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.
Continue reading “The Oldest Nuclear Reactor? Nature’s 2 Billion Year Old Experiment”
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.
Many of us have experimented with the ESP32 microcontroller, attracted by its combination of WiFi and a powerful processor core, but how many of us will have explored all of its many on-board features? One of the more interesting capabilities of this chip comes in the form of its ultra-low-power (ULP) co-processor, an extra core that allows an ESP32 to function while sipping tiny quantities of power with the ever-hungry main cores turned off.
It’s a feature that [Max Kern] has used to great effect in his low power ESP32 handheld computer, where he’s paired the chip with a low-power Sharp Memory LCD and used the ESP32’s ULP core to keep the display alive while the ESP cores are sleeping. Software wise the device sports basic PDA and clock functionality including an RSS parser, all of which can be seen in the video below the break. Its inspiration came from Panic’s crank-equipped Playdate console, with which it shares the Sharp display.
Seeing this device reminds us of some of the badges featuring ESP32 power that we’ve seen over the last few years. An event badge creator has a constant battle to give the device enough battery life to last the distance. It’s a problem the designers of the SHA 2017 badge solved with an e-ink unit, but perhaps the Sharp display could offer a cost-effective alternative for new designs.
Continue reading “How Low Can An ESP32 Go?”