| Course label : | Signal processing |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S2_SPR - Signal processing |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
• Usual signals ◦ Discrete and continuous signals, sampling, sensors ◦ Time series ◦ Images • The notion of representation ◦ Fourier transform, orthogonal bases / overcomplete representations ◦ Linear transforms in practice • Usual representations ◦ Global representations: FT, DFT, DCT ◦ STFT, Wavelets, Splines… ◦ Discrete cosines transform… • Sparse representations ◦ The notion of sparsity ◦ L1-penalty, LASSO… • Inverse problems in signal processing ◦ Denoising, Interpolation/inpainting ◦ Segmentation ◦ Filtering, smoothing
Educational goals
After successfully taking this course, a student should be able to: • Understand how to work with discrete representations of continuous signals • Manage usual changes of representation: Fourier, STFT, discrete cosines, splines, wavelets… • Choose an adequate representation depending on the data at hand • Solve data processing problems with continuous signals/functional data
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Exam, grading scale: (min) 0 – 20 (max)
Labs, grading scale: (min) 0 – 20 (max)
Average passing grade = 10/20
Online resources
Signal Processing & Linear Systems, B.P. Lathi 1998 Foundations of signal processing. Vetterli, Kovacevic & Goyal, 2014 A complete and recent overview of modern signal processing.
Pedagogy
24 hours, 7 lectures 5 exercises/labs Language of instruction is specified in the course offering information in the course and programme directory. English is the default language.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Bases of linear algebra, integration and functional analysis ; optimization Fundamental mathematics, Probability 1, Statistics 1, Python.