Randomness is a pursuit in a similar vein to metrology or time and frequency, in that inordinate quantities of effort can be expended in pursuit of its purest form. The Holy Grail is a source of completely unpredictable randomness, and the search for entropy so pure has taken experimenters into the sampling of lava lamps, noise sources, unpredictable timings of user actions in computer systems, and even into sampling radioactive decay. It’s a field that need not be expensive or difficult to work in, as [Henk Mulder] shows us with his 4-bit analogue random number generator.
One of the simplest circuits for generating random analogue noise involves a reverse biased diode in either Zener or avalanche breakdown, and it is a variation on this that he’s using. A reverse biased emitter junction of a transistor produces noise which is amplified by another transistor and then converted to a digital on-off stream of ones and zeroes by a third. Instead of a shift register to create his four bits he’s using four identical circuits, with no clock their outputs randomly change state at will.
A large part of his post is an examination of randomness and what makes a random source. He finds this source to be flawed because it has a bias towards logic one in its output, but we wonder whether the culprit might be the two-transistor circuit and its biasing rather than the noise itself. It also produces a sampling frequency of about 100 kbps, which is a little slow when sampling with he Teensy he’s using.
An understanding of random number generation is both a fascinating and important skill to have. We’ve featured so many RNGs over the years, here’s one powered by memes, and another by a fish tank.
Need a random number? Sure, you could just roll a die, but if you do, you might invite laughter from nearby quantum enthusiasts. If it’s truly, unpredictably random numbers you need, look no farther than the background radiation constantly bombarding us from the safety of its celestial hideout.
In a rare but much appreciated break from the Nixie tube norm of clock making, [Alpha-Phoenix] has designed a muon-powered random number generator around that warm, vintage glow. Muons are subatomic particles that are like electrons, but much heavier, and are created when pions enter the atmosphere and undergo radioactive decay. The Geiger-Müller tube, mainstay of Geiger counters the world over, detects these incoming muons and uses them to generate the number.
Inside the box, a 555 in astable mode drives a decade counter, which outputs the numbers 0-9 sequentially on the Nixie via beefy transistors. While the G-M tube waits for muons, the numbers just cycle through repeatedly, looking pretty. When a muon hits the tube, a second 555 tells the decade counter to stop immediately. Bingo, you have your random number! The only trouble we can see with this method is that if you need a number right away, you might have to go get a banana and wave it near the G-M tube.
Whether this all makes sense or not, you should check out [Alpha-Phoenix]’s project video, which is as entertaining as it is informative. He’s planning a follow-up video focused on the randomness of the G-M tube, so look out for that.
For the casual Monopoly or Risk player, using plain six-sided dice is probably fine. For other games you may need dice with much more than six sides, and if you really want to go overboard you can do what [John] did and build electronic dice with a random number generator if you really need to remove the pesky practice of rolling physical dice during your games of chance.
The “digital dice” he built are based on a multimeter from 1975 which has some hardware in it that was worth preserving, including a high quality set of nixie tubes. Nixies can be a little hard to come by these days, but are interesting pieces of hardware in their own right. [John] added some modern hardware to it as well, including an AVR microcontroller that handles the (pseudo) random number generation. A hardware switch tells the microcontroller how many sides the “die” to be emulated will need, and then a button generates the result of the roll.
This is a pretty great use for an old piece of hardware which would otherwise be obsolete by now. [John] considers this a “Resto-Mod” and the finish and quality of the build almost makes it look all original. It’s certainly a conversation piece at the D&D sessions he frequents.
Hackaday editors Elliot Williams and Mike Szczys highlight the most delightful hacks of the past week. Need a random-number showpiece for your office? Look no further than that fish tank. Maybe the showpiece you actually need is to complete your band’s stage act? You want one of Tristan Shone’s many industrial-chic audio controllers or maybe just a hacked turntable sitting between your guitar and amp.
Plus citizen science is alive and well in the astronomy realm, and piezo elements are just never going to charge your electric vehicle.
Take a look at the links below if you want to follow along, and as always tell us what you think about this episode in the comments!
While working towards his Computing and Information Systems degree at the University of London, [Jason Fenech] submitted an interesting proposal for generating random numbers using nothing more exotic than an aquarium and a sufficiently high resolution camera. Not only does his BubbleRNG make a rather relaxing sound while in operation, but according to tools such as ENT, NIST-STS, and DieHard, appears to be a source of true randomness.
If you want to build your own BubbleRNG, all you need is a tank of water and some air pumps to generate the bubbles. A webcam looking down on the surface of the water captures the chaos that ensues when the columns of bubbles generated by each pump collide. In the video after the break [Jason] uses two pumps, but considering they’re cheaper than lava lamps, we’d probably chuck a few more into the mix. To be on the safe side, he mentions that the placement and number of pumps should be arbitrary and not repeated on subsequent installations.
To turn this tiny maelstrom into a source of random numbers, OpenCV is first used to identify the bubbles in the video stream that are between a user-supplied minimum and maximum radius. The software then captures the X and Y coordinates of each bubble, and the resulting values are shuffled around and XOR’d until a stream of random numbers comes out the other end. What you do with this cheap source of infinite improbability is, of course, up to you.
While this project has been floating around (no pun intended) the Internet for a few years now, it seems to have gone largely overlooked, and was only just brought to our attention thanks to a tip from one of our illustrious readers. An excellent reminder that if you see something interesting out there, we’d love to hear about it.
Are you waiting for something that may never happen? Maybe it’s the end of your ennui, or the release of Half Life 3. While you wait, why not build a Godot Machine? Then you can diversify your portfolio and wait for two things that could happen today, tomorrow, or at sunrise on the 12th of Never.
The Godot Machine is a functional art piece that uses a solar panel and a joule thief to charge a bank of capacitors up to 5V. Whenever that happens, the Arduino comes online and generates a 20-bit random number, which is displayed on an LED bar. If the generated number matches the super-secret number that was generated at first boot and then stashed away in EEPROM, the Machine emits a victory beep and lights a green LED. Then you can go back to complaining about whatever.
We like that [kajnjaps] made his own chaos-based random number generator instead of just calling random(). It uses a guitar string to collect ambient electronic noise and an entropy generator to amplify it. Then the four least significant digits are used to seed the logistical map, so the initial value is always different.
Many languages feature a random number generator library for help with tasks like rolling a die or flipping a coin. Why, you may ask, is this necessary when humans are perfectly capable of randomly coming up with values?
The data from gathered from running the script with 200 pseudo-random inputs 100,000 times resulted in a distribution of correct guess approximately normal (µ=50% and σ=3.5%). The probability of the script correctly guessing the user’s input is >57% from calculating µ+2σ. The result? Humans aren’t so good at being random after all.
It’s almost intuitive why this happens. Finger presses tend to repeat certain patterns. The script already has a database of all possible combinations of five presses, with a counter for each combination. Every time a key is pressed, the latest five presses is updated and the counter increases for whichever combination of five presses this falls under. Based on this data, the script is able to make a prediction about the user’s next press.
In a follow-up statistic analysis, [ex-punctis] notes that with more key presses, the accuracy of the script tended to increase, with the exception of 1000+ key presses. The latter was thought to be due to the use of a psuedo random number generator to achieve such high levels of engagement with the script.
Some additional tests were done to see if holding shorter or longer sequences in memory would account for more accurate predictions. While shorter sequences should theoretically work, the risk of players keeping a tally of their own presses made it more likely for the longer sequences to reduce bias.
There’s a lot of literature on behavioral models and framing effects for similar games if you’re interested in implementing your own experiments and tricking your friends into giving you some cash.