Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione - 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf)riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
- Insegnamento
- Documentazione
- Esame
The goal of this module is to introduce the students to the powerful world of statistical digital signal processing. While at the bachelor level digital signal processing is most often taught with deterministic signals, in the real world most interesting signals are stochastic in nature. Hence in more advanced applications, such as prediction or noise removal, the theories presented in this module are essential.
The basic digital signal processing, linear algebra and probability theory necessary to understand the module are brushed-up at the beginning. Then stochastic processes are introduced which allows the proper formulation of the optimal filtering and spectral estimation problem later on. After an in-depth treatment of the optimal filtering and estimation problem, adaptive filters are introduced which are a popular choice for many advanced statistical digital signal processing problems.
Requisiti
Understanding of the following concepts at the Bachelor of Science level
• Calculus
• Linear algebra
• Probability/Statistics
• Digital signal processing
Obiettivi di apprendimento
• The student becomes familiar with stochastic signals and systems
• The student understands and can apply the different methods for signal modeling
• The student has an in-depth understanding of Wiener filtering and knows how a discrete Kalman filter can be used to solve a stochastic filtering problem
• The student understands and can apply the different methods for spectrum estimation
• The student knows the most common adaptive filters and is able to select the proper one for the application at hand
Categoria modulo
The module starts with a review of basic digital signal processing, linear algebra and probability theory. It then introduces some concepts about stochastic processes, which are necessary to understand the following applications of statistical signal processing. Then the module discusses several different ways of signal modeling which can be used for parametric methods later on. Then one of the core topics is presented, which is the optimal linear mean square error estimation of a signal which is corrupted by additive noise. The module then presents a chapter about the very important topic of spectral estimation and finally concludes with the application of the learned theory for designing adaptive filters.
The available 14 weeks are organized as follows:
• 2 weeks: Background (review of digital signal processing and linear algebra)
• 3 weeks. Discrete-time random processes (including a review of probability)
• 2 weeks: Signal modeling
• 3 weeks: Wiener filtering (including the discrete Kalman Filter)
• 2 weeks: Spectrum estimation
• 2 weeks: Adaptive filtering
Metodologie di insegnamento e apprendimento
• A three hour session each week for 14 weeks
• The first hour is a homework review session where the homework is discussed. The homework is “paper and pencil” homework and small Matlab programming assignments
• The next two hours are lecture hours, where new theory is introduced
Bibliografia
“Statistical Digital Signal Processing and Modeling” by Monson H. Hayes
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