Statistical Signal & Data Processing

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Related lectures (32)

Page 2 of 4

Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Signal Processing: Noise Filtering and Signal Estimation

Explores noise filtering, signal estimation, and signal-to-noise ratio optimization through Wiener-Khintchine theorem and power spectral density.

Stochastic Processes and Spectral Densities

Covers spectral densities, signal correlations, and white noise processes in stochastic systems.

Statistical Signal Processing for Wireless Communications

Introduces statistical signal processing tools for wireless communications, emphasizing practical applications and hands-on experience with Python or Matlab.

Neural Signals and Signal Processing

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

Gaussian Mixture Models & Noisy Signals

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Filtering and Sampling of Signals

Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.