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Matematikcentrum

Lunds universitet

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Stationary and Non-stationary spectral analysis

News

  • Project proposals. By Thursday February 6th we need your group's project preferences, ranking 1st, 2nd, and 3rd choice.
  • The project presentations will be held on March 18, at 13-15, and on April 29, at 10-12.

Course contents

The course provides an overview of different modern techniques in statistical spectral analysis, for both stationary and non-stationary signals and processes, with material that ranges between statistics and signal processing. The purpose of the course is to deepen and widen the knowledge for such methods, as there is a large need for more advanced techniques in many application areas, e.g., communication and medicine. The course will contain material on basic definitions and an overview of classical non-parametric methods. Furthermore, more statistically robust techniques that have become more common during recent years will be covered, such as subspace-based parametric techniques and non-parametric data-adaptive and multi-taper methods. The course also covers non-uniform sampling, non-circular processes, and spatial spectral analysis, topics that find applications in an ever-growing number of fields. Time-frequency analysis is a modern tool for investigation of non-stationary signals and processes. The research in this area has expanded during the last 20 years, making this is a common tool for analysis. The course will cover both classical and modern time-frequency approaches. Many applications will be presented and discussed during the course and the participants will work with real data.

Higher education credits: 7,5 Level: A

Language of instruction: This course may be offered in English (if non-Swedish speaking students are attending).

Required prequisites for FMSN35: FMSF10 Stationary stochastic processes.

Required prequisites for MASM26: FMSF10 Stationary stochastic processes and MASM17 Time series analysis.

Assessment: Written and oral project presentation and hand-ins.

Literature

Preliminary schedule

  • Lecture 1, Parametric and semi-parametric spectral estimation. (AJ)
  • Lecture 2, Semi-parametric spectral estimation. (AJ)
  • Lecture 3, Group sparsity, ADMM, damped modes. (AJ)
  • Lecture 4, Spatial signal processing. (AJ)
  • Project presentation, TBD. (AJ & MS) 

The detailed schedule can be found here.

Course start

First Lecture:
Monday, January 20, 2020, at 10.15, in MH:309A

Reading periods:
VT1

Contact

Lecturers:

Andreas Jakobsson, 046-222 45 20

Maria Sandsten, 046-222 49 53

Course secretary:

Susann Nordqvist 046-222 85 50