Coin Cell Challenge: Use Coin Cell, Win Prizes

Today, we’re calling all hackers to do the most with a single coin cell. It’s the Coin Cell Challenge, and we’re looking for everything from the most low-power electronics to a supernova in a button cell battery.

Electronics are sucking down fewer and fewer amps every year. Low power is the future, and we’re wondering how far we can push the capabilities of those tiny discs full of power. The Coin Cell Challenge is your chance to plumb the depths of what can be done with the humble coin cell.

This is a contest, and as with the tradition of the Open 7400 Logic Competition and the recent Flashing Light Prize, we want to see what the community can come up with. The idea is simple: do something cool with a single coin cell and you’ll secure your fifteen minutes of fame and win a prize.

Three Challenges

To kick this contest off, we’re opening up three challenges to all contenders to the world heavyweight champion of button cell exploits. The first, the Lifetime Award, will go to whoever can run something interesting the longest amount of time on a coin cell. The Supernova Award is the opposite – what is the most exciting thing you can do with a button cell battery, lifetime be damned? The Heavy Lifting Award will go to the project that is the most unbelievable. If you think you can’t do that with a coin cell battery — lifting a piano or starting a car, for example — odds are you probably can. We want to see it.

Prizes and Rules

All Hackaday hardware hacking challenges need prizes, and for this one, we’re rolling out the red carpet. We’re offering up cash prizes for the top coin cell hacks. There are three $500 USD cash prizes, one for each winner of the Lifetime, Supernova, and Heavy Lifting awards. We’re not stopping there, because the top twenty builds overall will each receive $100 in Tindie credit, where the winners can cash in on some artisanal electronics sold by the people who design them.

What do you have to do to get in on this action? First, you need to build something. This something must be powered by nothing more than a single coin cell battery and must include some type of electronics. We also want this to be Open Source, and you’ll need to start a project on The full rules are available over here, but don’t wait — the deadline for entry is January 8th, 2018.

We’re excited to see what the community comes up with, and who will find a production coin cell that’s the size of a dinner plate. This is going to be a great contest with overheating coin cells and tiny bits of metal flying across the room. This is going to be a contest filled with blinkies and wireless devices that run for far, far too long. Someone is going to misread the rules and tape together a meter tall pile of coin cells. It’s going to be awesome, so start your project now.

Peer Review In The Age Of Viral Video

Recently, a YouTube video has been making the rounds online which shows a rather astounding comparison between two printed models of the US Capitol. Starting with the line “3-D PRINTERS CAN NOW PRINT TWICE AS FAST”, the video shows that one print took four hours to complete, and the other finished in just two hours by virtue of vibration reducing algorithms developed at the University of Michigan. The excitement around this video is understandable; one of the biggest limitations of current 3D printer technology is how long it takes to produce a model of acceptable quality, and if improvements to the software that drives these machines could cut total print time in half, the ramifications would be immense.

In only a few weeks the video racked up tens of thousands of views, and glowing articles popped up with headlines such as: “How to cut 3D print times in half by the University of Michigan” and “University of Michigan professor doubles 3D printing speeds using vibration-mitigating algorithm“. Predictably, our tips line lit up with 3D printer owners who wanted to hear more about the incredible research that promised to double their print speed with nothing more than a firmware update.

The only problem is, the video shows nothing of the sort. What’s more, when pushed for details, the creators of the video are now claiming the same thing.

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MeatBagPnP Makes You The Automatic Pick And Place

It’s amazing how hackers are nowadays building increasingly complex hardware with SMD parts as small as grains of sand. Getting multilayer PCB’s and soldering stencils in small quantities for prototyping is easier than ever before. But Pick-and-Place — the process of taking parts and stuffing them on the PCB in preparation for soldering — is elusive, for several reasons. For one, it makes sense only if you plan to do volume production as the cost and time for just setting up the PnP machine for a small run is prohibitive. And a desktop PnP machine isn’t yet as ubiquitous as a 3D printer. Placing parts on the board is one process that still needs to be done manually. Just make sure you don’t sneeze when you’re doing it.

Of course the human is the slow part of this process. [Colin O’Flynn] wrote a python script that he calls MeatBagPnP to ease this bottleneck. It’s designed to look at a row in a parts position file generated from your EDA program and highlight on a render of the board where that part needs to be placed. The human then does what a robotic PnP would have done.

A bar code scanner is not necessary, but using one does make the process a bit quicker. When you scan a code on the part bag, the script highlights the row on the spreadsheet and puts a marker on the first instance of it on the board. After you’ve placed the part, pressing the space bar puts a marker on the next instance of the same value. The script shows it’s done after all parts of the same value are populated and you can then move on to the next part. If you don’t have a bar code scanner handy, you can highlight a row manually and it’ll tell you where to put that part. Check it out in the video below.

Of course, before you use this tool you need some prior preparation. You need a good PNG image of the board (both sides if it is double-sided) scaled so that it is the same dimensions as the target board. The parts position file generated from your EDA tool must use the lower left corner of the board as the origin. You then tell the tool the board dimensions and it scales up everything so that it can put the red markers at the designated XY positions. The script works for single and double-sided boards. For a board with just a few parts, it may not be worth the trouble of doing this, but if you are trying to manually populate a complex board with a lot of parts, using a script like this could make the process a lot less painful.

The project is still fresh and rough around the edges, so if you have comments or feedback to offer, [Colin] is listening.

[Colin]’s name ought to ring a bell — he’s the hacker who built ChipWhisperer which took 2nd Prize at The Hackaday Prize in 2014. The MeatBagPnP project is a result of having worked at building increasingly complex boards manually and trying to make the process easier. In addition to the walk-through of how the script works after the break we’ve embedded his other video from three years back when he was stuffing parts — including BGA’s — the hard way and then reflowing them in a Chinese oven with hacked firmware.

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High-Speed Drones Use AI To Spoil The Fun

Some people look forward to the day when robots have taken over all our jobs and given us an economy where we can while our days away on leisure activities. But if your idea of play is drone racing, you may be out of luck if this AI pilot for high-speed racing drones has anything to say about it.

NASA’s Jet Propulsion Lab has been working for the past two years to develop the algorithms needed to let high-performance UAVs navigate typical drone racing obstacles, and from the look of the tests in the video below, they’ve made a lot of progress. The system is vision based, with the AI drones equipped with wide-field cameras looking both forward and down. The indoor test course has seemingly random floor tiles scattered around, which we guess provide some kind of waypoints for the drones. A previous video details a little about the architecture, and it seems the drones are doing the computer vision on-board, which we find pretty impressive.

Despite the program being bankrolled by Google, we’re sure no evil will come of this, and that we’ll be in no danger of being chased down by swarms of high-speed flying killbots anytime soon. For now we can take solace in the fact that JPL’s algorithms still can’t beat an elite human pilot like [Ken Loo], who bested the bots overall. But alarmingly, the human did no better than the bots on his first lap, which suggests that once the AI gets a little creativity and intuition like that needed to best a Go champion, [Ken] might need to find another line of work.

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