Digital Signal Processing

Course Code: 
Credit Points: 

Course outline

A. Theory

  • Discrete time signals and systems
  • Sampling of continuous time signals
  • Discrete Time Fourier Transform, Discrete Fourier Transform and series, Fast Fourier Transform,
  • Circular Convolution and its relation to the linear one, fast computation of circular convolution,  
  • Digital filters and their Realizations
  • Design of FIR and IIR filters
  • Multirate Systems and filterbanks
  • Introduction to Stochastic signal processing
  • Strong and Weak Stochastic processes, stationarity, ergodicity, auto/cross-correlation function/sequence, Power spectrum density function.
  • Wide sense  stationary stochastic processes as the response of a LTI system to a white noise process, inverse system, whitening,
  • Optimum Linear Mean squares estimation and the optima IIR and FIR Wiener filter, autoregressive processes.   

B. Laboratory Exercises

  • Exercise 1: Sampling and Reconstruction of Signals
  • Exercise 2: DFT, FFT, Circular and Linear Convolution
  • Exercise 3: FIR and IIR Filter Design
  • Exercise 4: Stochastic processes and linear time invariant systems
  • Exercise 5: Optimum Linear Processing of stochastic signals, Wiener filters, AR processes

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