5.00 crédits
30.0 h + 30.0 h
Q2
Enseignants
Jacques Laurent; Rottenberg François (supplée Vandendorpe Luc); Vandendorpe Luc;
Langue
d'enseignement
d'enseignement
Acquis
d'apprentissage
d'apprentissage
A la fin de cette unité d’enseignement, l’étudiant est capable de : | |
1 | At the end of this learning unit, the student is able to :
|
Contenu
- Sampling: theorem, interpolation, sequence
- Sampling rate change: downsampling and interpolation for low-pass signals and bandpass signals, complex envelope
- Processing structures and graph theory: switching, transposition, direct and polyphase structures
- Discrete Fourier transform, properties, convolution, truncation and window
- Finite impulse response filters, phase linearity, types and properties of poles and zeros
- Synthesis of FIR filters: window method, frequency response sampling, minimax synthesis and Remez method
- Synthesis of IIR filters by impulse invariance and bilinear transformation
- Bayesian filtering, Kalman filtering, particle filter, and variants
- Theory of multiresolution and wavelet transforms (from the Haar system to other wavelets)
- Compressive sensing: principles and algorithms
- Numerical exercises in Python related to these topics
Méthodes d'enseignement
The course is composed of
- lectures on the topics listed in the course content,
- and practical sessions, both classical and numerical.
Modes d'évaluation
des acquis des étudiants
des acquis des étudiants
- Concerning the lectures, the students are individually evaluated with a (in-session) written exam, including problems solving, and questions on the theory.
- Regarding the numerical exercises on Python, the students are individually evaluated off-session, over two distinct evaluations, in a computer room. Each of these two evaluations are organized only once during the semester.
Ressources
en ligne
en ligne
Bibliographie
- Course and lecture notes available on Moodle
- Slides and reference articles available on Moodle
Faculté ou entité
en charge
en charge
ELEC