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ALARM eResearch Australasia 2012

ALARM eResearch Australasia 2012

Presented at eResearch Australasia.

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Christopher Bayliss

October 29, 2012
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  1. ALARM Richard Sinnott, Gerson Galang, Stephen Marshall and Christopher Bayliss

  2. Aims —  Monitor water quality —  Directly via sensors. — 

    Indirectly via animals. —  In real time or as close as possible. —  Correlate with catchment information. To locate sources of pollution.
  3. Current methods —  People in field taking samples —  People

    standing in rivers with sensors and nets. —  Automated samplers —  Grab and freeze a sample regularly. —  Someone collects it for lab analysis.
  4. Animal monitoring —  An animal reacts to its environment. — 

    By monitoring the behavior of an animal in the water we can deduce things about the water quality. —  Previous approaches —  Indicator species —  Tank
  5. Behaviors —  Movement —  Gill movement —  Feeding —  Hiding

  6. Rough Outline Sensors NBN Collector Analytics Website

  7. Sensors —  Cheap —  Low power —  Can handle multiple

    sensors
  8. Sensor Hardware —  BeagleBoard —  Arduino —  USB camera — 

    G4 Modem
  9. Sensor Software —  Linux —  Python —  Gstreamer —  WebM

    video codec —  OpenCV vision library —  Arduino flavour of C
  10. Collector —  Receives data from sensors —  Stores raw data

    —  Converts raw data into usable values. —  Uses python and PostgreSQL with PostGIS
  11. Website —  Java and JavaScript based —  Allows users to

    search and visualise collected data.
  12. Current status —  Lab based test bed for controlled testing.

    —  Laptop based test sensor. —  Building a library of video and sensor data. —  Prototype sensor package. —  Temperature, pH, Redox … —  Experimenting with WAN compatible data transport options. AMQP , XMPP , … —  Developing video analysis filters. —  Migrating from Matlab to Python + OpenCV
  13. Demo