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CAN-BUS Shield v2

SKU TPX00006 Barcode 103030215 Show more
Original price €0
Original price €33,92 - Original price €33,92
Original price
Current price €33,92
€33,92 - €33,92
Current price €33,92
VAT included

With its long travel distance, great communication speed and high reliability, CAN-BUS is one of the most common industrial bus.

Overview

It is commonly found on modern machine tools and as an automotive diagnostic bus.
Thanks to the CAN-BUS, makers are able to hack their cars! 

It adopts MCP2515 CAN-BUS controller with SPI interface and MCP2551 CAN transceiver to give you Arduino CAN-BUS capability. Default pinout is OBD-II and CAN standard pinout can be selected by switching jumpers on DB9 interface.
Moreover, it has the TF card slot for data storage and the CS pin that can be set to D4 or D5. 
The INT pin can also be set to D2 or D3 by switching jumpers on the back of the shield.

CAN-BUS Shield Works perfectly with Arduino UNO (ATmega328), Arduino Mega (ATmega1280/2560) as well as Arduino Leonardo (ATmega32U4).

 

Features:

  • Implements CAN V2.0B at up to 1 Mb/s  

  • Industrial standard 9 pin sub-D connector

  • OBD-II and CAN standard pinout selectable.

  • Changeable chip select pin

  • Changeable CS pin for TF card slot

  • Changeable INT pin

  • Screw terminal that easily to connect CAN_H and CAN_L

  • Arduino Uno pin headers 

  • 2 Grove connectors (I2C and UART

  • SPI Interface up to 10 MHz    

  • Standard (11 bit) and extended (29 bit) data and remote frames    

  • Two receive buffers with prioritized message storage  


Conformities

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

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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