J-Link PLUS Compact
USB powered JTAG debug probe supporting a large number of CPU cores.
Based on a 32-bit RISC CPU, it can communicate at high speed with the supported target CPUs.
SEGGER J-Link PLUS Compact is used around the world in tens of thousand places for development and production (flash programming) purposes.
Overview
Get the SEGGER J-Link PLUS Compact debug probe: a compact version of the J-Link PLUS. Mounts securely & unobtrusively into development and end user equipment.
Based on 32-bit RISC CPU, it communicates at high speed with supported target CPUs.
Thanks to a small size with two mounting holes, it can be placed into existing equipment housings.
Space can also be reserved for direct-to-PCB mounting.
All major IDEs (Eclipse & GDB-based IDEs) support J-Link debug probes, as does SEGGER Embedded Studio. 500,000 J-Links have been shipped so far, making this probably the most popular debug probe on the market for Arm cores and the de-facto standard.
Further Advantages
The SEGGER J-Link PLUS Compact has a built-in VCOM functionality and integrated licenses for unlimited breakpoints in flash memory, RDI/RDDI and J-Flash. It supports direct download into RAM and flash memory. It has a broad range of supported microcontrollers and CPUs.
Box Contents
- SEGGER J-Link PLUS Compact debug probe
- Micro USB cable
- 1" 20-pin ribbon cable (18 cm)
- Includes free software updates and one year of email support.
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
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.
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