Today it is easier than ever to learn how to program a computer. Everyone has one (and probably has several) and there are tons of resources available. You can even program entirely in your web browser and avoid having to install programming languages and other arcane software. But it wasn’t always like this. In the sixties and seventies, you usually learned to program on computers that didn’t exist. I was recently musing about those computers that were never real and wondering if we are better off now with a computer at every neophyte’s fingertips or if somehow these fictional computing devices were useful in the education process.
Back in the day, almost no one had a computer. Even if you were in the computer business, the chances that you had a computer that was all yours was almost unheard of. In the old days, computers cost money — a lot of money. They required special power and cooling. They needed a platoon of people to operate them. They took up a lot of space. The idea of letting students just run programs to learn was ludicrous.
Continue reading “Computers That Never Were” →
We see a lot of Raspberry Pis used to play games, but this is something entirely different from the latest RetroPie build. This Raspberry Pi is learning how to read playing cards, with the goal of becoming the ultimate card counting blackjack player.
If [Taxi-guy] hasn’t named his project Rain Man, we humbly suggest that he does so. Because a Pi that can count into a six-deck shoe would be quite a thing, even though it would never be allowed anywhere near a casino. Hurdle number one in counting cards is reading them, and [Taxi-guy] has done a solid job of leveraging the power of OpenCV on a Pi 3 for the task. His description in the video below is very detailed, but the approach is simple: find the cards in a PiCam image of the playing field using a combination of thresholding and contouring. Then, with the cards isolated, compare the rank and suit in the upper left corner of the rotated card image to prototype images to identify the card. The Pi provides enough horsepower to quickly identify an arbitrary number of non-overlapping cards; we assume [Taxi-guy] will have to address overlapping cards and decks that use different fonts at some point.
We’re keen to see this Pi playing blackjack someday. As he’s coding that up, he may want to look at algorithmic approaches to blackjack strategies, and the real odds of beating the house.
Continue reading “A Raspberry Pi Rain Man In The Making” →
When it comes to building a neural network to simulate complex behavior, Arduino isn’t exactly the first platform that springs to mind. But when your goal is to model the behavior of an organism with only a handful of neurons, the constraints presented by an Arduino start to make sense.
It may be the most important non-segmented worm you’ve never heard of, but Caenorhabditis elegans, mercifully abbreviated C. elegans, is an important model organism for neurobiology, having had its entire nervous system mapped in 2012. [Nathan Griffith] used this “connectome” to simulate a subset of the diminutive nematode’s behaviors, specifically movements toward attractants and away from obstacles. Riding atop a small robot chassis, the Arduino sends signals to the motors when the model determines it’s time to fire the virtual worm’s muscles. An ultrasonic sensor stands in for the “nose touch” neurons of the real worm, and when the model is not busy avoiding a touch, it’s actively seeking something to eat using the “chemotaxis” behavior. The model is up on GitHub and [Nathan] hopes it provides an approachable platform for would-be neuroroboticists.
This isn’t the first time someone has modeled the nematode’s connectome in silico, but kudos to [Nathan] for accomplishing it within the constraints an Arduino presents.
Continue reading “Nematoduino: A Roundworm Neural Model On An Arduino” →