Have you ever wished you could see in the RF part of the radio spectrum? While such a skill would probably make it hard to get a good night’s rest, it would at least allow you to instantly see dead spots in your WiFi coverage. Not a bad tradeoff.
Unwilling to go full [Geordi La Forge] to be able to visualize RF, [Ken Kawamoto] built the next best thing – an augmented-reality RF signal strength app for his smartphone. Built to aid in the repositioning of his router in the post-holiday cleanup, the app uses the Android ARCore framework to figure out where in the house the phone is and overlays a color-coded sphere representing sensor data onto the current camera image. The spheres persist in 3D space, leaving a trail of virtual breadcrumbs that map out the sensor data as you warwalk the house. The app also lets you map Bluetooth and LTE coverage, but RF isn’t its only input: if your phone is properly equipped, magnetic fields and barometric pressure can also be AR mapped. We found the Bluetooth demo in the video below particularly interesting; it’s amazing how much the signal is attenuated by a double layer of aluminum foil. [Ken] even came up with an Arduino with a gas sensor that talks to the phone and maps the atmosphere around the kitchen stove.
The app is called AR Sensor and is available on the Play Store, but you’ll need at least Android 8.0 to play. If your phone is behind the times like ours, you might have to settle for mapping your RF world the hard way.
Continue reading “Smartphone App Uses AR to Visualize The RF Spectrum”
Racing is certainly exciting for the person rocketing around the track fast enough to get the speedometer into the triple digits, and tends to be a decent thrill for the spectators if they’ve got good seats. But if you’re just watching raw race videos on YouTube from the comfort of your office chair it can be a bit difficult to appreciate. There’s a lack of context for the viewer, and it can be hard to get the same sense of speed and position that you’d have if you saw the event first hand.
In an effort to give his father’s racing videos a bit more punch, [DusteD] came up with a clever way of adding video game style overlays to the recordings. The system provides real-time speed, lap times, and even a miniature representation of the track complete with a marker to show where the action is taking place. The end result is that recordings of Dad’s exploits on the track could pass as gameplay footage from Gran Turismo (we know GT doesn’t have motorcycles, but you get the idea).
The first part of the system is the tracker itself, which consists of a GPS receiver, an Arduino Pro Micro, and an SD card module. [DusteD] powers the device with two 18650 cells in parallel, and a DC-DC boost converter to step it up to 5V. Everything is contained in a 3D printed enclosure that he designed in OpenSCAD, with the only external elements being a toggle switch, a momentary switch, and most critically, a set of LEDs.
These LEDs play into the second part of the system, the software. The blinking LEDs are positioned so they’ll get picked up by the camera, which is then used to help synchronize the data stored on the SD card with the video. [DusteD] came up with some software that will take the speed and position information from the card, and turn it into PNG files with transparent backgrounds. These are then placed on top of the video with the help of FFmpeg. It takes a little adjustment to get everything lined up properly, but as the video after the break shows the end result is very impressive.
This build reminds us of the Raspberry Pi powered GPS helmet camera we featured a few years back, and it’s interesting to see how the two projects achieved what’s essentially the same goal in different ways.
Continue reading “GPS Overlays Give Real Life Racing A Video Game Feel”
If you haven’t been paying attention, big wheel trikes are a thing. There are motor driven versions as well as OG pedal pushing types . [Flux Axiom] is of the OG (you only get one link, now its on you) flavor and has written an instructable that shows how to achieve some nice looking on screen data that he syncs up with the video for a professional looking finished product which you can see in the video after the break.
[Flux Axiom] is using an Arduino Mega in his setup along with a cornucopia of sensors and all their data is being logged onto an SD card. All the code used in his setup is available in his GitHub repository. [Flux Axiom] was also nice enough to include the calibration process he used for the sensors which is also located in the GitHub download.
Sadly [Flux Axiom] uses freedom hating software for combining the video and data, Race Render 3 is his current solution and he is pleased with the results. Leave it in the comments if you have an open source solution for combining the video and data that we can offer him as a replacement.
Edit: Correct spelling of handle.
Continue reading “Video With Sensor Data Overlay Via Arduino Mega”
[Daniel Paluska] is getting away from the point-and-click by editing videos from the command line. Using the free open source software packages FFmpeg, Imagemagick, and Sox he produces new clips from multiple videos with effects like overlaying, slicing, and assigning each video to a different quadrant. The last option would be useful for displaying different angled shots of the same thing all at once but we’re sure you can find a way to use them all. He is using shell scripts to automate some of the process but the commands are still easy enough to understand if this is your first foray into these tools. After all, great video production will go a long way toward becoming an Internet sensation.
Elaborating on an item previously mentioned among last weekend’s Cornell final projects list, this time with video:
For their ECE final project, [Adam Papamarcos] and [Kerran Flanagan] implemented a real-time video object tracking system centered around an ATmega644 8-bit microcontroller. Their board ingests an NTSC video camera feed, samples frames at a coarse 39×60 pixel resolution (sufficient for simple games), processes the input to recognize objects and then drives a TV output using the OSD display chip from a video camera (this chip also recognizes the horizontal and vertical sync pulses from the input video signal, which the CPU uses to synchronize the digitizing step). Pretty amazing work all around.
Sometimes clever projects online are scant on information…but as this is their final grade, they’ve left no detail to speculation. Along with a great explanation of the system and its specific challenges, there’s complete source code, schematics, a parts list, the whole nine yards. Come on, guys! You’re making the rest of us look bad… Videos after the break…
Continue reading “Human Tetris: object tracking on an 8-bit microcontroller”