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%). The student can be evaluated in a final exam if he/she has attended the laboratorial classes.    

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. Discrete-time filters. Design. Real time filtering.

2. Multi-rate signal processing

3. Discrete-time stochastic processes

4. Power spectrum estimation

5. Optimal filtering.

Head Lecturer(s)

Luís Alberto da Silva Cruz

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.