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Broadband Fish Identification of Great Lake Fishes

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. Overview. Why Broadband Sonar?

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Broadband Fish Identification of Great Lake Fishes

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  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) Q=3.4 • Active Surface 177-mm disc • Beam Pattern 4.1° beamwidth, sidelobes @ 20 dB • Rated Power 80 W transducer & wet electronics • Source Level 216 dB re 1 uPa @ 1 m @ 153.6 kHz • Transmit Sensitivity peak TVR of 181 dB re 1 uPa-m/V @ 169 kHz • Receive Sensitivity peak OCVR of -180 dB re 1 V/uPa @ 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. Features Input a a a a o o o F 1 n 3 2 A Nodes Hidden b b b b o o o F Nodes p 3 2 1 B Output c c c c o o o Nodes F m 3 2 1 C Classes 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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