On-Board Mining in the On-Board Mining in the Sensor Web Sensor Web

NSF Next Generation Data Mining

November 2, 2002

Dr. Rahul Ramachandranrramachandran@itsc.uah.edu

For Steve Tanner and the EVE Team

stanner@itsc.uah.eduInformation Technology and Systems Center

University of Alabama in Huntsville256.824.5157

www.itsc.uah.edu

Presentation OutlinePresentation Outline ITSC/UAH Data Mining

Overview Onboard Mining (EVE)

– Project Overview– System Design Overview– The EVE Editor– The On-board Components– EVE Operations– Example Plans– Current and Future Directions

ITSC and Scientific Data ITSC and Scientific Data MiningMining

Research primarily focused on – Developing Mining Environments for Scientific Data– Scientific Data Mining Applications

Developed Algorithm Development and Mining (ADaM) System

– NASA research grant– The system provides knowledge discovery, feature detection

and content-based searching for data values, as well as for metadata.

– It contains over 120 different operations that can be performed on the input data stream.

– Operations vary from specialized atmospheric science data-set specific algorithms to different digital image processing techniques, processing modules for automatic pattern recognition, machine perception, neural networks and genetic algorithms.

ADaM Engine ADaM Engine ArchitectureArchitecture

PreprocessedData

PreprocessedData

Patterns/ModelsPatterns/Models

ResultsResults

OutputGIF ImagesHDF-EOSHDF Raster ImagesHDF SDSPolygons (ASCII, DXF)SSM/I MSFC

Brightness TempTIFF ImagesOthers...

Preprocessing AnalysisClustering K Means Isodata MaximumPattern Recognition Bayes Classifier Min. Dist. ClassifierImage Analysis Boundary Detection Cooccurrence Matrix Dilation and Erosion Histogram Operations Polygon Circumscript Spatial Filtering Texture OperationsGenetic AlgorithmsNeural NetworksOthers...

Selection and Sampling Subsetting Subsampling Select by Value Coincidence SearchGrid Manipulation Grid Creation Bin Aggregate Bin Select Grid Aggregate Grid Select Find HolesImage Processing Cropping Inversion ThresholdingOthers...

Processing

InputHDFHDF-EOSGIF PIP-2SSM/I PathfinderSSM/I TDRSSM/I NESDIS Lvl 1BSSM/I MSFC

Brightness TempUS RainLandsatASCII GrassVectors (ASCII Text)

Intergraph RasterOthers...

TranslatedData

DataData

ADaM: Mining ADaM: Mining EnvironmentEnvironment

Classification Based on Classification Based on Texture Features and Edge Texture Features and Edge DensityDensity

Cumulus cloud fields have a very characteristic texture signature in the GOES visible imagery

Science Rationale: Man-made changes to land use cause changes in weather patterns, especially cumulus clouds

Comparison between mining techniques based on

– Accuracy of detection

– Amount of time required to classify

Automated Data Analysis for Automated Data Analysis for Boundary Detection and Boundary Detection and QuantificationQuantification

Analysis of polar cap auroras in large volumes of spacecraft UV images

Science rationale:– Indicators to predict

geomagnetic storm Damage satellites Disrupt radio connections

Developing different mining algorithms to detect and quantify polar cap boundary

Polar Cap Boundary

Detecting Mesocylone Detecting Mesocylone SignaturesSignatures

Detecting mesocyclone signatures from Radar data

Mesocyclone is an indicator of Tornadic activity

Developing an algorithm based on wind velocity shear signatures

– Improve accuracy and reduce false alarm rates

User

Community

InformationInformation

“…drowning in data but starving for knowledge” – John Naisbett

Data glut affects business, medicine,

military, scienceHow do we leverage data to make BETTER decisions???

Exploratory - Exploration of Specific Earth System Processes and Parameters and Demonstration of Technologies

GRACE

PICASSO

Cloudsat

EO-1

SRTM

QuickTOMS

GIFTS

Systematic Missions - Observation of Key Earth System Interactions

Terra AuraAquaLandsat 7

QuikSCATICEsat

Jason-1

Many On-board PlatformsMany On-board Platforms

Many Types of Sensor Many Types of Sensor DataData

Multispectral Hyperspectral

Synthetic Aperture RadarLidar Scatterometer

Thermal

A Reconfigurable Web of Interacting Sensors

Ground NetworkGround Network

Ground Network

Military

Weather

Satellite Constellations

Communications

Project OverviewProject Overview- EVE Requirements- EVE Requirements

• Prototype a processing framework for the on-board satellite environment.

• Provide specific capabilities within the framework– Data Mining

– Classification

– Feature Extraction

• Support research applications– Multi-sensor fusion

– Intelligent sensor control

– Real-time customized data products

• Create a ground-based testbed

EVE Software Architecture

etc.

Sensor Model

Library

On-boardConfiguration

Library

Sensor Data Simulations

IR

Passive Microwave

Testbed of On-board Systems

etc.

Flight Linux

RT Linux

Control Systems

Ground Control

Testbed Control

System Specific Modifications

OutputModules

AnalysisModules

InputModules

XML BasedProcessing

Plans

Inter Process Communcation

Decision Support

Processing Plan Editor

EVE Functional EVE Functional ComponentsComponents

EVE Functional Flow: EVE Functional Flow: Getting a plan on-boardGetting a plan on-board

1. The user edits a processing plan and sends an XML description to the ground station

Ground Station with SMAC

Editor

EVE On-board System

2. The ground station sends the plan on to the appropriate on-board system

3. The on-board system creates the carts for execution

Design Overview:Design Overview:What is a Plan?What is a Plan?

A Processing Plan:Specifies a set of operations and the data stream connections between them

Design Overview:Design Overview:What is a Cart?What is a Cart?

Holds the operations of a plan that will be executed as a single real-time unit

Has knowledge of resource limitations on a platform and resource usage of operations

Design Overview:Design Overview:Processing Plan EditorProcessing Plan Editor

Web-Based Editor– Accessible from everywhere– No need to distribute new code for

new versions– No client installations– Easy to build– Flexible (drag and drop)

Drag and Drop InterfaceDrag and Drop Interface•Developed during

’02

• Java based

•Web accessible

•Extensible

•Much reuse of existing code

•Will be incorporated into other projects

Close up of Major Editor Close up of Major Editor FeaturesFeatures

Editing tools

Cart building tools

Operations

Estimated Resource

Information

Actual On-boardResourceUsage

ActualOn-board Cart Information

Metrics Module

DownlinkCommCoordinator

Plan Manager

Cart Cart

CartCart

Conductor

Schedule

Schedule

System Monitor

Cart Factory

Operations Storage

Design:Design:EVE On-board EVE On-board SystemSystemNon RT RT

Plan Manager

Cart

Cart

EVE On-board SystemEVE On-board System Coordinator:

– Start a plan manager for each uploaded plan

Plan Manager: – Push Carts into the RT environment for execution

Conductor: – Schedule and execute Carts and events

Cart Factory:– Create Carts based upon the on-board resources and

the uploaded plans, and using modules stored in the Operation Storage

Metrics Module

DownlinkCommCoordinator

Plan Manager

Cart Cart

CartCart

Conductor

Schedule

Schedule

System Monitor

Cart Factory

Operations Storage

Design:Design:EVE On-board EVE On-board SystemSystemNon RT RT

Plan Manager

Cart

Cart

Downlink Communications receives a new plan from the ground station

The Coordinator takes the plan, and creates a Plan Manager process for that specific plan

The Plan Manager parses the plan, and contacts the Cart Factory to create a Cart for each one described in the plan

The Conductor manages both a temporal scheduler and an event scheduler. When a specified time or event occurs, the Conductor invokes the appropriate Cart for execution

Each Cart executes as an independent process, and can signal events by sending messages to the Conductor

The Cart Factory creates an executable module for each Cart, including all described operations and their I/O information

This information comes from the Operations Storage

The Metrics Module collects resource usage information and sends this to the ground station

The System Monitor watches both real-time and non-real-time system functions, and sends status to the ground station

The Plan Manager then pushes each Cart into the real-time kernel space and inserts schedule information about when the Carts should be invoked

Operations in EVEOperations in EVE

Each operation is a reusable component capable of functioning in a constrained real-time environment

Operation metadata (parameters, input, and output specifications) are specified in the metadata library

Plan description files document what and how operations are linked together for a complete plan

OperationsOperationsCurrently AvailableCurrently Available

Data I/O Format Conversion Image Processing

– Convolve– Resample– Rotate– Etc.

Complex number operations (e.g. fft) Signal generator operations Network operations

Plan branching and recombining

Multiple carts, real-time and non-real-time

vidop

user_to_rtf

convolve(vert)

convolve (horz)

add

Branch

Real-timethreshold

image_to_disk

Find edges

Recombine

Example Plan: Real–Time Edge Example Plan: Real–Time Edge DetectionDetection

Plan 1

from_rtf

split

user_from_rtf

to_rtf

Cart 1(NRT)

Cart 2(RT)

Cart 3(NRT)

Get sensor data

Store results

Example Plan: Real–Time Example Plan: Real–Time Edge Detection Edge Detection

• Significant speed improvement- 5+ images per second

• Can be used with many sensors

• Edge Detection output is used by other processes

• Can be the basis for further feature extraction plans

Example Plan: Threshold Example Plan: Threshold events in AMSU-A events in AMSU-A Streaming DataStreaming Data

Event triggering between plans

from_swath

AMSUA_detect

save_to_raw_file

Read_raw_data

convert_to_image

save_image_data

Plan 1

Plan 2

Get sensor data

Channel select Thresholding

Save resultsand signal event

Activate on event signal

Example Plan: Threshold Example Plan: Threshold events in AMSU-A Streaming events in AMSU-A Streaming Data Data

EVE

Current Issues and Future Current Issues and Future EnhancementsEnhancementsAdvanced on-board coordination

– Shared memory– Broadcasting from On-Board

Event Flagging on Multiple Platforms

Enhanced System Tools– Detection of Race Conditions– Monitor operation I/O

Year 3 ActivitiesYear 3 Activities Publish Processing Plan Syntax for use by others

Provide public access to web based user interface and beta testing of the EVE system framework

Implement and add new operations to the system

Incorporate additional operations from other sources

Increase data input components based upon known and expected sensors

Incorporate intelligent scheduling Port to cluster environment for sensor web

prototyping Possibly incorporate EVE into a flight of

opportunity (OMNI, UAV, Flight Linux, etc.)

Additional InformationAdditional Information Website:

– eve.itsc.uah.edu

Contact Person:

– Steve Tanner

– stanner@itsc.uah.edu

– (256)-824-6868