1 WALS-AIP Project: A Bridge to Sustained Competitive Performance WALS_AIP PROJECT (CNS ) From Sensor Signals to Knowledge: A Research Road Map Domingo Rodríguez – Project PI February 9, 2005

2 WALS-AIP Project: A Bridge to Sustained Competitive Performance Outline Introduction Problem Formulation WALS-AIP’s Conceptual Framework Collaboration Efforts Active Sensor Imaging Conclusion

3 WALS-AIP Project: A Bridge to Sustained Competitive Performance There is a need for a unified integrated infrastructure (cyber-infrastructure) to monitor, collect, process, and render array sensor-based information, in an automated and timely manner, for the assessment and proper management of Earth’s geophysical, environmental, and ecological issues such as: Landslides, deforestation, etc. River dynamics: watersheds, flashfloods, etc. Soil moisture, wetlands, land use, etc. Water pollution: pathogens, solid waste, etc. PROBLEM STATEMENT

4 WALS-AIP Project: A Bridge to Sustained Competitive Performance Research Approach The formulation of a conceptual framework for a wide area large scale cyber-infrastructure for the automated processing of signal based information pertaining to hydrological applications.

5 WALS-AIP Project: A Bridge to Sustained Competitive Performance CIP Environment A Set of Input Entities A Data Storage Infrastructure A Set of Generalized Operators A Set of Composition Rules A Set of Action Rules A Set of Output Entities A User Interface CIP: Computing and Information Processing

6 WALS-AIP Project: A Bridge to Sustained Competitive Performance Phase I – Formulate CIP Framework Apprehend Conceptual Framework Develop Fractal or Scaled View of a CIP Phase II – Develop a CIP Prototype Identify Appropriate Technology Integrate Proofs of Concept Phase III – Develop Best Practices Produce Appropriate Metrics Perform Benchmarking WALS_AIP ROADMAP

7 WALS-AIP Project: A Bridge to Sustained Competitive Performance A FRAMEWORK FOR COMPUTING AND INFORMATICS AUTOMATED INFORMATION PROCESSING AUTOMATED INFORMATION PROCESSING PARALLEL & DISTRIBUTED COMPUTING PARALLEL & DISTRIBUTED COMPUTING ADVANCED DATA MANAGEMENT ADVANCED DATA MANAGEMENT User Interface Information Substrate Information Flow Target Application Intellectual Infrastructure Framework

PDC AIP HCI ADM Computing Systems to Applications DATA MANAGEMENT Computing Systems to Applications DATA ANALYSIS Hydrological Applications

9 WALS-AIP Project: A Bridge to Sustained Competitive Performance  Advanced Data Management  Database Middleware Applications  e-government  Automated Information Processing  Signal Processing System Modeling  Sensor Array Computational Methods  Hardware/Software Co-design  Human-Computer Interfaces  Usability Engineering  Informatics  Parallel and Distributed Computing  Cluster & Grid Computing  Distributed Algorithms and Scheduling  Computer and Network Performance  Hydro-Ecological Research  Phenomenological Characterization  Hydro-Ecological Modeling Methods  Hydro-Ecological Risk Modeling and Assessment WALS_AIP Groups

SAS: Sensor Array Structures DRS: Data Representation System CSPS: Computational Signal Processing System SDP: Signal Data Post-processing A Signal-based Processing System as an Automated Computing and Information Processing (CIP) Environment SCS: Signal Conditioning System RDS: Raw Data Server CDS: Computed Data Server IRS: Information Rendering System DRSSCSSAS RDSCDSCSPSSDP IRS Pre-processing Stage Post-processing Stage Processing Stage SAS INTERNET

Internet DRS SCSSAS CSPS CDP IRS CDP IRS DRS SCS SAS RDS DBMS RDS DBMS CDS DBMS CIP over a Cyber-Infrastructure

NameAutomated Information Processing Domingo Rodríguez Sensor-based Computational Signal Processing and Information Representation over a Cyber Infrastructure for Hydrological Applications Manuel Jiménez Sensor Characterization and Hardware/Software Co-esign for Hydrological Applications Co-PI & PDC Group Leader Suggested Research Trends NameHuman Computer Intefaces Nestor Rodríguez Information Processing Interfaces for Hydrological Applications José Borges Web-based Graphical Data Representation for Hydrological Applications

NameAdvanced Data Management Manuel Rodríguez P2P Web Services for Scalable Data Management over a Cyber Infrastructure for Hydrological Applications Pedro Rivera Mirror Services over a Cyber Infrastructure for Hydrological Applications Bienvenido Vélez Information Discovery over a Cyber Infrastructure for Hydrological Applications Suggested Research Trends NameHydro-Ecological Research Ismael Pagán Phenomenological Characterization of Hydrological Models in Puerto Rico John Nestler Hydro-Ecological Modeling Methods Carlos Ruiz Hydro-Ecological Risk Modeling and Assessment

NameAutomated Information Processing Wilson Rivera Scheduling over a Cyber Infrastructure for Hydrological Applications Yi QianCharacterization and Reliable Communication of a Physical Cyber Infrastructure for Hydrological Applications Nayda Santiago Performance Measures over a Cyber Infrastructure for Hydrological Applications Jaime Seguel Process Tracking over a Cyber Infrastructure for Hydrological Applications Suggested Research Trends

15 UPRM R&D Center – Feb. 9, 2005 Active Sensor Array Imaging over a Cyber Infrastructure Domingo Rodriguez Automated Information Processing Group Institute for Computing and Informatics

16 WALS-AIP Project: A Bridge to Sustained Competitive Performance Sensor Imaging System Imaging Sensors Sensor Characterization Modeling and Simulation Computational Signal Processing Algorithms Soil Moisture Estimation GIS DataBase Validation Stage No Yes

17 UPRM R&D Center – Feb. 9, 2005  Active imaging is the act of image formation by processing signals from sensors/effectors  A sensor array is a set of sensors with a prescribed physical topology  Sensors/effectors are treated as peripherals and appliances in cyberspace, and vice versa. Basic Concepts

18 UPRM R&D Center – Feb. 9, 2005  Study active signal-based imaging in the context of signal representation for information rendering and understanding.  View signals as the information carrying entities in scales. Conceptual Framework

19 UPRM R&D Center – Feb. 9, 2005  There is a need to integrate active imaging sensors and signal processing systems for cyberspace applications.  Information flows from sensors to users at different scales, sometimes in an unmanageable manner. Problem Formulation

20 UPRM R&D Center – Feb. 9, 2005  Make sensors/effectors as simple as possible for signal acquisition.  Transform the imaging problem into a computational problem.  Automate the information rendering process in cyberspace Solution Approach Physics-based Information Processing System-based Information Processing

21 UPRM R&D Center – Feb. 9, 2005

22 UPRM R&D Center – Feb. 9, 2005

23 UPRM R&D Center – Feb. 9, 2005 SAR Image LANDSAT band 6 Image Radar and Optical Images of Mayaguez

4/23/2004 Theoretical Framework Raw Data Scene Reflectivity System’s Impulse Response Function SAR System Model

4/23/2004  Level Zero (Raw) Data Level one (Processed) Data FFT2 IFFT2 Based on Franceschetti [3] Theoretical Framework SAR Image Formation Model

4/23/2004 Theoretical Framework The expected output of an imaging radar system is related to the reflectivity density function by a 2-D convolution with the ambiguity function of the imaging waveform. An inverse processed must be perform in order to obtain and estimate of this reflectivity density function. Blahut Model Image Scene Reflectivity Mathematical Model of an Imaging Radar System Ambiguity Function

4/23/2004 Matlab Simulations Results L = Number of targets

4/23/2004 Uniform Linear Array Sampling Imaging: ULASI

29 HPL Visit – UPRM R&D Center – Feb. 9, 2005 Reflection Operator FFT Operator Conjugate Operator Padding Operator Reflection Operator Cyclic Shift Operator Cyclic Shift Operator FFT Operator Pre-Computation Parallel Point Spread Function Implementation

30 WALS-AIP Project: A Bridge to Sustained Competitive Performance Questions?