Connexion
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.
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.
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.
Watch Jess’s full interview:
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.
How do you train an AI model using machine learning?
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?
I think it is going to be very different in the future, but we’ll still need to learn how to build different types of algorithms and we’re going to need to understand the concepts behind coding as well. We’ll still need to ask questions like, ‘What is it that I want to build?’ and ‘Is this actually doing the correct thing?’
Watch Ashley’s full interview:
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.
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Many technology items are disposed of each year, either because they are broken, are no longer needed, or have been upgraded. Researchers from Germany have identified this as an opportunity to develop a scheme of work for Computing, while at the same time highlighting the importance of sustainability in hardware and software use. They hypothesised that by repairing defective devices, students would come to understand better how these devices work, and therefore meet some of the goals of their curriculum.
The research team visited three schools in Germany to deliver Computing lessons based around the concept of a repair café, where defective items are repaired or restored rather than thrown away. This idea was translated into a series of lessons about using and repairing smartphones. Learners first of all explored the materials used in smartphones and reflected on their personal use of these devices. They then spent time moving around three repair workstations, examining broken smartphones and looking at how they could be repaired or repurposed. Finally, learners reflected on their own ecological footprint and what they had learnt about digital hardware and software.
In the classroom, repair workstations were set up for three different categories of activity: fixing cable breaks, fixing display breaks, and tinkering to upcycle devices. Each workstation had a mentor to support learners in investigating faults themselves by using the question prompt, “Why isn’t this feature or device working?” At the display breaks and cable breaks workstations, a mentor was on hand to provide guidance with further questions about the hardware and software used to make the smartphone work. On the other hand, the tinkering workstation offered a more open-ended approach, asking learners to think about how a smartphone could be upcycled to be used for a different purpose, such as a bicycle computer. It was interesting to note that students visited each of the three workstations equally.
The feedback from the participants showed there had been a positive impact in prompting learners to think about the sustainability of their smartphone use. Working with items that were already broken also gave them confidence to explore how to repair the technology. This is a different type of experience from other Computing lessons, in which devices such as laptops or tablets are provided and are expected to be carefully looked after. The researchers also asked learners to complete a questionnaire two weeks after the lessons, and this showed that 10 of the 67 participants had gone on to repair another smartphone after taking part in the lessons.
The project drew on a theory called duality reconstruction that has been developed by a researcher called Carsten Schulte. This theory argues that in computing education, it is equally important to teach learners about the function of a digital device as about the structure. For example, in the repair café lessons, learners discovered more about the role that smartphones play in society, as well as experimenting with broken smartphones to find out how they work. This brought a socio-technical perspective to the lessons that helped make the interaction between the technology and society more visible.
Using this approach in the Computing classroom may seem counter-intuitive when compared to the approach of splitting the curriculum into topics and teaching each topic sequentially. However, the findings from this project suggest that learners understand better how smartphones work when they also think about how they are manufactured and used. Including societal implications of computing can provide learners with useful contexts about how computing is used in real-world problem-solving, and can also help to increase learners’ motivation for studying the subject.
The final aspect of this research project looked at collaborative problem-solving. The lessons were structured to include time for group work and group discussion, to acknowledge and leverage the range of experiences among learners. At the workstations, learners formed small groups to carry out repairs. The paper doesn’t mention whether these groups were self-selecting or assigned, but the researchers did carry out observations of group behaviours in order to evaluate whether the collaboration was effective. In the findings, the ideal group size for the repair workstation activity was either two or three learners working together. The researchers noticed that in groups of four or more learners, at least one learner would become disinterested and disengaged. Some groups were also observed taking part in work that wasn’t related to the task, and although no further details are given about the nature of this, it is possible that the groups became distracted.
The findings from this project suggest that learners understand better how smartphones work when they also think about how they are manufactured and used.
Further investigation into effective pedagogies to set group size expectations and maintain task focus would be helpful to make sure the lessons met their learning objectives. This research was conducted as a case study in a small number of schools, and the results indicate that this approach may be more widely helpful. Details about the study can be found in the researchers’ paper (in German).
If you’re thinking about setting up a repair café in your school to promote sustainable computing, either as a formal or informal learning activity, here are ideas on where to begin:
This article is from our free computing education magazine Hello World. Every issue is written by educators for educators and packed with resources, ideas, and insights to inspire your learners and your own classroom practice.
For more about computing education in the context of sustainability, climate change, and environmental impact, download issue 19 of Hello World, which focuses on these topics.
You can subscribe to Hello World for free to never miss a digital issue, and if you’re an educator in the UK, a print subscription will get you free print copies in the post.
PS If you’re interested in facilitating productive classroom discussions with your learners about ethical, legal, cultural, and environmental concerns surrounding computer science, take a look at our free online course ‘Impacts of Technology: How To Lead Classroom Discussions’.
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Last summer, the Raspberry Pi Foundation and the University of Cambridge Department of Computer Science and Technology created a new research centre focusing on computing education research for young people in both formal and non-formal education. The Raspberry Pi Computing Education Research Centre is an exciting venture through which we aim to deliver a step-change for the field.
Computing education research that focuses specifically on young people is relatively new, particularly in contrast to established research disciplines such as those focused on mathematics or science education. However, computing is now a mandatory part of the curriculum in several countries, and being taken up in education globally, so we need to rigorously investigate the learning and teaching of this subject, and do so in conjunction with schools and teachers.
To celebrate the official launch of the Raspberry Pi Computing Education Research Centre, we will be holding an in-person event in Cambridge, UK on Weds 20 July from 15.00. This event is free and open to all: if you are interested in computing education research, we invite you to register for a ticket to attend. By coming together in person, we want to help strengthen a collaborative community of researchers, teachers, and other education practitioners.
The launch event is your opportunity to meet and mingle with members of the Centre’s research team and listen to a series of short talks. We are delighted that Prof. Mark Guzdial (University of Michigan), who many readers will be familiar with, will be travelling from the US to join us in cutting the ribbon. Mark has worked in computer science education for decades and won many awards for his research, so I can’t think of anybody better to be our guest speaker. Our other speakers are Prof. Alastair Beresford from the Department of Computer Science and Technology, and Carrie Anne Philbin MBE, our Director of Educator Support at the Foundation.
The event will take place at the Department of Computer Science and Technology in Cambridge. It will start at 15.00 with a reception where you’ll have the chance to talk to researchers and see the work we’ve been doing. We will then hear from our speakers, before wrapping up at 17.30. You can find more details about the event location on the ticket registration page.
The aim of the Raspberry Pi Computing Education Research Centre is to increase our understanding of teaching and learning computing, computer science, and associated subjects, with a particular focus on young people who are from backgrounds that are traditionally under-represented in the field of computing or who experience educational disadvantage.
We have been establishing the Centre over the last nine months. In October, I was appointed Director, and in December, we were awarded funding by Google for a one-year research project on culturally relevant computing teaching, following on from a project at the Raspberry Pi Foundation. The Centre’s research team is uniquely positioned, straddling both the University and the Foundation. Our two organisations complement each other very well: the University is one of the highest-ranking universities in the world and renowned for its leading-edge academic research, and the Raspberry Pi Foundation works with schools, educators, and learners globally to pursue its mission to put the power of computing into the hands of young people.
In our research at the Centre, we will make sure that:
We are excited to work with a large community of teachers and researchers, and we look forward to meeting you at the launch event.
At the end of June, we’ll be launching a new website for the Centre at computingeducationresearch.org. This will be the place for you to find out more about our projects and events, and to sign up to our newsletter. For announcements on social media, follow the Raspberry Pi Foundation on Twitter or Linkedin.
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We are delighted to share the news that Carrie Anne Philbin, Raspberry Pi’s Director of Educator Support, has been awarded an MBE for her services to education in the Queen’s Birthday Honours 2020.
Carrie Anne was one of the first employees of the Raspberry Pi Foundation and has helped shape our educational programmes over the past six years. Before joining the Foundation, Carrie Anne was a computing teacher, YouTuber, and author.
She’s also a tireless champion for diversity and inclusion in computing; she co-founded a grassroots movement of computing teachers dedicated to diversity and inclusion, and she has mentored young girls and students from disadvantaged backgrounds. She is a fantastic role model and source of inspiration to her colleagues, educators, and young people.
As a young girl, Carrie Anne enjoyed arts and crafts and when her dad bought the family a Commodore 64, she loved the graphics she could make on it. She says, “I vividly remember typing in the BASIC commands to create a train that moved on the screen with my dad.” Being able to express her creativity through digital patterns sparked her interest in technology.
After studying history at university, Carrie Anne followed her passion for technology and became an ICT technician at a secondary school, where she also ran several extra-curricular computing clubs for the students. Her school encouraged and supported her to apply for the Graduate Teacher Programme, and she qualified within two years.
Carrie Anne admits that her first experience in a new school as a newly qualified teacher was “pretty terrifying”, and she says her passion for the subject and her sense of humour are what got her through. The students she taught in her classroom still inspire her today.
As well as co-founding CAS #include, a diversity working group for computing teachers, Carrie Anne started the successful YouTube channel Geek Gurl Diaries. Through video interviews with women working in tech and hands-on computer science tutorials, Carrie Anne demonstrates that computing is fun and that it’s great to be a girl who likes computers.
On the back of her own YouTube channel’s success, Carrie Anne was invited to host the Computer Science video series on Crash Course, the extremely popular educational YouTube channel created by Hank and John Green. There, her 40+ videos have received over 2 million views so far.
Carrie Anne says that the Raspberry Pi computer brought her to the Raspberry Pi Foundation, and that she stayed “because of the community and the Foundation’s mission“. She came across the Raspberry Pi while searching for new ways to engage her students in computing, and joined a long waiting list to get her hands on the single-board computer. After her Raspberry Pi finally arrived, she carried it in her handbag to community meetups to learn how other people were using it in education.
Since joining the Foundation, Carrie Anne has helped to build an incredible team, many of them also former computing teachers. Together they have trained thousands of educators and produced excellent resources that are used by teachers and learners around the world. Most recently, the team created the Teach Computing Curriculum of over 500 hours of free teaching resources for primary and secondary teachers; free online video lessons for students learning at home during the pandemic (in partnership with Oak National Academy); and Isaac Computer Science, a free online learning platform for A level teachers and students.
Carrie Anne says, “We’re living in an ever-changing world that is facing many challenges right now: climate change, democracy and human rights, oh and a global pandemic. These are issues that young people care about. I’ve witnessed this year after year at our international Coolest Projects technology showcase event for young people, where passionate young creators present the tech solutions they are already building to address today’s and tomorrow’s problems. I believe that equipped with a deeper understanding of technology, young people can change the world for the better, in ways we’ve not even imagined.”
Carrie Anne has already achieved a huge amount in her career, and we honestly believe that she is only just getting started. On behalf of all your colleagues at the Foundation and all the educators and young people whose lives you’ve changed, congratulations Carrie Anne!
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Tom Lee decided to combine his household with his sister-in-law during lockdown so that she could help him make childcare more manageable. The problem was, Tom’s household was a smidge frantic in the mornings, as the family struggled to be up and ready in time for his sister-in-law’s arrival.
Enter this Raspberry Pi–powered tracking device, which tells Tom when the family car is on its way with childcare support. The DIY appliance helps his household manage childcare routines like clockwork.
When the family car is moving, a light turns on, and an antique electrical meter points to 30…20…10 to show the estimated minutes until the driver arrives. The movements of the car come in from a cellular Sinotrack OBD2 dongle pointed at a traccar server running on Raspberry Pi 3.
Tom explains: “I have not found traccar to be the greatest to work with, but you can make it forward everything it decodes to your own script pretty easily.”
The case (below) is a lasercut design Tom had made by online laser cutting business Ponoko.
Inside there’s a solid state relay and a first-generation Raspberry Pi (hidden under the black cable in the photo below). This Raspberry Pi model doesn’t have wireless connectivity, and Tom found that getting wireless working was a bit tricky for this project.
Tom produced a nice long webinar to show you exactly how this all works. So if you’d like to give this project a try, watch it for yourself.
Oh, and he’s only gone and uploaded every single bit of code you’ll need on GitHub (what an angel):
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Bonsai trees are the most glorious of miniature shrubbery. But caring for them takes seriously green fingers. Luckily, this Raspberry Pi–powered bonsai watering system doesn’t require much to get started. Also, the Reddit user who shared the project is named Lord-of-the-Pis, so, we love.
The Pimoroni Explorer HAT Pro isn’t essential to make this project work, it just makes things a whole lot easier by removing the need for a relay. It also comes with a Python library for interfacing with Raspberry Pi. The project uses an I2C connection, so it would also be possible to not use the HAT and instead plug a moisture sensor into an analogue-to-digital converter and then into Raspberry Pi’s GPIO pins.
Lord-of-the-Pis explains: “I used the Pimoroni Explorer HAT Pro in order to make the entire system on a small breadboard on top of Raspberry Pi. The Explorer HAT has inbuilt analogue inputs over I2C, which I used for the input of the moisture sensor (two wires pushed into the soil as probes). Furthermore, the output GPIO pins on this HAT sink all current to ground when activated so they can be used as a transistor to power the small 5V motor (which was also attached to the 5V power pins on Raspberry Pi).”
Using the HAT also allowed this maker to simply hook the pump up to the GPIO pins and turn these on and off, so there’s no need for an on/off switch.
This project’s code is in Python 3, and you can find it all on GitHub.
The main watering program (plantWater.py) takes input from the moisture sensor, and if the soil moisture level is below a set amount, the bonsai gets watered.
Lord-of-the-Pis built a simple web interface for the project on a localhost site that’s hosted using Apache. Apache SSI is used to execute the Python scripts. Due to the use of SSI, the index page is called index.shtml.
An image of the website. The Dip and then steadiness of the graph is due to the faulty moisture sensor. The maker has ordered another!
A lot more detail about the hardware and software involved is available in this second reddit post about the project.
Lord-of-the-Pis is now working on a dashboard that plots the soil moisture over time, as well as tracking other things like light intensity, temperature, and humidity.
May no other plant perish due to overwatering on our watch ever again!
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Since we first launched Raspberry Pi, an SD card (or microSD card) has always been a vital component. Without an SD card to store the operating system, Raspberry Pi is pretty useless*! Over the ensuing eight years, SD cards have become the default removable storage technology, used in cameras, smartphones, games consoles and all sorts of other devices. Prices have plummeted to the point where smaller size cards are practically given away for free, and at the same time storage capacity has increased to the point where you can store a terabyte on your thumbnail.
However, the fact that SD cards are now so commonplace sometimes conceals the fact that not all SD cards are created equal. SD cards have a speed rating – how fast you can read or write data to the card – and as card sizes have increased, so have speed ratings. If you want to store 4K video from your digital camera, it is important not just that the card is big enough to hold it, but also that you can write it to the card fast enough to keep up with the huge amount of data coming out of the camera.
The speed of an SD card will also directly affect how fast your Raspberry Pi runs, in just the same way as the speed of a hard drive affects how fast a conventional desktop computer runs. The faster you can read data from the card, the faster your Raspberry Pi will boot, and the faster programs will load. Equally, write speed will also affect how well any programs which save large quantities of data run – so it’s important to use a good-quality card.
The speed rating of an SD card should be printed either on the card itself or on the packaging.
The 32GB card shown below is Class 4, denoted by the 4 inside the letter C – this indicates that it can write at 4MB/s.
The 64GB card shown below is Class 10, and so can write at 10MB/s. It also shows the logo of UHS (“ultra high speed”) Class 1, the 1 inside the letter U, which corresponds to the same speed.
More recently, speeds have started to be quoted in terms of the intended use of the card, with Class V10 denoting a card intended for video at 10MB/s, for example. But the most recent speed categorisation – and the one most relevant to use in a Raspberry Pi – is the new A (for “application”) speed class. We recommend the use of Class A1 cards (as the one above – see the A1 logo to the right of the Class 10 symbol) in Raspberry Pi – in addition to a write speed of 10MB/s, these support at least 1500 read operations and 500 write operations per second. All the official Raspberry Pi microSD cards we sell meet this specification.
We’ve all heard the stories of people who have bought a large capacity SD card at a too-good-to-be-true price from a dodgy eBay seller, and found that their card labelled as 64GB can only actually hold 2GB of data. But that is at least fairly easy to spot – it’s much harder to work out whether your supposedly fast SD card is actually meeting its specified speed, and unscrupulous manufacturers and sellers often mislabel low quality cards as having unachievable speeds.
Today, as the first part of a new suite of tests which will enable you to perform various diagnostics on your Raspberry Pi hardware, we are releasing a tool which allows you to test your SD card to check that it performs as it should.
To install the new tool, from a terminal do
sudo apt update sudo apt install agnostics
(“agnostics”? In this case it’s nothing to do with religion! I’ll leave you to work out the pun…)
Once installed, you will find the new application “Raspberry Pi Diagnostics” in the main menu under “Accessories”, and if you launch it, you’ll see a screen like this:
In future, this screen will show a list of the diagnostic tests, and you will be able to select which you want to run using the checkboxes in the right-hand column. But for now, the only test available is SD Card Speed Test; just press “Run Tests” to start it.
One thing to note is that the write performance of SD cards declines over time. A new card is blank and data can be written to what is effectively “empty” memory, which is fast; but as a card fills up, memory needs to be erased before it can be overwritten, and so writes will become slower the more a card is used. The pass / fail criteria in this test assume a new (or at least freshly formatted) card; don’t be alarmed if the write speed test fails when run on the SD card you’ve been using for six months! If you do notice your Raspberry Pi slowing down over time, it may be worth backing up your SD card using the SD Card Copier tool and reformatting it.
The test takes a minute or so to run on a Raspberry Pi 4 (it’ll take longer on older models), and at the end you’ll see a results screen with either (hopefully) PASS or (if you are less fortunate) FAIL. To see the detailed results of the speed test, press “Show Log”, which will open the test log file in a text editor. (The log file is also written to your home directory as rpdiags.txt.)
We are testing against the A1 specification, which requires a sequential write speed of 10MB/s, 500 random write operations per second, and 1500 random read operations per second; we run the test up to three times. (Tests of this nature are liable to errors due to other background operations accessing the SD card while the test is running, which can affect the result – by running the test multiple times we try to reduce the likelihood of a single bad run resulting in a fail.)
If the test result was a pass, great! Your SD card is good enough to provide optimum performance in your Raspberry Pi. If it failed, have a look in the log file – you’ll see something like:
Raspberry Pi Diagnostics - version 0.1 Mon Feb 24 09:44:16 2020 Test : SD Card Speed Test Run 1 prepare-file;0;0;12161;23 seq-write;0;0;4151;8 rand-4k-write;0;0;3046;761 rand-4k-read;9242;2310;0;0 Sequential write speed 4151 kb/s (target 10000) - FAIL Note that sequential write speed declines over time as a card is used - your card may require reformatting Random write speed 761 IOPS (target 500) - PASS Random read speed 2310 IOPS (target 1500) - PASS Run 2 prepare-file;0;0;8526;16 ...
You can see just how your card compares to the stated targets; if it is pretty close to them, then your card is only just below specification and is probably fine to use. But if you are seeing significantly lower scores than the targets, you might want to consider getting another card.
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[*] unless you’re using PXE network or USB mass storage boot modes of course.
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Hey there! I’ve just come back from a two-week vacation, Liz and Helen are both off sick, and I’m not 100% sure I remember how to do my job.
So, while I figure out how to social media and word write, here’s this absolutely wonderful video from Ian Charnas, showing how he hacked his car windscreen wipers to sync with his stereo.
FINALLY! Wipers Sync to Music
In this video, I modify my car so the windshield wipers sync to the beat of whatever music I’m listening to. You can own this idea!
Ian will be auctioning off the intellectual property rights to his dancing wipers on eBay, will all proceeds going to a charity supporting young makers.
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Let the accelerometer and gyroscope of your Raspberry Pi Sense HAT measure and record impact sustained in a car collision.
The Raspberry Pi Sense HAT was originally designed for the European Astro Pi Challenge, inviting schoolchildren to code their own experiments for two Raspberry Pi units currently orbiting the Earth upon the International Space Station.
The Sense HAT is kitted out with an 8×8 RGB LED matrix and a five-button joystick, and it houses an array of useful sensors, including an accelerometer and gyroscope.
And it’s these two sensors that Instructables user Ashu_d has used for their Impact Recorder for Vehicles.
“Impact Recorder is designed to record impact sustained to a vehicle while driving or stationary,” Ashu_d explains. Alongside the Raspberry Pi and Sense HAT, the build also uses a Raspberry Pi Camera Module to record footage, saving video and/or picture files to the SD card for you to examine after a collision. “The impacts are stored in the database in the form of readings as well as video/picture.”
By following Ashu_d’s Instructables tutorial, you’re essentially building yourself a black box for your car, recording impact data as the Sense HAT records outside the standard parameters of your daily commute.
“Upon impact, remote users can be verified in real time,” they continue, “and remote users can then watch the saved video or take remote access to the Pi Camera Module and watch events accordingly.”
Ashu_d goes into great detail on how to use Node-RED and MQTT to complete the project, how you can view video in real time using VLC, and how each element works to create the final build over at Instructables.
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Could the future of driverless cars be shaped by Raspberry Pi? For undergraduate researchers at the University of Cambridge, the answer is a resounding yes!
Can cars talk to each other?
A fleet of driverless cars working together to keep traffic moving smoothly can improve overall traffic flow by at least 35 percent, researchers have shown. The researchers, from the University of Cambridge, programmed a small fleet of miniature robotic cars to drive on a multi-lane track and observed how the traffic flow changed when one of the cars stopped.
By using Raspberry Pis and onboard sensors to program scale-model versions of commercially available cars, undergraduate researchers have built a fleet of driverless cars that ‘talk to each other’. They did this because they are studying how driverless technology can help reduce traffic incidents on our roads.
The researchers investigated how a car stalled on a multi-lane track affects the buildup of traffic, and how communication between driverless cars can prevent these buildups.
When the cars acted independently of each other, a stalled car caused other vehicles in the same lane to slow or stop in order to merge into the adjacent lane. This soon led to queues forming along the track. But when the cars communicated via Raspberry Pis, they could tell each other about obstacles on the track, and this allowed cars to shift lanes with the cooperation of other road users.
The researchers recently presented their paper on the subject at the International Conference on Robotics and Automation (ICRA 2019) in Montréal, Canada. You can find links to their results, plus more information, on the University of Cambridge blog.
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Living with a toddler is the best thing. It really is. Seen through their eyes, everything you’re jaded about becomes new and exciting. Every piece of music is new. Frog and Toad are real people. Someone doesn’t care that you’re really, really bad at drawing, believing that you’re actually a kind of cross between Leonardo and Picasso; and you have a two-foot-tall excuse to sing Gaston at the top of your voice in public. The parents of toddlers are allowed into the ball pit at soft play. There’s lots of cake. The hugs and kisses are amazing.
Frog and Toad. Real people. If you are in charge of small children and do not own any of the Frog and Toad series, get yourself to a bookshop pronto. You can thank me later.
However. If my experience here is anything to go by, you may also be so tired you’re walking into things a lot. It doesn’t matter. The hugs and kisses are, like I said, amazing. And there are things you can do to mitigate that tiredness. Enter the Pi.
I’m lucky. My toddler sleeps through. But sometimes she has an…aggravating habit of early wakefulness. After 7am I’m golden. I can do 6.30 at a push. Any earlier than that, though, and I am dead-eyed and leather-visaged for the rest of the day. It’s not a good look. Enter equally new parent Cary Ciavolella, who has engineered a solution. This is a project so simple even the most sleep-deprived parent should be able to put it together, using Pimoroni parts you can easily buy online. Cary has thoughtfully made all the code available for you so you don’t have to do anything other than build the physical object.
Cary’s nightlight can produce a number of different sorts of white noise, and changes colour from red (YOU’RE MEANT TO BE ASLEEP, KID) through orange (you can play in your room) to green (it’s time to get up). Coloured lights are a sensible option: toddlers can’t read numbers, let alone a clock face. It’s all addressable via a website, which, if you’re feeling fancy, you can set up with a favicon on your phone’s home screen so it feels like an app.
White noise – I use a little box from Amazon which plays the sound of the sea – and red-spectrum nightlights have solid research behind them if you’re trying to soothe a little one to sleep. Once you cross over into blue light, you’ll stop the pineal gland from producing melatonin, which is why I hate the fan I bought for our bedroom with a burning, fiery passion. Some smart-alec thought that putting a giant blue led on the front to demonstrate that the fan was on was a smart idea, never mind the whirling blades which are obvious to at least three of the senses. (I have never tried tasting it.)
With this in mind, I’ve one tiny alteration to make to Cary’s setup: you can permanently disable the green LED on the Pi Zero itself so that the only lights visible are the Pimoroni Blinkt – namely the ones that your little one should be looking at to figure out whether it’s time to get up yet. Just add the following to the Zero’s /boot/config.txt and reboot.
# Disable the ACT LED on the Raspberry Pi. dtparam=act_led_trigger=none dtparam=act_led_activelow=on
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