If you think coffee people are opinionated, then you’ve never met an espresso person. There is a lot of art and science that goes into making the perfect little cup of espresso and a good barista will control every factor, from temperature to pressure to pour rate. It isn’t rocket science, but it isn’t far off. So the LanderShot Lunar Espresso Module, a CNC-machined high-tech espresso machine, has a fitting theme.
This is, at its core, a premium espresso machine that merges designer aesthetics with cutting-edge electronics. The founder of LanderShot, Ted Ciamillo, lives in the state of Washington, but is of Italian descent. He wanted to honor his Italian heritage — and the origin of espresso — so he turned to Arduino.
Temperature control is crucial when making espresso; heat the water too much and you’ll burn the coffee, but you’ll lose the flavor and strength if you heat it too little. For that reason, the Lunar Espresso Module utilizes PID (proportional-interval-derivative) control for the 1000-watt heater. That ensures that water comes up to temperature quickly without overshooting the target, helping it go from 20 °C to 100 °C in just 180 seconds. A pneumatic lever lets the user increase the pressure to the desired level, with each stroke adding one bar.
An Arduino Nano Every board controls the heat, monitors temperature and pressure, and displays the results on a small screen offset to the side. “The Nano Every is excellent at performing the several jobs in the machine. We chose it for its low-profile architecture, easy access to the pinouts, robustness and accessible price point. And, of course, we are pleased that the brains of our machine were designed in Italy,” Ciamillo says.
He adds that the greatest technical challenge was fitting all of the electronic components into the compact machine. While that may be true, we think that he’s selling himself short on the design and CNC work. The milled parts are stunning to look at, and we can only imagine that they’re even more pleasing to touch while pulling a shot.Ready to take your daily coffee to new heights? A limited number of LanderShot Lunar Espresso Module machines are available for pre-order and should ship out in June.
Matt began with a series of traditional telescopes, but found them “a little frustrating to use in practice”. It was hard to locate objects in the sky and most of what he could see was “often just a grey smudge”. Digital telescopes were not that common and were expensive, but when Raspberry Pi launched the HQ camera sensor Matt wondered whether he could build a really simple digital telescope with the Raspberry Pi at the heart of it.
He had already built an Aurora clock that lets you know if there’s a chance of seeing the Northern Lights, and a similar device using Raspberry Pi to track the International Space Station. With ten years of Raspberry Pi familiarity to draw upon, Matt was confident he could handle both high- and low-level functions and decided it would be the ideal basis for his own telescope and observatory design. “I knew there were Python packages available and I hoped to find pre-existing solutions to most of my needs”. The initial plans were for a Raspberry Pi Pico project but Matt soon realised he needed more power, switching to “Raspberry Pi 4B with RP2040 help”.
Matt was less confident of his 3D design skills: he needed to create more parts than he’d ever done previously and regarded this aspect as a distraction at the time, but says it’s a new skill that has come in useful elsewhere since.
Seeing the lights
The Mini Observatory design emerged from successive experiments using Raspberry Pi Pico. Having started tinkering, Matt gradually developed routines to solve specific problems, learning how to control stepper motors and the technical aspects of the HQ camera. He needed precision here and went to a specialist company called Stepperonline to source them. Matt bought other parts from well-known Raspberry resellers such as The Pi Hut and Pimoroni, with more generic nuts and bolts from general hardware stores. He cautions over scrimping on potentially dangerous items such as power supplies: “always buy them from a trusted source”.
The project provided a great learning experience, with the observatory, gears and mechanism all home-grown. The ‘semi-intelligent’ motor controller for the telescope is probably the most novel element. Matt needed a way for the telescope to move while Raspberry Pi was busy taking photographs, so gave the motors a little RP2040 microprocessor brain. “They were released at the perfect time.”
Matt was able to make use of Python packages such as Skyfield, OpenCV, PiDNG and Astroalign and says it was a good choice for his Mini Observatory project. He is also really keen to process the photographs in real time onboard the telescope. “I haven’t solved that yet, so I still need to do some offline processing afterwards. Realtime processing must be possible, I just have to research more.”
Since Matt first unveiled the project, several other makers have created versions, providing invaluable feedback and prompting him to tweak a few elements such as removing the infrared cutoff filters from his cameras which will make more objects visible.
Matt is also plotting a second version of his mini observatory, and is excited about the possibilities of Raspberry Pi with improved imagery and support for a second camera: one camera to do the tracking another to photograph the heavens. In fact “Raspberry Pi 5 may trigger quite a rewrite!”
We love hearing from members of the community and sharing the stories of amazing young people, volunteers, and educators who are using their passion for technology to create positive change in the world around them.
Arno helping young coders at the CoderDojo Netherlands tenth birthday celebrations
In our latest story, we’re heading to Alkmaar, the Netherlands, to meet Arno and Timo, CoderDojo enthusiasts who have transitioned from club members to supportive mentors. Their journey at CoderDojo and their drive to give back and support the next generation of coders in their community has been an inspiration to those around them.
Arno and Timo have been friends since childhood, and embarked on their CoderDojo journey at the age of 12, eager to explore the world of coding. Under the guidance of mentors like Sanneke, Librarian and Chair of CoderDojo Netherlands, they not only honed their technical skills, but also learned about the value of collaboration, curiosity, and perseverance. As they grew older, they in turn were inspired to support young coders, and wanting to remain part of the CoderDojo community, they decided to become mentors to the next generation of club attendees.
Having been helping younger members of the club for years, the transition to official mentors and proud owners of the much-coveted mentor T-shirt was seamless.
Timo with Mirthe and Linus, two young CoderDojo members
The power of mentorship
Sanneke reflects on the impact young mentors like Timo and Arno have on the young learners at CoderDojo:
“Having young mentors who are just slightly older than our youngest… I think it helps them to see what happens when you grow up and how they can help. They can be examples for how to help others.” – Sanneke, Librarian, CoderDojo mentor, and Chair of CoderDojo Netherlands
Timo echoes this sentiment, highlighting how mentoring provides a fantastic opportunity to help people and make a positive impact in the local community:
“I think volunteering is important, because you’re doing something for the community, in a city or village, supporting them in their journey in learning coding.” – Timo
As they continue their journey, Timo and Arno remain committed to supporting and inspiring the next generation of coders. They also encourage anyone who is thinking of volunteering at a club to give it a go:
“If you want to volunteer at the CoderDojo, just go for it. You don’t really need that much experience. […] The kids can learn it, so can you.” – Arno
The CoderDojo movement in the Netherlands is celebrating a decade of impact, and champions a culture of growth and learning. Arno and Timo’s story serves as an inspiration to us all, shining a light on the power of mentorship and the impact of volunteering in building stronger, more supportive communities.
Inspire the next generation of young coders
Arno and Timo’s story showcases the importance of mentorship for both individuals and communities, and the real impact you can have by donating an hour of your time a week. If you’re interested in becoming a CoderDojo volunteer, head to coderdojo.com to find out how to get started.
Help us celebrate Arno and Timo and their inspiring journey by sharing their story on X (formerly Twitter), LinkedIn, and Facebook.
Arduino Cloud recently received a major upgrade renewing the user interface and the Arduino Cloud editor. And as we promised it was just the beginning. This article dives into some of the new IoT monitoring dashboard features that may seem small, but pack a big punch for your connected projects.
1. Duplicate IoT Dashboards
Dashboards are the heart of the Arduino Cloud, allowing you to effortlessly monitor and control your devices. That’s why improving their functionality is crucial.
Even though creating an IoT dashboard is an easy task with the intuitive drag-and-drop interface, it can become a bit tedious when you have to replicate many of them and apply minor adjustments.
Now it is possible to duplicate your IoT dashboards with just one click. Just click on the three dots (?) of the dashboard that you want to clone and select “Duplicate.” This creates a copy that you can customize to meet your specific needs.
2. Customize your IoT Value Widget
Customizing widgets has been a common request from our user community, and we’ve recently addressed this with the introduction of decimal settings in the “Value” widget. This much-awaited feature allows users to configure the number of decimal digits displayed in the widget when dealing with floating-point variable types.
When selecting a floating-point variable type, users can specify the decimal precision shown in the widget and choose whether to truncate or round the value. Importantly, this setting only affects the visualization, not the actual variable value.
3. Enjoy the new data aggregation method in Advanced Chart widget
Advanced chart widgets are one of the most popular widgets for data analysis as they help you improve your data analysis. The widget now includes support for configuring the data aggregation method.
But what does it mean?
The chart widgets come with a smart implicit feature known as data aggregation. To prevent the chart from becoming too messy with an excess of data points, there is a fixed limit of data points per chart:
If the number of data points to show is lower than the maximum number of data points, there is no aggregation.
If the number of data points to show is bigger than the maximum number of data points, data is aggregated.
Before this update, there was only an implicit aggregation method, which was the average.
With this new Advanced Chart widget feature, you can now choose the aggregation method that suits your needs. Options include average, max value, and min value:
Average: Calculates the average of the data for each aggregation period.
Max value: Uses the maximum value within the aggregation period.
Min value: Uses the minimum value within the aggregation period.
This enhancement is a direct result of the feedback from our community. It’s a feature that has long been requested by users, and we’re happy to finally deliver it.
4. Deploy ready-to-use dashboards and firmware for your ESP32 devices
Templates are one of the most popular features of the Arduino Cloud. You can select a ready-to-use solution and deploy it with one click. You get the software and an IoT monitoring dashboard. If you need to do modifications for your IoT project, you can just edit the code and dashboard and off you go! It’s a fun and easy way to get started.
The exciting thing is that two new dashboard templates for ESP32 boards are now available, complementing the offering for Arduino boards:
As a bonus, we’re also introducing a handy new feature – now when you add a widget to a dashboard, you’ll see a preview and description of the widget. This makes it even easier to choose the right widget for your dashboard and streamline your project setup process.
On the smooth opposite side of the two parts are thermal pads; for the top piece, there are three that stick to the SoC, power management IC, and Wi-Fi and Bluetooth module. The base section is almost totally covered by a single thermal pad that sticks to the underside of Raspberry Pi 5.
Unlimited access
The two case parts are secured in place with long bolts. With no side pieces, access to Raspberry Pi 5’s ports is unobstructed. Cutouts in the top part give access to the GPIO pins and PoE header. There are also slots for the two camera/display MIPI ports and the UART and RTC battery connectors, while the fan connector remains uncovered.
So, how much cooling does this case provide? A lot! When idle, Raspberry Pi 5 was 10–15°C cooler than without a case; running a stress test, the difference was around 40°C.
Verdict
9/10
Provides an impressive amount of cooling while giving full access to all Raspberry Pi 5’s ports.
Specs
Cooling: Two large heatsink panels (top and base) with thermal pads attached
Features: Cutouts/slots for all Raspberry Pi 5 top-side ports; open sides for the others
Our team at Embedded World (April 9th-11th in Nuremberg) has announced not one, but two groundbreaking additions to the Arduino Pro range that are ready to elevate your prototyping and connectivity experiences. Say hello to the Arduino Portenta Mid Carrier and the Arduino Pro 4G Module!
This ultimate companion to the Portenta boards family is your gateway to seamless prototyping and expanded connectivity, designed to grant you zero hassles and maximum efficiency.
From CAN lines to Ethernet, microSD, USB, camera interfaces, and more, it allows you to effortlessly tap into high-density signals through dedicated headers. Plus, debug pins and the RTC battery backup will simplify your development journey even more.
Compatible with Portenta C33, Portenta H7, and Portenta X8, it adapts to your evolving development needs with ease. Whether you’re delving into machine vision prototyping or testing cellular connectivity, this is the carrier for you.
Arduino Pro 4G Module: revolutionizing connectivity
Are you ready to revolutionize your connectivity game? Engineered to seamlessly integrate with the Portenta family, the Arduino Pro 4G Module comes with a plethora of benefits, ensuring your projects thrive with lightning-fast data throughput and high bandwidths, powered by a robust Cat.4 modem from Quectel.
Enjoy secure data transfer, long-range global coverage even in the most isolated locations, and cost-efficient flexibility – all in the widely adopted Mini PCIe form factor: from remote maintenance to building safety inspection, the possibilities are endless.
The full details are available on our website, but you can also jump right to purchase from the Arduino Store!
Embark on your journey of innovation with Arduino’s expanding ecosystem
Our end-to-end ecosystem of hardware, software, and cloud solutions keeps expanding to meet your needs. The Portenta Mid Carrier and Arduino Pro 4G Module are only the latest additions that promise to unlock new realms of creativity and innovation for seasoned developers and passionate hobbyists alike.
So, what are you waiting for? Dive into a world of seamless prototyping and unparalleled connectivity today, with the Portenta Mid Carrier and Arduino Pro 4G Module.
“Edge AI is a crucial technology in this world of finite resources. It allows us to monitor and optimize consumption in real time: so the use of electricity or water, for example, can be optimized not just for today, but for the future. Manufacturing, agriculture and logistics can minimize their impact, with huge potential for cost savings as well as lowering our carbon footprint,” explains Fabio Violante, CEO of Arduino.
Edge AI has witnessed a remarkable surge in recent years, driven among other factors by the urgent need for efficient resource management and sustainability. Indeed, this technology leverages real-time data analytics and predictive modeling to enable proactive decision-making in a wide variety of sectors.
The 2024 “State of Edge AI” Report, curated by Wevolver, contains a plethora of examples and insights relevant to applications ranging from healthcare to automotive.
For example, edge AI solutions facilitate precision farming practices by analyzing soil moisture levels, weather patterns, and crop health data to optimize irrigation and fertilization, thereby maximizing yields while minimizing environmental impact.
In logistics and transportation networks, deploying AI-powered edge devices in vehicles and infrastructure makes real-time monitoring of traffic conditions and route optimization feasible. This not only improves operational efficiency but also enhances safety by mitigating the risks of accidents and breakdowns. Edge AI also facilitates the development of smart cities by enabling intelligent management of utilities, transportation systems, and public services through seamless integration with IoT devices and sensors deployed across urban environments. This empowers municipalities to optimize resource allocation, reduce congestion, and enhance the overall quality of life for residents.
In addition to optimizing resource and energy use to reduce financial and environmental impacts, edge AI-powered systems can lead to significant cost savings by foreseeing equipment failures. Predictive maintenance was indeed the focus of our contribution to this year’s report, showcasing products like Opta, Nicla Sense ME and Portenta Machine Control and success stories (like AROL’s and Engapplic’s) that bring the benefits of edge AI into the realm of present, tangible opportunities for enterprises in any industry and at any stage of their development.
“Simplicity is the key to success. In the tech world, a solution is only as successful as it is widely accepted, adopted and applied — and not everyone can be an expert. You don’t have to know how electricity works to turn on the lights, how an engine is built to drive a car, or how large language models were developed to write a ChatGPT prompt: that plays a huge part in the popularity of these tools,” Violante adds. “That’s why, at Arduino, we make it our mission to democratize technologies like edge AI — providing simple interfaces, off-the-shelf hardware, readily available software libraries, free tools, shared knowledge, and everything else we can think of. We believe edge AI today can become an accessible, even easy-to-use option, and that more and more people across all industries, in companies of all sizes, will be able to leverage this innovation to solve problems, create value, and grow.”
It’s been almost a year since we launched our first set of Experience AI resources in the UK, and we’re now working with partner organisations to bring AI literacy to teachers and students all over the world.
Developed by the Raspberry Pi Foundation and Google DeepMind, Experience AI provides everything that teachers need to confidently deliver engaging lessons that will inspire and educate young people about AI and the role that it could play in their lives.
Over the past six months we have been working with partners in Canada, Kenya, Malaysia, and Romania to create bespoke localised versions of the Experience AI resources. Here is what we’ve learned in the process.
Creating culturally relevant resources
The Experience AI Lessons address a variety of real-world contexts to support the concepts being taught. Including real-world contexts in teaching is a pedagogical strategy we at the Raspberry Pi Foundation call “making concrete”. This strategy significantly enhances the learning experience for learners because it bridges the gap between theoretical knowledge and practical application.
The initial aim of Experience AI was for the resources to be used in UK schools. While we put particular emphasis on using culturally relevant pedagogy to make the resources relatable to learners from backgrounds that are underrepresented in the tech industry, the contexts we included in them were for UK learners. As many of the resource writers and contributors were also based in the UK, we also unavoidably brought our own lived experiences and unintentional biases to our design thinking.
Therefore, when we began thinking about how to adapt the resources for schools in other countries, we knew we needed to make sure that we didn’t just convert what we had created into different languages. Instead we focused on localisation.
Localisation goes beyond translating resources into a different language. For example in educational resources, the real-world contexts used to make concrete the concepts being taught need to be culturally relevant, accessible, and engaging for students in a specific place. In properly localised resources, these contexts have been adapted to provide educators with a more relatable and effective learning experience that resonates with the students’ everyday lives and cultural background.
Working with partners on localisation
Recognising our UK-focused design process, we made sure that we made no assumptions during localisation. We worked with partner organisations in the four countries — Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup — drawing on their expertise regarding their educational context and the real-world examples that would resonate with young people in their countries.
A video call with educators in Kenya.
We asked our partners to look through each of the Experience AI resources and point out the things that they thought needed to change. We then worked with them to find alternative contexts that would resonate with their students, whilst ensuring the resources’ intended learning objectives would still be met.
Spotlight on localisation for Kenya
Tech Kidz Africa, our partner in Kenya, challenged some of the assumptions we had made when writing the original resources.
An Experience AI resource in English and Swahili.
Relevant applications of AI technology
Tech Kidz Africa wanted the contexts in the lessons to not just be relatable to their students, but also to demonstrate real-world uses of AI applications that could make a difference in learners’ communities. They highlighted that as agriculture is the largest contributor to the Kenyan economy, there was an opportunity to use this as a key theme for making the Experience AI lessons more culturally relevant.
This conversation with Tech Kidz Africa led us to identify a real-world use case where farmers in Kenya were using an AI application that identifies disease in crops and provides advice on which pesticides to use. This helped the farmers to increase their crop yields.
Training an AI model to classify healthy and unhealthy cassava plant photos.
We included this example when we adapted an activity where students explore the use of AI for “computer vision”. A Google DeepMind research engineer, who is one of the General Chairs of the Deep Learning Indaba, recommended a data set of images of healthy and diseased cassava crops (1). We were therefore able to include an activity where students build their own machine learning models to solve this real-world problem for themselves.
Access to technology
While designing the original set of Experience AI resources, we made the assumption that the vast majority of students in UK classrooms have access to computers connected to the internet. This is not the case in Kenya; neither is it the case in many other countries across the world. Therefore, while we localised the Experience AI resources with our Kenyan partner, we made sure that the resources allow students to achieve the same learning outcomes whether or not they have access to internet-connected computers.
An Experience AI activity related to farming.
Assuming teachers in Kenya are able to download files in advance of lessons, we added “unplugged” options to activities where needed, as well as videos that can be played offline instead of being streamed on an internet-connected device.
What we’ve learned
The work with our first four Experience AI partners has given us with lots of localisation learnings, which we will use as we continue to expand the programme with more partners across the globe:
Cultural specificity: We gained insight into which contexts are not appropriate for non-UK schools, and which contexts all our partners found relevant.
Importance of local experts: We know we need to make sure we involve not just people who live in a country, but people who have a wealth of experience of working with learners and understand what is relevant to them.
Adaptation vs standardisation: We have learned about the balance between adapting resources and maintaining the same progression of learning across the Experience AI resources.
Throughout this process we have also reflected on the design principles for our resources and the choices we can make while we create more Experience AI materials in order to make them more amenable to localisation.
Join us as an Experience AI partner
We are very grateful to our partners for collaborating with us to localise the Experience AI resources. Thank you to Digital Moment, Tech Kidz Africa, Penang Science Cluster, and Asociația Techsoup.
We now have the tools to create resources that support a truly global community to access Experience AI in a way that resonates with them. If you’re interested in joining us as a partner, you can register your interest here.
(1) The cassava data set was published open source by Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, and Jeremy Tusubira. Read their research paper about it here.
Find My Device is secure by default and private by design. Multi-layered protections built into the Find My Device network help keep you safe and your personal information private, while keeping you in control of the devices connected to the Find My Device network. This includes end-to-end encryption of location data as well as aggregated device location reporting, a first-of-its-kind safety feature that provides additional protection against unwanted tracking back to a home or private location. Read more about how our multi-layered protections for the Find My Device network work.
The new Find My Device works with devices running Android 9+. And look out for software updates coming to headphones from JBL, Sony and others, which will join the Find My Device network soon.
Sri Yash Tadimalla from the University of North Carolina and Dr Mary Lou Maher, Director of Research Community Initiatives at the Computing Research Association, are exploring how student identities affect their interaction with AI tools and their perceptions of the use of AI tools. They presented findings from two of their research projects in our March seminar.
How students interact with AI tools
A common approach in research is to begin with a preliminary study involving a small group of participants in order to test a hypothesis, ways of collecting data from participants, and an intervention. Yash explained that this was the approach they took with a group of 25 undergraduate students on an introductory Java programming course. The research observed the students as they performed a set of programming tasks using an AI chatbot tool (ChatGPT) or an AI code generator tool (GitHub Copilot).
Highly confident students rely heavily on AI tools and are confident about the quality of the code generated by the tool without verifying it
Cautious students are careful in their use of AI tools and verify the accuracy of the code produced
Curious students are interested in exploring the capabilities of the AI tool and are likely to experiment with different prompts
Frustrated students struggle with using the AI tool to complete the task and are likely to give up
Innovative students use the AI tool in creative ways, for example to generate code for other programming tasks
Whether these attitudes are common for other and larger groups of students requires more research. However, these preliminary groupings may be useful for educators who want to understand their students and how to support them with targeted instructional techniques. For example, highly confident students may need encouragement to check the accuracy of AI-generated code, while frustrated students may need assistance to use the AI tools to complete programming tasks.
An intersectional approach to investigating student attitudes
Yash and Mary Lou explained that their next research study took an intersectional approach to student identity. Intersectionality is a way of exploring identity using more than one defining characteristic, such as ethnicity and gender, or education and class. Intersectional approaches acknowledge that a person’s experiences are shaped by the combination of their identity characteristics, which can sometimes confer multiple privileges or lead to multiple disadvantages.
In the second research study, 50 undergraduate students participated in programming tasks and their approaches and attitudes were observed. The gathered data was analysed using intersectional groupings, such as:
Students who were from the first generation in their family to attend university and female
Students who were from an underrepresented ethnic group and female
Although the researchers observed differences amongst the groups of students, there was not enough data to determine whether these differences were statistically significant.
Who thinks using AI tools should be considered cheating?
Participating students were also asked about their views on using AI tools, such as “Did having AI help you in the process of programming?” and “Does your experience with using this AI tool motivate you to continue learning more about programming?”
The same intersectional approach was taken towards analysing students’ answers. One surprising finding stood out: when asked whether using AI tools to help with programming tasks should be considered cheating, students from more privileged backgrounds agreed that this was true, whilst students with less privilege disagreed and said it was not cheating.
This finding is only with a very small group of students at a single university, but Yash and Mary Lou called for other researchers to replicate this study with other groups of students to investigate further.
Acknowledging differences to prevent deepening divides
As researchers and educators, we often hear that we should educate students about the importance of making AI ethical, fair, and accessible to everyone. However, simply hearing this message isn’t the same as truly believing it. If students’ identities influence how they view the use of AI tools, it could affect how they engage with these tools for learning. Without recognising these differences, we risk continuing to create wider and deeper digital divides.
For our next seminar on Tuesday 16 April at 17:00 to 18:30 GMT, we’re joined by Brett A. Becker (University College Dublin), who will talk about how generative AI can be used effectively in secondary school programming education and how it can be leveraged so that students can be best prepared for continuing their education or beginning their careers. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.
Artists are understandably less than thrilled that AI is producing facsimiles of their work without giving them credit, or payment. For coders: AI changes everything. It can help you write, explain, understand, and improve the quality of code, and increase productivity by enhancing performance. It’s versatile in all programming languages and can help translate code between them.
On the downside, GPT can spit out code that kind of works for people who sort of understand it. And, as it gets better, they may not need, or even want, to understand it.
Abstract arts
Everybody involved in technology knows about abstraction. The process whereby the intricate technology stack gets hidden away, and the user is presented with a simpler interface. The iPhone is easier than the GUI PC, which is easier than the DOS PC, which is easier than the PDP.
This next step: the ChatGPT “How can I help today?” rather than an IDE and knowledge of coding.
Raspberry Pi exists, on some level, counter to abstraction. We want to tear people away from their shiny slabs of glass and glue, and show them the insides of a computer. “It isn’t magic, it’s just billions of on/off switches flicking on and off at a billion times per second!” Which is, in itself, a form of magic.
Anybody doubting the importance of GPT and similar technologies isn’t really paying attention. The negative responses remind me of Douglas Adams’ three rules:
Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.
Anything that’s invented between when you’re 15 and 35 is new and exciting and revolutionary and you can probably get a career in it.
Anything invented after you’re 35 is against the natural order of things.
Most of us are somewhere between two and three, but objections to AI aren’t just Ludditism. When applied to creative arts, AI devalues human involvement and can be accused of plagiarism. The same can be said of code, of course, but art feels instinctively more personal.
Coding is an incredibly cerebral process and requires creativity and deep thought. But coders stand on the shoulders of giants. I may understand a merge-sort algorithm, but I sure as heck didn’t come up with it. And using AI to put it to work and explain it to me feels inherently useful. John von Neumann might disagree if he was around, but somehow I feel he’d be delighted.
If you’ve ever tried to produce an analog video signal with an Arduino, then you know that it isn’t easy. That’s a bit counterintuitive if you think of analog video as “old” and assume that generating an analog video signal would be trivial with our powerful modern hardware. But there are many ways in which analog signals are tricky and that’s especially true if you want something like VGA output, which requires very precise timing. That’s why it is so impressive that Slu4 was able to build this retro computer with just an Arduino Nano and a shift register.
This was no simple feat and it really showcases Slu4’s programming prowess. His creation can output 320×200 resolution VGA video while reading PS/2 keyboard inputs, with enough processing power leftover to handle basic video game logic and graphics. He demonstrates that with a Tetris-like games that runs very smoothly. And Slu4 says that it is even possible to add 16 colors per row, though he doesn’t show that in action.
Slu4 first achieved a similar result a few years ago, but that required several IC (Integrated Circuit) chips. This version only needs one: a standard 74HC166 shift register. That helped him overcome some of the challenges related to VGA timing, which the Nano’s ATmega328 microcontroller can just barely keep up with. This did necessitate some low-level programming to maximize efficiency, but Slu4 was able to pull it off. Even more impressive, he was able to read PS/2 keyboard input at the same time so the player can control the game.
The Backpack Cyberdeck was inspired by Davide’s need to move his experiments easily without setup breakdowns – he works in the metalworking industry, and reasoned that a custom-designed frame that fits inside a commercially available backpack would come in mighty useful for other people as well as himself. He says the idea is that hobbyists and professionals can carry, use and interact with a variety of devices on the go. Raspberry Pi 4 was chosen for its compactness and power efficiency and serves as the ‘brain’ of Davide’s mobile setup. It allows him to remotely control devices mounted on the frame. The project runs on open-source software – “primarily GNU Radio for wireless communications analysis, and Kali Linux tools for security and penetration testing tasks”. He says the build cost was “moderate, reflecting the price of the Raspberry Pi, the backpack, and some additional electronic components like the RTL-SDR”.
Sharing the build photos on Facebook, Davide explains that it transforms a simple backpack into a customisable platform, allowing for the creation of mobile workstations, entertainment systems, or unique projects through 3D-printed attachments”. The Backpack Cyberdeck avoids the risk of damage or subsequent discomfort because there is no desk outdoors. Having cycled to his destination he can conduct his outdoor experiments “the most comfortable way”.
Bag your own
For Davide, one advantage of using Raspberry Pi is its compact size, which allowed him to design a system that was both powerful and practical. The design is entirely original, with all parts created or modified by him to suit his project’s requirements.
Interest in the backpack led Davide to set up Bag Builds as an online custom bag business, but he designed the Backpack Cyberdeck’s frame for DIY enthusiasts with a passion for 3D printing. He shares STL files for several versions of the Backpack Cyberdeck on his GitHub page, with customisation options for the hardware components all designed to fit neatly into a standard backpack. There is plenty of opportunity for DIY builds to be customised, since some users may need holes to allow antennae through, and to fit cables.
Reassuring curious but impressed project followers, Davide says all the components meet flight-safe guidelines for power output, so it could potentially be taken on board aircraft too. Davide is at pains to point out that everything he’s done with the Backpack Cyberdeck is legal and poses no security issues. He has posted several assembly videos on YouTube that show the breadth of uses for his backpacks, including using one as a mobile radio station.
“The idea could be adapted for various other purposes, such as mobile video streaming, electronic repair setups, or even as a simple organiser,” he says. “My advice to anyone interested in similar projects is to start small, and to pay attention to the interference that the various devices and cables could generate, compromising the functionality of some parts.”
Having constant, reliable access to a working HVAC system is vital for our way of living, as they provide a steady supply of fresh, conditioned air. In an effort to decrease downtime and maintenance costs from failures, Yunior González and Danelis Guillan have developed a prototype device that aims to leverage edge machine learning to predict issues before they occur.
The duo went with a Nicla Sense ME due to its onboard accelerometer, and after collecting many readings from each of the three axes at a 10Hz sampling rate, they imported the data into Edge Impulse to create the model. This time, rather than using a classifier, they utilized a K-means clustering algorithm — which is great at detecting anomalous readings, such as a motor spinning erratically, compared to a steady baseline.
Once the Nicla Sense ME had detected an anomaly, it needed a way to send this data somewhere else and generate an alert. González and Guillan’s setup accomplishes the goal by connecting a Microchip AVR-IoT Cellular Mini board to the Sense ME along with a screen, and upon receiving a digital signal from the Sense ME, the AVR-IoT Cellular Mini logs a failure in an Azure Cosmos DB instance where it can be viewed later on a web app.
As educators, it’s important that we showcase the wide range of career opportunities available in the field of computing, not only to inspire learners, but also to help them feel sure they’re choosing to study a subject that is useful for their future. For example, a survey from the BBC in September 2023 found that more than a quarter of UK teenagers often feel anxious, with “exams and school life” among the main causes. To help young people chart their career paths, we recently hosted two live webinars for National Careers Week in the UK.
Our goal for the webinars was to highlight the breadth of careers within computing and to provide insights from professionals who are pursuing their own diverse and rewarding paths. Each webinar featured engaging discussions and an interactive Q&A session with learners who use our Ada Computer Science platform. The learners could ask their own questions to get firsthand knowledge and perspectives from our guest speakers.
Our guest speakers
Jess Van Brummelen is a Human–Computer Interaction Research Scientist at Niantic, the video games company behind augmented reality game Pokémon Go. After developing an interest in programming during her undergraduate degree in mechanical engineering, she went on to complete a Master’s degree and PhD in computer science at MIT.
Ashley Edwards is a Senior Research Scientist at Google DeepMind, working on reinforcement learning. She received her PhD in 2019 from Georgia Tech, spent time as an intern at Google Brain, and worked as a research scientist at Uber AI Labs.
You can read extracts from our interviews with Jess and Ashley and watch the full videos below. Teachers have contacted us to say they’ll be using the webinars for careers-focused sessions with their students. We hope you will do the same!
Please note that we have edited the extracts below to add clarity.
Jess Van Brummelen
Hi Jess. What advice would you give to a student who is thinking about a career in human–computer interaction in the gaming industry?
In terms of HCI and gaming, I’d actually recommend that you keep gaming! It’s a small part of my job but it’s really important to understand what’s fun and enjoyable in games. Not only that; gaming can be great for learning to problem-solve — there’s been all sorts of research on the positive impact of gaming.
A second thing, going back to how I felt in my mechanical engineering classes, I really felt like an ‘other’ and not someone who is the standard computer scientist or engineer. I would encourage students to pursue their dreams anyway because it’s so important to have diversity in these types of careers, especially technology, because it goes out to so many different people and it can really affect society. It’s really important that the people who make it come from many different backgrounds and cultures so we can create technology that is better for everyone.
[From Owen, a student on the livestream] What’s the most impossible idea you’ve come up with while working at Niantic?
I’m currently publishing a paper addressing the question, ‘Can we guide people without using anything visual on their phone?’ That means using audio and haptic (technology that transmits information via touch, e.g. vibrations) prompts instead. We tried out different commands where the phone said ‘turn left’ and ‘turn right’, but we really wanted to test how to guide someone more specifically in a game environment. For example, if there was a hidden object on a wall in a game that a person couldn’t see, could we guide them to that object while they’re walking? So I ran a study where I guided people to scan a statue by moving around it. Scanning is the process of using the camera on your phone to scan an object in real life, which is then reconstructed on your phone. Scanning objects can trigger other augmented reality experiences within a game. For example, you might scan a real-life box in a room and this might trigger an animation of that box opening to reveal a secret within the game. We tested a lot of different things. For example, test subjects listened to music as they were walking and when they were on the right path, the music sounded really good. But when they were off the path, it sounded terrible. So it helped them to look for the right path. Then if you were pointing the phone in the wrong direction for scanning objects, you would get warning vibrations on the phone. So we did the study and we were hoping it would improve safety. It turns out it was neutral on improving safety — I think this is because it was such a novel system. People weren’t used to using it and still bumped into things! But it did make people better at scanning the objects, which was interesting.
Hi Ashley. Is there something you studied in school that you found to be more useful now than you ever thought it would be?
Maths! I always enjoyed doing maths, but I didn’t realise I would need it as a computer scientist. You see it popping up all the time, especially in machine learning. Having a strong knowledge of calculus and linear algebra is really helpful.
You start by asking the question, ‘What is the problem I’m trying to solve?’ Then typically you need input data and the outputs you want to achieve, so you ask two more questions, ‘What data do I want to come in?’ and ‘What do I want to come out?’ Let’s say you decide to use a supervised learning model (a category of machine learning where labelled data sets are used to train algorithms to detect patterns and predict outcomes) to predict whether a photo contains a cat. You train the model using a giant set of images with labels that say either ‘This is a cat’ or ‘This isn’t a cat’. By training the model with the images, you get to a point where your model can analyse the features of any image and predict whether it contains a cat or not.
In my field of research, I work on something called reinforcement learning, which is where you train your model through trial and error and the use of ‘rewards’. Let’s imagine we are trying to train a robot. We might write a program that tells the robot, ‘I am going to give you a reward if you take the right step forward and it’s going to be a positive reward. If you fall over, I’m going to give you a negative reward.’ So you train the robot to prioritise the right behaviours to optimise the rewards it’s getting.
[From a student] Will I still need to learn to code in the future?
Jess and Ashley are forging successful careers not only through a combination of smart choices, hard work, talent, and a passion for technology; they also had access to opportunities to discover their passion and receive an education in this field. Too many young people around the world still don’t have these opportunities.
That is why we provide free resources and training to help schools broaden access to computing education. For example, our free learning platform, Ada Computer Science, provides students aged 14 to 19 with high-quality computing resources and interactive questions, written by experts from our team. To learn more, visit adacomputerscience.org.
You’ll find dartboards in just about every dive bar in the world, like cheaper and pokier alternatives to pool. But that doesn’t mean that darts is a casual game to everyone. It takes a lot of skill to play on a competitive level and many of us struggle to perform well. Niklas Bommersbach decided that years of practice was too much of a commitment, so he built this robot that can dominate dart games.
This robot can, essentially, throw a dart perfectly every time to hit the desired target on the board. If you’re unfamiliar with the game, you might think that a bullseye is always best. But that isn’t true — especially for certain rulesets. To play strategically, Bommersbach needed his robot to nail the desired space on the board on-demand.
His first step was to make throws repeatable and predictable. His robot has a balanced arm that spins up to a precise rotational speed. At the set angle, it releases the dart. By monitoring many throws with computer vision, Bommersbach was able to dial in the speed and angle variables until the result became very predictable. An Arduino UNO Rev3 board controls the arm speed and calculates the release. But Bommersbach struggled to get the timing of the release exactly right, as the Arduino was running its code sequentially and so there was a small variance — just enough to throw off the throw.
His solution was to add a second Arduino, which has the sole responsibility of releasing the dart using a stepper-actuated mechanism. That allowed for very precise timing and repeatable throws. The timing influences the dart’s vertical position on the board, while a linear motion system controls its horizontal position.
In a world where industrial automation is rapidly advancing, education often struggles to keep pace.
This disconnect leaves a big gap in practical, industry-relevant skills among graduates.
Addressing this critical need, we’re excited to introduce the Arduino PLC Starter Kit. Powered by the robust Arduino Opta mini PLC and backed by the intuitive Arduino PLC IDE, this kit is set to revolutionize programmable logic control education.
Continue reading to find out more about the PLC Starter Kit.
What is the Arduino PLC Starter Kit?
Before we delve into the details of this exciting new offering, let’s explain exactly what a PLC is. A Programmable Logic Controller (PLC) is a type of industrial computer that’s used to automate, control and coordinate a wide range of manufacturing processes and machinery.
The Arduino PLC Starter Kit isn’t just another educational tool; it’s a comprehensive solution designed to bridge the gap between theoretical knowledge and practical application in industrial automation — a powerful simulation tool specifically created for vocational and university students considering a career in manufacturing.
Here’s what this groundbreaking kit offers:
20 hours of in-depth lessons — Explore the world of programmable logic control with the ‘Explore PLC’ course. The course has been created by educators and covers all the essential contents including the history of programmable logic controllers, Modbus RS-485 communications, and how PLCs integrate with industrial simulated systems.
Arduino Opta WiFi — Built with industrial IoT capabilities, our versatile and easy-to-use micro PLC offers real-time control, monitoring, and predictive maintenance for a variety of applications. Based on the existing Arduino Opta WiFi, it includes the STM32H747XI dual-core Arm® Cortex®-M7 +M4 MCU, making it exceptionally reliable and robust for your classroom.
Digital input and output simulators – The kit’s custom-designed hardware helps bring learning to life by allowing users to replicate real-world situations. The input simulator (DIN Simul8) includes 8 switches and power control, while the output simulator (DIN Celsius) features a resistor array and a temperature sensor.
Arduino PLC IDE — Our popular programming tool, Arduino PLC IDE, makes programming simple. Choose from any of the five programming languages defined by the IEC 61131-3 standard (Ladder, Functional Block Diagram, Structured Text, Sequential Function Chart, or Instruction List) to quickly code a range of PLC applications
Arduino IDE 2 — Another benefit of the kit is that it can be programmed using our powerful IDE 2. A step up from the classic Arduino IDE, the Arduino IDE 2 offers increased performance, an improved user interface and other new features, such as autocompletion and a built-in debugger.
And let’s not forget that all the kit’s hardware is fully compatible with the Arduino Cloud.
Get industry-ready with real-life simulations
Thanks to their exceptional flexibility, programmable logic controllers are being used more and more frequently in a wide range of industries. From production plants, assembly lines and packaging machines to heating control systems, traffic lights and elevators, PLC applications are vast and varied.
To help students prepare for the demands of these competitive industries and the challenges they face, we believe they need access to high-quality PLC simulation tools. After all, there’s a big difference between reading about programming in a textbook and actually doing it. As Benjamin Franklin once famously said, “Tell me and I forget, teach me and I may remember, involve me and I learn”. And that’s where the PLC Starter Kit comes into play.
Boasting industrial IoT capabilities, the kit’s Opta WiFi mini PLC — together with the input and output simulators – allows students to design and implement programs that simulate real-life industrial automation projects.
Take a manufacturing production line, for instance. To prevent overheating and a potential fire, the equipment’s PLC might be programmed to shut down when the input sensor detects a high temperature. With the PLC Starter Kit, students can simulate a similar scenario and gain practical knowledge about how inputs and outputs interface with a PLC.
It’s a great way to promote a deeper understanding of industrial automation and system behavior, while giving students the hands-on experience and critical thinking skills they’ll need to tackle real-world challenges in their professional careers.
Try the Arduino PLC Starter Kit today
With its powerful industrial IoT capabilities, easy programming software and wealth of online content, the PLC Starter Kit is the perfect introduction to automated programming for students and educators.
Interested in using the PLC Starter Kit in your education setting? Order the PLC Starter Kit here or get in touch through your local distributor and discover how it can transform your learning environment.
“Growing up, I went to art school in Italy,” Sara says. “It felt like the right path for me to undertake, and through the years I pushed myself to try different things; I would find myself at events drawing on walls or floors, other times I would be at home testing lino printing using a DIY device made from an olive press. I’d even try painting on T-shirts or home-printing personalised stickers to stick everywhere. At the time I couldn’t figure out a specific definition of what I wanted to do, but nowadays I feel that the best way to explain it is that I love to work as a visual communicator. So here I am.”
How did you join Raspberry Pi?
At the end of 2021 I was submitting my final major project for an illustration MA at Falmouth University. In January I was ready to get back into the creative industry, so I began looking for job vacancies when I came across to a catchy one on LinkedIn. In a series of paragraphs, I saw described what I enjoy doing, and I applied for it straight away. The interview was great and I remember coming back home very enthusiastic about it.
What Raspberry Pi design stuff have you worked on?
I’m lucky enough to be in a place where projects vary, and the nice part is that depending on the scale of them I might be working on my own or with the skillset of my colleagues. So far I’ve worked on a variety of projects of different scales, such as packaging design, visual communication for events and related merch, design layout for case studies, books, flyers, brochures and now magazines!
Have you made anything with a Raspberry Pi, or have any plans to?
I’d never done any coding before joining Raspberry Pi, so I’m still in a phase of learning while watching [my partner] doing some small home projects. Last year I participated in a couple of workshops on using Pico on a breadboard, and learned how to turn on some LEDs!
In regards to future plans… I have an idea for creating something that involves my artwork, like a sort of flipping book installation, or a projected animated GIF, but I need to define the idea first and understand how to use Raspberry Pi with it.
What other hobbies do you have?
I love creating bodies of work that mix illustration with fine art, along with testing new materials and techniques. I usually do this at the studio space I share with other creatives in the heart of Cambridge. I enjoy going there to meet them for a coffee or to chat about what they’re up to. Sometimes we organise open studios or small exhibitions next door.
Recently I also started to explore working with ceramics, and how to bring out the personal artwork in such tactile material.
How do unstable things stay upright? True passive balancing is very difficult and isn’t dynamic, so it doesn’t help when there is movement. Active balancing is all about inertia and this is how a tightrope walker can traverse a chasm by making small adjustments with a long pole. This is the same principle behind “self-balancing robots” that utilize reaction wheels. But the control scheme necessary to get that right is very difficult to perfect, as demonstrated by Nikodem Bartnik’s project that was three years in the making.
Physically, this is about as simple as a self-balancing robot can be. It stands on a single foot designed to be unstable in one horizontal axis, but stable in the other. It is long, front-to-back, so the robot can’t tip forward or backward. But the bottom of the foot has a curve to it, so it can’t stand upright without tipping to one side or the other. A reaction wheel with bolts for weights is responsible for preventing that tipping.
This is supposed to work by spinning in order to “push” against nothing (thanks to inertia), which generates torque to stop the tip. But Bartnik discovered that it was a massive challenge to tune that spin.
An Arduino Nano board controls a small brushless DC motor that spins the reaction wheel. A gyroscope sensor lets the Arduino monitor tilt and power comes from a hobby LiPo battery. The Arduino utilizes PID (proportional-integral-derivative) algorithms to try an apply just enough spin to counteract tipping, but not so much that it overcorrects.
That’s where Bartnik ran into trouble, because PID tuning is hard. Each variable has to be at the exact value — relative to the others — for PID to work as intended. After countless hours of struggling, Bartnik added a Bluetooth module to the Arduino to change those values wirelessly without flashing new code every time. That sped up the process dramatically, allowing Bartnik to find a set of values that works pretty well to keep the robot upright.
Here at the Raspberry Pi Foundation, we believe that it’s important that our academic research has a practical application. An important area of research we are engaged in is broadening participation in computing education by investigating how the subject can be made more culturally relevant — we have published several studies in this area.
Licensed under the Open Government Licence.
However, we know that busy teachers do not have time to keep abreast of all the latest research. This is where our Pedagogy Quick Reads come in. They show teachers how an area of current research either has been or could be applied in practice.
Our new Pedagogy Quick Reads summarises the central tenets of culturally relevant pedagogy (the theory) and then lays out 10 areas of opportunity as concrete ways for you to put the theory into practice.
Why is culturally relevant pedagogy necessary?
Computing remains an area where many groups of people are underrepresented, including those marginalised because of their gender, ethnicity, socio-economic background, additional educational needs, or age. For example, recent stats in the BCS’ Annual Diversity Report 2023 record that in the UK, the proportion of women working in tech was 20% in 2021, and Black women made up only 0.7% of tech specialists. Beyond gender and ethnicity, pupils who have fewer social and economic opportunities ‘don’t see Computing as a subject for somebody like them’, a recent report from Teach First found.
The fact that in the UK, 94% of girls and 79% of boys drop Computing at age 14 should be of particular concern for Computing educators. This last statistic makes it painfully clear that there is much work to be done to broaden the appeal of Computing in schools. One approach to make the subject more inclusive and attractive to young people is to make it more culturally relevant.
As part of our research to help teachers effectively adapt their curriculum materials to make them culturally relevant and engaging for their learners, we’ve identified 10 areas of opportunity — areas where teachers can choose to take actions to bring the latest research on culturally relevant pedagogy into their classrooms, right here, right now.
Applying the areas of opportunity in your classroom
The Pedagogy Quick Read gives teachers ideas for how they can use the areas of opportunity (AOs) to begin to review their own curriculum, teaching materials, and practices. We recommend picking one area initially, and focusing on that perhaps for a term. This helps you avoid being overwhelmed, and is particularly useful if you are trying to reach a particular group, for example, Year 9 girls, or low-attaining boys, or learners who lack confidence or motivation.
For example, one simple intervention is AO1 ‘Finding out more about our learners’. It’s all too easy for teachers to assume that they know what their students’ interests are. And getting to know your students can be especially tricky at secondary level, when teachers might only see a class once a fortnight or in a carousel.
However, finding out about your learners can be easily achieved in an online survey homework task, set at the beginning of a new academic year or term or unit of work. Using their interests, along with considerations of their backgrounds, families, and identities as inputs in curriculum planning can have tangible benefits: students may begin to feel an increased sense of belonging when they see their interests or identities reflected in the material later used.
How we’re using the AOs
The Quick Read presents two practical case studies of how we’ve used the 10 AO to adapt and assess different lesson materials to increase their relevance for learners.
Case study 1: Teachers in UK primary school adapt resources
As we’ve shared before, we implemented culturally relevant pedagogy as part of UK primary school teachers’ professional development in a recent research project. The Quick Read provides details of how we supported teachers to use the AOs to adapt teaching material to make it more culturally relevant to learners in their own contexts. Links to the resources used to review 2 units of work, lesson by lesson, to adapt tasks, learning material, and outcomes are included in the Quick Read.
Extract from the booklet used in a teacher professional development workshop to frame possible adaptations to lesson activities.
Case study 2: Reflecting on the adaption of resources for a vocational course for young adults in a Kenyan refugee camp
In a different project, we used the AOs to reflect on our adaptation of classroom materials from The Computing Curriculum, which we had designed for schools in England originally. Partnering with Amala Education, we adapted Computing Curriculum materials to create a 100-hour course for young adults at Kakuma refugee camp in Kenya who wanted to develop vocational digital literacy skills.
The diagram below shows our ratings of the importance of applying each AO while adapting materials for this particular context. In this case, the most important areas for making adaptations were to make the context more culturally relevant, and to improve the materials’ accessibility in terms of readability and output formats (text, animation, video, etc.).
Importance of the areas of opportunity to a course adaptation.
You can use this method of reflection as a way to evaluate your progress in addressing different AOs in a unit of work, across the materials for a whole year group, or even for your school’s whole approach. This may be useful for highlighting those areas which have, perhaps, been overlooked.
Applying research to practice with the AOs
The ‘Areas of opportunity’ Pedagogy Quick Read aims to help teachers apply research to their practice by summarising current research and giving practical examples of evidence-based teaching interventions and resources they can use.
The set of AOs was developed as part of a wider research project, and each one is itself research-informed. The Quick Read includes references to that research for everyone who wants to know more about culturally relevant pedagogy. This supporting evidence will be useful to teachers who want to address the topic of culturally relevant pedagogy with senior or subject leaders in their school, who often need to know that new initiatives are evidence-based.
Our goal for the Quick Read is to raise awareness of tried and tested pedagogies that increase accessibility and broaden the appeal of Computing education, so that all of our students can develop a sense of belonging and enjoyment of Computing.
Let us know if you have a story to tell about how you have applied one of the areas of opportunity in your classroom.
To date, our research in the field of culturally relevant pedagogy has been generously supported by funders including Cognizant and Google. We are very grateful to our partners for enabling us to learn more about how to make computing education inclusive for all.
Go to any arcade and the air hockey table will probably be one of the most popular games they have. Everyone loves air hockey, but a lot of people don’t want to go to an arcade just to play. If you fall into that category, then you can follow LloydB’s Instructables guide to make your own scorekeeping air hockey table.
The key to air hockey is right there in the name: air. All of those little holes in the table’s surface allow air flow. That creates an air cushion for the puck and paddles to float on, reducing friction and enabling knuckle-shattering gameplay. For that to work, the table needs something pushing at least as much air as escapes through the holes. This table isn’t very big, so it doesn’t need a high volume of air. Three 12V PC fans are enough. They push air into a chamber beneath the hole-filled top board. Power for the fans comes from a battery holder with 8 AA batteries.
Those batteries also power the Arduino UNO Rev3 that handles the scorekeeping, which is the other important part of air hockey. Each goal chute has a laser break-beam sensor to detect when the puck comes shooting in. The Arduino then updates the scores shown on a 16×2 LCD screen. The Arduino will also emit a tone through a buzzer. That increases in pitch with each point, so players get audible cues as the game progresses.
When you hear the word “joystick,” you probably think of the standard dual-axis joysticks that we see on video game controllers. As the name implies, those move and provide signals for two axes (X and Y). But there is no reason that a joystick needs two axes and, in fact, that may not be desirable. To demonstrate the practicality of single-axis joysticks, Austin Allen built this simple controller suitable for several different applications.
Allen’s device controls three different things with its three single-axis joysticks: an RGB LED, a servo motor, and a stepper motor. Each of those is an example of a single-axis at work. That axis maps to color (red and green) and brightness for the LED, horn position for the servo, and rotation direction/speed for the stepper motor. There are, of course, several other viable use cases for single-axis joysticks.
To showcase this, Allen’s unit provides signals to an Arduino Nano board, which then controls the LED and motors. It controls the LED and servo motor directly, but goes through a TMC2208 driver to handle the stepper motor. The signals from the joysticks are easy to read, because they’re just potentiometers. Each joystick accepts positive and negative power, then outputs a voltage between those two based on its position. With a standard analogRead() function, the Arduino can check the voltage and determine the joystick position.
You may not have any use for this specific controller, but it does do a good job of illustrating potential applications for single-axis joysticks and you should consider them for future projects.
Um dir ein optimales Erlebnis zu bieten, verwenden wir Technologien wie Cookies, um Geräteinformationen zu speichern und/oder darauf zuzugreifen. Wenn du diesen Technologien zustimmst, können wir Daten wie das Surfverhalten oder eindeutige IDs auf dieser Website verarbeiten. Wenn du deine Einwillligung nicht erteilst oder zurückziehst, können bestimmte Merkmale und Funktionen beeinträchtigt werden.
Funktional
Immer aktiv
Die technische Speicherung oder der Zugang ist unbedingt erforderlich für den rechtmäßigen Zweck, die Nutzung eines bestimmten Dienstes zu ermöglichen, der vom Teilnehmer oder Nutzer ausdrücklich gewünscht wird, oder für den alleinigen Zweck, die Übertragung einer Nachricht über ein elektronisches Kommunikationsnetz durchzuführen.
Vorlieben
Die technische Speicherung oder der Zugriff ist für den rechtmäßigen Zweck der Speicherung von Präferenzen erforderlich, die nicht vom Abonnenten oder Benutzer angefordert wurden.
Statistiken
Die technische Speicherung oder der Zugriff, der ausschließlich zu statistischen Zwecken erfolgt.Die technische Speicherung oder der Zugriff, der ausschließlich zu anonymen statistischen Zwecken verwendet wird. Ohne eine Vorladung, die freiwillige Zustimmung deines Internetdienstanbieters oder zusätzliche Aufzeichnungen von Dritten können die zu diesem Zweck gespeicherten oder abgerufenen Informationen allein in der Regel nicht dazu verwendet werden, dich zu identifizieren.
Marketing
Die technische Speicherung oder der Zugriff ist erforderlich, um Nutzerprofile zu erstellen, um Werbung zu versenden oder um den Nutzer auf einer Website oder über mehrere Websites hinweg zu ähnlichen Marketingzwecken zu verfolgen.