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Arduino Nano Matter: Community Preview

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SKU ABX00112 Barcode 7630049204867 Show more
Original price €0
Original price €21,60 - Original price €21,60
Original price
Current price €21,60
€21,60 - €21,60
Current price €21,60
VAT included

The Arduino Nano Matter stems from a partnership between Arduino and Silicon Labs® to make Matter®, one of the most popular IoT connectivity standards for smart home devices, accessible to all.

Overview

Nano Matter merges Arduino’s signature ease of use with with the powerful Silicon Labs® MGM240S, wrapping the best of two worlds into one of the smallest form factors currently on the market. Experimenting with Matter-compatible devices has never been easier! 
With Nano Matter, makers – at all levels of expertise – can leverage the popular Matter IoT connectivity standard to build interactive solutions, upgrade previous Nano-based projects to fully function as smart home devices, and even experiment with protocols like Zigbee® and OpenThread.

 

Key benefits include:

  • Matter-ready for quick prototyping, thanks to hardware support and a user-friendly software layer.
  • Based on the MGM240SD22VNA from Silicon Labs, a 32-bit Arm® Cortex®-M33.
  • Secure Vault™ technology: enjoy industry-leading, state-of-the art security from Silicon Labs against escalating IoT threats.
  • Multiprotocol connectivity enables 802.15.4 (Thread) and Bluetooth® Low Energy 
  • Nano-family compact size and pinout.
  • Debugging over USB via SWD interface: no external debugging probe needed!
  • Low energy consumption, designed for battery powered IoT devices.

Arduino IoT Cloud Compatible

Use your board on Arduino's IoT Cloud, a simple and fast way to ensure secure communication for all of your connected Things.

TRY THE ARDUINO IOT CLOUD FOR FREE

Need Help?

Check the Arduino Forum for questions about the Arduino Language or how to make your own Projects with Arduino. If you need any help with your product, please contact the official Arduino User Support through our Contact us page.

 

The Matter Color Light will be the only officially Matter-certified profile for the Nano Matter. Currently under certification.


Tech specs

Microprocessor MGM240SD22VNA (32-bit Arm® Cortex®-M33 with DSP instruction and FPU)
Connectivity 802.15.4 (Thread), Bluetooth® Low Energy 5.3, Bluetooth® Mesh, Matter-ready Smart Home Connectivity
Memory 1536 kB Flash, 256 kB RAM
USB Connector USB-C®
Security Secure Vault™ High
Debugging Over USB
UART 2
I2C 2
SPI 2
Digital I/O 22
Analog Inputs 20 (12 bits resolution)
DAC 4 (8-12 bits resolution)
PWM pins 22 (A maximum of 5 pins simultaneously)
External interrupts Available within all Digital pins
User Interface On-board RGB LED, User pushbutton
Circuit operating voltage 3.3 V
Input Voltage (VIN) 5 V
Source Current per I/O Pin 40 mA
Sink Current per I/O Pin 28 mA
Clock Speed 78 MHz
Antenna On-board 2.4 GHz
Dimensions 18x45 mm
Environmental Temperature  -40 °C to + 85 °C 

 

Conformities

The following Declarations of Conformities have been granted for this board:
UKCA
CE
For any further information about our certifications please visit docs.arduino.cc/certifications

Resources for Safety and Products

Manufacturer Information

The production information includes the address and related details of the product manufacturer.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/ 

Responsible Person in the EU

An EU-based economic operator who ensures the product's compliance with the required regulations.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc

 

Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

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BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

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