Presentation on theme: "The Use of PRAGMA on Distributed Virtual Instrumentation for Signal Analysis (DiVISA) Domingo Rodriguez Wilson Rivera ECE Department University of Puerto."— Presentation transcript:
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
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).
An Infrastructure for Human Collaboration Applications Layer Network Layer Physical Layer Physical World Distributed Sensor NetworksDSN Medium Access ControlMAC Service Oriented ArchitectureSOA
Environmental Observatory Users Target Application Observables Signals Data Sensors Effectors Signal Processing Information Processing Information Knowledge Processing Knowledge Decision System Intelligence PRAGMA: A Grid Service Community Resource Framework (GSCRF) for Information Flow PRAGMA
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 Environmental Surveillance* Monitoring (ESM) * From French: sur- 'over' + veiller- 'watch' ESM: WALSAIPs Main Research Objective WALSAIP: Wide Area Large Scale Automated Information Processing
Photo: Gail S Ross Searching for the endangered Bufo [Peltophryne] lemur through environmental surveillance monitoring
Aromas basin Atolladoras basin Tamarindos basin Picture: DRNA Tamarindos basin Master Sensor: gumstix embedded PC, Power supply for basic sensors, and remote internet access. Basic sensors: gumstix embedded PC based acoustic recorders (frogloggers) Ethernet and power cables Environmental Surveillance Monitoring Region
WALSAIP Sensor Grid (WSG) N S0 N S1 N S2 N SN-1 Basic Interface Module (BIM) Linear Sensor Array (LSA) N Sk : k th Sensor Node Embedded Computer System (ECS a ) Storage Device ~2TB USA China Global users ECS-G interface Grid-S interface Memory ~2GB Grid Environment Japan Others r th Master Sensor Node (MSN) JBNERR, PR
The Concept of the Acoustical Map (A-MAP) Type I Microphone Array x y A-MAP processor A-MAP Output Type I: Direction of Arrival (DoA) x y seagull_01 coqui_01
The Concept of the Acoustical Map (A-MAP) Type II Sensor Array x y A-MAP Processor A-MAP Output Type II: Time-Frequency Distribution (TFD) Time Frequency CoquiSeagull Analyzed sound Full length sound Analyzed sound Full length sound
Signal Analysis Tools for Information Flow Cohen-Class Type Time-frequency Distribution (TFD), C (t,f ) An example of distance measure between C 1 (t,f)=p1 and C 2 (t,f)=p2 Another example of distance measure: Kullback-Leibler Divergence Rényi Divergence: Generalized Formulation of Kullback-Leibler Divergence
System Information Flow Characterization Shannon entropy when applied to TFDs The α th order Rényi entropy Energy Flow Characterization: Power – Estimation in energy change/unit time Information Flow Characterization: – Estimation in entropy change/unit scale
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.
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!
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.
Cyclic Short Time Fourier Transform (CSTFT) CSTFT:
Virtual Sensor Grid Resource Infrastructure iGIAB (INTEGRIDS Grid-in-a-Box) Network-Centric System iGIAB More Interaction Less Interaction iGIAB USGS Server NWS Server EPA Server DRNA Server (NOAA-JBNERRS) Jobos NERRS Sensors (YSI 6600EDS, Weather Station, etc.) DRNA Server (Guanica Dry Forest Reserve) UPRM-AIP Sensors (Xbow, Tmote, Gumstix, Acoustics, etc.) WALSAIP Server Portal Host
Real-World Physical Signals Operator Algebras Framework for Signal Analysis 2D Discrete Signal Spaces One-Dimensional Discrete Finite Signals Two-Dimensional Discrete Finite Signals One-Dimensional Signal Algebra Operators Time-Frequency Tools Physical Signals Sampling and Windowing Two-Dimensional Signal Algebra Operators 1D and 2D Discrete Signal Spaces
Implementation on PRAGMA Hardware Level Programming Level PRAGMA CS 1 PRAGMA CS 2 PRAGMA CS N … CS: Compute Site C MPI FFTW C MPI FFTW C MPI FFTW NINF-G G-FARM Application Level LOCAL CPUs LOCAL CPUs LOCAL CPUs
Application Development Tools C MPI 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. Gfarm File System: A next-generation network shared file system used as an infrastructure software. PROGRAMMING TOOLS SYSTEM RESOURCES
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).
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