Least-squares spectral analysis

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Spectral Estimation in Time Series

Covers spectral estimation in time series analysis, including imaging kernels, tapering methods, and AR models.

Numerical Methods: Boundary Value Problems

Covers numerical methods for solving boundary value problems, including applications with the Fast Fourier transform (FFT) and de-noising data.

Power Spectral Density Computation

Covers the computation of power spectral density and the design of communication systems.

Mixture models: nesting structures

Covers mixture models, nesting structures, and identification issues in discrete choice analysis.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Spectral & Parametric Estimation: Time Series

Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.

NMR spectral analysis

Corrects mistakes in NMR spectrum analysis, emphasizing integration values and chemical shifts.

Computational Quantum Physics

Explores computational methods in quantum physics, emphasizing exact diagonalization and space discretization techniques.

Fast Fourier Transform (FFT): Lecture 4

Covers the Fast Fourier Transform (FFT) algorithm, interpolation, filters, image processing, and experimental techniques in TEM and STM.

Laser Noise: Understanding Laser Line Width and Coherence

Explores laser noise, emphasizing laser line width and coherence for precise measurements in amplitude and phase quadrature.