Month: June 2017

Getting Data Off Proprietary Glucometers Gets A Little Easier

June 23, 2017 by 22 Comments

Glucometers (which measure glucose levels in blood) are medical devices familiar to diabetics, and notorious for being proprietary. Gentoo Linux developer [Flameeyes] has some good news about his open source tool to read and export data from a growing variety of glucometers. For [Flameeyes], the process started four years ago when he needed to send his glucometer readings to his doctor and ended up writing his own tool. Previously it was for Linux only, but now has Windows support.

Glucometers use a variety of different data interfaces, and even similar glucometers from the same manufacturer can use different protocols. Getting the data is one thing, but more is needed. [Flameeyes] admits that the tool is still crude in many ways, lacking useful features such as HTML output. Visualization and analysis are missing as well. If you’re interested in seeing if you can help, head over to the GitHub repository for glucomerutils. Also needed are details on protocols used by different devices; [Flameeyes] has only been able to reverse-engineer the protocols of meters he owns.

Speaking of glucometers, there is a project for a Universal Glucometer which aims to be able to use test strips from any manufacturer without needing to purchase a different meter.

Thanks for the tip, [Stuart]!

Autonomous Transatlantic Seafaring

June 23, 2017 by 11 Comments

[Andy Osusky]’s project submission for the Hackaday Prize is to build an autonomous sailboat to cross the Atlantic Ocean. [Andy]’s boat will conform to the Microtransat Challenge – a transatlantic race for autonomous boats. In order to stick to the rules of the challenge, [Andy]’s boat can only have a maximum length of 2.5 meters, and it has to hit the target point across the ocean within 25 kilometers.

The main framework of the boat is built from aluminum on top of a surfboard, with a heavy keel to keep it balanced. Because of the lightweight construction, the boat can’t sink and the heavy keel will return it upright if it flips over. The sail is made from ripstop nylon reinforced by nylon webbing and thick carbon fiber tubes, in order to resist the high ocean winds.

The electronics are separated into three parts. A securely sealed Pelican case contains the LiFePo4 batteries, the solar charge controller, and the Arduino-based navigation controller. The communications hardware is kept in polycarbonate cases for better reception. One case contains an Iridium satellite tracker, compass, and GPS, the other contains two Globalstar trackers. The Iridium module allows the boat to transmit data via the Iridium Short Burst Data service. This way, data such as GPS position, wind speed, and compass direction can be transmitted.

[Andy]’s boat was launched in September from Newfoundland headed towards Ireland. However, things quickly seemed to go awry. Storms and crashes caused errors and the solar chargers seemed not to be charging the batteries. The test ended up lasting about 24 days, during which the boat went almost 1000km.

[Andy] is redesigning the boat, changing to a rigid sail and enclosing the hardware inside the boat. In the meantime, the project is open source, so the hardware is described and software is available on GitHub. Be sure to check out the OpenTransat website, where you can see the data from the first sailing. Also, check out this article on autonomous kayaks, and this one about a swarm of autonomous boats.

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Catastrophic Forgetting: Learning’s Effect On Machine Minds

June 23, 2017 by 22 Comments

What if every time you learned something new, you forgot a little of what you knew before? That sort of overwriting doesn’t happen in the human brain, but it does in artificial neural networks. It’s appropriately called catastrophic forgetting. So why are neural networks so successful despite this? How does this affect the future of things like self-driving cars? Just what limit does this put on what neural networks will be able to do, and what’s being done about it?

Numerical weights in an artificial neural network
Neurons in the brain

The way a neural network stores knowledge is by setting the values of weights (the lines in between the neurons in the diagram). That’s what those lines literally are, just numbers assigned to pairs of neurons. They’re analogous to the axons in our brain, the long tendrils that reach out from one neuron to the dendrites of another neuron, where they meet at microscopic gaps called synapses. The value of the weight between two artificial neurons is roughly like the number of axons between biological neurons in the brain.

To understand the problem, and the solutions below, you need to know a little more detail.

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Simple Electric Bike Conversion From 3D-Printed Parts

June 23, 2017 by 27 Comments

Challenge: Perform an electric conversion on a bicycle. Problem: No significant metal working skills or equipment. Solution: 3D print everything needed to electrify the bike.

At least that’s the approach that [Tom Stanton] took to his electric bike build. Having caught the electric locomotion bug on a recent longboard build, [Tom] undertook the upgrade of a cheap “fixie,” or fixed-gear bike. His delta printer was big enough for the motor mount and weather-resistant ESC enclosure, but he needed to print the drive pulley in four quadrants that were later glued together. We can’t say we hold much faith in the zip ties that transmit all the torque through the rear wheel’s spokes, but as a proof of concept it seems sturdy enough. With a throttle from an electric scooter and a battery in a saddle bag, the bike turns in pretty decent performance — at least after a minor gearing change. And everything blends in or accents the black frame of the bike, so it’s a good-looking build to boot.

Want to catch the cheap electric personal transportation bug too? Check out this electric longboard, or this all-terrain hoverboard.

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Go Small, Get Big: The Hack That Revolutionized Bioscience

June 23, 2017 by 11 Comments

Few people outside the field know just how big bioscience can get. The public tends to think of fields like physics and astronomy, with their huge particle accelerators and massive telescopes, as the natural expressions of big science. But for decades, biology has been getting bigger, especially in the pharmaceutical industry. Specialized labs built around the automation equipment that enables modern pharmaceutical research would dazzle even the most jaded CERN physicist, with fleets of robot arms moving labware around in an attempt to find the Next Big Drug.

I’ve written before on big biology and how to get more visibility for the field into STEM programs. But how exactly did biology get big? What enabled biology to grow beyond a rack of test tubes to the point where experiments with millions of test occasions are not only possible but practically required? Was it advances in robots, or better detection methodologies? Perhaps it was a breakthrough in genetic engineering?

Nope. Believe it or not, it was a small block of plastic with some holes drilled in it. This is the story of how the microtiter plate allowed bioscience experiments to be miniaturized to the point where hundreds or thousands of tests can be done at a time.

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A Minority Report Arduino-Based Hand Controller

June 23, 2017 by 14 Comments

Movies love to show technology they can’t really build yet. Even in 2001: A Space Oddessy (released in 1968), for example, the computer screens were actually projected film.  The tablet they used to watch the news looks like something you could pick up at Best Buy this afternoon. [CircuitDigest] saw Iron Man and that inspired him to see if he could control his PC through gestures as they do on that film and so many others (including Minority Report). Although he calls it “virtual reality,” we think of VR as being visually immersed and this is really just the glove, but it is still cool.

The project uses an Arduino on the glove and Processing on the PC. The PC has a webcam which tracks the hand motion and the glove has two Hall effect sensors to simulate mouse clicks. Bluetooth links the glove and the PC. You can see a video of the thing in action, below.

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