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Tux Machines


Open Hardware: DIY, Raspberry Pi, and Arduino


Posted by Roy Schestowitz on Oct 15, 2022


FUD and Openwashing

Android Leftovers



This DIYer built themselves an awesome open-source HDMI capture card for only $10


↺ This DIYer built themselves an awesome open-source HDMI capture card for only $10


> Capture cards aren't cheap, especially if you want to record or stream footage at a high resolution and framerate. Even the best capture cards (opens in new tab) can cost you upwards of $200. So, if you don't have that sort of cash lying about, why not try making your own for just a tiny fraction of that? That's what one DIYer did.



Acorn Archimedes A3010: Restoration Part 4


↺ Acorn Archimedes A3010: Restoration Part 4


> And that is it. The A3010 appears to be in full working order. Now I just need to find some time to play on it!



10 amazing Raspberry Pi cases


↺ 10 amazing Raspberry Pi cases


> You may have noticed that a Raspberry Pi is sold essentially naked. However, you can easily get a case for your Raspberry Pi and give it some extra protection from the elements and spilled drinks. Here are ten of our favourites, including some nice cases for your Pico.



Spotting defects in solar panels with machine learning | Arduino Blog


↺ Spotting defects in solar panels with machine learning | Arduino Blog


> Large solar panel installations are vital for our future of energy production without the massive carbon dioxide emissions we currently produce. However, microscopic fractures, hot spots, and other defects on the surface can expand over time, thus leading to reductions in output and even failures if left undetected. Manivannan Sivan’s solution for tackling this issue revolves around using computer vision and machine learning to find small defects at the surface before automatically reporting the information.


> Sivan compiled his dataset by first gathering images of solar panels that have visible cracks using an Arduino Portenta H7 and Vision Shield and then drawing bounding boxes around each one. From here, he trained a MobileNetV2 model with the addition of Edge Impulse’s recent FOMO object detection algorithm for better performance. He was able to improve the model’s accuracy even further by augmenting the dataset with images taken at different camera angles and lighting conditions in order to prevent mistaking the white boundary lines for cracks.




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