Digital Signal Processing

Recommended Prerequisites

Probability and Statistics; Signal Processing

Teaching Methods

Expository lessons with a detailed presentation of the concepts and solving of practical examples of application with demonstrations.

Laboratorial classes where the student will perform computational simulations of the presented topics.

Evaluation:

The continuous evaluation consists of theoretical-practical tests (60%) and laboratorial assignments and practical laboratory work (40%).

Learning Outcomes

It is intended that students acquire a strong training in statistical and deterministic processing of discrete-time signals. The skills obtained will allow developing modern applications and signaling processing algorithms in different areas of knowledge.    

Work Placement(s)

No

Syllabus

1. Multi-rate signal processing

Decimation, expansion, band-limited interpolation. Bank of filters.

2. Discrete-time stochastic processes

Definitions. Estimation of autocorrelation. Harmonic and white noise processes. Regular stochastic processes: AR, MA and ARMA.

3. Power spectrum estimation

Periodogram. Classical smoothing methods. Maximum Entropy method. Structural Methods.

4. Optimal filtering

Wiener filtering. Wiener-Hopf equations. Practical cases: echo canceling; noise canceling; linear prediction, noise reduction in time and in frequency. 

Head Lecturer(s)

Fernando Manuel dos Santos Perdigão

Assessment Methods

Assessment
Frequency: 30.0%
Exam: 30.0%
Laboratory work or Field work: 40.0%

Bibliography

S. Mitra, Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 3rd. ed., 2005.

Monson H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley, 1996.

Oppenheim and Schafer, Discrete-Time Signal Processing, 3rd edition, Pearson, 2010.