Visualization and Networking Toolkits with Wavelets

Gordon ErlebacherFlorida State University

David A. YuenUniversity of Minnesota

May 5-10, 2002 ACES 2002, Maui, HW 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

May 5-10, 2002 ACES 2002, Maui, HW 3

Curvelet Transform

Do & Vetterli 2001

Original Orig + noise

Wavelet Transform Curvelet Transform

Based on ridgelets

wavelet

constant

Multiscale

Donoho & Huo

May 5-10, 2002 ACES 2002, Maui, HW 4

Beamletse.g., Edge Extraction

256x256 = 65k pixels 900 beamlets

Hierarchical beam basis

May 5-10, 2002 ACES 2002, Maui, HW 5

Fault extraction via beamlets

Feature extraction via wavelets

Shear zones on venus Ice ridges and grooveson Europa

San Andreas fault Microstructural image of mylonitc shear zone

Image from Regenauer & Yuen 2002

May 5-10, 2002 ACES 2002, Maui, HW 6

Returning to wavelets …

May 5-10, 2002 ACES 2002, Maui, HW 7

May 5-10, 2002 ACES 2002, Maui, HW 8

May 5-10, 2002 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

May 5-10, 2002 ACES 2002, Maui, HW 10

Temperature field, 2D grid: 3400x500

Ra = 3×107

Ra = 3×108

Ra = 109

Ra = 1010

May 5-10, 2002 ACES 2002, Maui, HW 11

May 5-10, 2002 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

/p u

May 5-10, 2002 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

May 5-10, 2002 ACES 2002, Maui, HW 14

Wavelet ThresholdingModule development in Amira

Wavelets: 1.2% of coefficients

Full resolution

Flowchart

GUI

May 5-10, 2002 ACES 2002, Maui, HW 15

Wavelet ThresholdingFeature identification

May 5-10, 2002 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!!

May 5-10, 2002 ACES 2002, Maui, HW 17

Video Streamingwith wavelets

VisualizationServer

Frame

Wavelettransform

Encode

VisualizationIpaq

Frame

Wavelettransform

Decode

Color animations at 4 frames/sec on Ipaq (320 x 200) and 802.11b wireless network

CORBA/SOAP GUIIpaq

May 5-10, 2002 ACES 2002, Maui, HW 18

May 5-10, 2002 ACES 2002, Maui, HW 19

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 referencesystem [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).

(slide provided by Fox)

May 5-10, 2002 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

May 5-10, 2002 ACES 2002, Maui, HW 21

Two-way flow of information!!

Map of data

Histogram

May 5-10, 2002 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!!

May 5-10, 2002 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

May 5-10, 2002 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

May 5-10, 2002 ACES 2002, Maui, HW 25

The End

May 5-10, 2002 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.

May 5-10, 2002 ACES 2002, Maui, HW 27

May 5-10, 2002 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

, ,,

n j n jn j

I c B x ,n jB x

May 5-10, 2002 ACES 2002, Maui, HW 29

ANALYSIS FLOWS (KNOWLEDGE PATHS)Schematic of Slide Shown Earlier By Geoffry Fox

(Monday afternoon, March 25).

DATA SOURCE

MIDDLE TIER

USER

EARTHSCOPE FRAMEWORK

SERVICES

Flows Vary

branches

iterations

DATA STRUCTURE

Data Mining,

Imaging/ Analysis,

Visualization

Raw data Raw data

(Web) Service

Portal

May 5-10, 2002 ACES 2002, Maui, HW 30

DATA STRUCTURES

*EarthScope has all Data Types:point matrixvector volumetime series volume & time (4D)polygon/surface

* Plus Higher Level Application Data Structuree.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).

May 5-10, 2002 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

May 5-10, 2002 ACES 2002, Maui, HW 32

Wireless Portal

May 5-10, 2002 ACES 2002, Maui, HW 33

Web Services 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

G. Fox

May 5-10, 2002 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