Neural Network Learns SDR Ham Radio

Identifying ham radio signals used to be easy. Beeps were Morse code, voice was AM unless it sounded like Donald Duck in which case it was sideband. But there are dozens of modes in common use now including TV, digital data, digital voice, FM, and more coming on line every day. [Randaller] used CUDA to build a neural network that could interface with an RTL-SDR dongle and can classify the signals it hears. Since it is a neural network, it isn’t so much programmed to do it as it is trained. The proof of concept has training to distinguish FM, SECAM, and tetra. However, you can train it to recognize other modulation schemes if you want to invest the time into it.

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Colorize Your Election Party

[Eric] has put together a simple python script to scrape election results from It uses urllib2 to return the popular and electoral votes for each party and throws an ElectionWon exception when CNN calls the race. He’s planning on hooking this to DMX controlled RGB LED lighting that will shift to either blue or red as the night progresses. It’s a great starting point if you want to pull off something similar.

You may remember [Eric] for building the IKEA MAME table and the TRS-80 wireless terminal.

[photo: skenmy]

UPDATE: [Garrett] of macetech is putting the finishing touches on his version which uses 32 ShiftBrite modules and 2 4-digit displays controlled by a CuBLOC.