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Sonified ELF signals from California, Hawaii and Virgin Island locations with 16x frequency shift

Much of my ELF measurement was done at my home using a 150' pine tree as antenna. During the recording, there was a power failure at 30s...

Saturday, October 21, 2023

Forwarded my Integrated-Vision.com domain to this Blog page.

Godaddy stopped providing hosting many years ago,  I was mad at them for dropping it,  so I didn't have website.  

My plan is to create an official site, with access my library of ELF reading, which is spotty, but does cover several years of measurements.  Mainly with the same receiver.  

For now I only have the important ones available in this blog.  See previous entries for reading that I have sonified.

The ELF receiver has a 3.3V level asynchronous serial output at 9600 baud.  It can be connected to almost any computer using a USB serial converter.  The ones I use have 3.3V and 5V sources and you jumper the Vcc of the converter to the 3.3V. The 5V source is also sent to the receiver to power it.  

The host computer controls the amplification and DC offset of the signal, along with the sampling rate.  The gain of the unit has a range from 28 to ~40,000 times  Some of the gain. up to 32x, can be provided by the TI MSP430F2013 microcontroller which also does the 16bit analog to digital conversion.  Power and serial data are connected to the unit using RJ11 4 conductor telephone cable.  I've been running mine with about 70' of cable between the receiver and my raspberry Pi.  The serial lines can be directly connected to the GPIO pins for /dev/serial0, but I use the USB dongle to protect the board.  

The control software is written in Python and ran in a Jupyter Notebook on either a Raspberry Pi or PC computer. Jupyter was great for experimentation but not for automation.  Notebook has FFT for a small window, 3d FFT plot for entire measurement and spectrogram.  

Also working on a portable communications unit, based on a ESP32 microcontroller board that has WiFi and LORA wireless connectivity.

The received signal can be displayed on the OLED display, saved to micro SD card or published over WiFi to a Mosquito broker. 

I have a Python application on my android phone, that subscribes to the data and plots both the raw signal and a 4 sample running average.  The averaging effectively removes the 60 Hz noise.

Since the received frequencies are so low, ~5 to 40 Hz, I further process the audio to pitch shift it by 16 time (4 octaves) so it can be heard.