Eloquent Arduino Blog
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A blog about Machine learning on Arduino, programming & electronics. Features Arduino coding, machine learning on microcontrollers, and computer programming. Authored by Simone, a Computer Science Graduate who loves to spend his spare time with Arduino boards.
Eloquent Arduino Blog
2y ago
Person detection on Arduino and ESP32 microcontrollers doesn't have to be difficult: with the right library, you only need 3 lines of code to perform state-of-the-art person detection . You use TensorFlow Neural Networks without any boilerplate and verbose code using the ..read more
Eloquent Arduino Blog
2y ago
Arduino gesture recognition - Vertical gesture gesture recognition system based on an accelerometer ..read more
Eloquent Arduino Blog
2y ago
RGB histogram from "Secure Content-Based Image Retrieval in the Cloud With Key Confidentiality" to extract an RGB histogram from your ESP32-cam images for computer vision tasks ..read more
Eloquent Arduino Blog
2y ago
This project was conceived for the and it's a simple realization of a trackpad-like, AI-powered, programmable "touch" surface made of cheap LDRs (light dependant resistors). In it's current form, it is a small surface, but I see scaling it up to create big touch surface, without the expensiveness of true touch sensing ..read more
Eloquent Arduino Blog
2y ago
At the end of the post you may be wandering: "do I really need Neural Networks ..read more
Eloquent Arduino Blog
3y ago
Check the full project code on how to load Tensorflow Lite Tinyml models from an SD card in Arduino library because it makes using Tf painless. how to download models from internet ..read more
Eloquent Arduino Blog
3y ago
is probably the most well known algorithm for feature extraction from time-dependent data (in particular speech data), where frequency holds a great deal of information. Sadly, computing the transform over the whole spectrum of the signal still requires O(NlogN) with the best implementation ( ); we would like to achieve faster computation on our microcontrollers. In this post I propose a partial, naive implementation of the Fourier Transform you can use to extract features from your data for Machine Learning models ..read more
Eloquent Arduino Blog
3y ago
You will be surprised by how much accuracy you can achieve in just a few kylobytes of resources: Decision Tree, Random Forest and XGBoost (Extreme Gradient Boosting) are now available on your microcontrollers: highly RAM-optmized implementations for super-fast classification on embedded devices ..read more
Eloquent Arduino Blog
3y ago
Look no further: this post explains STEP-BY-STEP all you need to know to build one yourself ..read more