Pi Pico Learns Morse Code

When [101 Things] didn’t want to copy Morse code, he decided to build a Pi Pico system to read it for him. On the face of it, this doesn’t seem particularly hard, until you look at the practical considerations. With perfectly timed dots and dashes, it would be trivial. But in real life, you get an audio signal. It has been mangled and mixed with noise and interference as it travels through the air. Then there’s the human on the other end who will rarely send at a constant speed with no errors.

Once you consider that, this becomes quite the project, indeed. The decoder captures audio via the Pi’s analog-to-digital converter. Then it resamples the input, applies an FFT, and converts the output via a complex classification pipeline that includes, among other things, Bayesian decoding. Part of the pipeline makes simple typo corrections. You can see the device do its thing in the video below.

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Living In The (LLM) Past

In the early days of AI, a common example program was the hexapawn game. This extremely simplified version of a chess program learned to play with your help. When the computer made a bad move, you’d punish it. However, people quickly realized they could punish good moves to ensure they always won against the computer. Large language models (LLMs) seem to know “everything,” but everything is whatever happens to be on the Internet, seahorse emojis and all. That got [Hayk Grigorian] thinking, so he built TimeCapsule LLM to have AI with only historical data.

Sure, you could tell a modern chatbot to pretend it was in, say, 1875 London and answer accordingly. However, you have to remember that chatbots are statistical in nature, so they could easily slip in modern knowledge. Since TimeCapsule only knows data from 1875 and earlier, it will be happy to tell you that travel to the moon is impossible, for example. If you ask a traditional LLM to roleplay, it will often hint at things you know to be true, but would not have been known by anyone of that particular time period.

Chatting with ChatGPT and telling it that it was a person living in Glasgow in 1200 limited its knowledge somewhat. Yet it was also able to hint about North America and the existence of the atom. Granted, the Norse apparently found North America around the year 1000, and Democritus wrote about indivisible matter in the fifth century. But that knowledge would not have been widespread among common people in the year 1200. Training on period texts would surely give a better representation of a historical person.

The model uses texts from 1800 to 1875 published in London. In total, there is about 90 GB of text files in the training corpus. Is this practical? There is academic interest in recreating period-accurate models to study history. Some also see it as a way to track both biases of the period and contrast them with biases found in data today. Of course, unlike the Internet, surviving documents from the 1800s are less likely to have trivialities in them, so it isn’t clear just how accurate a model like this would be for that sort of purpose.

Instead of reading the news, LLMs can write it. Just remember that the statistical nature of LLMs makes them easy to manipulate during training, too.


Featured Art: Royal Courts of Justice in London about 1870, Public Domain

Ask Hackaday: How Do You Detect Hidden Cameras?

The BBC recently published an exposé revealing that some Chinese subscription sites charge for access to their network of hundreds of hidden cameras in hotel rooms. Of course, this is presumably without the consent of the hotel management and probably isn’t specifically a problem in China. After all, cameras can now be very tiny, so it is extremely easy to rent a hotel room or a vacation rental and bug it. This is illegal, China has laws against spy cameras, and hotels are required to check for them, the BBC notes. However, there is a problem: At least one camera found didn’t show up on conventional camera detectors. So we wanted to ask you, Hackaday: How do you detect hidden cameras?

How it Works

Commercial detectors typically use one of two techniques. It is easy to scan for RF signals, and if the camera is emitting WiFi or another frequency you expect cameras to use, that works. But it also misses plenty. A camera might be hardwired, for example. Or store data on an SD card for later. If you have a camera that transmits on a strange frequency, you won’t find it. Or you could hide the camera near something else that transmits. So if your scanner shows a lot of RF around a WiFi router, you won’t be able to figure out that it is actually the router and a small camera.

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He’s A Wrapper (Wire Wrapper, That Is)

Before PCBs, wiring electronic circuits was a major challenge in electronics production. A skilled person could make beautiful wire connections between terminal strips and components with a soldering iron, but it was labor-intensive and expensive. One answer that was very popular was wire wrapping, and [Sawdust & Circuits] shows off an old-fashioned wire wrap gun in the video below.

The idea was to use a spinning tool to tightly wrap solid wire on square pins. A proper wrap was a stable alternative to soldering. It required less skill, no heat, and was easy to unwrap (using a different tool) if you changed your mind. The tech started out as wiring telephone switchboards but quickly spread.

Not all tools were guns or electric. Some used a mechanical handle, and others were like pencils — you simply rotated them by hand. You could specify levels for sockets and terminals to get a certain pin length. A three-level pin could accept three wire wrap connections on a single pin, for example. There were also automated machines that could mass-produce wire-wrapped circuits.

The wire often had thin insulation, and tools usually had a slot made to strip the insulation on the tiny wires. Some guns created a “modified wrap” that left insulation at the top one or two wraps to relieve stress on the wire as it exited the post. If you can find the right tools, wires, and sockets, this is still a viable way to make circuits.

Want to know more about wire wrapping? Ask [Bil Herd].

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CIA’s World Factbook Is Gone

Before the Internet, there was a certain value to knowing how to find out about things. Reference librarians could help you locate specialized data like the Thomas Register, the EE and IC Masters for electronics, or even an encyclopedia or CRC handbook. But if you wanted up-to-date info on any country of the world, you’d often turn to the CIA. The originally classified document was what the CIA knew about every country in the world. Well, at least what they’d admit to knowing, anyway. But now, the Factbook is gone.

The publication started in 1962 as the classified “The National Basic Intelligence Factbook,” it went public in 1971 and became “The World Factbook” in the 1980s. While it is gone, you can rewind it, including a snapshot taken just before it went dark on Archive.org.

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Thomas Edison May Have Discovered Graphene

Thomas Edison is well known for his inventions (even if you don’t agree he invented all of them). However, he also occasionally invented things he didn’t understand, so they had to be reinvented again later. The latest example comes from researchers at Rice University. While building a replica light bulb, they found that Thomas Edison may have accidentally created graphene while testing the original article.

Today, we know that applying a voltage to a carbon-based resistor and heating it up to over 2,000 °C can create turbostratic graphene. Edison used a carbon-based filament and could heat it to over 2,000 °C.

This reminds us of how, in the 1880s, Edison observed current flowing in one direction through a test light bulb that included a plate. However, he thought it was just a curiosity. It would be up to Fleming, in 1904, to figure it out and understand what could be done with it.

Naturally, Edison wouldn’t have known to look for graphene, how to look for it, or what to do with it if he found it. But it does boggle the mind to think about graphene appearing many decades earlier. Or maybe it would still be looking for a killer use. Certainly, as the Rice researchers note, this is one of the easier ways to make graphene.

The Amazing Maser

While it has become a word, laser used to be an acronym: “light amplification by stimulated emission of radiation”. But there is an even older technology called a maser, which is the same acronym but with light switched out for microwaves. If you’ve never heard of masers, you might be tempted to dismiss them as early proto-lasers that are obsolete. But you’d be wrong! Masers keep showing up in places you’d never expect: radio telescopes, atomic clocks, deep-space tracking, and even some bleeding-edge quantum experiments. And depending on how a few materials and microwave engineering problems shake out, masers might be headed for a second golden age.

Simplistically, the maser is — in one sense — a “lower frequency laser.” Just like a laser, stimulated emission is what makes it work. You prepare a bunch of atoms or molecules in an excited energy state (a population inversion), and then a passing photon of the right frequency triggers them to drop to a lower state while emitting a second photon that matches the first with the same frequency, phase, and direction. Do that in a resonant cavity and you’ve got gain, coherence, and a remarkably clean signal.

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