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J-Link EDU Mini classroom package

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SKU TPX00070 Barcode 7630049203105 Show more
Original price €0
Original price €748,99 - Original price €748,99
Original price
Current price €748,99
€748,99 - €748,99
Current price €748,99
VAT included

SEGGER J-Link EDU Mini is a version of the SEGGER J-Link EDU debugger in a reduced form factor with identical functionality.

It has been designed to allow students and educational facilities as well as hobbyists access to top of the line debug probe technology.

Overview

Get the J-Link EDU Mini, offering the same functionality as the J-Link EDU but in a reduced form factor.
The J-Link EDU Mini Classroom Edition includes twelve J-Link EDU Mini units on special offer. Designed for education purposes and hobbyists, it provides access to top-of-the-line debug probe functionality. With a tiny form factor (18mm by 50mm, similar to a USB stick), users can enjoy full functionality.
It is JTAG and SWD supported and can only be used for non-commercial education purposes.


Other Details:

Various cores are supported by the J-Link EDU Mini. Find a complete list of supported cores here. J-Link also allows applications to access a CPU simultaneously, such as being used in parallel as a debugger. Like all SEGGER products, it is cross platform working on Windows, Linux and macOS.

Box Contents:

  • 12 units of J-Link EDU mini
  • 12 .05" 19-pin target cable
  • 12 .05" 9-pin target cable
  • 12 Micro USB cable

SEGGER J-Link debuggers are the most popular choice for optimizing the debugging and flash programming experience.

 


Documentation

Debugging with the Arduino IDE 2.0

Learn how to set up a Zero board, J-Link and Atmel-ICE debuggers with the Arduino IDE 2.0, and how to debug a program.

Using the Segger J-Link debugger with the MKR boards

Learn how to set up a MKR board with the Segger J-link debugger.

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.

read more
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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