Time Series: Spectral Analysis and Linear Filtering

Related lectures (32)

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.

Covers digital RII filters, elliptical and Chebyshev filters, frequency transformations, and filter realization.

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Explores aliasing, sampling, equivalence of analog and digital signals, and digital filter design.

Covers the frequency response of linear systems and filter design principles.

Covers the structure and implementation of digital filters.

Explores the Sampling Theorem, digital control, signal reconstruction, and anti-aliasing filters.

Explores feedback, stability, and active filters, covering various amplifier structures, filter fundamentals, and stability analysis.

Explores image filtering techniques, including linear and nonlinear filters, for artifact removal and feature enhancement.

Explores the design and simulation of high-order LC ladder filters using active-RC circuits and impedance normalization techniques.

Explores time series estimation, spectral representation, and p-variate analysis in depth.

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

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