Fabric(ated) Drum Machine

January 7, 2020 by 3 Comments

Some folks bring out an heirloom table runner when they have company, but what if you sewed your own and made it musical? We’d never put it away! [kAi CHENG] has an Instructable about how to recreate his melodic material, and there is a link to his website, which describes his design process, not just the finished product. We have a video below showing a jam session where he exercises a basic function set.

GarageBand is his DAW of choice, which receives translated MIDI from a Lilypad. If you don’t have a Lilypad, any Arduino based on the ATmega328P chip should work seamlessly. Testing shows that conductive threads in the soft circuit results in an occasional short circuit, but copper tape makes a good conductor  at the intersections. Wide metallic strips make for tolerant landing pads beneath modular potentiometers fitted with inviting foam knobs. Each twist controls a loop in GarageBand, and there is a pressure-sensitive pad to change the soundset. Of course, since this is all over MIDI, you can customize to your heart’s content.

MIDI drums come in all shapes and sizes, from a familiar game controller to hand rakes.

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Liquid Cooling Keeps This Electronic Load’s MOSFETs From Burning

January 7, 2020 by 21 Comments

Problem: your electronic load works fine, except for the occasional MOSFET bursting into flames. Solution: do what [tbladykas] did, and build a water-cooled electronic load.

One can quibble that perhaps there are other ways to go about preventing your MOSFETs from burning, including changes to the electrical design. But he decided to take a page from [Kerry Wong]’s design book and go big. [Kerry]’s electronic load was air-cooled and capable of sinking 100 amps; [tbladykas] only needed 60 or 70 amps or so. Since he had an all-in-one liquid CPU cooler on hand, it was only natural to use that for cooling.

The IXYS linear MOSFET dangles off the end of the controller PCB, where the TO-247 device is soldered directly to the copper cold plate of the AiO cooler. This might seem sketchy as the solder could melt if things got out of hand, but then again drilling and tapping the cold plate could lead to leakage of the thermal coupling fluid. It hasn’t had any rigorous testing yet – his guesstimate is 300 Watts dissipation at this point – but as his primary endpoint was to stop the MOSFET fires, the exact details aren’t that important.

We’ve seen a fair number of liquid-cooled Raspberry Pis and Arduinos before, but we can’t find an example of a liquid-cooled electronic load. Perhaps [tbladykas] is onto something with this design.

Guitar Hero Controller Gets A New Musical Life

January 7, 2020 by 3 Comments

Guitar Hero was a big deal, right up until it wasn’t. The best efforts of the video game industry couldn’t resurrect the once-off rush of enthusiasm for rhythm gaming, and thrift stores around the globe are now littered with little plastic instruments. [Analog Sketchbook] decided to give one of these guitars for the Wii a new life, repurposing it as a synth controller.

The build is a straightforward one, thanks to the prevalence of modern maker solutions to electronic problems. Hooking up to the guitar is a solved problem, with an Adafruit Nunchucky breakout board allowing the Guitar Hero controller to be connected via jumper wires to the Raspberry Pi’s IO pins.

Communication is via I2C, and is easy to work with in Pure Data, running on the Pi. [Analog Sketchbook] created a patch that runs a synthesizer, controlled by the buttons and controls on the guitar itself. With this setup, you could create any number of different routines to allow the guitar to be played differently. We’d love to see a chiptune-esque arpeggio patch, or something that plays fat FM synth tones a la the Genesis, but that’s just our opinion. The sky really is the limit here, with plenty of grunt on the Pi for various forms of synthesis.

It’s a fun build that gives new life to an otherwise forgotten gaming accessory. We’ve seen them repurposed before too, as far back as 2010. Video after the break.

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Choosing The Right Battery For Your Electric Vehicle Build

January 7, 2020 by 43 Comments

Many a hacker has looked at their scooter, bike, or skateboard, and decided that it would be even better if only it had a motor on it. Setting out to electrify one’s personal transport can be an exciting and productive journey, and one that promises to teach many lessons about mechanical and electronic engineering. Fundamentally, the key to any build is the battery, which has the utmost say in terms of your vehicle’s performance and range. To help out, we’ve prepared a useful guide on selecting the right battery for your needs.

One Chemistry To Rule Them All

Batteries come in all shapes and sizes, and a variety of different chemistries that all have their own unique properties and applications. When it comes to small electric vehicles, it’s desirable to have a battery with a low weight, compact size, plenty of current delivery for quick acceleration, and high capacity for long range.

30 years ago, options were limited to lead acid, nickel cadmium, and nickel metal hydride batteries. These were heavy, with low current output, poor capacity, and incredibly slow charge times. Thankfully, lithium polymer batteries have come along in the meantime and are more capable across the board. Offering huge discharge rates, fast charging, light weight and high capacity, they’re undeniably the ultimate choice for a high performance electric vehicle. They’re also wildly popular, and thus cheap, too!

There are some hangups, however. It’s important to keep all the cells in a pack at the same voltage in order to avoid cells back-charging each other. This can cause damage to the pack, or even explosions or fire. Maintaining the battery voltages to avoid this is called “balancing”. It can be handled in various ways, depending on the exact style of battery you’re using, as we’ll cover later.

Additionally, lithium batteries do not like being over-discharged. As a rule of thumb, it’s a good idea not to let your batteries drop below 3.0 V per cell. Failure to keep this in check can lead to ruining a pack, hurting its maximum capacity and ability to deliver current.

There are thankfully ways around these issues, and which ones you use depends on the battery you choose for your application. Continue reading “Choosing The Right Battery For Your Electric Vehicle Build” →

DMCA-Locked Tractors Make Decades-Old Machines The New Hotness

January 7, 2020 by 125 Comments

It’s fair to say that the hearts and minds of Hackaday readers lie closer to the technology centres of Shenzhen or Silicon Valley than they do to the soybean fields of Minnesota. The common link is the desire to actually own the hardware we buy. Among those working the soil there has been a surge in demand (and consequently a huge price rise) in 40-year-old tractors.

Second-hand farm machinery prices have made their way to the pages of Hackaday due to an ongoing battle between farmers and agricultural machinery manufacturers over who has the right to repair and maintain their tractors. The industry giant John Deere in particular uses the DMCA and end-user licensing agreements to keep all maintenance in the hands of their very expensive agents. It’s a battle we’ve reported on before, and continues to play out across the farmland of America, this time on the secondary market. Older models continue to deliver the freedom for owners to make repairs themselves, and the relative simplicity of the machines tends to make those repairs less costly overall.

Tractors built in the 1970s and 80s continue to be reliable and have the added perk of predating the digital shackles of the modern era. Aged-but-maintainable machinery is now the sweetheart of farm sales. It confirms a trend I’ve heard of anecdotally for a few years now, that relatively new tractors can be worth less than their older DMCA-free stablemates, and it’s something that I hope will also be noticed in the boardrooms. Perhaps this consumer rebellion can succeed against the DMCA where decades of activism and lobbying have evidently failed.

They just don’t build ’em like they used to.

[Image Source: John Deere 2850 by Raf24 CC-BY-SA 3.0]

[Via Hacker News]

The Oldest Nuclear Reactor? Nature’s 2 Billion Year Old Experiment

January 7, 2020 by 34 Comments

When was the first nuclear reactor created? You probably think it was Enrico Fermi’s CP-1 pile built under the bleachers at the University of Chicago in 1942. However, you’d be off by — oh — about 2 billion years.

The first reactors formed naturally about 2 billion years ago in what is now Gabon in West Africa. This required several things coming together: natural uranium deposits, just the right geology in the area, and a certain time in the life of the uranium. This happened 17 different times, and the average output of these natural reactors is estimated at about 100 kilowatts — a far cry from a modern human-created reactor that can reach hundreds or thousands of megawatts.

The reactors operated for about a million years before they spent their fuel. Nuclear waste? Yep, but it is safely contained underground and has been for 2 billion years.

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Tiny Machine Learning On The Attiny85

January 7, 2020 by 19 Comments

We tend to think that the lowest point of entry for machine learning  (ML) is on a Raspberry Pi, which it definitely is not. [EloquentArduino] has been pushing the limits to the low end of the scale, and managed to get a basic classification model running on the ATtiny85.

Using his experience of running ML models on an old Arduino Nano, he had created a generator that can export C code from a scikit-learn. He tried using this generator to compile a support-vector colour classifier for the ATtiny85, but ran into a problem with the Arduino ATtiny85 compiler not supporting a variadic function used by the generator. Fortunately he had already experimented with an alternative approach that uses a non-variadic function, so he was able to dust that off and get it working. The classifier accepts inputs from an RGB sensor to identify a set of objects by colour. The model ended up easily fitting into the capabilities of the diminutive ATtiny85, using only 41% of the available flash and 4% of the available ram.

It’s important to note what [EloquentArduino] isn’t doing here: running an artificial neural network. They’re just too inefficient in terms of memory and computation time to fit on an ATtiny. But neural nets aren’t the only game in town, and if your task is classifying something based on a few inputs, like reading a gesture from accelerometer data, or naming a color from a color sensor, the approach here will serve you well. We wonder if this wouldn’t be a good solution to the pesky problem of identifying bats by their calls.

We really like how approachable machine learning has become and if you’re keen to give ML a go, have a look at the rest of the EloquentArduino blog, it’s a small goldmine.

We’re getting more and more machine learning related hacks, like basic ML on an Arduino Uno, and Lego sortings using ML on a Raspberry Pi.