Every time we watch Minority Report we want to make wild hand gestures at our computer — most of them polite. [Rootsaid] wanted to do the same and discovered that the PAJ7620 is an easy way to read hand gestures. The little sensor has a serial interface and can recognize quite a bit of hand waving. To be precise, the device can read nine different motions: up, down, left, right, forward, backward, clockwise, anticlockwise, and wave.

There are plenty of libraries to read it for common platforms. If you have an Arduino that can act as a keyboard for a PC, the code almost writes itself. [Rootsaid] uses a specific library for the PAJ7620 and another — Nicohood — for sending media keys.

With those two libraries, it is very simple to write the code. You simply read a register from the sensor and determine which key to send using the Nicohood library. The serial communications is I2C and there’s a tiny optical sensor onboard along with an IR LED.

Of course, you could send other keys than media controls. We wouldn’t mind going back and forward on web pages with a gesture, for example.

We’ve seen gesture recognition with radar. We’ve also seen it with ultrasonics.

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Toy pianos are fun to plink around on for a minute, but their small keyboards and even smaller sound make them musically uninteresting pretty quickly. [Måns Jonasson] found a way to jazz up a two-octave toy piano almost beyond recognition. All it took was thirty solenoids, a few Arduinos, a MIDI shield, and a lot of time and patience.

This particular piano’s keys use lever action to strike thin steel tines. These tines are spaced just wide enough for tiny 5V solenoids to fit over them. Once [Måns] got a single solenoid striking away via MIDI input, he began designing 3D printed holders to affix them to the soundboard.

Everything worked with all thirty solenoids in place, but the wiring was a bird’s nest of spaghetti until he upgraded to motor driver shields. Then he designed a new bracket to hold eight solenoids at once, with a channel for each pair of wires. Every eight solenoids, there’s an Arduino and a motor shield.

The resulting junior player piano sounds like someone playing wind chimes like a xylophone, or a tiny Caribbean steel drum. Check out the build video after the break.

Hate the sound of toy pianos, but dig the convenient form factor? Turn one into a synth.

[W8BH] attended a talk by another ham, [W8TEE] that showed a microcontroller sending and receiving Morse code. He decided to build his own, and documented his results in an 8 part tutorial. He’s using the Blue Pill board and the resulting device sends code with paddles, sends canned text, provides an LCD with a rotary knob menu interface, and even has an SD card for data storage.

All the code is on GitHub. If you are interested in Morse code or in learning how to write a pretty substantial application using the Blue Pill and the Arduino IDE (or any other similar processor), this is a great exposition that is also a practical tool.

[W8BH] takes good advantage of breakout boards with things such as the displays and jacks on them. Of course, you don’t absolutely have to use those, but it does make life easier. You can see [W8TEE’s] version posted in an online forum.

The parts of the tutorial all build on each other, so you start out simple and get deeper and deeper. The tutorials are PDF files, but they are well organized and easy to read.

We’ve done our tutorials and videos on the Blue Pill. If you don’t want to rely on the Arduino IDE, there are ways around that, too.

Blue Pill header pic: Popolon [CC BY-SA 4.0]

Somehow [hvde] wound up with a CB radio that does AM and SSB on the 11 meter band. The problem was that the radio isn’t legal where he lives. So he decided to change the radio over to work on the 6 meter band, instead.

We were a little surprised to hear this at first. Most radio circuits are tuned to pretty close tolerances and going from 27 MHz to 50 MHz seemed like quite a leap. The answer? An Arduino and a few other choice pieces of circuitry.

In particular, [hvde] removed much of the RF portion of the radio, leaving just the parts that dealt with the intermediate frequency at 7.8 MHz. Even the transmitter generates this frequency because it is easier to create an SSB signal at a fixed frequency. The Arduino drives a frequency synthesizer and an OLED display. A mixer combines the IF signal with the frequency the Arduino commands.

The radio had a “clarifier” which acts as a fine tuning control. With the new setup, the Arduino has to read this, also, and make small adjustments to the frequency. The RF circuits in the radio took some modifications, too. It is all documented, although we will admit this probably isn’t a project for the faint of heart.

As much as we admired this project, we think we will just stick with SDR. If you want to learn more about the digital synthesis of signals, check out [Bil Herd’s] post.

If you treat your Pi as a wearable or a tablet, you will already have a battery. If you treat your Pi as a desktop you will already have a plug-in power supply, but how about if you live where mains power is unreliable? Like [jwhart1], you may consider building an uninterruptible power supply into a USB cable. UPSs became a staple of office workers when one-too-many IT headaches were traced back to power outages. The idea is that a battery will keep your computer running while the power gets its legs back. In the case of a commercial UPS, most generate an AC waveform which your computer’s power supply converts it back to DC, but if you can create the right DC voltage right to the board, you skip the inverting and converting steps.

Cheap batteries develop a memory if they’re drained often, but if you have enough space consider supercapacitors which can take that abuse. They have a lower energy density rating than lithium batteries, but that should not be an issue for short power losses. According to [jwhart1], this quick-and-dirty approach will power a full-sized Pi, keyboard, and mouse for over a minute. If power is restored, you get to keep on trucking. If your power doesn’t come back, you have time to save your work and shut down. Spending an afternoon on a power cable could save a weekend’s worth of work, not a bad time-gamble.

We see what a supercap UPS looks like, but what about one built into a lightbulb or a feature-rich programmable UPS?

Miniaturization has made smart watches possible, even for the DIY maker to tinker with. For those just getting to grips with basic digital electronics, it can be daunting, however. For those just starting out, [陳亮] put together a handy guide to building the core of an Arduino-based watch.

The writeup starts at the beginning, going over the basic hardware requirements for a smart watch. This involves considering size, packaging and power draw, as well as the user interface. The build settles on an Arduino Pro Micro, as it uses the ATmega32U4 which eliminates secondary USB-to-serial chips, helping cut down on power consumption. A square IPS LCD display is used to display an analog-style watch face, and time is kept by a DS3231 real-time clock. A pair of small vibration sensors are used to wake the watch when the user moves their wrist to check the time.

While it doesn’t cover the final assembly into a watch-like form factor, it’s a handy guide on what it takes to build a working watch for those who are still getting their feet wet with hardware. Once you’ve got that down, it’s time to contemplate how you’ll build the sleek exterior. Naturally, a good maker has that covered, too.

Creating a video signal from a computer, a job that once required significant extra hardware, is now a done deal with a typical modern microcontroller. We’ve shown you more NTSC, PAL, and VGA projects than you can shake a stick at over the years. Creating an HDMI video signal however is not so straightforward. It’s not a loosely defined analogue standard but a tightly controlled digital one upon which the clever hacks that eke full colour composite video from a single digital I/O pin will have little effect. Surely creating them from a simple microcontroller will be impossible! Not according to [techtoys], who has created an Arduino shield that creates an HDMI output from an SPI control input.

At its heart are two interesting integrated circuits that give us a little bit of insight into creating graphics at this level. First up is an RA8876 MIPI TFT controller which is a full graphics engine that produces a digital RGB output, followed by a CH7035B HDMI encoder that produces an HDMI output from the RGB. This combination of chips is particularly interesting one, because the RA8876 supports a variety of different interfaces that between them should be able to talk to most microcontrollers. In the Arduino world the only other HDMI options come via the use of an FPGA.

This is a project that seems to have been around for a couple of years, but which is still an active one. The classic Arduino shield form factor may now seem a little past its zenith, but as this board shows it’s still capable of being used for interesting new applications.

Thanks [th_in_gs] for the tip.

For years [Edward] has been building professional grade underwater sensing nodes at prices approachable for an interested individual without a government grant. An important component of these is temperature, and he has been on a quest to get the highest accuracy temperature readings from whatever parts hit that sweet optimum between cost and complexity. First there were traditional temperature sensor ICs, but after deploying numerous nodes [Edward] was running into the limit of their accuracy. Could he use clever code and circuitry to get better results? The short answer is yes, but the long answer is a many part series of posts starting in 2016 detailing [Edward]’s exploration to get there.

The first step is a thermistor, a conceptually simple device: resistance varies with temperature (seriously, how much more simple can a sensor get?). You can measure them by tapping the center of a voltage divider the same way you’d measure any other resistance, but [Edward] had discarded this idea because the naive approach combined with his Arduino’s 10 bit ADC yielded resolution too poor to be worthwhile for his needs. But by using the right analog reference voltage and adjusting the voltage divider he could get a 20x improvement in resolution, down to 0.05°C in the relevant temperature range. This and more is the subject of the first post.

What comes next? Oversampling. Apparently fueled by a project featured on Hackaday back in 2015 [Edward] embarked on a journey to applying it to his thermistor problem. To quote [Edward] directly, to get “n extra bits of resolution, you need to read the ADC four to the power of n times”. Three bits gives about an order of magnitude better resolution. This effectively lets you resolve signals smaller than a single sample but only if there is some jitter in the signal you’re measuring. Reading the same analog line with no perturbation gives no benefit. The rest of the post deals with the process of artificially perturbing the signal, which turns out to be significantly complex, but the result is roughly 16 bit accuracy from a 10 bit ADC!

What’s the upside? High quality sensor readings from a few passives and a cheap Arduino. If that’s your jam check out this excellent series when designing your next sensing project!

A neat little hacker project that’s flying off the workbenches recently is the Arduboy. This tiny game console looks like a miniaturized version of the O.G. Game Boy, but it is explicitly designed to be hacked. It’s basically an Arduino board with a display and a few buttons, anyway.

[rv6502] got their hands on an Arduboy and realized that while there were some 3D games, there was nothing that had filled polygons, or really anything resembling a modern 3D engine. This had to be rectified, and the result is pretty close to Star Fox on a microcontroller.

This project began with a simple test on the Arduboy to see if it would be even possible to render 3D objects at any reasonable speed. This test was just a rotating cube, and everything looked good. Then began a long process of figuring out how fast the engine could go, what kind of display would suit the OLED best, and how to interact in a 3D world with limited controls.

Considering this is a fairly significant engineering project, the fastest way to produce code isn’t to debug code on a microcontroller. This project demanded a native PC port, so all the testing could happen on the PC without having to program the Flash every time. That allowed [rv] to throw out the Arduino IDE and USB library; if you’re writing everything on a PC and only uploading a hex file to a microcontroller at the end, you simply don’t need it.

One of the significant advances of the graphics capability of the Arduboy comes from exploring the addressing modes of the OLED. By default, the display is in a ‘horizontal mode’ which works for 2D blitting, but not for rasterizing polygons. The ‘vertical addressing mode’, on the other hand, allows for a block of memory, 8 x 128 bytes, that maps directly to the display. Shove those bytes over, and there’s no math necessary to display an image.

This is, simply, one of the best software development builds we’ve seen. It’s full of clever tricks (like simply not doing math if you’ll never need the result) and stuffing animations into far fewer bytes than you would expect. You can check out the demo video below.

Even though machine learning AKA ‘deep learning’ / ‘artificial intelligence’ has been around for several decades now, it’s only recently that computing power has become fast enough to do anything useful with the science.

However, to fully understand how a neural network (NN) works, [Dimitris Tassopoulos] has stripped the concept down to pretty much the simplest example possible – a 3 input, 1 output network – and run inference on a number of MCUs, including the humble Arduino Uno. Miraculously, the Uno processed the network in an impressively fast prediction time of 114.4 μsec!

Whilst we did not test the code on an MCU, we just happened to have Jupyter Notebook installed so ran the same code on a Raspberry Pi directly from [Dimitris’s] bitbucket repo.

He explains in the project pages that now that the hype about AI has died down a bit that it’s the right time for engineers to get into the nitty-gritty of the theory and start using some of the ‘tools’ such as Keras, which have now matured into something fairly useful.

In part 2 of the project, we get to see the guts of a more complicated NN with 3-inputs, a hidden layer with 32 nodes and 1-output, which runs on an Uno at a much slower speed of 5600 μsec.

This exploration of ML in the embedded world is NOT ‘high level’ research stuff that tends to be inaccessible and hard to understand. We have covered Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino before, but not with such an easy and thoroughly practical example.