Broadband Fish Identification of Great Lake Fishes - PowerPoint PPT Presentation

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Broadband Fish Identification of Great Lake Fishes

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Broadband Fish Identification of Great Lake Fishes. Patrick Simpson ... DOS 6.22 running ORCA.EXE Interface & Processing. Data Processing. ACOUSTIC RECEIVER ... – PowerPoint PPT presentation

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Title: Broadband Fish Identification of Great Lake Fishes


1
Broadband Fish Identification of Great Lake Fishes
  • Patrick Simpson and Mike Tuohey
  • Scientific Fishery Systems, Inc.
  • Anchorage, AK
  • Guy Fleischer and Ray Argyle
  • Biological Resources Division - U.S. Geological
    Survey
  • Great Lakes Science Center
  • Ann Arbor, MI

2
Overview
  • Why Broadband Sonar?
  • System Overview
  • Data Collection
  • Classification Results
  • Future Work

3
Broadband vs. Narrowband
4
Benefits of Broadband Fish Identification
  • Full-Column Assessment
  • Continuous Assessment
  • Remote Assessment
  • Cost Savings

5
Broadband Sonar Fish Identification System
Prototype
6
Broadband Transducer
  • Resonant Frequency 153,600 Hz
  • 3 dB Operation Band 45 kHz (138 - 183 kHz) Q3.4
  • Active Surface 177-mm disc
  • Beam Pattern 4.1 beamwidth, sidelobes _at_ 20 dB
  • Rated Power 80 W transducer wet electronics
  • Source Level 216 dB re 1 uPa _at_ 1 m _at_ 153.6 kHz
  • Transmit Sensitivity peak TVR of 181 dB re 1
    uPa-m/V _at_ 169 kHz
  • Receive Sensitivity peak OCVR of -180 dB re 1
    V/uPa _at_ 169 kHz

7
Processing Platform
  • Mid-tower computer case with 230 W power supply
  • Plato motherboard with ISA/PCI bus adapters, 256
    kByte cache
  • Intel Pentium 90 MHz CPU with 16 Mbyte DRAM
  • PCI SCSI-2 host adapter controlling three SCSI-2
    devices below
  • 1.0 GB hard disk, 1.3 GB magneto-optical drive,
    and quad-speed SCSI-2 CD-ROM
  • 12 bit 770 kS/s ADC/DSP card with 486DX2/66 and 4
    MB on-board DRAM
  • 1280 x 1024 video monitor and video adapter card
    with 2 MB DRAM
  • DOS 6.22 running ORCA.EXE Interface Processing

8
Data Processing
A/D CONVERSION
ECHO DETECTION
ACOUSTIC RECEIVER
FEATURE EXTRACTION
MAN-MACHINE INTERFACE
CLASSIFICATION
SIGNATURE DATABASE
9
Features
  • Single Ping Parameters
  • Multiple Ping Parameters
  • Environmental Parameters

10
Neural Net Classifier
11
Tethered Individual Fish
12
Free-Swimming Fish
13
Tethered Data Collection
14
Classification Results
15
Tethered Vs. Free-Swimming Results
16
Future Work
  • Wider Beam (15 degrees)
  • Dual / Split Beam
  • Greater Source Level
  • Streamline Data Storage and Analysis
  • More Data Collection
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