Meeting: Wednesdays, 2:30 - 5:20, SMI 109
Instructor: Bo Zhao, SMI 416B, Office hours by appointment
Authors: Bo Zhao, Angel Lin
Contact: 206.685.3846, zhaobo@uw.edu, jakobzhao (skype/wechat)
This practical exercise includes six sections:
- PART 1: Preparation
- PART 2: Using Sense HAT to Collect Environmental Data
- PART 3: Monitoring the Environmental Variables with Real-Time GIS
- PART 4: Running TensorFlow Lite Object Detection Models on the Raspberry Pi
- PART 5: Deliverable
- PART 6: References
In this practical exercise, you will learn how to set up a Raspberry Pi, collect environmental data (pressure, temperature, and humidity) using a Sense HAT, synchronize the collected data to GitHub for real-time GIS, schedule an auto-run task using crontab, and use TensorFlow Lite to run an object detection model. The aim of this project is to briefly introduce how a Raspberry Pi can be a great platform for building Internet of Things (IoT).
At the end of the tutorial, you are expected to know how to connect sensors to a Raspberry Pi, and how to set up an automated task to synchronize data from local device to the cloud. Ok, let's get started!