2001
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Beamletse.g., Edge Extraction
Hierarchical beam basis
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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
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• Returning to wavelets

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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

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Temperature field, 2D grid 3400x500
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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

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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

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Wavelet ThresholdingModule development in Amira
Wavelets 1.2 of coefficients
Flowchart
GUI
Full resolution
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Wavelet ThresholdingFeature identification
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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!!

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Video Streamingwith wavelets
CORBA/SOAP
GUIIpaq
Color animations at 4 frames/sec on Ipaq (320 x
200) and 802.11b wireless network
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(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).
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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

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Map of data
Histogram
Two-way flow of information!!
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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!!

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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

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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

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• The End

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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 2x104 x
  2x104 x 1 something you cannot do in numerical
  experiments so easily but beamlets would be a
  definite application.

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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

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

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Wireless Portal
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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

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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