Hackaday Prize Entry: Russian Roulette With A Soldering Gun

You’re driving along a lonely, dark highway with the knowledge that suicide rates are highly correlated with fatal single vehicle car accidents. A highway overpass bridge appears ahead. You might be able to make it around the guard rail. Might is the operative word. You’ve failed at everything else so far, and there’s no reason to believe this would be any exception.

The suffering will not end, but you can delay it a bit. That’s what the Internet is all about. Cat pictures. Memes. Rare Pepes. Distraction is your digital analgesic. Like this post if you agree. The problem with using distraction as a candle of hope in your empty, wind-blown existence is simply finding new things to distract yourself with. This Hackaday Prize entry is the solution to that. It’s a randomizer for Hackaday.io. Russian roulette with a soldering gun.

This Hackaday.io project randomizer works on a property unique to the greatest project hosting site. All the links have a number and the project name in the URL. Remove the project name, and the link still works. It’s a handy pseudo-URL shortener if you ever want to put a link to your project on a PCB, but also a great way to look at all the projects on .io – all you need is a bit of Python, Perl, or some other scripting language

Right now, [Greg] has a Perl script running on one of his servers (sure to be down by the time you read this), that chooses a random number, and tries to grab that Hackaday.io project. If 404 is returned, it tries again until it succeeds.

 

Asking The Security Question Of Home Automation

“Security” is the proverbial dead horse we all like to beat when it comes to technology. This is of course not unjust — we live in a technological society built with a mindset of “security last”. There’s always one reason or another proffered for this: companies need to fail fast and will handle security once a product proves viable, end users will have a harder time with setup and use if systems are secured or encrypted, and governments/law enforcement don’t want criminals hiding behind strongly secured systems.

This is an argument I don’t want to get bogged down in. For this discussion let’s all agree on this starting point for the conversation: any system that manages something of value needs some type of security and the question becomes how much security makes sense? As the title suggests, the technology du jour is home automation. When you do manage to connect your thermostat to your door locks, lights, window shades, refrigerator, and toilet, what type of security needs to be part of the plan?

Join me after the break for an overview of a few Home Automation security concerns. This article is the third in our series — the first asked What is Home Automation and the second discussed the Software Hangups we face.

These have all been inspired by the Automation challenge round of the Hackaday Prize. Document your own Automation project by Monday morning to enter. Twenty projects will win $1000 each, becoming finalists with a chance at the grand prize of $150,000. We’re also giving away Hackaday T-shirts to people who leave comments that help carry this discussion forward, so let us know what you think below.

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Hackaday Prize Entry: Robot Shore

Everyone knows the ocean is not a gigantic garbage can, but unless you live in the middle of Asia, below sea level, Utah, or some other inhospitable place, all trash eventually makes it to sea. This is a problem not only for the the sea and the Great Pacific Garbage Patch, but for shorelines as well. For her Hackaday Prize entry, [Erin] is building a series of robots designed to walk the shore, pick up garbage, deposit it in a bin, and do it again after the next high tide.

The key problem for a robot that picks up trash is simply finding that trash. This means cameras and a lot of computing power. Lucky for [Erin], smartphones are cheap and have excellent cameras and a ton of processing power. The brains for these robots will be built around an off-the-shelf smartphone mounted on a pan/tilt mast on the bot.

[Erin] is already testing her bot, and after a few field tests she noticed a family left behind their trash on the beach. The robot moved into action before the flying rats could choke on a bottlecap, and the cleanup operation was a success. Not bad for a prototype, and an excellent entry to the Hackaday Prize.

Hackaday Prize Entry: Grasshopper Neurons

A plague of locusts descends on your garden, and suddenly you realize grasshoppers are very hard to catch. Grasshoppers are nature’s perfect collision avoidance system, and this is due to a unique visual system that includes neurons extending directly from the eye to the animal’s legs. For this Hackaday Prize entry – and as a research project for this summer at Backyard Brains, [Dieu My Nguyen] is studying the neuroscience of grasshopper vision with stabs and shocks.

We visited Backyard Brains about two years ago, and found three very interesting projects. The first was a project on optogenetics, or rewiring neurons so flies taste something sweet when they’re exposed to red light. The second was remote-controlled cockroaches. Number three will shock you: a device that allowed me to expand my megalomania by shocking people with the power of my mind. It’s not all fun and games, though. This grasshopper neuron probe will use the Backyard Brains SpikerBox to investigate when those neurons are activated in response to a stimuli.

The utility of looking at the common grasshopper to learn about collision and object avoidance may not be very apparent at first. The more you learn about neuroscience, the more apparent the biological connection to common computer vision tasks becomes. That makes this a great research project and an excellent entry into the Hackaday Prize.

Hackaday Prize Entry: Polling The Polling Places

A decade and a half ago, a developer testified that he was contracted to make code that would swing an election using electronic voting machines. In this year’s presidential primaries, exit polling significantly differed from official results, but only in precincts using unverifiable electronic voting machines. A democracy can only exist if the integrity of the voting process can be assured, and there is no international electoral oversight committee that would verify the elections in every precinct of the United States.

Your vote may not count, but that doesn’t mean you should wait for hours to cast it. This Hackaday Prize aims to end excessive waiting times at polling places, by giving voters a handy app to check the wait times they’re about to face.

The Qubie is a device that simply keeps track of how long voters are waiting in line at their polling place. The tech behind this is extremely simple – just a Raspberry Pi, WiFi adapter, and a battery. The device keeps track of how long voters have been waiting in line by looking at WiFi coming from smartphones. This data, which has a MAC address in there somewhere, is pseudorandomized and checked every minute or so to get a very good idea of how long a specific smartphone has been in range of the Pi. This data is then broadcast out to a server which figures out how long wait times are at a specific polling place.

In the recent California primary election, the Qubie was used at ten polling places in Shasta county. They logged a total of over 30,000 WiFi contacts and after a cursory examination of the data, saw the phenomena you would expect: surges in activity around lunchtime and at the end of the day. It’s a great project that gathers data that should be automated and public, and a great entry for the Hackaday Prize.

Hackaday Prize Entry: A Cheap STM32 Dev Board

Dev boards sporting a powerful ARM microcontroller are the future, despite what a gaggle of Arduino clones from China will tell you. Being the future doesn’t mean there’s not plenty of these boards around, though. The LeafLabs Maple has been around since 2009, and is a fine board if you want all that Processing/Wiring/Arduino goodies in a in an ARM dev board. The Maple has been EOL’d, and that means it’s time for a few new boards that build on what LeafLabs left behind.

This Hackaday Prize entry is for an almost-clone of the Leaflabs Maple Mini. It sports a newer microcontroller, but still has the same bootloader and pinout. The best part? It costs less than four dollars.

The microcontroller inside this Maple Mini clone is the STM32F103, a 32-bit ARM Cortex-M3 running at 72 MHz with 128K of Flash and 20K of SRAM. That’s enough for just about everything you would want to throw at it. It also follows the pinout of the original Maple Mini, and the team also has a version that’s a slight improvement of the original Maple.

The big deal is, of course, the price of the board. It’s four bucks, or about the same price as an Arduino clone from the usual online retailers. Now, finally, there’s a reason for you to wash your hands of the Arduino too.

Hackaday Prize Entry: An Oven Of Raspberry Pis

When the Raspberry Pi was introduced, the world was given a very cheap, usable Linux computer. Cheap is good, and it enables one kind of project that was previously fairly expensive. This, of course, is cluster computing, and now we can imagine an Aronofsky-esque Beowulf cluster in our apartment.

This Hackaday Prize entry is for a 100-board cluster of Raspberry Pis running Hadoop. Has something like this been done before? Most certainly. The trick is getting it right, being able to physically scale the cluster, and putting the right software on it.

The Raspberry Pi doesn’t have connectors in all the right places. The Ethernet and USB is on one side, power input is on another, and god help you if you need a direct serial connection to a Pi in the middle of a stack. This is the physical problem of putting a cluster of Pis together. If you’re exceptionally clever and are using Pi Zeros, you’ll come up with something like this, but for normal Pis, you’ll need an enclosure, a beefy, efficient power supply, and a mess of network switches.

For the software, the team behind this box of Raspberries is turning to Hadoop. Yahoo recently built a Hadoop cluster with 32,000 nodes used for deep learning and other very computationally intensive tasks. This much smaller cluster won’t be used for very demanding work. Instead, this cluster will be used for education, training, and training those ever important STEAM students. It’s big data in a small package, and a great project for the Hackaday Prize.