Short summary Frequency-domain tools for measurement signals: FFT, windowing, spectral interpretation, and basic filtering.
Learning objectives
Compute and interpret discrete Fourier transforms and spectra.
Understand windowing, spectral leakage, and resolution trade-offs.
Apply simple spectral filtering and reconstruction concepts.
Key concepts (brief)
Frequency resolution, Nyquist limit, and window-induced spectral effects.
Interpreting power spectra vs amplitude spectra.
Practical filtering: time-domain vs frequency-domain considerations.
Recommended notebooks to run
simple_fft_two_sine.ipynb
spectrum_example.ipynb
FFT_based_filtering.ipynb
Fourier_transform_with_windowing.ipynb
Frequency_content_of_a_periodic_signal.ipynb
Suggested exercises
Demonstrate aliasing by downsampling and explain observed artifacts.
Compare window functions on a mixed-frequency signal and discuss leakage.
Prerequisites Discrete signals, sampling basics, and NumPy FFT usage.