Raspberry Pi Learns Slow Morse Code

November 13, 2017 by 11 Comments

It wasn’t long ago that you needed to know Morse code to be a ham radio operator. That requirement has gone in most places, but code is still useful and many hams use it, especially hams that like to hack. Now, hams are using the Raspberry Pi to receive highly readable Morse code using very low power. The software is QrssPiG and it can process audio or use a cheap SDR dongle.

There are a few reasons code performs better than voice and many other modes. First, building transmitters for Morse is very simple. In addition, Morse code is highly readable, even under poor conditions. This is partly because it is extremely narrow bandwidth and partly because your brain is an amazing signal processor.

Like most communication methods, the slower you go the easier it is to get a signal through. In ham radio parlance, QRS means “send slower”, so QRSS has come to mean mean “send very slowly”. So hams are using very slow code, and listening for it using computerized methods. Because the data rate is so slow, the computer has time to do extreme methods to recover the signal — essentially, it can employ an extremely narrow filter. Having a QRSS signal detected around the world from a transmitter running much less than a watt is quite common. You can see a video introduction to the mode from [K6BFA] and [KI4WKZ], below.

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Quick And Simple Morse Decoder

July 1, 2021 by 14 Comments

[Rostislav Persion] wrote a simple Morse Code decoder to run on his Arduino and display the text on an LCD shield. This is probably the simplest decoder possible, and thus its logic is pretty straightforward to follow. Simplicity comes at a price — changing the speed requires changing constants in the code. We would like to see this hooked up to a proper Morse code key, and see how fast [Rostislav] could drive it before it conks out.

In an earlier era of Morse code decoders, one tough part was dealing with the idiosyncrasies of each sender. Every operator’s style, or “fist”, has subtle variations in the timings of the dots, dashes, and the pauses between these elements, the letters, and the words. In fact, trained operators can recognize each other because of this, much like we can often recognize who is speaking on the phone just by hearing their voice. The other difficulty these decoders faced was detecting the signal in low signal-to-noise ratio environments — pulling the signal out of the noise.

A Morse decoder built today is more likely to be used to decode machine-generated signals, for example, debugging information or telemetry. This would more than likely be sent at fixed, known speeds over directly connected links with very high S/N ratios (a wire, perhaps). In these situations, a simple decoder like [Rostislav]’s is completely sufficient.

We wrote about a couple of Morse code algorithms back in 2014, the MorseDetector and the Magic Morse algorithm. While Morse code operators usually rank their skills by speed — the faster the better — this Morse code project for very low power transmitters turns that notion on its head by using speeds more suitably measured in minutes per word (77 MPW for that project). Have you used Morse code in any of your projects before? Let us know in the comments below.