Teacher(s)
Language
English
> French-friendly
> French-friendly
Learning outcomes
At the end of this learning unit, the student is able to : | |
| 1 | At the end of this learning unit, the student is able to :
|
Content
- 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
Teaching methods
The course is composed of
- lectures on the topics listed in the course content,
- and practical sessions, both classical and numerical.
Evaluation methods
- 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.
Online resources
Bibliography
- Course and lecture notes available on Moodle
- Slides and reference articles available on Moodle
Faculty or entity
