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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
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
Overview

  • Why Broadband Sonar?

  • System Overview

  • Data Collection

  • Classification Results

  • Future Work



Benefits of broadband fish identification
Benefits of Broadband Fish Identification

  • Full-Column Assessment

  • Continuous Assessment

  • Remote Assessment

  • Cost Savings



Broadband transducer
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


Processing platform
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


Data processing
Data Processing

A/D

CONVERSION

ECHO

DETECTION

ACOUSTIC RECEIVER

FEATURE EXTRACTION

MAN-MACHINE

INTERFACE

CLASSIFICATION

SIGNATURE

DATABASE


Features
Features

  • Single Ping Parameters

  • Multiple Ping Parameters

  • Environmental Parameters


Neural net classifier

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







Future work
Future Work

  • Wider Beam (15 degrees)

  • Dual / Split Beam

  • Greater Source Level

  • Streamline Data Storage and Analysis

  • More Data Collection