ArduinoMania

[Tommy] has a great write-up about designing and building a minimalistic handheld electronic game called
“Higher Lower”. It’s an audio-driven game in which the unit plays two tones and asks the player to choose whether the second tone was higher in pitch, or lower. The game relies on 3D printed components and minimal electronics, limiting player input to two buttons and output to whatever a speaker stuck to an output pin from an ATtiny85 can generate.

Gameplay may be straightforward, but working with so little raises a number of design challenges. How does one best communicate game state (and things like scoring) with audio tones only? What’s the optimal way to generate a random seed when the best source of meaningful, zero-extra-components entropy (timing of player input) happens after the game has already started? What’s the most efficient way to turn a clear glue stick into a bunch of identical little light pipes? [Tommy] goes into great detail for each of these, and more.

In addition to the hardware and enclosure design, [Tommy] has tried new things on the software end of things. He found that using tools intended to develop for the Arduboy DIY handheld console along with a hardware emulator made for a very tight feedback loop during development. Being able to work on the software side without actually needing the hardware and chip programmer at hand was also flexible and convenient.

We’ve seen [Tommy]’s work before about his synth kits, and as usual his observations and shared insights about bringing an idea from concept to kit-worthy product are absolutely worth a read.

You can find all the design files on the GitHub repository, but Higher Lower is also available as a reasonably-priced kit with great documentation suitable for anyone with an interest. Watch it in action in the video below.

Do you ever get tired of stressing your neck looking for planes in the sky? Worry not! Here is a neat and cheap Arduino/Ras Pi project to keep your neck sore free! [BANK ANGLE] presents a wonderfully simple plane tracking system using an affordable camera and basic microcontrollers.

The bulk of the system relies on a cheap rotating security camera that gets dissected to reveal its internals. Here stepper control wires can be found and connected to the control boards required to allow an Arduino nano to tell the motors when and where to spin. Of course, the camera system doesn’t just look everywhere until it finds a plane, a Raspberry Pi takes in data from local ADS-B data to know where a nearby plane is.

After that, all that’s left is a nifty overlay to make the professional look. Combining all these creates a surprisingly capable system that gives information on the aircraft’s azimuth, elevation, and distance.

If you want to try your hand at making your own version of [BLANK ANGLE]’s tracker, check out his GitHub page. Of course, tracking planes gets boring after a while so why not try tracking something higher with this open-source star tracker?

Thank you Israel Brunini for the tip.

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Cats are no respecters of personal property, as [Joe Mattioni] learned when one of his cats, [Layla] needed a special prescription diet. Kitty didn’t care for it, and since the other cat, [Foxy]’s bowl was right there– well, you see where this is going. To keep [Layla] out of [Foxy]’s food and on the vet-approved diet, [Joe] built an automatic feeding system with feline facial recognition. As you do.

The hardware consists of a heavily modified feed bowl with a motorized lid that was originally operated by motion-detection, an old Android phone running a customized TensorFlow Lite model, and hardware to bridge them together. Bowl hardware has yet to be documented on [Joe]’s project page, aside from the hint that an Arduino (what else?) was involved, but the write up on feline facial recognition is fascinating.

See, when [Joe] started the project, there were no cat-identifying models available– but there were lots of human facial recognition models. Since humans and cats both have faces, [Joe] decided to use the MobileFaceNet model as a starting point, and just add extra training data in the form of 5000 furry feline faces. That ran into the hurdle that you can’t train a TFLite model, which MobileFaceNet is, so [Joe] reconstructed it as a Keras model using Google CoLab. Only then could the training occur, after which the modified model was translated back to TFLite for deployment on the Android phone as part of a bowl-controller app he wrote.

No one, [Joe] included, would say that this is the easiest, fastest, or possibly even most reliable solution– a cat smart enough not to show their face might sneak in after the authorized feline has their fill, taking advantage of a safety that won’t close a bowl on a kitty’s head, for example–but that’s what undeniably makes this a hack. It sounds like [Joe] had a great learning adventure putting this together, and the fact that it kept kitty on the proper diet is really just bonus.

Want to go on a learning adventure of your own? Click this finely-crafted link for all the details about this ongoing contest.

We wrote up a video about speeding up Arduino code, specifically by avoiding DigitalWrite. Now, the fact that DigitalWrite is slow as dirt is long known. Indeed, a quick search pulls up a Hackaday article from 2010 demonstrating that it’s fifty times slower than toggling the pin directly using the native pin registers, but this is still one of those facts that gets periodically rediscovered from generation to generation. How can this be new again?

First off, sometimes you just don’t need the speed. When you’re just blinking LEDs on a human timescale, the general-purpose Arduino functions are good enough. I’ve written loads of useful firmware that fits this description. When the timing requirements aren’t tight, slow as dirt can be fast enough.

But eventually you’ll want to build a project where the old slow-speed pin toggling just won’t cut it. Maybe it’s a large LED matrix, or maybe it’s a motor-control application where the loop time really matters. Or maybe it’s driving something like audio or video that just needs more bits per second. One way out is clever coding, maybe falling back to assembly language primitives, but I would claim that the right way is almost always to use the hardware peripherals that the chipmakers gave you.

For instance, in the end of the video linked above, the hacker wants to drive a large shift register string that’s lighting up an LED matrix. That’s exactly what SPI is for, and coming to this realization makes the project work with timing to spare, and in just a few lines of code. That is the way.

Which brings me to the double-edged sword that the Arduino’s abstraction creates. By abstracting away the chips’ hardware peripherals, it makes code more portable and certainly more accessible to beginners, who don’t want to learn about SPI and I2C and I2S and DMA just yet. But by hiding the inner workings of the chips in “user friendly” libraries, it blinds new users to the useful applications of these same hardware peripherals that clever chip-design engineers have poured their sweat and brains into making do just exactly what we need.

This isn’t really meant to be a rant against Arduino, though. Everyone has to start somewhere, and the abstractions are great for getting your feet wet. And because everything’s open source anyway, nothing stops you from digging deeper into the datasheet. You just have to know that you need to. And that’s why we write up videos like this every five years or so, to show the next crop of new hackers that there’s a lot to gain underneath the abstractions.

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