Aquatic Identification and Sorting System (A.I.S.S)
CAPSTONE PRESENTATION JOHN SCHULZ AND REINER LINTAG ADVISOR: DR. IMTIAZ MAY 4 TH, 2019
- Dept. of Electrical Engineering
(A.I.S.S) CAPSTONE PRESENTATION JOHN SCHULZ AND REINER LINTAG - - PowerPoint PPT Presentation
Aquatic Identification and Sorting System (A.I.S.S) CAPSTONE PRESENTATION JOHN SCHULZ AND REINER LINTAG ADVISOR: DR. IMTIAZ MAY 4 TH , 2019 Dept. of Electrical Engineering Agenda Problem Statement Architecture & Design Results
Aquatic Identification and Sorting System (A.I.S.S)
CAPSTONE PRESENTATION JOHN SCHULZ AND REINER LINTAG ADVISOR: DR. IMTIAZ MAY 4 TH, 2019
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Figure 1 “Simulation Tank”
Camera GPU:GTX 1060 ATmega128a IOT Sorting Servo Data Cache
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Battery Powered
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Images Training DNN DNN FPGA/ GPU Camera Laptop
Powered Components System Prototype
µC Servos Solar Panel Li Battery Power Controller Figure 2 “High Level System Overview”
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Figure 3 “A.I.S.S. Architecture”
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Figure 4 “Current Simulation” Figure 5 “Tank Design”
1. Safety Management System Guidelines
1. Plan to track safety 2. Safe working environment
2. Emergency Contingency Plan
1. Accident procedure 2. Notification plan
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Figure 6 “GFCI Protection”
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Total database size:
Images per class:
~33k
Hours captured:
+7 Hrs.
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Figure 7 “Imaging Angles”
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Figure 8 “Database Layout”
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color accuracy provided for each color
color that exist due to a lack of image intensity.
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Figure 10 “2-Bit Color” Figure 11 “3-Bit Color” Figure 9 “8-Bit Color” Figure 12 “4-Bit Color”
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Figure 13 “Tested FPGA Accuracy” 5,000 10,000 15,000 20,000 25,000 30,000 35,000 GPU 2016 GPU 2017 GPU 2018 FPGA 2018 FPGA 2019 Images/Second
Inference Performance
Source: Xilinx Developers Conference
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Figure 14 “Classical AlexNet”
Figure 15 “Embedded AlexNet”
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Figure 16 “Training Accuracy”
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Figure 17 “Verification Accuracy Known Classes”
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Figure 18 “F18_ST05” Figure 19 “F12_CD09” Figure 20 “F10_CD03” Actual Class Names Class Predictions
Distribution
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Figure 22 “Solar Panel” Figure 21 “Inverter”
System Power Consumption
System Total Wattage = 62 [W] to 63 [W] Calculation: Low = FPGA + Water Pump (Low) + LED High = FPGA + Water Pump (High) + LED
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Potential Off-grid Power System Hydro-Generator
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Figure 23 “Stator and Rotor Model”
Potential Off-grid Power System Solar Panels
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Figure 24 “Solar Panel”
Tank components
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Figure 25 “Pump” Figure 26 “Tank Light”
Safety
The team is working with and in possible water spill areas. These parts were bought and inspected before making the fully assembled tank “Live”. Safety is the number one priority for this project.
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Figure 27 “GFCI”
Data Acquisition
Data Storage
AI Implementation
AI Training
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JOHN SCHULZ REINER LINTAG
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Shared Labor
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