(A.I.S.S) CAPSTONE PRESENTATION JOHN SCHULZ AND REINER LINTAG - - PowerPoint PPT Presentation

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(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


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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
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Agenda

  • Problem Statement
  • Architecture & Design
  • Results
  • Parts
  • Division of Labor

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Problem Statement

  • Modular fish sorting system to address:
  • Invasive species
  • Avoid premature fish harvesting.

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Figure 1 “Simulation Tank”

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Hardware Design

Camera GPU:GTX 1060 ATmega128a IOT Sorting Servo Data Cache

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Battery Powered

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Prototype System Design

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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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Sorting Architecture

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Figure 3 “A.I.S.S. Architecture”

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System Prototype

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Figure 4 “Current Simulation” Figure 5 “Tank Design”

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Safety

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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Results

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Database

  • 20 Different lure classes
  • 3 Water clarity levels
  • 3 Imaging angles captured:

Total database size:

+700k created in house

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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Image Intensity

  • Image Intensity – the number of levels of

color accuracy provided for each color

  • False Contouring – the appearance of lines of

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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Neural Network & FPGAs

  • Video Card: GTX 1060 6GB
  • Training Accuracy: FP16
  • FPGA Accuracy: FP12 (“FIX_12_8”)

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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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Neural Network

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Figure 14 “Classical AlexNet”

  • AlexNet structure
  • Space efficient on FPGAs
  • Designed for low latency time

Figure 15 “Embedded AlexNet”

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Neural Network

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  • Optimizer: Stochastic Gradient Descent (SGD) Method

Figure 16 “Training Accuracy”

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Inferencing: Known Data

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Figure 17 “Verification Accuracy Known Classes”

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Inferencing: Unknown Data

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Figure 18 “F18_ST05” Figure 19 “F12_CD09” Figure 20 “F10_CD03” Actual Class Names Class Predictions

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Power

Distribution

  • Currently using grid power for the system
  • Implement a maneuverable power distribution system
  • Portable Power Station
  • Car Battery
  • Solar Panel

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Figure 22 “Solar Panel” Figure 21 “Inverter”

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Power

System Power Consumption

  • Xilinx ZYNQ XC7Z020 FPGA – 36 [W]
  • Jebao DCP Water Pump – 22 [W] to 23 [W]
  • Mingdak LED Aquarium Light – 4 [W]

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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Power

Potential Off-grid Power System Hydro-Generator

  • Stator
  • Rotor Disk
  • Projected Watt Generation: 59kWh

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Figure 23 “Stator and Rotor Model”

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Power

Potential Off-grid Power System Solar Panels

  • BP350
  • Solar Panel: 150[W]
  • Solar Panel Efficiency: 24%
  • Portable

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Figure 24 “Solar Panel”

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Parts

Tank components

  • Jebao DCP – 2500 Water Pump
  • Cambro – Lid
  • Cambro – Container
  • PVC Pipe ¾”
  • Mingdak – B00X84LQ5S Top Light
  • Hefty – Black Garbage Bags

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Figure 25 “Pump” Figure 26 “Tank Light”

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Parts

Safety

  • Watt’s Wire – WW-G12T003Y GFCI
  • AmazonBasics - MW-A1/B3-1650 Extension Cord

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”

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Parts

Data Acquisition

  • Canon 70D DSLR camera using a 35-50mm
  • Nikon D3500 DSLR camera using 18-55mm

Data Storage

  • 1TB Samsung 970 EVO

AI Implementation

  • PYNQ Dev. Board w/ Xilinx ZYNQ FPGA

AI Training

  • Alienware 13, 32GB Ram, GTX1060 6GB

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Division of Labor

JOHN SCHULZ REINER LINTAG

  • Build simulation tank
  • Testing image processing methods
  • Power system
  • Develop electrical safety guidelines
  • Creating website
  • Update Bill of Material (BOM)

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  • Build simulation tank
  • Testing image processing methods
  • Building database
  • AI training and implementation
  • Microcontroller & Servos

Shared Labor

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Questions?

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