September 05, 2022 by regression0707221
We’ll be walking you through creating a robotic subsystem with a voice-activated motor leveraging machine learning (ML) and an Arduino Nano 33 BLE.
With a solid foundational understanding of the concepts that underlie the field of TinyML, we’ll be applying our knowledge to a real-life project.
A note before digging into this project, I just wanted to make clear that this project will be using pre-existing datasets, Google Colabs, and Arduino code developed by both Pete Warden and the TinyML team at Harvard University. To deploy on our microcontroller unit (MCU), their resources will provide us with:
Access to datasets
Model architectures
Training scripts
Quantization scripts
Evaluation tools
Arduino code
As a disclaimer, we did not develop the vast majority of this code, and we do not own the rights to it.
All said and done, this project assumes a basic understanding of programming and electronics.
Upload Image (Automation)
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| namespace MovingForm | |
| { | |
| public partial class Form1 : Form | |
| { | |
| System.Windows.Forms.Timer timer = new(); | |
| DateTime startTime; | |
| public Form1() | |
| { | |
| InitializeComponent(); | |
| timer.Interval = 500; | |
| timer.Tick += new EventHandler(TimerTick); | |
| startTime = DateTime.Now; | |
| timer.Start(); | |
| } | |
| void TimerTick(object? sender, EventArgs e) | |
| { | |
| var distance = DateTime.Now - startTime; | |
| int milliseconds = distance.Minutes * 60 * 1000 + distance.Seconds * 1000 + distance.Milliseconds; | |
| Text = milliseconds.ToString(); | |
| } | |
| } | |
| } |
