Explores statistical signal processing tools for wireless communications, including spectral estimation and signal detection, classification, and adaptive filtering.
Covers statistical signal processing tools for wireless communications, including spread spectrum, spectral analysis, ultra-wide band communications, and heart rate variability analysis.
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.
Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.
Covers statistical signal processing tools for wireless communications, focusing on signals like train of pulses, harmonic signals, and smooth spectrum signals.
Introduces statistical signal processing tools for wireless communications, emphasizing practical applications and hands-on experience with Python or Matlab.
