Grove Shield for Arduino Nano
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Grove Shield for Arduino Nano provides you a simple way to connect with Arduino Nano.
This shield help you get rid of breadboard and jumper wires by pulling out the pins of the motherboard and expands to 8 Grove connectors.
Overview
This shield pulls out the pins of the motherboard and expands to 8 Grove connectors, including 3 Grove digital connectors, 3 Grove analog connectors, 1 Grove I2C connector, and 1 Grove UART connector.
With the help of the Grove system, you can connect hundreds of Grove sensors, Grove actuators and Grove displays to the Arduino Nano with a simple plug.
V1.1 Support List:
- Arduino Nano
- Arduino Nano Every
- Arduino Nano 33 IoT*
- Arduino Nano 33 BLE*
- Arduino Nano 33 BLE Sense*
*Attention:
To work with Arduino 33 serial boards, please switch the VCC power to 3.3V. If the VCC power supply is switched to 5V, the development board may be damaged!
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