Signal processing for Physics

Code 338BB

Credits 6

Learning outcomes

Signal properties. Convolution, cross correlation.

Random processes, stationarity and ergodicity. The autocorrelation function and its estimation.

Discrete and continuous time Fourier transforms and series.

Convolution and Fourier transform.

The Parseval theorem and energy spectrum of signals.

Power spectral density and the Wiener-Kintchine theorem.

The sampling theorem and the Nyquist frequency.

Non parametric spectral estimation: aliasing, leaking, windowing.

LTI systems. Filter.

Finite and Infinite impulse response filters.

Design of FIR filters.

The z transform. Stability and instability for recursive systems.

Commonly used IIR filters.

A/D and D/A conversion in practice.

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Random processes, stationarity and ergodicity. The autocorrelation function and its estimation.

Discrete and continuous time Fourier transforms and series.

Convolution and Fourier transform.

The Parseval theorem and energy spectrum of signals.

Power spectral density and the Wiener-Kintchine theorem.

The sampling theorem and the Nyquist frequency.

Non parametric spectral estimation: aliasing, leaking, windowing.

LTI systems. Filter.

Finite and Infinite impulse response filters.

Design of FIR filters.

The z transform. Stability and instability for recursive systems.

Commonly used IIR filters.

A/D and D/A conversion in practice.

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