Bandsaw Tension Gauge Uses Raspberry Pi And Load Cell

January 28, 2017 by Dan Maloney 13 Comments

No matter what material you’re cutting, getting the blade tension right is one of the keys to quality cuts on the bandsaw. Unfortunately, most bandsaws come with only a rudimentary tension gauge, and while there are plenty of tricks for measuring blade tension indirectly, nothing beats a digital blade tension gauge for repeatable results.

Despite being an aftermarket accessory for his beefy Hitachi CB-75F bandsaw, [Stephen B. Kirby]’s Pi-based tension guide looks like an OEM product. Housed in a sturdy case and sporting a sealed membrane keypad and four-line LCD display, the interface electronics are pretty straightforward. The tricky bit is sensing the amount of tension on the bandsaw blade. For that task, [Stephen] mounted a load cell in place of the original tensioning spring. A few adapters helped that job, and with a little calibration, the gauge is capable of displaying the tension by measuring the force over the cross-sectional area of the current blade.

We really like it when electronics can bring a new level of precision to old-school hardware, whether it’s a simple DRO for a manual lathe or a more accomplished build like [Stephen]’s. Sometimes adding new silicon can make old iron a little easier to use.

Raspberry Pi SDR

January 27, 2017 by Al Williams 13 Comments

[Chris D] noticed that the excellent software defined radio (SDR) software gqrx will run on the Raspberry Pi now. So he married a Raspberry Pi 3, a touchscreen, an RTL-SDR dongle, and an upconverter to make a very nice receiver setup. You can see the receiver in action below.

The video is a little light on build details, but there is a shot of the setup with the pieces labeled, and you should be able to figure it out from there. Of course, gqrx works with lots of different SDR devices so you might have to make adjustments depending on what you use (for example, many of the supported dongles won’t need the upconverter that [Chris] uses).

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Objectifier: Director Of Domestic Technology

January 26, 2017 by Anool Mahidharia 37 Comments

[Bjørn Karmann]’s Objectifier is a device that lets you control domestic objects by allowing them to respond to unique actions or behaviour, using machine learning and computer vision. The Objectifier can turn on a table lamp when you open a book, and turn it off when you close the book. Switch on the coffee maker when you place the mug next to the pot, and switch it off when the mug is removed. Turn on the belt sander when you put on the safety glasses, and stop it when you remove the glasses. Charge the phone when you put a banana in front of it, and stop charging it when you place an apple in front of it. You get the drift — the possibilities are endless. Hopefully, sometime in the (near) future, we will be able to interact with inanimate objects in this fashion. We can get them to learn from our actions rather than us learning how to program them.

The device uses computer vision and a neural network to learn complex behaviours associated with your trigger commands. A training mode, using a phone app, allows you to train it for the On and Off actions. Some actions require more human effort in training it — such as detecting an open and closed book — but eventually, the neural network does a fairly good job.

The current version is the sixth prototype in the series and [Bjørn] has put in quite a lot of work refining the project at each stage. In its latest avatar, the device hardware consists of a Pi Zero, a Raspberry-Pi camera module, an SMPS power brick, a relay block to switch the output, a 230 V plug for input power and a 230 V socket outlet for the final output. All the parts are put together rather neatly using acrylic laser cut support pieces, and then further enclosed in a nice wooden enclosure.

On the software side, all of the machine learning part is taken care of using “Wekinator” — a free, open source software that allows building musical instruments, gestural game controllers, computer vision or computer listening systems using machine learning. The computer vision is handled via Processing. All the code is wrapped using openframeworks, with ml4A providing apps for working with machine learning.

All of the above is what we could deduce looking at the pictures and information on his blog post. There isn’t much detail about the hardware, but the pictures are enough to tell us all. The software isn’t made available, but maybe this could spur some of you hackers into action to build another version of the Objectifier. Check out the video after the break, showing humans teaching the Objectifier its tricks.

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Pikelet – A Pi-Zero PC

January 25, 2017 by Pedro Umbelino 12 Comments

There are many uses for an old 10 Mbps Ethernet hub besides using it as a speed-bump in your network. (No fun in that!) [thinkerzone] decided to gut an old EN104 Bay Networks ‘Netgear Hub’ to re-purpose the solid steel case as a Raspberry Pi Zero PC housing. The project, which [thinkerzone] called Pikelet, aims to be an ‘IoT server’ with the feel of a PC. Note: a PC, not a Gameboy. In his hackaday.io project, he describes the minimum set of features for the Pikelet.

Power button – PCs need a power button
Power and Status LEDS – Blue for power, RGB for the programmable status LED
USB ports – using a Zero4U hub to expand the Pi Zero usb ports
Ethernet – using a ENC28J60 module was the original idea, but it burned while making the project
HDMI access – the case should ensure the HDMI port is accessible
Minimum storage – a 32 Gb SD card was found to be “enough to be useful”
UART – via a FT232 module
WiFi – a WiFi dongle with an external antenna, since the metal case would degrade the signal if it was inside, so a WiFi hat was not an option

On the software side, it runs the latest version of Raspbian with some additional configuration for the UART port and status LED pins.

In the project logs we can follow along as [thinkerzone] battles through the implementation of the project and, well, Murphy’s Law. One of the things that a descriptive log is useful for is that it serves as a reminder that an apparently simple project can have a lot of setbacks. Sometimes an easy-to-describe project is quite a challenge to implement. And it can be annoying when explaining the challenges to other (non hackers/makers) persons and they go: “That’s just connecting some wires…”

Is the feeling familiar? It’s nice to see someone else going through it too.

Bring A Modern Mouse To An Atari ST

January 23, 2017 by Jenny List 35 Comments

Human input devices are a consumable on our computers today. They are so cheap and standardised, that when a mouse or a keyboard expires we don’t think twice, just throw it away and buy another one. It’ll work for sure with whatever computer we have, and we can keep on without pause.

On earlier machines though, we might not be so lucky. The first generation of computers with mice didn’t have USB or even PS/2 or serial, instead they had a wide variety of proprietary mouse interfaces that usually carried the quadrature signals direct from the peripheral’s rotary sensors. If you have a quadrature mouse that dies then you’re in trouble, because you won’t easily find a new one.

Fortunately there is a solution. In the intervening decades the price of computing power has fallen to the extent that you can buy a single board computer with far more than enough power to interface with a standard USB mouse and emulate a quadrature mouse all at the same time. This was exactly the solution [Andrew Armstrong] took to provide a replacement mouse for his Atari ST, he used a Raspberry Pi as both USB host and quadrature mouse emulator (YouTube link) through its GPIOs.

He’s put together a comprehensive description of his work in the video we’ve placed below the break, meanwhile if you’d like to have a go yourself you’ll find all you need to know in his GitHub repository.

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Ping Pong Ball-Juggling Robot

January 22, 2017 by Bryan Cockfield 6 Comments

There aren’t too many sports named for the sound that is produced during the game. Even though it’s properly referred to as “table tennis” by serious practitioners, ping pong is probably the most obvious. To that end, [Nekojiru] built a ping pong ball juggling robot that used those very acoustics to pinpoint the location of the ball in relation to the robot. Not satisfied with his efforts there, he moved onto a visual solution and built a new juggling rig that uses computer vision instead of sound to keep a ping pong ball aloft.

The main controller is a Raspberry Pi 2 with a Pi camera module attached. After some mishaps with the planned IR vision system, [Nekojiru] decided to use green light to illuminate the ball. He notes that OpenCV probably wouldn’t have worked for him because it’s not fast enough for the 90 fps that’s required to bounce the ping pong ball. After looking at the incoming data from this system, an algorithm extracts 3D information about the ball and directs the paddle to strike the ball in a particular way.

If you’ve ever wanted to get into real-time object tracking, this is a great project to look over. The control system is well polished and the robot itself looks almost professionally made. Maybe it’s possible to build something similar to test [Nekojiru]’s hypothesis that OpenCV isn’t fast enough for this. If you want to get started in that realm of object tracking, there are some great projects that make use of that piece of software as well.

Persistence Of Vision Death Star

January 21, 2017 by Steven Dufresne 22 Comments

Death Stars were destroyed twice in the Star Wars movies and yet one still lives on in this 168 LED persistence of vision globe made by an MEng group at the University of Leeds in the UK. While Death Stars are in high demand, they mounted it on an axis tilted 23.4° (the same as the Earth) so that they can show the Earth overlaid with weather information, the ISS position, or a world clock.

More details are available on their system overview page but briefly: rotating inside and mounted on the axis is a Raspberry Pi sending either video or still images through its HDMI port to a custom made FPGA-based HDMI decoder board. That board then controls 14 LED driver boards mounted on a well-balanced aluminum ring. All that requires 75W which is passed through a four-phase commutator. Rotation speed is 300 RPM with a frame rate of 10 FPS and as you can see in the videos below, it works quite well.

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