| Course label : | Signal processing |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | French |
| Potential ects : | 2 |
| Results grid : | |
| Code and label (hp) : | G1_S5_SC_EEA_TSI - Traitement du Signal |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS / Mister LOUIS FILSTROFF / Mister MARC GOUEYGOU / Mister MAXIME BOUTON / Mister OLIVIER BOU MATAR-LACAZE / Mister PHILIPPE VANHEEGHE / Mister YANNICK DUSCH
External contributors (business, research, secondary education): various temporary teachers
Summary
This course introduces the main notions underlying any signal processing application. After introducing the different class of signals and the associated concepts (auto / cross-correlation, spectral density), the course will focus on the notion of spectral representation based on the Fourier transform, at the core of signal processing. In particular, an introduction to linear time-invariant filtering will motivate the use of spectral representations to easily alter / recover specific features of a signal (harmonic equalization, ...). Finally, an introduction to sampling theory will provide key notions to understand the principles at the basis of analog-to-digital conversion, which is a necessary step to acquire measures and numerically analyze the content of any signal. Most of these notions will be numerically illustrated on audio signals and images during the practical sessions.
Educational goals
By the end of the course, students will be able to: - identify the class a signal and understand the objects associated to this clase (auto/cross-correlation, spectral density); - perform an elementary spectral analysis of a signal; - use a consistent representation of a signal in both the temporal and the frequency domains; - understand and leverage the properties of linear time-invariant filters to recover specific features of an input signal; - acquire good practices to acquire measurements of a signal (sampling frequency, anti-aliasing filter).
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Continuous evaluation based on:
- 2 quizzes on Moodle in preparation for the assessment;
- 2 short assessments (~15 min) to evaluate the understanding of the concepts covered during the lectures;
- 1 individual homework;
- reports and codes produced during the practical sessions, combining numerical illustrations and questions about the underlying theory.
Online resources
- lecture slides, exercise sheets; - reference books made available on Moodle; - links towards online videos showing illustrative applications of concepts covered in class; - set of Python codes and notebooks.
Pedagogy
Lectures, practical sessions (Python notebooks Python on Moodle) and tutorials.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 7 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 5 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 12 |
| 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
Undergraduate maths (linear algerbra, analysis) and physics classes, elementary Python programming skills, 1st year math lecture provided at Centrale Lille.