EE3S1 Signal Processing

This course was previously taught as EE2S31 prior to 2025-2026. The DSP part has somewhat changed.

Signal processing plays an important role in many applications, such as consumer electronics (mp3 player, mobile phone, CD player, TV (HD)), radar and medical applications. This course covers two topics: an introduction into random signals (processes), and a first course on digital signal processing (following the Signals and Systems course EE2S1).

In this course the following topics are discussed:

Digital signal processing

The part on signal processing considers in particular one-dimensional signals and discusses digital filter design, filter structures, the DFT spectral analysis, filter implementation, and multirate filters.

1. Repetition ((discrete-time) signal processing, z-transform, poles and zeros, filter functions); DTFT
2. Sampling and reconstruction; upsampling and downsampling
3. Discrete Fourier transform (DFT)
4. Spectral analysis
5. Quantization; Sigma-Delta ADC
6. The FFT; efficient convolution using FFT
7. Time Discrete filter structures; or Multirate filters

Stochastic processes

The part on stochastic processes introduces the concept of stochastic models and random processes to describe systems and signals that are not deterministic in nature.

1. Pairs of random variables
2. Random vectors & conditional probability models
3. Sums of random variables,
4. Derived random variables
5. moment generating function
6. central limit theorem.
7. Deviation of RVs from its expected value:Markov ineq., Chebyshev ineq. and the Chernoff bound.
8. Sample mean, unbiased estimators, consistency.
9. Estimation of Random variables, blind estimation, conditional estimation, MMSE, MAP and ML estimators.
10. Stochastic processes.
11. Estimation of autocorrelation functions, ergodicity, the autocorrelation function & signal processing for WSS signals.
12. The autocorrelation function & signal processing, PSD, CPSD & frequency domain relationships.

Teachers

Last modified: 2025-12-08