The build uses the laptop’s Enhanced Dictation functionality to convert text into speech, and when a Python program receives the proper keywords, it sends an “H” character over serial to an Arduino Uno to activate the vehicle.
The Uno uses a transistor to control a 12V relay, which passes current to the Jeep’s starter solenoid. After a short delay, the MacBook then transmits an “L” command to have it release the relay, ready to do the job again when needed!
As a fan of Iron Man, Forsyth channeled his inner Tony Stark and even programmed the system to respond to “JARVIS, let’s get things going!”
Music and synchronized lighting can be a beautiful combination, evident by panGenerator’s recent installation that was commissioned by the M?skie Granie concert tour in Poland.
The interactive sculpture was comprised of 15 drums that trigger waves of light traveling toward a huge helium-filled sphere floating above the area, appearing to charge it with sound and light energy as the instruments are played.
“The audience was invited to drum collectively and together create an audio-visual spectacle – intensity of which depended on the speed and intensity of the drumming. That fulfilled the main goal of creating interactive art experience in which the audience can actively participate in the event rather than just passively enjoy the music, gathering and playing together.”
The project incorporated 200 meters of addressable RGB LEDs and measured in at roughly 300 square meters, making it likely the biggest such build ever seen there. According to the designers, each of the drums featured a custom PCB equipped with an Arduino Nano and microphone, and used an MCP2515-based CAN setup for communication.
All of this was assembled and taken down seven times over two months in cities around the country. Be sure to check out this dazzling display in action in the video below!
This post is from Massimiliano Pippi, Senior Software Engineer at Arduino.
The Arduino IoT Cloud platform aims to make it very simple for anyone to develop and manage IoT applications and its REST API plays a key role in this search for simplicity. The IoT Cloud API at its core consists of a set of endpoints exposed by a backend service, but this alone is not enough to provide a full-fledge product to your users. What you need on top of your API service are:
Good documentation explaining how to use the service.
A number of plug-and-play API clients that can be used to abstract the API from different programming languages.
Both those features are difficult to maintain because they get outdated pretty easily as your API evolves but clients are particularly challenging: they’re written in different programming languages and for each of those you should provide idiomatic code that works and is distributed according to best practices defined by each language’s ecosystem.
Depending on how many languages you want to support, your engineering team might not have the resources needed to cover them all, and borrowing engineers from other teams just to release a specific client doesn’t scale much.
Being in this exact situation, the IoT Cloud team at Arduino had no other choice than streamlining the entire process and automate as much as we could. This article describes how we provide documentation and clients for the IoT Cloud API.
Clients generation workflow
When the API changes, a number of steps must be taken in order to ship an updated version of the clients, as it’s summarized in the following drawing.
As you can see, what happens after an engineer releases an updated version of the API essentially boils down to the following macro steps:
1. Fresh code is generated for each supported client. 2. A new version of the client is released to the public.
The generation process
Part 1: API definition
Every endpoint provided by the IoT Cloud API is listed within a Yaml file in OpenAPI v3 format, something like this (the full API spec is here):
- description: The id of the thing
description: Not Found
The format is designed to be human-readable, which is great because we start from a version automatically generated by our backend software that we manually fine-tune to get better results from the generation process. At this stage, you might need some help from the language experts in your team in order to perform some trial and error and determine how good the generated code is. Once you’ve found a configuration that works, operating the generator doesn’t require any specific skill, the reason why we were able to automate it.
Part 2: Code generation
To generate the API clients in different programming languages we support, along with API documentation we use a CLI tool called openapi-generator. The generator parses the OpenAPI definition file and produces a number of source code modules in a folder on the filesystem of your choice. If you have more than one client to generate, you will notice very soon how cumbersome the process can get: you might need to invoke openapi-generator multiple times, with different parameters, targeting different places in the filesystem, maybe different git repositories; when the generation step is done, you have to go through all the generated code, add it to version control, maybe tag, push to a remote… You get the gist.
To streamline the process described above we use another CLI tool, called Apigentools, which wraps the execution of openapi-generator according to a configuration you can keep under version control. Once Apigentools is configured, it takes zero knowledge of the toolchain to generate the clients – literally anybody can do it, including an automated pipeline on a CI system.
Part 3: Automation
Whenever the API changes, the OpenAPI definition file hosted in a GitHub repository is updated accordingly, usually by one of the backend engineers of the team. A Pull Request is opened, reviewed and finally merged on the master branch. When the team is ready to generate a new version of the clients, we push a special git tag in semver format and a GitHub workflow immediately starts running Apigentools, using a configuration stored in the same repository. If you look at the main configuration file, you might notice for each language we want to generate clients for, there’s a parameter called ‘github_repo_name’: this is a killer feature of Apigentools that let us push the automation process beyond the original plan. Apigentools can output the generated code to a local git repository, adding the changes in a new branch that’s automatically created and pushed to a remote on GitHub.
The release process
To ease the release process and to better organize the code, each API client has its own repo: you’ll find Python code in https://github.com/arduino/iot-client-py, Go code in https://github.com/arduino/iot-client-go and so on and so forth. Once Apigentools finishes its run, you end up with new branches containing the latest updates pushed to each one of the clients’ repositories on GitHub. As the branch is pushed, another GitHub workflow starts (see the one from the Python client as an example) and opens a Pull Request, asking to merge the changes on the master branch. The maintainers of each client receive a Slack notification and are asked to review those Pull Requests – from now on, the process is mostly manual.
It doesn’t make much sense automate further, mainly for two reasons:
We want to be sure a human validates the code before it’s generally available through an official release.
We’ve been generating API clients for the IoT Cloud API like this for a few months, performing multiple releases for each supported programming language and we now have a good idea of the pros and cons of this approach.
On the bright side:
The process is straightforward, easy to read, easy to understand.
The system requires very little knowledge to be operated.
The time between a change in the OpenAPI spec and a client release is within minutes.
We had an engineer working two weeks to set up the system and the feeling is that we’re close to paying off that investment if we didn’t already.
On the not-so-bright side:
If operating the system is trivial, debugging the pipeline if something goes awry requires a high level of skill to deep dive into the tools described in this article.
If you stumble upon a weird bug on openapi-generator and the bug doesn’t get attention, contributing patches upstream might be extremely difficult because the codebase is complex.
Overall we’re happy with the results and we’ll keep building up features on top of the workflow described here. A big shoutout to the folks behind openapi-generator and Apigentools!
Nixie tubes are, of course, an elegant display method from a more civilized age, but actually powering and controlling them can be a challenge. This can mean a great project and learning opportunity, but if you’d rather just skip ahead to programming these amazing lights, then Marcin Saj’s IN-2 binary Nixie clock is definitely worth a look.
This retro-style unit features a 6 x 3 array of small IN-2 tubes, which are turned to “1” or “0” depending on the time. Reading the results takes a bit of binary math, but it would be good practice for those that would like to improve their skills.
Researchers in Thailand have developed a ZigBee-based wireless monitoring solution for off-grid PV installations capable of tracking the sun across the sky, tilting the panel hourly. The elevation for the setup is adjusted manually once per month for optimum energy collection. The prototype is controlled by a local Arduino Uno board, along an H-bridge motor driver to actuate the motor and a 12V battery that’s charged entirely by solar power.
The system features a half-dozen sensors for measuring battery terminal voltage, solar voltage, solar current, current to the DC-DC converter, the temperature of the power transistor of DC-DC converter, and the tilt angle of solar panels according to the voltage across the potentiometer.
Data is transmitted wirelessly via an XBee ZNet 2.5 module to a remote Uno with an XBee shield. The real-time information is then passed on to and analyzed by a computer, which is also used to set the system’s time.
Wireless sensing is an excellent approach for remotely operated solar power system. Not only being able to get the sensor data, such as voltage, current, and temperature, the system can also have a proper control for tracking the Sun and sensing real-time data from a controller. In order to absorb the maximum energy by solar cells, it needs to track the Sun with proper angles. Arduino, H-bridge motor driver circuit, and Direct Current (DC) motor are used to alter the tilt angle of the solar Photovoltaic (PV) panel following the Sun while the azimuth and the elevation angles are fixed at noon. Unlike the traditional way, the tilt rotation is proposed to be stepped hourly. The solar PV panel is tilted in advance of current time to the west to produce more output voltage during an hour. As a result, the system is simple while providing good solar-tracking results and efficient power outputs.
This project uses an Adafruit Feather M0 Basic Proto board to control a group of Color Kinetics or other RGB light fixtures using the DMX-512 protocol. We’ll build a DMX-512 interface FeatherWing then connect it to the Feather M0 using a Particle Ethernet FeatherWing. Once the hardware is built and assembled, we’ll write software with a web-based GUI to generate RGB lighting effects and control the attached RGB lights using the DMX protocol. By modifying the software on the Feather M0, different effects can be generated and added to the web-based GUI.