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Adafruit Circuit Playground Express or Bluefruit Enclosure

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SKU TPX00090 Barcode P3915A Show more
Original price €0
Original price €6,07 - Original price €6,07
Original price
Current price €6,07
€6,07 - €6,07
Current price €6,07
VAT included

This minimal but elegant acrylic case will turn your board into an electronic snowflake.

Overview

The enclosure is designed to snap together securely with no tools required.
You can still press all the buttons and slide the switch thanks to a clever plastic widget fitted inside. The case winds around all the touch pads so you can still touch them or attach alligator clips.

On the back, we have two slots that can be used to pass through ribbon or webbing up to 1" /2.5mm wide There is also a slim 1/4"-20 nut that is embedded in the center back.
This is compatible with any camera mounting accessory, so if you need a solid attachment, you can use any camera positioning tool or tripod.

Since it's crystal clear, you can see all the NeoPixels shining in glorious color. The light sensor also works just fine and the case will not block IR messages going in or out. Even though there are no microphone or speaker holes, audio passes through the case via various small openings - we tested it and the difference is barely noticeable.

This enclosure is only compatible with the Circuit Playground Express and the Circuit Playground Bluefruit! It will not fit the older Classic.

This case will protect your Circuit Playground board from every day usage, but it is not waterproof or weatherproof. This product comes as 2 molded plastic halves with the buttons in place. No boards, cables, or accessories are included.


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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