On-Board Mining in the Sensor Web

1. On-Board Mining in the Sensor Web NSF Next Generation Data Mining November 2, 2002 Dr. Rahul Ramachandran rramachandran@itsc.uah.edu For Steve Tanner and the EVE Team stanner@itsc.uah.edu Information Technology and Systems Center University of Alabama in Huntsville 256.824.5157 www.itsc.uah.edu

2. Presentation 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

3. ITSC and Scientific Data Mining • 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.

4. Input Output HDF HDF-EOS GIF PIP-2 SSM/I Pathfinder SSM/I TDR SSM/I NESDIS Lvl 1B SSM/I MSFC Brightness Temp US Rain Landsat ASCII Grass Vectors (ASCII Text) Intergraph Raster Others... GIF Images HDF-EOS HDF Raster Images HDF SDS Polygons (ASCII, DXF) SSM/I MSFC Brightness Temp TIFF Images Others... ADaM Engine Architecture Preprocessed Data Patterns/ Models Results Data Translated Data Processing Preprocessing Analysis Selection and Sampling Subsetting Subsampling Select by Value Coincidence Search Grid Manipulation Grid Creation Bin Aggregate Bin Select Grid Aggregate Grid Select Find Holes Image Processing Cropping Inversion Thresholding Others... Clustering K Means Isodata Maximum Pattern Recognition Bayes Classifier Min. Dist. Classifier Image Analysis Boundary Detection Cooccurrence Matrix Dilation and Erosion Histogram Operations Polygon Circumscript Spatial Filtering Texture Operations Genetic Algorithms Neural Networks Others...

5. ADaM: Mining Environment

6. Classification Based on Texture Features and Edge Density • 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 Cumulus cloud fields have a very characteristic texture signature in the GOES visible imagery

7. Automated Data Analysis for Boundary Detection and Quantification • 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

8. Detecting Mesocylone Signatures • 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

10. “…drowning in data but starving for knowledge” – John Naisbett Data glut affects business, medicine, military, science How do we leverage data to make BETTER decisions??? User Community Information

11. Many On-board Platforms Landsat 7 Terra Aqua Aura ICEsat QuikSCAT Jason-1 Systematic Missions- Observation of Key Earth System Interactions SRTM GRACE GIFTS Cloudsat QuickTOMS PICASSO EO-1 Exploratory - Exploration of Specific Earth System Processes and Parameters and Demonstration of Technologies

12. Many Types of Sensor Data Multispectral Hyperspectral Thermal Lidar Scatterometer Synthetic Aperture Radar

13. A Reconfigurable Web of Interacting Sensors Communications Weather Satellite Constellations Military Ground Network Ground Network Ground Network

14. Project Overview - 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

15. XML Based Processing Plans On-board Configuration Library Processing Plan Editor Input Modules Analysis Modules Output Modules Sensor Model Library Inter Process Communcation Decision Support Sensor Data Simulations Testbed of On-board Systems Passive Microwave RT Linux IR Flight Linux etc. etc. EVE Functional Components EVE Software Architecture System Specific Modifications Control Systems Testbed Control Ground Control

16. EVE On-board System Ground Station with SMAC Editor EVE Functional Flow: Getting a plan on-board 2. The ground station sends the plan on to the appropriate on-board system 1. The user edits a processing plan and sends an XML description to the ground station 3. The on-board system creates the carts for execution

17. Design Overview: What is a Plan? A Processing Plan:Specifies a set of operations and the data stream connections between them

18. Design Overview: 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

19. Design Overview:Processing 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)

20. Drag and Drop Interface • Developed during ’02 • Java based • Web accessible • Extensible • Much reuse of existing code • Will be incorporated into other projects

21. Close up of Major Editor Features Editing tools • Estimated • Resource • Information • Cart • building • tools • Actual • On-board • Resource • Usage Operations • Actual • On-board • Cart • Information

22. Design: EVE On-board System Non RT RT Metrics Module Schedule Conductor DownlinkComm Coordinator Schedule Plan Manager Cart Cart Cart Cart Factory Plan Manager Cart Cart Cart Operations Storage System Monitor

23. EVE 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

24. 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 Design: EVE On-board System The Metrics Module collects resource usage information and sends this to the ground station The Coordinator takes the plan, and creates a Plan Manager process for that specific plan Downlink Communications receives a new plan from the ground station The Plan Manager parses the plan, and contacts the Cart Factory to create a Cart for each one described in the plan The Plan Manager then pushes each Cart into the real-time kernel space and inserts schedule information about when the Carts should be invoked Non RT RT Metrics Module Schedule Conductor DownlinkComm Coordinator Schedule 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 The System Monitor watches both real-time and non-real-time system functions, and sends status to the ground station Plan Manager Cart Cart Cart Cart Factory Each Cart executes as an independent process, and can signal events by sending messages to the Conductor Plan Manager Cart Cart Cart Operations Storage System Monitor

25. Operations 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

26. OperationsCurrently Available • Data I/O • Format Conversion • Image Processing • Convolve • Resample • Rotate • Etc. • Complex number operations (e.g. fft) • Signal generator operations • Network operations

27. Example Plan: Real–Time Edge Detection Plan 1 Cart 1 (NRT) image_to_disk Cart 3 (NRT) vidop • Plan branching and recombining • Multiple carts, real-time and non-real-time Store results user_to_rtf Cart 2 (RT) user_from_rtf convolve (vert) Get sensor data from_rtf to_rtf split add Real-time threshold convolve (horz) Branch Recombine Find edges

28. Example Plan: Real–Time 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

29. Example Plan: Threshold events in AMSU-A Streaming Data Plan 1 Channel select Thresholding • Event triggering between plans from_swath AMSUA_detect Get sensor data save_to_raw_file Save results and signal event Plan 2 Read_raw_data convert_to_image save_image_data Activate on event signal

30. Example Plan: Threshold events in AMSU-A Streaming Data EVE

31. Current Issues and Future Enhancements • Advanced 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

32. Year 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.)

33. Additional Information • Website: • eve.itsc.uah.edu • Contact Person: • Steve Tanner • stanner@itsc.uah.edu • (256)-824-6868