A TinyML model using Edge Impulse and wio terminal to predict the faulty lithium ion cell in BMS pack
This tutorial shows the different ways how to perform an inference in AIfES®.
A computer vision model using Edge Impulse to assist the driver to park the vehicle in parking space.
WAiT is a project that track the movement of wild animal at the application area, identify the animal class and report it on the webserver.
Classify a fan's operational state on the 8-bit AVR Curiosity Nano using the SensiML Toolkit.
Fall detected, help connected—your safety, our priority
This project uses TinyML model and Arduino TinyML kit to monitor animals in the surroundings.
Waste management on your campus with advanced AI-powered waste sorting system, promoting sustainability and inclusivity.
Empowering Students for a Sustainable Future through Real-time Water Monitoring and Wastage Prevention with TinyML and IoT
Transforming air quality monitoring with cutting-edge technology. Our system combines advanced sensors and real-time analytics for proactive
Exploring AAC to substitute hearing through Neosensory Buzz's haptic feedback for deaf parents to connect to their kids.
Train an artificial neural network directly on the Wio terminal with AIfES® to recognize simple gestures.
Using a Raspberry Pi and web camera, do periodic photo capture of cars on the parking and store the car count in the database
Detecting short circuits, open circuits and missing holes in PCB boards with Object Detection and deploying to a Raspberry Pi
My first tinnyML project, Fitness-Band using Arduino Nano BLE sense and Edge Impulse especially on how to collect training data via BLE.
Edge Impulse automatically improves model accuracy with one weird trick!
Identify leaks by using machine learning and a flowmeter to measure and classify the flow of liquid through a pipe.
Use the Sony Spresense with camera board and LTE board to detect potholes and then notify via MQTT the GPS location!
This tutorial shows how to use AIfES® on PC or in other IDEs and run it on your Arduino board afterwards.
Learns the colors of three different objects and can later classify them.
Energy Usage Optimisation With IoT Assisted Machine Learning
Use an Arduino Nicla Sense ME and Edge Impulse model to determine your rowing cadence and provide feedback via the IoT Remote app.
Deploy a TinyML model to detect the states of a 3D printer and monitor those states by sending them over cellular to the cloud.
A vending machine with gesture controlled touch-free console using computer vision and TinyML with Raspberry-pi Zero W and Arduino Nano.
