The Use of PRAGMA on Distributed Virtual Instrumentation for Signal Analysis (DiVISA)

1. The Use of PRAGMA on Distributed Virtual Instrumentation for Signal Analysis (DiVISA) Domingo Rodriguez Wilson Rivera ECE Department University of Puerto Rico at Mayaguez September 24, 2007

2. Our Vision Developing the concept of  distributed virtual instrumentation for signal analysis (DiVISA) as a means of fostering interdisciplinary collaboration in signal-based  information processing (SbIP) through the PRAGMA grid service community resource framework (GSCRF).

3. An Infrastructure for Human Collaboration Applications Layer SOA Service Oriented Architecture Network Layer MAC Medium Access Control Physical Layer DSN Distributed Sensor Networks Physical World

4. PRAGMA: A Grid Service Community Resource Framework (GSCRF) for Information Flow Environmental Observatory Observables Sensors Effectors Signals “PRAGMA” Knowledge Processing Information Data Information Processing Signal Processing Knowledge User’s Target Application Intelligence Decision System

5. ESM: WALSAIP’s Main Research Objective *From French: sur- 'over' + veiller- 'watch' Environmental Surveillance* Monitoring (ESM) It deals with the gathering and processing of appropriate environmental information to aid in the process of effective decision making! http://www.walsaip.uprm.edu WALSAIP: Wide Area Large Scale Automated Information Processing

6. Searching for the endangered Bufo [Peltophryne] lemur through environmental surveillance monitoring Photo: Gail S Ross

7. Basic sensors: gumstix embedded PC based acoustic recorders (frogloggers) Ethernet and power cables Master Sensor: gumstix embedded PC, Power supply for basic sensors, and remote internet access. Tamarindo’s basin Environmental Surveillance Monitoring Region Atolladora’s basin Aroma’s basin Tamarindo’s basin Picture: DRNA

8. WALSAIP Sensor Grid (WSG) NS0 NS1 Basic Interface Module (BIM) Global users NS2 JBNERR, PR USA Japan Grid-S interface ECS-G interface NSN-1 China Embedded Computer System (ECSa) Linear Sensor Array (LSA) Others Storage Device ~2TB Memory ~2GB Grid Environment rth Master Sensor Node (MSN) NSk: kth Sensor Node

9. The Concept of the Acoustical Map (A-MAP) Type I A-MAP Output Type I: Direction of Arrival (DoA) y seagull_01 y x x coqui_01 Microphone Array A-MAP processor

10. The Concept of the Acoustical Map (A-MAP) Type II A-MAP Output Type II: Time-Frequency Distribution (TFD) Coqui Seagull y x Frequency Time Analyzed sound Analyzed sound Full length sound Full length sound Sensor Array A-MAP Processor

11. Signal Analysis Tools for Information Flow Cohen-Class Type Time-frequency Distribution (TFD), C (t,f ) An example of distance measure between C1(t,f)=p1 and C2(t,f)=p2 Another example of distance measure: Kullback-Leibler Divergence Rényi Divergence: Generalized Formulation of Kullback-Leibler Divergence

12. System Information Flow Characterization Shannon entropy when applied to TFDs The αth order Rényi entropy • Energy Flow Characterization: Power • Estimation in “energy change/unit time” • Information Flow Characterization: • Estimation in “entropy change/unit scale”

13. Raw Data Generation Requirements • Analyzing acoustic data to extract relevant information from a single site sensor array (M nodes) may be a “24/7/365” activity. • At a 48K samples/sec rate, 16 bits A/D, single node raw data acquisition may generate about 5 Terabytes of data yearly. • If a “single laptop” approach is taken for single node data analysis using existing software packages, it would take about four (4) person-year for a one (1) year raw data.

14. Advanced Computational Requirements • Large scale signal analysis techniques such as multivariate analysis and multispectral analysis of time-frequency distributions (TFD) bring orders of magnitude to initial raw data. This work seeks to introduce automation techniques to large scale signal analysis by efficiently using distributed computing resources and data on a grid infrastructure!

15. On Going Works • Developing a framework for automating large scale signal analysis • Integrating large scale signal analysis tools with a graphical user interface. • Formulating a real time signal analysis framework for connecting to WSG testbeds.

16. Cyclic Short Time Fourier Transform (CSTFT) CSTFT:

17. Virtual Sensor Grid Resource Infrastructure WALSAIP Server Portal Host Network-Centric System USGS Server NWS Server EPA Server DRNA Server (NOAA-JBNERRS) DRNA Server (Guanica Dry Forest Reserve) iGIAB iGIAB iGIAB iGIAB iGIAB UPRM-AIP Sensors (Xbow, Tmote, Gumstix, Acoustics, etc.) Jobos NERRS Sensors (YSI 6600EDS, Weather Station, etc.) More Interaction Less Interaction iGIAB (INTEGRIDS Grid-in-a-Box)

18. Operator Algebras Framework for Signal Analysis Physical Signals One-Dimensional Discrete Finite Signals Two-Dimensional Discrete Finite Signals Real-World Physical Signals Sampling and Windowing 1D and 2D Discrete Signal Spaces One-Dimensional Signal Algebra Operators Time-Frequency Tools 2D Discrete Signal Spaces Two-Dimensional Signal Algebra Operators

19. Implementation on PRAGMA FFTW MPI C FFTW FFTW MPI MPI C C G-FARM Application Level NINF-G Programming Level PRAGMACS 1 PRAGMACS 2 PRAGMACS N … Hardware Level LOCAL CPUs LOCAL CPUs LOCAL CPUs CS: Compute Site

20. Application Development Tools MPI C MPI PROGRAMMING TOOLS “Fastest Fourier Transform in the West.” Ninf: A programming middleware which enables users to access resources on the Grid with an easy-to-use interface. SYSTEM RESOURCES Gfarm File System: A next-generation network shared file system used as an infrastructure software.

21. Conclusion and Future Works • Conclusion: • The Concept of DiVISA • Time-Frequency Signal Analysis for Acoustical Environmental Applications • Real/Virtual Sensor Grid Resources • PRAGMA as Community Resource • Future Works: • Development of MPI-based Signal Analysis Applications • Study Dynamic Behavior of PRAGMA Infrastructure for Signal-based Information Processing (SbIP).

22. QUESTIONS?