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

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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
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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).
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An Infrastructure for Human Collaboration
Applications Layer
Service Oriented Architecture
SOA
Network Layer
Medium Access Control
MAC
Physical Layer
Distributed Sensor Networks
DSN
Physical World
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PRAGMA A Grid Service Community Resource
Framework (GSCRF) for Information Flow
Environmental Observatory
Observables
Sensors Effectors
Signals
PRAGMA
Knowledge Processing
Data
Information
Signal Processing
Information Processing
Knowledge
Users Target Application
Intelligence
Decision System
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ESM WALSAIPs 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
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Searching for the endangered Bufo Peltophryne
lemur through environmental surveillance
monitoring
Photo Gail S Ross
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Environmental Surveillance Monitoring Region
Atolladoras basin
Aromas basin
Tamarindos basin
Picture DRNA
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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
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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
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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
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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
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System Information Flow Characterization
Shannon entropy when applied to TFDs
The ath order Rényi entropy

• Energy Flow Characterization Power
• Estimation in energy change/unit time
• Information Flow Characterization
• Estimation in entropy change/unit scale

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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.

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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!
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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.

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Cyclic Short Time Fourier Transform (CSTFT)
CSTFT

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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)
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Operator Algebras Framework for Signal Analysis
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
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Implementation on PRAGMA
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
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Application Development Tools
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.
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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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QUESTIONS?