A common theme in The Hackaday Prize and Hackaday.io in general is tools to make more tools. There are a lot of people out there trying to make the next Bus Pirate, and simply measuring things is the first step towards automating a house or creating the next great blinkey invention.
In what is probably the most capable measurement system in the running for this year’s Hackaday Prize, [jithin] is working on a Python Powered Scientific Instrumentation Tool. It’s a microcontroller-powered box containing just about every imaginable benchtop electronics tool, from constant current supplies, LCR meters, waveform generators, frequency counters, and a logic analyzer.
This project is stuffed to the gills with just about every electronic tool imaginable; there are programmable gain amplifiers, voltage references, DACs and constant current sources, opamps and comparators, all connected to a bunch of banana jacks. All of these components are tied up in a nifty Python framework, allowing a bunch of measurements to be taken by a single box.
If that’s not enough, [jithin] is also working on wireless extension nodes for this box to get data from multiple acquisition points where wires would be unfeasible. This feature uses a NRF24L01+ radio module; it’s more than enough bandwidth for a lot of sensors, and there’s enough space all the wireless sensors you would ever need.
[Myrijam Stoetzer] and her friend [Paul Foltin], 14 and 15 years old kids from Duisburg, Germany are working on a eye movement controller wheel chair. They were inspired by the Eyewriter Project which we’ve been following for a long time. Eyewriter was built for Tony Quan a.k.a Tempt1 by his friends. In 2003, Tempt1 was diagnosed with the degenerative nerve disorder ALS and is now fully paralyzed except for his eyes, but has been able to use the EyeWriter to continue his art.
This is their first big leap moving up from Lego Mindstorms. The eye tracker part consists of a safety glass frame, a regular webcam, and IR SMD LEDs. They removed the IR blocking filter from the webcam to make it work in all lighting conditions. The image processing is handled by an Odroid U3 – a compact, low cost ARM Quad Core SBC capable of running Ubuntu, Android, and other Linux OS systems. They initially tried the Raspberry Pi which managed to do just about 3fps, compared to 13~15fps from the Odroid. The code is written in Python and uses OpenCV libraries. They are learning Python on the go. An Arduino is used to control the motor via an H-bridge controller, and also to calibrate the eye tracker. Potentiometers connected to the Arduino’s analog ports allow adjusting the tracker to individual requirements.
The web cam video stream is filtered to obtain the pupil position, and this is compared to four presets for forward, reverse, left and right. The presets can be adjusted using the potentiometers. An enable switch, manually activated at present is used to ensure the wheel chair moves only when commanded. Their plan is to later replace this switch with tongue activation or maybe cheek muscle twitch detection.
First tests were on a small mockup robotic platform. After winning a local competition, they bought a second-hand wheel chair and started all over again. This time, they tried the Raspberry Pi 2 model B, and it was able to work at about 8~9fps. Not as well as the Odroid, but at half the cost, it seemed like a workable solution since their aim is to make it as cheap as possible. They would appreciate receiving any help to improve the performance – maybe improving their code or utilising all the four cores more efficiently. For the bigger wheelchair, they used recycled car windshield wiper motors and some relays to switch them. They also used a 3D printer to print an enclosure for the camera and wheels to help turn the wheelchair. Further details are also available on [Myrijam]’s blog. They documented their build (German, pdf) and have their sights set on the German National Science Fair. The team is working on English translation of the documentation and will release all design files and source code under a CC by NC license soon.
Lightning photography is a fine art. It requires a lot of patience, and until recently required some fancy gear. [Saulius Lukse] has always been fascinated by lightning storms. When he was a kid he used to shoot lightning with his dad’s old Zenit camera — It was rather challenging. Now he’s figured out a way to do it using a GoPro.
He films at 1080@60, which we admit, isn’t the greatest resolution, but we’re sure the next GoPro will be filming 4K60 next. This means you can just set up your GoPro outside during the storm, and let it do it what it does best — film video. Normally, you’d then have to edit the footage and extract each lightning frame. That could be a lot of work.
[Saulius] wrote a Python script using OpenCV instead. Basically, the OpenCV script spots the lightning and saves motion data to a CSV file by detecting fast changes in the image.
The result? All the lightning frames plucked out from the footage — and it only took an i7 processor about 8 minutes to analyze 15 minutes of HD footage. Not bad.
Now if you feel like this is still cheating, you could build a fancy automatic trigger for your DSLR instead…
Two years ago, [Matt] made a move away from his software hacks and into the physical world. He was part of a pilot program to provide mentorship to children as part of the Maker Education Initiative. This program gave him access to 3D printers, CNC machines, and laser cutters within the New York Hall of Science makerspace. [Matt] chose to build an illuminated notification cube for his first physical project. The idea being that smart phones have so many alerts, many of which are unimportant. His project would help him to visualize and categorize each alert to better understand its importance.
The brain of the system is a Raspberry Pi. [Matt] found a Python library that allowed him to directly control an RGB LED strip based on the LPD8806 chip. He wired the data pins directly to the Pi and used an old 5V cell phone charger to power the LEDs. The strip was cut into smaller strands. Each face of the cube would end up with three strands of two LEDs each, or six LEDs per side. [Matt] found a mount for the Pi on Thingiverse and used a 3D printer to bring it into existence. The sides were made of frosted laser cut acrylic. The frosted look helps to diffuse the light from the LEDs.
Over time [Matt] found that the cube wasn’t as useful as he originally thought it would be. He just didn’t have enough alerts to justify the need. He ended up reprogramming the Pi to pull weather information instead, making use of the exact same hardware for another, more useful purpose.
[François] lives in Canada, and as you might expect, he loves hockey. Since his local team (the Habs) is in the playoffs, he decided to make an awesome setup for his living room that puts on a light show whenever his team scores a goal. This would be simple if there was a nice API to notify him whenever a goal is scored, but he couldn’t find anything of the sort. Instead, he designed a machine-learning algorithm that detects when his home team scores by listening to his TV’s audio feed.
[François] started off by listening to the audio of some recorded games. Whenever a goal is scored, the commentator yells out and the goal horn is sounded. This makes it pretty obvious to the listener that a goal has been scored, but detecting it with a computer is a bit harder. [François] also wanted to detect when his home team scored a goal, but not when the opposing team scored, making the problem even more complicated!
Since the commentator’s yell and the goal horn don’t sound exactly the same for each goal, [François] decided to write an algorithm that identifies and learns from patterns in the audio. If a home team goal is detected, he sends commands to some Phillips Hue bulbs that flash his team’s colors. His algorithm tries its best to avoid false positives when the opposing team scores, and in practice it successfully identified 75% of home team goals with 0 false positives—not bad! Be sure to check out the setup in action after the break.
Continue reading “Audio Algorithm Detects When Your Team Scores”
If you are interested in local wildlife, you may want to consider this wildlife camera project (Google cache). [Arnis] has been using his to film foxes and mice. The core components of this build are a Raspberry Pi and an infrared camera module specifically made for the Pi. The system runs on a 20,000 mAh battery, which [Arnis] claims results in around 18 hours of battery life.
[Arnis] appears to be using a passive infrared (PIR) sensor to detect motion. These sensors work by detecting sudden changes in the amount of ambient infrared radiation. Mammals are good sources of infrared radiation, so the sensor would work well to detect animals in the vicinity. The Pi is also hooked up to a secondary circuit consisting of a relay, a battery, and an infrared light. When it’s dark outside, [Arnis] can enable “night mode” which will turn on the infrared light. This provides some level of night vision for recording the furry critters in low light conditions.
[Arnis] is also using a Bluetooth dongle with the Pi in order to communicate with an Android phone. Using a custom Android app, he is able to connect back to the Pi and start the camera recording script. He can also use the app to sync the time on the Pi or download an updated image from the camera to ensure it is pointed in the right direction. Be sure to check out the demo video below.
If you like these wildlife cameras, you might want to check out some older projects that serve a similar purpose. Continue reading “Remote Controlled Wildlife Camera with Raspberry Pi”
The Raspberry Pi is a great machine to learn the ins and outs of blinking pins, but for doing anything that requires blinking pins fast, you’re better off going with a BeagleBone. This has been the conventional wisdom for years now, and now that the updated Raspberry Pi 2 is out, there’s the expectation that you’ll be able to blink a pin faster. The data are here, and yes, you can.
The method of testing was connecting a PicoScope 5444B to a pin on the GPIO pin and toggling between zero and one as fast as possible. The original test wasn’t very encouraging; Python maxed out at around 70 kHz, Ruby was terrible, and only C with the native library was useful for interesting stuff – 22MHz.
Using the same experimental setup, the Raspberry Pi 2 is about 2 to three times faster. The fastest is still the C native library, topping out at just under 42 MHz. Other languages and libraries are much slower, but the RPi.GPIO Python library stukk sees a 2.5x increase.