Signal Processing — Introduction and Learning Goals

Signal Processing — Introduction and Learning Goals#

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.

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