EE-726 / 4 credits

Teacher: Unser Michaël

Language: English

Remark: Next time: Fall 2022


Frequency

Every 2 years

Summary

We cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a wavelet basis and are relevant to coding, compressed sensing, and biomedical imaging.

Content

Keywords

Signal and image processing, sparsity, stochastic modeling, wavelets, compressed sensing.

Learning Prerequisites

Recommended courses

Theory of linear systems, Fourier transform, Signal processing, statistics.

Assessment methods

Midterm and final oral examination.

In the programs

  • Number of places: 20
  • Exam form: Multiple (session free)
  • Subject examined: Sparse stochastic processes
  • Lecture: 28 Hour(s)
  • Exercises: 28 Hour(s)

Reference week