2024
Signal analysis and processing
Name: Signal analysis and processing
Code: INF13255M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Informatics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
To acquire a global view of signal processing and analysis problems and of their practical applications.
To know several kinds of signals and the processes that can transform signals and extract information from them. To understand signal time and frequency representations.
To develop the ability to select the most appropriate signal processing techniques to obtain certain desired results.
To design and implement, in software, signal processing algorithms like digital filters or parameter estimation.
To acquire enough knowledge that enables research in areas related to processing and analysis of digital signals.
To know several kinds of signals and the processes that can transform signals and extract information from them. To understand signal time and frequency representations.
To develop the ability to select the most appropriate signal processing techniques to obtain certain desired results.
To design and implement, in software, signal processing algorithms like digital filters or parameter estimation.
To acquire enough knowledge that enables research in areas related to processing and analysis of digital signals.
Contents
Types of signals:
Discrete and continuous time signals.
Unidimensional and bidimensional signals, audio and image.
Sampling: sampling frequency, Nyquist theorem, aliasing.
Frequency analysis of discrete time signals:
Discrete Fourier Transform, Fast Fourier Transform (FFT).
Z-Transform.
Linear systems: time response, convolution, FIR and IIR systems.
Frequency response of linear systems.
Low-pass, band-pass and high-pass filters.
Linear filter design: Butterworth and Chebychev. Filters based on the FFT.
Linear prediction: minimization of the mean square error, Yule-Walker equations, Levinson and Durbin algorithms.
Nonlinear filters: median filter for noise and ouliers removal.
Stochastic processes/
Prediction, filtering and smoothing problems.
Space state and Kalman Filter.
Digital Signal Processors (DSP).
Discrete and continuous time signals.
Unidimensional and bidimensional signals, audio and image.
Sampling: sampling frequency, Nyquist theorem, aliasing.
Frequency analysis of discrete time signals:
Discrete Fourier Transform, Fast Fourier Transform (FFT).
Z-Transform.
Linear systems: time response, convolution, FIR and IIR systems.
Frequency response of linear systems.
Low-pass, band-pass and high-pass filters.
Linear filter design: Butterworth and Chebychev. Filters based on the FFT.
Linear prediction: minimization of the mean square error, Yule-Walker equations, Levinson and Durbin algorithms.
Nonlinear filters: median filter for noise and ouliers removal.
Stochastic processes/
Prediction, filtering and smoothing problems.
Space state and Kalman Filter.
Digital Signal Processors (DSP).
Teaching Methods
The classes are divided into theoretical classes, where the problems and techniques are presented and where algorithms are developed and analysed, and practical laboratorial classes, where the presented techniques are used to solve practical problems.
It is supported by a learning environment that directs the student to the application of the learned knowledge, and to enable students to search new knowledge. The learning environment is supported by an informatic learning platform (e.g. moodle), that allows components to be learned in e-Learning.
It is supported by a learning environment that directs the student to the application of the learned knowledge, and to enable students to search new knowledge. The learning environment is supported by an informatic learning platform (e.g. moodle), that allows components to be learned in e-Learning.
Assessment
Assessment is performed by one of the following methods:
- Continuous assessment: average of the tests done during the semester (minimum 2) with weight 50%; practical projects with weight 50%.
- Final assessment: final exam with weight 50%; practical projects with weight 50%.
- Continuous assessment: average of the tests done during the semester (minimum 2) with weight 50%; practical projects with weight 50%.
- Final assessment: final exam with weight 50%; practical projects with weight 50%.
