Signal processing

lelec2900  2020-2021  Louvain-la-Neuve

Signal processing
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Jacques Laurent; Macq Benoît; Vandendorpe Luc;

At the end of this learning unit, the student is able to :

1 With respect to the AA referring system defined for the Master in Electrical Engineering, the course contributes to the develoopment, mastery and assessment of the following skills :
  • AA1.1, AA1.2, AA1.3
  • AA2.1, AA2.2
  • AA6.1
  • 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: Prony method, synthesis method by bilinear transformation
  • Comparison of the IIR and FIR filters: discussion on the linear phase and the complexity
  • Non-parametric spectral analysis by the discrete Fourier transform: compromise between the resolution and the level of the secondary lobes
  • Fast Fourier Transform (FFT) algorithm
  • Parametric spectral analysis: identification of a auto regressive model - Yule-Walker equation and Levinson-Durbin algorithm
  • Adapted and adaptive filtering.
  • Theory of multiresolution and wavelet transforms: links between sampling and projection on a vector space generated by orthonormal basic functions of index type. Examplification by the Haar Transform.
  • Compressive sensing.
  • Exercises on the use of Python for the prototyping of signal processing systems
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The course is organized in
  • 14 lectures
  • 12 training sessions
Depending on the Covid-19 security measures, the lectures and the practical sessions will be organized face-to-face, distance or hybrid.
The details of this organization, as well as the exact schedule, will be provided on the moodle of the course.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

  • Concerning the lectures, the students are individually evaluated with a written exam, including problems solving, and questions on the theory.
  • For the numerical exercises on Python, the students are evaluated in computer room (in-session or out-of-session).
  • Syllabus de cours disponible sur Moodle
  • Transparents et articles de référence disponibles sur Moodle
  • Enregistrement de la 1ère moitié du cours, disponible en podcast
Lectures notes on Moodle
Faculty or entity
Force majeure
Teaching methods
If the sanitary situation allows it, courses and exercise classes will be held in presence.
Otherwise, courses and exercise classes will be organised on line, or in comodal format.
Students may also be invited to watch podcasts.
Evaluation methods
The evaluation will address subjects covered in the courses, the podcasts and the exercise classes.
The examination will be written, individual, and composed of open questions. 
If the sanitary situation allows it, the examination will be on campus and no material will be allowed.
If the situation requires the examination to be organised on line, then it will be an open book examination.

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Master [120] in Electrical Engineering

Master [120] in Mathematical Engineering

Master [120] in Biomedical Engineering