Course Code:
CEID_NY381
Type:
Semester:
Instructors:
Credit Points:
6
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