Overview
The Arduino Prototyping Shield makes it easy for you to design custom circuits. You can solder parts to the prototyping area to create your project,or use it with a small solderless breadboard (not included) to quickly test circuit ideas without having to solder. It's got extra connections for all of the Arduino MEGA I/O pins, and it's got space to mount through-hole and surface mount integrated circuits. It's a convenient way to make your custom Arduino circuit into a single module.
Getting Started
You can find in the Getting Started section all the information you need to configure your board, use the Arduino Software (IDE), and start tinker with coding and electronics..
Need Help?
- On the Software on the Arduino Forum
- On Projects on the Arduino Forum
- On the Product itself through our Customer Support
Tech specs
General
PCB Size | 101.5 x 53.3 mm |
Weight | 0.013 Kg |
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
OSH: Schematics
The Arduino Mega Proto Shield is open-source hardware! You can build your own board using the following files:
EAGLE FILES IN .ZIP SCHEMATICS IN .PDF
Description
Board features as follows:
- 1.0 Arduino pinout
- 1 Reset button
- 1 ICSP connector
- 14 pins SMD footprint (50 mils pitch)
- 32 double row through Hole pads, standard Arduino breakout layout
- Proto aerea with multiple THT pads, 100 mils pitch
Power
The Proto Shield bring the power from the Arduino standard 5V and GND pins to the two power bus rows placed between the Through Hole package footprint, which can be used for powering the DIP sockets, or for power and ground rows.
Physical Characteristics
The maximum length and width of the Proto Shield PCB are 2.7 and 2.1 inches respectively. Three screw holes allow the shield to be attached to a surface or case. Note that the distance between digital pins 7 and 8 is 160 mil (0.16"), not an even multiple of the 100 mil spacing of the other pins.
SPI Connection
On the ICSP connector only 5V, GND and RST are wired to the respective pins on the header. MOSI and MISO are present only on the connector pads. For more information about the SPI communication see the SPI library.
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