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Team of four CSUF seniors creating an innovative 'Automatic Waste Sorter' that utilizes advanced technology to sort waste accurately and efficiently. The system's core is driven by a Google Coral Dev Board, with additional peripherals such as an Arduino, stepper motors, proximity sensors, a webcam, and object detection (machine learning).

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Computer Engineering Capstone Project!

Important

Below is a demonstration of the proof of concept:
Recycling, Composting, and Waste using Object Detection.

IMG_9676_compressed.mp4

Poster for project expos


Coral commands

  • use mdt shell to connect to coral via USB
  • use ssh mendel@192.168.100.2 to connect via SSH, then use password to connect.
  • connect board to wifi nmtui
  • check wifi connection hostname -I shows IP address of Coral
  • can also pin ping google.com verifies your are connected to WIFI
  • load file to board mdt push PycharmProjects/InferenceCoral/main.py
  • run Python script on Coral python3 main.py
  • sudo apt-get update && sudo apt-get dist-upgrade
  • sudo reboot now
  • sudo shutdown now
  • Connect to dev board serially with mac

Git commands.

  • git config --global user.name "Your username" setup username for Repo
  • git config --global user.email "Your email address" setup account email
  • `git remote set-url origin

After above has been completed does not need to be done again.

  • git pull origin main
  • git add .
  • git commit -m "message you want to describe commit"

Update index.html on Google Coral dev board.

  • cd templates
  • nano index.html makes changes then do the following commands
  • using control k will delete line by line code in index.html file
  • then use control o to save file, then press enter to make changes
  • lastly, use control x to exit nano editor (used with bin/bash normally)



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Team of four CSUF seniors creating an innovative 'Automatic Waste Sorter' that utilizes advanced technology to sort waste accurately and efficiently. The system's core is driven by a Google Coral Dev Board, with additional peripherals such as an Arduino, stepper motors, proximity sensors, a webcam, and object detection (machine learning).

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