Xilinx just announced Kria™, their newest product portfolio. This project takes a sneak peek at the adaptive and production ready SOM.
Collates local weather data on Google Sheets and interprets it with a neural network model built w/ TensorFlow to predict rainfall intensity
Train a neural network model in 10 minutes, and use it on ESP32 with MicroPython to control a light switch. Everything done in browser.
My yard is under attack. That’s why I used a Raspberry Pi, a Pi camera, and some machine learning to catch my yard’s attackers in action.
A voice-enabled machine which reads book and replies to the questions.
Your neighbor loves to blast Reggaeton music at full volume every morning at 9 am. Could this be an opportunity for an AI device?
An intelligent device for seniors which detects falls and sends emergency alert messages with location information using a cellular network
Via MKR GSM 1400, collate water quality data from resources over GPRS to train a Neuton model, run the model, and transmit results via SMS.
Combine the power of autonomous flight and computer vision in a UAV that can detect people in search and rescue operations.
Creating a faster, smaller, and more accurate magic wand than the famous experiment provided in Arduino Sketch, given the same hardware.
Learning Image Classification on embedding devices (ESP32-CAM)
Get Xilinx Vitis AI hardware accelerated inference up and running with minimal effort using Python and PYNQ!
In this tutorial we show how to build a cough detection system for the Arduino Nano BLE Sense using TinyML and Edge Impulse.
This smart dog collar offers an immediate reward marker to reinforce correctly obeying the trainer's commands.
Allows the reading impaired to hear both printed and handwritten text by converting recognized sentences into synthesized speech.
Learn how to turn audio into text with the free, open-source spchcat tool on a Raspberry Pi.
Use Wio Terminal and Tensorflow Lite for Microcontrollers to predict the weather and precipitation for next 24 hours.
Learn how to deploy the best object detection algorithm to Jetson Nano and start building powerful edge computing applications.
Train your machine learning models in Google Colab and easily optimize them for hardware accelerated inference!
Learn how to use machine learning on a Raspberry Pi in a remote environment (complete with cellular connectivity and solar power!).
They took our jobs, they will take our fun!
Learn how to create a custom Kria Vision AI Starter Kit (KV260) ML application with PL overlay.
While it was just a proof of concept demonstration, it really shows TinyML is up to something big.
This is my homemade CNC machine using Arduino, Easydrivers and old CD-Rom(s). I use Grbl and G-Code sender to execute the G-Code.
