I had seen the Edge Impulse development platform for machine learning on edge devices being used by several boards, but I hadn't had an opportunity to try it out so far. So when Seeed Studio asked me whether I'd be interested to test the nRF52840-powered XIAO BLE Sense board, I thought it might be a good idea to review it with Edge Impulse as I had seen a motion/gesture recognition demo on the board. It was quite a challenge as it took me four months to complete the review from the time Seeed Studio first contacted me, mostly due to poor communications from DHL causing the first boards to go to customs' heaven, then wasting time with some of the worse instructions I had seen in a long time (now fixed), and other reviews getting in the way. But I finally managed to get it working (sort of), so let's have a look. Since the gesture recognition demo used an OLED display, I also asked for it and I received the XIAO BLE board (without sensor), the XIAO BLE Sense board, and the Grove OLED Display 0.66″.

This article is a quick getting started guide for the ESP32-CAM board. Note: in this tutorial we use the example from the arduino-esp32 library. This tutorial doesn't cover how to modify the example. You can watch the video tutorial or keep reading this page for the written instructions. You can use the preceding links or go directly to MakerAdvisor.com/tools to find all the parts for your projects at the best price!

If you're interested in embedded machine learning (TinyML) on the Arduino Nano 33 BLE Sense, you'll have found a ton of on-board sensors -- digital microphone, accelerometer, gyro, magnetometer, light, proximity, temperature, humidity and color -- but realized that for vision you need to attach an external camera. In this article, we will show you how to get image data from a low-cost VGA camera module. We'll be using the Arduino_OVD767x library to make the software side of things simpler. You can of course get a board without headers and solder instead, if that's your preference. The one downside to this setup is that (in module form) there are a lot of jumpers to connect.

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog. Arduino is on a mission to make machine learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.

Arduino is on a mission to make Machine Learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands. Next, we'll introduce a more in-depth tutorial you can use to train your own custom gesture recognition model for Arduino using TensorFlow in Colab.

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog. Arduino is on a mission to make machine learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.

Each successive generation of Raspberry Pi has brought something new to the table. The latest release, the Raspberry Pi 4, is no exception, upgrading the low-cost single-board computer to include true gigabit Ethernet connectivity, a high-performance 64-bit central processor, more powerful graphics processor, and up to 4GB of RAM. It's a low-cost way to play with RISC-V and Kendryte's KPU, but more expensive than an Arduino for microcontroller use and too limited for general-purpose AI work. Even with these impressive-for-the-price specifications, though, there's something the Raspberry Pi can't easily do unaided: deep learning and other artificial intelligence workloads. With an explosion of interest in AI-at-the-edge, though, there's a market for Raspberry Pi add-ons which offer to fill in the gap - and the Grove AI HAT is just such a device, billed by creator Seeed Studio as ideal for AI projects in fields from hobbyist robotics to the medical industry.

This example demonstrates the training, conversion and deployment of a simple TensorFlow model to Arduino: https://t.co/wdRW8G8s0n As it says, it's a quick demo of the training, conversion and deployment of a simple TensorFlow model to an Arduino MKR device. It's actually for the Arduino MKRZERO, but I had a MKR WIFI 1010 sitting in my desk and quickly fired it up with the example TensorFlow Lite library specified (hello_world.zip)… Of course, full declaration, this is the baby step of baby steps. The programme is outputting a sine-wave.

To celebrate another year in the books, here are our top 10 best selling Adafruit products from 2019! The Adafruit Trinket M0 may be small, but do not be fooled by its size! It's a tiny microcontroller board, built around the Atmel ATSAMD21, a little chip with a lot of power. We wanted to design a microcontroller board that was small enough to fit into any project, and low cost enough to use without hesitation. Perfect for when you don't want to give up your expensive dev-board and you aren't willing to take apart the project you worked so hard to design.