Intellectually, we all know that we exist in a complex soup of RF energy. Cellular, WiFi, TV, public service radio, radar, ISM-band transmissions from everything from thermometers to garage door openers — it’s all around us. It would be great to see these transmissions, but alas, most of us don’t come from the factory with the correct equipment.
Luckily, aftermarket accessories like RadioFieldAR by [Manahiyo] make it possible to visualize RF signals. As the name suggests, this is an augmented reality system that lets you inspect the RF world around you. The core of the system is a tinySA, a pocket-sized spectrum analyzer that acts as a broadband receiver. A special antenna is connected to the tinySA; unfortunately, there are no specifics on the antenna other than it needs to have a label with an image of the Earth attached to it, for antenna tracking purposes. The tinySA is connected to an Android phone — one that supports Google’s ARCore — by a USB OTG cable, and a special app on the phone runs the show.
By slowly moving the antenna around in the field of view of the phone’s camera, a heat map of signal strength at a particular frequency is slowly built up. The video below shows it in action, and the results are pretty cool. If you don’t have a tinySA, fear not — [Manahiyo] has a version of the app that supports a plain old RTL-SDR dongle too. That should make it easy for just about anyone to try this out.
It seems like most hackers have never played a game without at least wondering how to cheat at it. It’s not that we’re a dishonest lot, at least not as a rule. It’s more that most games hold less challenge for us than does figuring out how to reverse engineer the game’s mechanics. We don’t intend to cheat; it just sort of happens.
Or at least that’s the charitable way to look at such smartphone game cheats as this automated word-search puzzle solver. The game is Wordblitz, which is basically an implementation of classic Boggle along with extra features to release more dopamine and keep you playing. Not one to fall for that trick, [ghettobastler] whipped up a quick X-Y gantry from MDF using a laser cutter, added a stylus in the form of a cotton swab tipped with aluminum foil, and a vision system based on a simple web camera. The bed of the gantry has a capacitive plate so the stylus can operate the phone, along with a frame of ArUco fiducial marker to aid in locating the phone.
A Raspberry Pi handles the machine vision part of the process, which uses OpenCV to estimate the phone’s location and extract the current game tiles. The words in the game field are located by a solver that [ghetto] had previously written; a script then streams G-code to the plotter to peck out the answers at blazing speed, or at least faster than even [Peggy Hill] could manage. See the video below for a sample game being solved.
One word of warning if you choose to build this: [ghettobastler]’s puzzle-solving algorithm is based on a French dictionary, so you’ll have to re-teach it for other languages. But whatever language it’s in, this reminds us a bit of some of the Wordle solvers we’ve seen recently.
What is it that’s not quite either a plane or a boat, but has characteristics of both? There are probably a lot of things that fit that description, but the one that [Nick Rehm] is working on is known as an ekranoplan. Specifically, he’s looking to make the surface-skimming ground-effect vehicle operate autonomously.
If you think you’ve heard about ekranoplans around here before, you’d be right — we’ve covered a cool LIDAR-controlled model ekranoplan that [rctestflight] worked on about a year ago, and more recently, [ThinkFlight]’s attempts to make an autonomous ekranoplan that can follow behind a boat. The latter is where [Nick] enters the collaboration, and the featherweight foam ground-effect vehicle shown in the video below is his test platform.
After sorting out the basic airframe design and getting the LIDAR integrated, he turned his attention to the autonomous bit, which relies on a Raspberry Pi 4 running ROS and a camera with a wide-angle lens. The Pi uses machine vision algorithms to find an “AprilTag” fiducial marker in the scene, which gives the flight controller information about the relative orientation of the ekranoplan to the tag. [Nick] tested tag tracking using an electric longboard, and the model ekranoplan did an admirable job of not only managing the ground-effect, but also staying on target right behind him. And hats off to [Nick] for keeping all the balls in the air and not breaking his neck in the process.
We’re looking forward to seeing what [Nick] built here end up in [ThinkFlight]’s big ekranoplan build. Ground-effect vehicles like these are undeniably cool, and it seems like they’ve got the potential to solve some interesting transportation problems.
Have you ever wanted to own a full-sized ShopBot? What if some geniuses somewhere made a tool the size of a coffee maker that had the same capabilities? Does an augmented reality, real-time feedback, interactive, handheld CNC router that can make objects ranging in size from a pillbox to an entire conference room table sound like a thing that even exists? It didn’t to me at first, but then I visited the Shaper Tools office in San Francisco and they blew my mind with their flagship tool, Shaper Origin.
It’s impossible for me not to sound like a fan boy. Using Shaper Origin was one of those experiences where you just don’t know what to say afterwards. This is what the future looks like.
I’ve used a lot of CNC tools in my life, from my first home-built CNC conversion, to 1980s monstrosities that ran off the floppy kind of floppy disks, and all the way over to brand new state-of-the-art vertical machining centers. I had to shake a lot of that knowledge off when they demoed the device to me.
Origin is a CNC router built into the form factor of a normal wood router. The router knows where it is on the work piece. You tell it where on the piece you would like to cut out a shape, drill a hole, or make a pocket. It tells you where to go, but as you move it keeps the cutting bit precisely on the path with its three axes of control.
[Matthiew] needed to create a system that would allow a computer to read braille. An electromechanical system would be annoying to develop and would require many hardware iterations as the system [Matthew] is developing evolves. Instead, he came up with a much better solution using a webcam and OpenCV that still gets 100% accuracy.
Instead of using a camera to look for raised or lowered pins in this mechanical braille display, [Matthiew] is using OpenCV to detect the shadows. This requires calibrating the camera to the correct angle, or in OpenCV terms, pose.
After looking at the OpenCV tutorials, [Matthiew] found a demo that undistorts an image of a chess board. Using this same technique, he used fiducials from the ARTag project to correctly calibrate an image of his mechanical braille pins.
As for why [Matthiew] went through all the trouble to get a computer to read braille – something that doesn’t make a whole lot of sense if you think about it – he’s building a braille eBook reader, something that just screams awesome mechanical design. We’d be interested in seeing some more info on that project as well.