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by Eric L. Morgan

Implementing Remote Real-time Biosensing in Watershed Management: Historical Perspectives (1970 to 2005). by Eric L. Morgan Dennis. B. George, Ester. T.Ososanya, Anil. U. Kukreja, Ninetha Thirunavukkarasu, Subramanian S. Meiyappan, and Erik Suffridge

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by Eric L. Morgan

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  1. Implementing Remote Real-time Biosensing in Watershed Management: Historical Perspectives (1970 to 2005) by Eric L. Morgan Dennis. B. George, Ester. T.Ososanya, Anil. U. Kukreja, Ninetha Thirunavukkarasu, Subramanian S. Meiyappan, and Erik Suffridge Center for the Management, Utilization, and Protection of Water Resources, Department of Electrical & Computer Engineering and Department of Biology Tennessee Technological University, Cookeville, TN 38505 and Joel H. Allen, W. Thomas Waller , Kenneth L. Dickson University of North Texas, Denton, Texas 86203

  2. Introduction • Biological Monitoring: Orderly use of biological responses to evaluate changes in environment with intent to use the information in quality control (Morgan et al 1999) • Automated Biomonitoring: Designed to support quality control by continuously recording biological responses of organisms while subjected to in-situ, ambient environmental conditions Providing real-time data on physiological and behavioral status of organism

  3. Advantages of Automated Biosensing • Provides multi-species assessments • Provides continuous, real-time monitor of biological responses • Operates at remote locations on solar cells • Transmits data from remote sites via satellite, cell phone or radio • Gives an Early Warning of Toxic stress • Intelligent System provides emergency response

  4. Project Objective • Objective: To design and implement an Early Warning Biosensing System that will detect toxic stress in aquatic animals at remote river site and transmit sensed data to a distant data processing center for stress detection/toxic prevention.

  5. Automated Biosensing System • Automated acquisition and storage of data from a small population of aquatic organisms and data transmission to a distant data processing system (for stress detection)

  6. Remote In-stream Fish Chamber

  7. Example of Bioelectric Signals Monitored

  8. Multiple Species- Water Quality Monitoring Logic

  9. Automated Biological Monitoring Network for Watershed Assessment:1970 (Concept Proposed by Cairns, et. al.)

  10. Automated Biological Monitoring Network for Watershed Assessment:1976 (Remote Platforms Proposed by Morgan, et. al)

  11. COMPLEX WATERSHEDS

  12. REGIONAL STUDY AREA

  13. RESEARCH WATERSHED- Tellico, TN

  14. Instrumented Watershed- Tellico (1986-91)

  15. Remote Platform- Tellico

  16. Remote Platform (Side view)- Tellico

  17. Remote Platform (Fish Chambers)- Tellico

  18. Remote Platform (Fish Chambers)- Stilling Well

  19. Remote Platform (Inside Instrumentation)

  20. Remote Platform (Water Quality Instruments)

  21. Date Collection Platform (DCP)- Tellico

  22. Data Collection Platform Logistics

  23. Effects of Hydrograph on Trout Breathing Rates

  24. Effects of Acidic Flows on Trout Breathing Rates

  25. Little Miami River System • Prototype Multi-species Automated Biosensing System installed at the Little Miami River, Cincinnati, Ohio. (collaborative effort between Univ. North Texas, Tenn. Tech. Univ. and the US-EPA) • Two biosensing systems on one remote river platform • One common data communication device • Handshaking between two system controllers • Two important system design requirements • Low power consumption • Small Size/configuration

  26. System Requirements • Sensor Designed to Detect Bioelectric Responses from Aquatic Animals • Signal Conditioning System • Multi-channel data Acquisition System • Compact Low-power Consuming System Controller • Remote Data Communication System • Remote Data Transmission via Cell Phone

  27. Biosensing System • Sensors: Pairs of Stainless Steel Probes • Signal Conditioning System Rack with plug-in cards • Keithley PC-add on Data Acquisition Board with Channel Expansion Modules • System Controller - 133MHz Pentium PC • Data Transmission from Remote Platform to Biology & Electrical Engineering Department at Tennessee Tech. Univ. and Made Available over the Internet

  28. Proposed New-Generation Biosensing System for Little Miami River, OH

  29. In-stream Sensor Housing (torpedo)

  30. On-site Experimental System Logic

  31. Experimental Platform On-site

  32. Field Work Review • Coaxial cables used to carry the signal from the torpedo to the cabinet found to be expensive and difficult to work with • Signal conditioning system found to be bulky • Poor signal resolution of bioelectric signals after digitization • Loss of data during transmission: Data flow control incorporated between the computer and the modem

  33. System Modifications • Build a signal conditioning system made up of small modules • Attach potted signal conditioning modules to the fish chambers • Use simple instrumentation wire to carry the signals • Build a multi-channel data acquisition system with automatic gain control for each channel

  34. System Components - 3rd Generation Remote Biosensing Network Probe, Chamber, and Animal Module Signal Conditioning Module Data Acquisition Module Signal Processing Module Pattern Recognition Module Early Warning Control Module Proposed Coordinated Watershed Network Module

  35. 3rd- Generation Biosensing System Logic

  36. Potted Signal Conditioning Module • Original Design by US-Tennessee Valley Authority • Two components : • Fixed-gain Instrumentation Amplifier, • Bandpass Filter

  37. Experimentally Generated Magnitude Response

  38. System Components - 3rd Generation Remote Biosensing Network • Probe, Chamber, and Animal Module • Signal Conditioning Module • Signal Processing Module • Pattern Recognition Module • Early Warning Control Module • Proposed Coordinated Watershed Network Module

  39. AGC Multi-Channel Data Acquisition System

  40. AGC 16-channel Data Acquisition Board

  41. System Operation System operation split up into three phases • Gain Setting Phase • Data Acquisition Phase • Data Transmission Phase

  42. Gain Setting Phase • Calculate Vmax, Vmin, delta_v, and V_average • Select gain_value from Table and calculate new range, range = (delta_v * gain_value) + V_average • Store gain_values to be used in the data acquisition phase

  43. Data Acquisition phase • Data is acquired simultaneously from all the channels • Based on the channel selected, the appropriate gain control bits , bit[1..0] are fed to the programmable gain amplifier • The data is stored in a 2-dimensional array in the system memory

  44. System Testing System testing split into two parts • Data acquisition system tested in the laboratory • Data communication system tested in the field

  45. Data Acquisition System

  46. System Components - 3rd Generation Remote Biosensing Network • Probe, Chamber, and Animal Module • Signal Conditioning Module • Data Acquisition Module • Pattern Recognition Module • Early Warning Control Module • Proposed Coordinated Watershed Network Module

  47. Example of Bioelectric Signals Monitored

  48. Waveforms (1)

  49. Waveforms (2)

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