Base Knowledge
Phasor representation, operations with complex numbers, differentiation and integration.
Teaching Methodologies
In the lectures, an oral exposition of the subject is done with the support of slide projection. The theoretical exposition of the concepts is complemented by the formulation and resolution of some application exercises.
Slides are available to students on the moodle platform. In the laboratory classes (mandatory), exercises are solved and simulations are performed with the help of Matlab support software.
Learning Results
The main objective of this course is to familiarize students with the theory and analysis of systems and signals.
It is intended that students develop the following skills:
– Understand the fundamental concepts of the theory of continuous and discrete time signals;
– Know how to apply the Fourier series and transforms and solve problems;
– Understand, know how to design and apply filters.
– Understand and apply sampling techniques and methods in the time and frequency domains;
– Acquire the ability to analyze and process the most common signals.
Program
1. Signal classification:
Continuous and discrete time signals. Even and odd signals, periodic signals, energy signals and power signals.
Unit Step and Unit Impulse. Exponential Complex Signal (Continuous and discrete time).
Basic operations on signals: operations performed in the dependent variables and operations performed in the independent variables.
2. Signal representation in the time and frequency domains.
Signals as functions of real variable on time.
Signals as functions of real variable on frequency.
3. Continuous and discrete time signals and systems .
System Definition.
Systems Interconnection.
System properties: linearity; memory; causality; invertibility; invariance and stability.
Characterization of an LTI System through Impulse Response.
Convolution. Convolution properties.
Step Response.
Systems with Finite Impulse Response (FIR) and Infinite Impulse Response (IIR).
4. Series and Fourier Transform:
Series and Fourier Transform.
Fourier Series and Transform Properties.
Applications.
5. Transfer function and analog filters.
6. Noise and interference – characterization and minimization techniques.
Thermal noise
Interference noise: common, electrical, magnetic and electromagnetic impedance.
7. Digitalization – signal conditioning and sampling.
Sampling theorem.
Ideal Sampling (Impulse Train).
Reconstruction by Interpolation.
Aliasing.
8. Digital signal processing – objectives and applications.
Discrete Time Series and Fourier Transform.
Convolution in discrete time.
Sampling of discrete time signals.
Decimation and Interpolation.
9. Application of spectral estimation techniques.
Application and interpretation of FFT results.
Applications using Matlab.
10. Design and application examples of digital filters.
Architecture of FIR filters.
Design of digital filters using matlab.
11. Signal processing applications in power and automation systems.
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Textos de Apoio disponibilizados no Inforestudante.
Oppenheim, A., Willsky, A., & Nawab, S. (1997). Signals & Systems. Prentice Hall.
Oppenheim, A., Willsky, A., & Nawab, S. (2010). Sinais e sistemas. Pearson Prentice Hall.
McClellan, J., Schafer, R., & Yoder , M. (2003). Signal Processing First. Prentice Hall.
Haykin, S., & Veen, B. (2001). Sinais e Sistemas. Bookman.
Lourtie, I. Sinais e Sistemas. (2002). Escolar Editora.
Buck, J., Daniel, M., Singer, A. (2002). Computer Exploration in Signals and Systems using Matlab. Prentice Hall.