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Introduces statistical signal processing tools for wireless communications, emphasizing practical applications and hands-on experience with Python or Matlab.
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
Explores psychoacoustics, signal processing, and the brain's interpretation of sound frequencies, covering topics like the Missing Fundamental phenomenon and the inner workings of the cochlea.
Covers statistical signal processing tools for wireless communications, focusing on signals like train of pulses, harmonic signals, and smooth spectrum signals.
Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.
