MSE Master of Science in Engineering

The Swiss engineering master's degree

Chaque module vaut 3 ECTS. Vous sélectionnez 10 modules/30 ECTS parmi les catégories suivantes:

  • 12-15 crédits ECTS en Modules technico-scientifiques (TSM)
    Les modules TSM vous transmettent une compétence technique spécifique à votre orientation et complètent les modules de spécialisation décentralisés.
  • 9-12 crédits ECTS en Bases théoriques élargies (FTP)
    Les modules FTP traitent de bases théoriques telles que les mathématiques élevées, la physique, la théorie de l’information, la chimie, etc., vous permettant d’étendre votre profondeur scientifique abstraite et de contribuer à créer le lien important entre l’abstraction et l’application dans le domaine de l’innovation.
  • 6-9 crédits ECTS en Modules contextuels (CM)
    Les modules CM vous transmettent des compétences supplémentaires dans des domaines tels que la gestion des technologies, la gestion d’entreprise, la communication, la gestion de projets, le droit des brevets et des contrats, etc.

Le descriptif de module (download pdf) contient le détail des langues pour chaque module selon les catégories suivantes:

  • leçons
  • documentation
  • examen 
Statistical Digital Signal Processing and Modeling (TSM_StatDig)

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.

Compétences préalables

Understanding of the following concepts at the Bachelor of Science level

•    Calculus
•    Linear algebra
•    Probability/Statistics
•    Digital signal processing

Objectifs d'apprentissage

•    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

Catégorie de module

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

Méthodes d'enseignement et d'apprentissage

•    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


“Statistical Digital Signal Processing and Modeling” by Monson H. Hayes

Télécharger le descriptif complet