Tick-Tock Goes the GNSS Timer

via SparkFun: Commerce Blog

This week, we finally get to show off a board that we have been working on for the past couple of months, the ZED-F9T GNSS Timing Breakout! This is a new piece of technology that we are excited to finally release and we eagerly await some of the new projects you make with it! Following that we have a new telemetry radio kit from our friends at HolyBro, as well as a few new cables from SparkX. Now, let's jump in and take a closer look at all of this week's new products.

If you've ever been curious about GNSS Timing, the ZED-F9T from u-blox is right for you!

SparkFun GNSS Timing Breakout - ZED-F9T (Qwiic)

SparkFun GNSS Timing Breakout - ZED-F9T (Qwiic)


The SparkFun GNSS Timing Breakout offers a unique entry into SparkFun's geospatial catalog by featuring the ZED-F9T GNSS receiver from u-blox. The ZED-F9T provides up to five nanosecond timing accuracy under clear skies with no external GNSS correction, making it perfect for applications where timing accuracy is imperative. Need an extremely accurate time reference to maximize the efficiency of your IoT network of 5G devices? The ZED-F9T GNSS Timing Breakout could be the perfect solution.

SiK Telemetry Radio V3 - 915MHz, 100mW

SiK Telemetry Radio V3 - 915MHz, 100mW


The SiK Telemetry Radio from Holybro, is a small, lightweight, and inexpensive open source radio platform that can transmit serial data more than 300m out of the box. The radio uses open source SiK firmware, which allows for a simple serial cable replacement to transmit any serial data including telemetry, RTK correction data (RTCM), or simple Serial.print() statements without any configuration required.

smôl 100mm 16-way Flexible Printed Circuit

smôl 100mm 16-way Flexible Printed Circuit

smôl 36mm 16-way Flexible Printed Circuit Z-shaped 18mm

smôl 36mm 16-way Flexible Printed Circuit Z-shaped 18mm


These are the 100mm straight and 36mm Z-shaped 16-way 0.5mm-pitch Flexible Printed Circuits from SparkX used to interconnect smôl boards end to end.

That's it for this week. As always, we can't wait to see what you make. Shoot us a tweet @sparkfun, or let us know on Instagram, Facebook or LinkedIn. Please be safe out there, be kind to one another, and we'll see you next week with even more new products!

Never miss a new product!

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Santagostino’s predictive maintenance for HVAC uses Nano RP2040 Connect

via Arduino Blog

Santagostino predictive maintenance Nano RP2040 Connect

Prevention is better than cure is pretty much every respect. Heating, ventilation and air conditioning included. The Arduino Pro team has been working with Italy’s Santagostino to deploy an impressive array of predictive maintenance solutions across the region’s medical sector.

Environment Management in Medical Centers

Santagostino operates a network of 35 medical centers across Italy. It’s work includes diagnostic tests, procedures and setting up and maintaining suitable, medical-grade environments within the centers. The HVAC systems played an important part of that even before the COVID pandemic, but is even more essential now.

So if a fault arose in the HVAC system it required the staff to notice it, in the first place. Then they’d need to report it, and wait for a technician to arrive and fix it. The inevitable delays could meant whole departments could potentially be unable to operate until the repairs took place.

But that’s the nature of a breakdown. The fault occurs, it gets reported, it gets fixed. You can’t fix something that isn’t faulty, right?

Well, maybe you can.

Predictive Maintenance Solutions with Arduino

Santagostino set about finding a monitoring solution that was modular, scalable, operated remotely and was adaptable enough to suit whatever HVAC system was in place. Ultimately it was built around a series of Arduino Nano RP2040 Connects. These have been installed in the HVAC units, and sending a constant stream of data back for analysis.

The Nano RP2040 Connect’s built-in accelerometer detects vibrations, and monitors if a system is running or not. By detecting unexpected stoppages, excessive vibrations, errant motion and analyzing that data with machine learning, a network of predictive maintenance systems was built across the facilities.

Not only is it working to alert the maintenance teams of imminent breakdowns, it allows them to schedule timely maintenance schedules before a fault occurs. A welcome side effect is that the system also allows machinery to be reduce operation when it’s not needed, saving budget and extending equipment life cycles in the process.

There’s a case study over on the Arduino Pro website that gives you a lot more details on the system. In it you can see how it can be deployed across different industries, scenarios and sectors. And our own Stefano Implicito spoke with Santagostino’s CTO Andrea Codini about the system, which you can take a look at below.

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Sending Sensor Data Over WiFi Tutorial

via SparkFun: Commerce Blog

We’re all familiar with WiFi. It runs our home, let’s us stream our favorite movies, and keeps us from having to talk with other people when we’re at a coffee shop. But there's more ways to use WiFi than simply accessing the internet through different applications. In this tutorial, we'll show you how to set up your own peer-to-peer network to sense data from one area and send that data to an LCD screen somewhere else without needing any internet connection or routers. This a great first step in being able to remove the wires from any embedded physical computing application.


Sending Sensor Data Over WiFi

January 16, 2022

This tutorial will show you how connect, send and receive sensor data between two ESP32 WiFi boards.

For this build, we're going to create a simple point-to-point closed WiFi system that reads the data from an environmental sensor and sends it to a display somewhere else. We'll keep this example as simple as possible by using our hardware, utilizing the Qwiic Connect System to connect without the need for soldering. The required hardware includes a pair of ESP32 Thing Plus Wroom modules, a Qwiic Environmental Combo Breakout, a Qwiic 20x4 SerLCD RGB Backlight Display, and a couple of Qwiic Cables. (And of course, a power supply - either battery or wall charger - for each.)

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The AI4K12 project: Big ideas for AI education

via Raspberry Pi

What is AI thinking? What concepts should we introduce to young people related to AI, including machine learning (ML), and data science? Should we teach with a glass-box or an opaque-box approach? These are the questions we’ve been grappling with since we started our online research seminar series on AI education at the Raspberry Pi Foundation, co-hosted with The Alan Turing Institute.

Over the past few months, we’d already heard from researchers from the UK, Germany, and Finland. This month we virtually travelled to the USA, to hear from Prof. Dave Touretzky (Carnegie Mellon University) and Prof. Fred G. Martin (University of Massachusetts Lowell), who have pioneered the influential AI4K12 project together with their colleagues Deborah Seehorn and Christina Gardner-McLure.

The AI4K12 project

The AI4K12 project focuses on teaching AI in K-12 in the US. The AI4K12 team have aligned their vision for AI education to the CSTA standards for computer science education. These Standards, published in 2017, describe what should be taught in US schools across the discipline of computer science, but they say very little about AI. This was the stimulus for starting the AI4K12 initiative in 2018. A number of members of the AI4K12 working group are practitioners in the classroom who’ve made a huge contribution in taking this project from ideas into the classroom.

Dave Touretzky presents the five big ideas of the AI4K12 project at our online research seminar.
Dave gave us an overview of the AI4K12 project (click to enlarge)

The project has a number of goals. One is to develop a curated resource directory for K-12 teachers, and another to create a community of K-12 resource developers. On the AI4K12.org website, you can find links to many resources and sign up for their mailing list. I’ve been subscribed to this list for a while now, and fascinating discussions and resources have been shared. 

Five Big Ideas of AI4K12

If you’ve heard of AI4K12 before, it’s probably because of the Five Big Ideas the team has set out to encompass the AI field from the perspective of school-aged children. These ideas are: 

  1. Perception — the idea that computers perceive the world through sensing
  2. Representation and reasoning — the idea that agents maintain representations of the world and use them for reasoning
  3. Learning — the idea that computers can learn from data
  4. Natural interaction — the idea that intelligent agents require many types of knowledge to interact naturally with humans
  5. Societal impact — the idea that artificial intelligence can impact society in both positive and negative ways

Sometimes we hear concerns that resources being developed to teach AI concepts to young people are narrowly focused on machine learning, particularly supervised learning for classification. It’s clear from the AI4K12 Five Big Ideas that the team’s definition of the AI field encompasses much more than one area of ML. Despite being developed for a US audience, I believe the description laid out in these five ideas is immensely useful to all educators, researchers, and policymakers around the world who are interested in AI education.

Fred Martin presents one of the five big ideas of the AI4K12 project at our online research seminar.
Fred explained how ‘representation and reasoning’ is a big idea in the AI field (click to enlarge)

During the seminar, Dave and Fred shared some great practical examples. Fred explained how the big ideas translate into learning outcomes at each of the four age groups (ages 5–8, 9–11, 12–14, 15–18). You can find out more about their examples in their presentation slides or the seminar recording (see below). 

I was struck by how much the AI4K12 team has thought about progression — what you learn when, and in which sequence — which we do really need to understand well before we can start to teach AI in any formal way. For example, looking at how we might teach visual perception to young people, children might start when very young by using a tool such as Teachable Machine to understand that they can teach a computer to recognise what they want it to see, then move on to building an application using Scratch plugins or Calypso, and then to learning the different levels of visual structure and understanding the abstraction pipeline — the hierarchy of increasingly abstract things. Talking about visual perception, Fred used the example of self-driving cars and how they represent images.

A diagram of the levels of visual structure.
Fred used this slide to describe how young people might learn abstracted elements of visual structure

AI education with an age-appropriate, glass-box approach

Dave and Fred support teaching AI to children using a glass-box approach. By ‘glass-box approach’ we mean that we should give students information about how AI systems work, and show the inner workings, so to speak. The opposite would be a ‘opaque-box approach’, by which we mean showing students an AI system’s inputs and the outputs only to demonstrate what AI is capable of, without trying to teach any technical detail.

AI4K12 advice for educators supporting K-12 students: 1. Use transparent AI demonstrations. 2. Help students build mental models. 3. Encourage students to build AI applications.
AI4K12 teacher guidelines for AI education

Our speakers are keen for learners to understand, at an age-appropriate level, what is going on “inside” an AI system, not just what the system can do. They believe it’s important for young people to build mental models of how AI systems work, and that when the young people get older, they should be able to use their increasing knowledge and skills to develop their own AI applications. This aligns with the views of some of our previous seminar speakers, including Finnish researchers Matti Tedre and Henriikka Vartiainen, who presented at our seminar series in November

What is AI thinking?

Dave addressed the question of what AI thinking looks like in school. His approach was to start with computational thinking (he used the example of the Barefoot project’s description of computational thinking as a starting point) and describe AI thinking as an extension that includes the following skills:

  • Perception 
  • Reasoning
  • Representation
  • Machine learning
  • Language understanding
  • Autonomous robots

Dave described AI thinking as furthering the ideas of abstraction and algorithmic thinking commonly associated with computational thinking, stating that in the case of AI, computation actually is thinking. My own view is that to fully define AI thinking, we need to dig a bit deeper into, for example, what is involved in developing an understanding of perception and representation.

An image demonstrating that AI systems for object recognition may not distinguish between a real banana on a desk and the photo of a banana on a laptop screen.
Image: Max Gruber / Better Images of AI / Ceci n’est pas une banane / CC-BY 4.0

Thinking back to Matti Tedre and Henriikka Vartainen’s description of CT 2.0, which focuses only on the ‘Learning’ aspect of the AI4K12 Five Big Ideas, and on the distinct ways of thinking underlying data-driven programming and traditional programming, we can see some differences between how the two groups of researchers describe the thinking skills young people need in order to understand and develop AI systems. Tedre and Vartainen are working on a more finely granular description of ML thinking, which has the potential to impact the way we teach ML in school.

There is also another description of AI thinking. Back in 2020, Juan David Rodríguez García presented his system LearningML at one of our seminars. Juan David drew on a paper by Brummelen, Shen, and Patton, who extended Brennan and Resnick’s CT framework of concepts, practices, and perspectives, to include concepts such as classification, prediction, and generation, together with practices such as training, validating, and testing.

What I take from this is that there is much still to research and discuss in this area! It’s a real privilege to be able to hear from experts in the field and compare and contrast different standpoints and views.

Resources for AI education

The AI4K12 project has already made a massive contribution to the field of AI education, and we were delighted to hear that Dave, Fred, and their colleagues have just been awarded the AAAI/EAAI Outstanding Educator Award for 2022 for AI4K12.org. An amazing achievement! Particularly useful about this website is that it links to many resources, and that the Five Big Ideas give a framework for these resources.

Through our seminars series, we are developing our own list of AI education resources shared by seminar speakers or attendees, or developed by us. Please do take a look.

Join our next seminar

Through these seminars, we’re learning a lot about AI education and what it might look like in school, and we’re having great discussions during the Q&A section.

On Tues 1 February at 17:00–18:30 GMT, we’ll hear from Tara Chklovski, who will talk about AI education in the context of the Sustainable Development Goals. To participate, click the button below to sign up, and we will send you information about joining. I really hope you’ll be there for this seminar!

The schedule of our upcoming seminars is online. You can also (re)visit past seminars and recordings on the blog.

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Supplino is a variable benchtop power supply that you can build yourself

via Arduino Blog

Working with electronics requires access to stable power in a variety of voltages. Some components require 3.3V and others require 5V. Still others need 9V or 12V — there are many possibilities. You could keep a variety of wall warts on hand, but a variable benchtop power supply is a more convenient option. Supplino is one choice and this guide from Giovanni Bernardo and Paolo Loberto will walk you through how to build one.

Supplino can accept anything from 4 to 40 volts and can output anything from 1.25 to 36 volts, with a maximum of 5A. An XH-M401 module with an XL4016E1 DC-DC buck converter handles the voltage regulation. Technically, you could use that alone to power your components. But the addition of an Arduino Nano board (or Nano Every) makes the experience far friendlier. It monitors the power supply output and drives a 1.8″ 128×160 TFT LCD screen, which displays the present voltage, amperage, and wattage.

The Arduino receives power from a second 5V buck converter. It uses a relay to control power going to the primary buck converter. A relocated potentiometer controls the voltage. Two banana plug socket make it easy to attach alligator clips or whatever other leads your project requires. You can wrap up all of these components in a tidy and attractive 3D-printed enclosure, which is compact and fits on any desktop. You have many options for the input power, but a laptop power supply is a good choice.

More details on the Supplino can be found in its post here.

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DIY jet engine powered by a Portenta H7

via Arduino Blog

Projects don’t get much more ambitious than DIY GUY Chris’ Arduino-powered jet engine. We’ve been following the work he’s done building a custom carrier board for the Portanta H7, and now we get to see it in action.

Portenta Jet Engine

To be honest, just building a working DIY jet engine model is incredible enough. But the model Chris has created is so much more than that.

The 3D-printed model has a breakaway section that lets us see the engine in action. A superb educational tool that covers everything from design and control to operation. And it looks like so much fun to make and play with, too.

His latest project puts the custom built Portenta H7 “Throne” board to use. This is a breakout, or carrier board, that he developed to explore ways to use the Portenta H7’s high density connectors. In this application it’s driving a high powered a DC motor that runs his jet engine model.

It’s an elaborate build, with a lot of printed, moving parts. In many respects the application that the H7 is used for is pretty simple, at least on the surface. But what’s great about Chris’ latest project is that it’s an excellent example of how the Arduino board could be implemented in industrial applications.

His excellent (and very professional) breakout board — the Throne — is a further demonstration of this, showing how adaptable devices like the H7 are in combination with custom solutions. So it’s worth taking a look at Chris’ other videos about the Throne’s development, as well as his mightily impressive DIY jet engine.

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