Matlab Signal Onramp course notes - Part 1: Generating Signals
Introduction to Signal Processing and generating synthetic signals in MATLAB.
Matlab Academy Signal Processing Onramp
Signal processing is a crucial part of many engineering and scientific fields. MATLAB provides powerful tools for analyzing and manipulating signals. As part of my MATLAB learning journey, I’m taking the Signal Onramp course to build a foundation in signal processing concepts.
Course Overview
We live in a world of signals—from radio waves and Bluetooth to GPS and automotive sensors. Signal processing is a set of techniques used to preprocess, analyze, and extract information from these signals.
In this course, we use MATLAB and the Signal Processing Toolbox to compare vibration signals from seismic stations, identify earthquake patterns, and separate signals from local noise.
Generating Signals
When working with real-world data, you might not know the expected result at each step. To verify algorithms, we often start by creating a known signal (like a sine wave) and verifying the output.
1. Specifying Sample Times
To generate a signal, you must choose the sample times. Key attributes:
- Sample rate ($f_s$): Measured in Hertz (Hz).
- Start time ($a$): In seconds.
- End time ($b$): In seconds.
The distance between two samples is $1/f_s$. In MATLAB, we can use the colon operator: a:1/fs:b.
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% Specify a sample rate of 100 Hz
fs = 100;
% Create a time vector from 0 to 1 second
t = (0:1/fs:1);
2. Creating a Sine Wave
We can generate a sine wave using the formula: $s = \sin(2\pi f t)$, where $f$ is the frequency.
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% Create a 5 Hz sine signal
sig = sin(2*pi*5*t);
% Plot the signal
plot(t, sig)
title('5 Hz Sine Wave')
xlabel('Time (s)')
ylabel('Amplitude')
3. Adding Noise
Adding noise makes signals more realistic and tests the robustness of signal processing techniques. Normally distributed random numbers can be generated using randn.
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% Create scaled white noise
noise = randn(size(sig)) * 0.1;
% Add noise to the original signal
sigNoisy = sig + noise;
% Plot the noisy signal
plot(t, sigNoisy)
title('Noisy 5 Hz Sine Wave')
4. Specialized Waveforms
The Signal Processing Toolbox contains functions for common waveforms like square and sawtooth.
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% Create a 5 Hz square wave
sq = square(2*pi*5*t);
% Plot the square wave with added noise
plot(t, sq + noise)
title('Noisy 5 Hz Square Wave')
In the next part, we will look into importing real earthquake data and using the Signal Analyzer app.