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The New Age of QRSS

October 3, 2020
qrss amateur radio

QRSS is an experimental radio mode that uses frequency-shift-keyed (FSK) continuous wave (CW) Morse code to transmit messages that can be decoded visually by inspecting the radio frequency spectrogram. The name “QRSS” is a derivation of the Q code “QRS”, a phrase Morse code operators send to indicate the transmitter needs to slow down. The extra “S” means slow way, way down, and at the typical speed of 6 second dots and 18 second dashes most QRSS operators have just enough time to send their call sign once every ten minutes (as required by federal law). These slow Morse code messages can be decoded by visual inspection of spectrograms created by computer software processing the received audio. A QRSS grabber is a radio/computer setup configured to upload the latest radio spectrogram to the internet every 10 minutes. QRSS Plus is an automatically-updating list of active QRSS grabbers around the world, allowing the QRSS community to see QRSS transmitters being detected all over the world.

TLDR: Get Started with QRSS

  • Tune your radio to 10.140 MHz (10.1387 MHz USB)
  • Install spectrogram software like FSKview
  • Inspect the spectrogram to decode callsigns visually
  • Join the QRSS Knights mailing list to learn what’s new
  • Go to QRSS Plus to see QRSS signals around the world
  • Design and build a circuit (or buy a kit) to transmit QRSS

What is QRSS?

QRSS allows miniscule amounts of power to send messages enormous distances. For example, 200 mW QRSS transmitters are routinely spotted on QRSS grabbers thousands of miles away. The key to this resilience lies in the fact that spectrograms can be designed which average several seconds of audio into each pixel. By averaging audio in this way, the level of the noise (which is random and averages toward zero) falls below the level of the signal, allowing visualization of signals on the spectrogram which are too deep in the noise to be heard by ear.

If you have a radio and a computer, you can view QRSS! Connect your radio to your computer’s microphone, then run a spectrogram like FSKview to visualize that audio as a spectrogram. The most QRSS activity is on 30m within 100 Hz of 10.140 MHz, so set your radio to upper sideband (USB) mode and tune to 10.1387 MHz so QRSS audio will be captured as 1.4 kHz audio tones.

FSKview is radio frequency spectrogram software for viewing QRSS and WSPR simultaneously. I wrote FSKview to be simple and easy to use, but it’s worth noting that Spectrum Lab, Argo, LOPORA, and QRSSpig are also popular spectrogram software projects used for QRSS, with the last two supporting Linux and suitable for use on the Raspberry Pi.

QRSS Transmitter Design

QRSS transmitters can be extraordinarily simple because they just transmit a single tone which shifts between two frequencies. The simplicity of QRSS transmitters makes them easy to assemble as a kits, or inexpensively designed and built by those first learning about RF circuit design. The simplest designs use a crystal oscillator (typically a Colpitts configuration) followed by a buffer stage and a final amplifier (often Class C configuration using a 2N7000 N-channel MOSFET or 2N2222 NPN transistor). Manual frequency adjustments are achieved using a variable capacitor, supplemented in this case with twisted wire to act as a simple but effective variable capacitor for fine frequency tuning within the 100 Hz QRSS band. Frequency shift keying to transmit call signs is typically achieved using a microcontroller to adjust voltage on a reverse-biased diode (acting as a varactor) to modulate capacitance and shift resonant frequency of the oscillator. Following a low-pass filter (typically a 3-pole Chebyshev design) the signal is then sent to an antenna.

QRP Labs is a great source for QRSS kits. The kit pictured above and below is one of their earliest kits (the 30/40/80/160m QRSS Kit), but they have created many impressive new products in the last several years. Some of their more advanced QRSS kits leverage things like direct digital synthesis (DDS), GPS time synchronization, and the ability to transmit additional digital modes like Hellschreiber and WSPR.

Radio Frequency Spectral Phenomena

Atmospheric phenomena and other special conditions can often be spotted in QRSS spectrograms. One of the most common special cases are radio frequency reflections off of airplanes resulting in the radio waves arriving at the receiver simultaneously via two different paths (a form of multipath propagation). Due to the Doppler shift from the airplane approaching the receiver the signal from the reflected path appears higher frequency than the direct path, and as the airplane flies over and begins heading away the signal from the reflected path decreases in frequency relative to the signal of the direct path. The image below is one of my favorites, captured by Andy (G0FTD) in the 10m QRSS band. QRSS de W4HBK is a website that has many blog posts about rare and special grabs, demonstrating effects of meteors and coronal mass ejections on QRSS signals.

QRSS Transmitters are Not Beacons

Radio beacons send continuous, automated, unattended, one-way transmissions without specific reception targets. In contrast, QRSS transmitters are only intended to be transmitting when the control operator is available to control them, and the recipients are known QRSS grabbers around the world. To highlight the distinction from radio beacons, QRSS transmitters are termed Manned Experimental Propagation Transmitters (MEPTs). Users in the United States will recall that the FCC (in Part 97.203) confines operation of radio beacons to specific regions of the radio spectrum and disallows operation of beacons below 28 MHz. Note that amateur radio beacons typically operate up to 100 W which is a power level multiple orders of magnitude greater than QRSS transmitters. MEPTs, in contrast, can transmit in any portion of the radio frequency spectrum where CW operation is permitted.

The New Age of QRSS

QRSS was first mentioned in epsisode 28 of The Soldersmoke Podcast on July 30, 2006. It was discussed in several episodes over the next few years, and a 2009 post about QRSS on Hack-A-Day brought it to my attention. In the early days of QRSS the only way to transmit QRSS was to design and build your own transmitter. David Hassall (WA5DJJ), Bill Houghton (W4HBK), Hans Summers (G0UPL), and others would post their designs on their personal websites along with notes about where their transmitters had been spotted. In the following years the act of creating QRSS grabbers became streamlined, and websites like I2NDT’s QRSS Grabber Compendium and QRSS Plus made it easier to see QRSS signals around the world. Hans Summers (G0UPL) began selling QRSS transmitter kits at amateur radio conventions, then later through the QRP Labs website. As more people started selling and buying kits (and documenting their experiences) it became easier and easier to get started with QRSS. Before QRSS kits were easy to obtain the only way to participate in the hobby was to design and build a transmitter from scratch, representing a high barrier to entry for those potentially interested in this fascinating hobby. Now with the availability of high quality QRSS transmitter kits and the ubiquity of internet tools and software to facilitate QRSS reception, it’s easier than ever to get involved in this exciting field! For these reasons I believe we have entered into a New Age of QRSS.

QRSS Frequency Bands

To receive QRSS, configure your radio for uper sideband using a dial frequency from the table below and QRSS will be audible as 1-2 kHz audio tones. This radio configuration is identical to the recommendation by WSPRnet for receiving WSPR using WSJT-X software, allowing QRSS and WSPR to be monitored simultaneously.

The entire QRSS band is approximately 200 Hz wide centered on the QRSS frequencies listed in the table below. Testing and experimentation is encouraged in the 100 Hz below the listed frequency, and the upper portion is typically used for more stable transmitters. An exception is the 10m band where the QRSS band is 400 Hz wide, extending ±200 Hz from the center frequency listed below.

Band QRSS Frequency (±100 Hz) USB Dial Frequency (Hz) Audio Frequency (Hz)
600m 476,100 474,200 1,900
160m 1,837,900 1,836,600 1,300
80m 3,569,900 ⭐ popular 3,568,600 1,300
60m 5,288,550 5,287,200 1,350
40m 7,039,900 ⭐ popular 7,038,600 1,300
30m 10,140,000 🌟 most popular 10,138,700 1,300
22m 13,555,400 13,553,900 1,300
20m 14,096,900 ⭐ popular 14,095,600 1,300
17m 18,105,900 18,104,600 1,300
15m 21,095,900 21,094,600 1,300
12m 24,925,900 24,924,600 1,300
10m 28,125,700 28,124,600 1,100
6m 50,294,300 50,293,000 1,300

Similar QRSS frequency tables:

⚠️ WARNING: It may not be legal for you to transmit on these frequencies. Check license requirements and regulations for your region before transmitting QRSS.

⚠️ WARNING: These frequencies sometimes change based upon community discussion. Frequency tables can be found on the Knights QRSS Wiki. Outdated or alternate frequencies include 160m (1,843,200 Hz), 80m (3,593,900 Hz), 12m (24,890,800 Hz), 10m (28,000,800 Hz and 28,322,000 ±500 Hz), and 6m (50,000,900 Hz). Experimentation on 10m is encouraged in the 100Hz above the band.

When tuning your radio your dial frequency may be lower than the QRSS frequency. If you are using upper-sideband (USB) mode, you have to tune your radio dial 1.4 kHz below the QRSS band to hear QRSS signals as a 1.4 kHz tone. Recommended dial frequencies in the table above are suitable for receiving QRSS and WSPR.

QRSS Knights

The QRSS Knights is a group of QRSS enthusiasts who coordinate events and discuss experiments over email. The group is kind and welcoming to newcomers, and those interested in learning more about QRSS are encouraged to join the mailing list.

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ECG Simulator Circuit

September 27, 2020
circuit diyECG

This page describes a simple circuit which produces ECG-like waveform. The waveform is not very detailed, but it contains a sharp depolarizing (rising) component, a slower hyperpolarizing (falling) component, and a repetition rate of approximately one beat per second making it potentially useful for testing heartbeat detection circuitry.

In 2019 I released a YouTube video and blog post showing how to build an ECG machine using an AD8232 interfaced to a computer’s sound card. At the end of the video I discussed how to use a 555 timer to create a waveform roughly like an ECG signal, but I didn’t post the circuit at the end of that video. I get questions about it from time to time, so I’ll share my best guess at what that circuit was here using LTSpice to simulate it.

Design Notes

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Using MOD Files in LTSpice

September 27, 2020
circuit

This page shows how to use the LM741 op-amp model file in LTSpice. This is surprisingly un-intuitive, but is a good thing to know how to do. Model files can often be downloaded by vendor sites, but LTSpice only comes pre-loaded with models of common LT components.

Step 1: Download a Model (.mod) File

I found LM741.MOD available on the TI’s LM741 product page.

Save it wherever you want, but you will need to know the full path to this file later.

Step 2: Determine the Name

Open the model file in a text editor and look for the line starting with .SUBCKT. The top of LM741.MOD looks like this:

* connections:      non-inverting input
*                   |   inverting input
*                   |   |   positive power supply
*                   |   |   |   negative power supply
*                   |   |   |   |   output
*                   |   |   |   |   |
*                   |   |   |   |   |
.SUBCKT LM741/NS    1   2  99  50  28

The last line tells us the name of this model’s sub-circuit is LM741/NS

Step 3: Include the Model File

Click the “.op” button on the toolbar, then add .include followed by the full path to the model file. After clicking OK place the text somewhere on your LTSpice circuit diagram.

Step 4: Insert a General Purpose Part

We know the part we are including is a 5-pin op-amp, so we can start by placing a generic component. Notice the description says you must give the value a name and include this file. We will do this in the next step.

Step 5: Configure the Component to use the Model

Right-click the op-amp and update its Value to match the name of the subcircuit we read from the model file earlier.

Step 6: Simulate Your Circuit

Your new component will run using the properties of the model you downloaded.

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Exponential Fit with Python

September 24, 2020
python

Fitting an exponential curve to data is a common task and in this example we’ll use Python and SciPy to determine parameters for a curve fitted to arbitrary X/Y points. You can follow along using the fit.ipynb Jupyter notebook.

import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt

xs = np.arange(12) + 7
ys = np.array([304.08994, 229.13878, 173.71886, 135.75499,
               111.096794, 94.25109, 81.55578, 71.30187, 
               62.146603, 54.212032, 49.20715, 46.765743])

plt.plot(xs, ys, '.')
plt.title("Original Data")

To fit an arbitrary curve we must first define it as a function. We can then call scipy.optimize.curve_fit which will tweak the arguments (using arguments we provide as the starting parameters) to best fit the data. In this example we will use a single exponential decay function.

def monoExp(x, m, t, b):
    return m * np.exp(-t * x) + b

In biology / electrophysiology biexponential functions are often used to separate fast and slow components of exponential decay which may be caused by different mechanisms and occur at different rates. In this example we will only fit the data to a method with a exponential component (a monoexponential function), but the idea is the same.

# perform the fit
p0 = (2000, .1, 50) # start with values near those we expect
params, cv = scipy.optimize.curve_fit(monoExp, xs, ys, p0)
m, t, b = params
sampleRate = 20_000 # Hz
tauSec = (1 / t) / sampleRate

# determine quality of the fit
squaredDiffs = np.square(ys - monoExp(xs, m, t, b))
squaredDiffsFromMean = np.square(ys - np.mean(ys))
rSquared = 1 - np.sum(squaredDiffs) / np.sum(squaredDiffsFromMean)
print(f"R² = {rSquared}")

# plot the results
plt.plot(xs, ys, '.', label="data")
plt.plot(xs, monoExp(xs, m, t, b), '--', label="fitted")
plt.title("Fitted Exponential Curve")

# inspect the parameters
print(f"Y = {m} * e^(-{t} * x) + {b}")
print(f"Tau = {tauSec * 1e6} µs")


Y = 2666.499 * e^(-0.332 * x) + 42.494
Tau = 150.422 µs
R² = 0.999107330342064

Extrapolating the Fitted Curve

We can use the calculated parameters to extend this curve to any position by passing X values of interest into the function we used during the fit.

The value at time 0 is simply m + b because the exponential component becomes e^(0) which is 1.

xs2 = np.arange(25)
ys2 = monoExp(xs2, m, t, b)

plt.plot(xs, ys, '.', label="data")
plt.plot(xs2, ys2, '--', label="fitted")
plt.title("Extrapolated Exponential Curve")

Constraining the Infinite Decay Value

What if we know our data decays to 0? It’s not best to fit to an exponential decay function that lets the b component be whatever it wants. Indeed, our fit from earlier calculated the ideal b to be 42.494 but what if we know it should be 0? The solution is to fit using an exponential function where b is constrained to 0 (or whatever value you know it to be).

def monoExpZeroB(x, m, t):
    return m * np.exp(-t * x)

# perform the fit using the function where B is 0
p0 = (2000, .1) # start with values near those we expect
paramsB, cv = scipy.optimize.curve_fit(monoExpZeroB, xs, ys, p0)
mB, tB = paramsB
sampleRate = 20_000 # Hz
tauSec = (1 / tB) / sampleRate

# inspect the results
print(f"Y = {mB} * e^(-{tB} * x)")
print(f"Tau = {tauSec * 1e6} µs")

# compare this curve to the original
ys2B = monoExpZeroB(xs2, mB, tB)
plt.plot(xs, ys, '.', label="data")
plt.plot(xs2, ys2, '--', label="fitted")
plt.plot(xs2, ys2B, '--', label="zero B")

Y = 1245.580 * e^(-0.210 * x)
Tau = 237.711 µs

The curves produced are very different at the extremes (especially when time is 0), even though they appear to both fit the data points nicely. Which curve is more accurate? That depends on your application. A hint can be gained by inspecting the time constants of these two curves.

Parameter Fitted B Fixed B
m 2666.499 1245.580
t 0.332 0.210
Tau 150.422 µs 237.711 µs
b 42.494 0

By inspecting Tau I can gain insight into which method may be better for me to use in my application. I expect Tau to be near 250 µs, leading me to trust the fixed-B method over the fitted B method. Choosing the correct method has great implications on the value of m (which is also the value of the curve when time is 0).

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Signal Filtering in Python

September 23, 2020
python

Over a decade ago I posted code demonstrating how to filter data in Python, but there have been many improvements since then. My original posts (1, 2, 3, 4) required creating discrete filtering functions, but modern approaches can leverage Numpy and Scipy to do this more easily and efficiently. In this article we will use scipy.signal.filtfilt to apply low-pass, high-pass, and band-pass filters to reduce noise in an ECG signal (stored in ecg.wav (created as part of my Sound Card ECG project).

Moving-window filtering methods often result in a filtered signal that lags behind the original data (a phase shift). By filtering the signal twice in opposite directions filtfilt cancels-out this phase shift to produce a filtered signal which is nicely aligned with the input data.

import scipy.io.wavfile
import scipy.signal
import numpy as np
import matplotlib.pyplot as plt

# read ECG data from the WAV file
sampleRate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sampleRate

# apply a 3-pole lowpass filter at 0.1x Nyquist frequency
b, a = scipy.signal.butter(3, 0.1)
filtered = scipy.signal.filtfilt(b, a, data)


# plot the original data next to the filtered data

plt.figure(figsize=(10, 4))

plt.subplot(121)
plt.plot(times, data)
plt.title("ECG Signal with Noise")
plt.margins(0, .05)

plt.subplot(122)
plt.plot(times, filtered)
plt.title("Filtered ECG Signal")
plt.margins(0, .05)

plt.tight_layout()
plt.show()

Cutoff Frequency

The second argument passed into the butter method customizes the cut-off frequency of the Butterworth filter. This value (Wn) is a number between 0 and 1 representing the fraction of the Nyquist frequency to use for the filter. Note that Nyquist frequency is half of the sample rate. As this fraction increases, the cutoff frequency increases. You can get fancy and express this value as 2 * Hz / sample rate.

plt.plot(data, '.-', alpha=.5, label="data")

for cutoff in [.03, .05, .1]:
    b, a = scipy.signal.butter(3, cutoff)
    filtered = scipy.signal.filtfilt(b, a, data)
    label = f"{int(cutoff*100):d}%"
    plt.plot(filtered, label=label)
    
plt.legend()
plt.axis([350, 500, None, None])
plt.title("Effect of Different Cutoff Values")
plt.show()

Improve Edges with Gustafsson’s Method

Something weird happens at the edges. There’s not enough data “off the page” to know how to smooth those points, so what should be done?

Padding is the default behavior, where edges are padded with with duplicates of the edge data points and smooth the trace as if those data points existed. The drawback of this is that one stray data point at the edge will greatly affect the shape of your smoothed data.

Gustafsson’s Method may be superior to padding. The advantage of this method is that stray points at the edges do not greatly influence the smoothed curve at the edges. This technique is described in a 1994 paper by Fredrik Gustafsson. “Initial conditions are chosen for the forward and backward passes so that the forward-backward filter gives the same result as the backward-forward filter.” Interestingly this paper demonstrates the method by filtering noise out of an EKG recording.

# A small portion of data will be inspected for demonstration
segment = data[350:400]

filtered = scipy.signal.filtfilt(b, a, segment)
filteredGust = scipy.signal.filtfilt(b, a, segment, method="gust")

plt.plot(segment, '.-', alpha=.5, label="data")
plt.plot(filtered, 'k--', label="padded")
plt.plot(filteredGust, 'k', label="Gustafsson")
plt.legend()
plt.title("Padded Data vs. Gustafsson’s Method")
plt.show()

Band-Pass Filter

Low-pass and high-pass filters can be selected simply by customizing the third argument passed into the filter. The second argument indicates frequency (as fraction of Nyquist frequency, half the sample rate). Passing a list of two values in for the second argument allows for band-pass filtering of a signal.

b, a = scipy.signal.butter(3, 0.05, 'lowpass')
filteredLowPass = scipy.signal.filtfilt(b, a, data)

b, a = scipy.signal.butter(3, 0.05, 'highpass')
filteredHighPass = scipy.signal.filtfilt(b, a, data)

b, a = scipy.signal.butter(3, [.01, .05], 'band')
filteredBandPass = scipy.signal.lfilter(b, a, data)

Filter using Convolution

Another way to low-pass a signal is to use convolution. In this method you create a window (typically a bell-shaped curve) and convolve the window with the signal. The wider the window is the smoother the output signal will be. Also, the window must be normalized so its sum is 1 to preserve the amplitude of the input signal.

There are different ways to handle what happens to data points at the edges (see numpy.convolve for details), but setting mode to valid delete these points to produce an output signal slightly smaller than the input signal.

# create a normalized Hanning window
windowSize = 40
window = np.hanning(windowSize)
window = window / window.sum()

# filter the data using convolution
filtered = np.convolve(window, data, mode='valid')


plt.subplot(131)
plt.plot(kernel)
plt.title("Window")

plt.subplot(132)
plt.plot(data)
plt.title("Data")

plt.subplot(133)
plt.plot(filtered)
plt.title("Filtered")

Different window functions filter the signal in different ways. Hanning windows are typically preferred because they have a mostly Gaussian shape but touch zero at the edges. For a discussion of the pros and cons of different window functions for spectral analysis using the FFT, see my notes on FftSharp.

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