# Agenda

## Signal Processing Seminar

- Thursday, 28 September 2017
- 13:30-14:30
- HB 17.150

### Graph Sampling for Covariance Estimation

**Geert Leus**

In this talk, the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers.

Additional information ...### Agenda

- Fri, 16 Nov 2018
- 14:30
- 3ME room J

### MSc BME Thesis Presentation

#### Jack Tchimino

#### The effect of dopamine release on electrical neural activity in the prefrontal cortex

How can certain oscillations be detected from the measured brain signals?

- Fri, 7 Dec 2018
- 12:30
- Aula Senaatszaal

### PhD Thesis Defence

#### Shahrzad Naghibzadeh

#### Image formation for future radio telescopes

Future telescopes such as the SKA consist of millions of antennas and will look at the early universe. From this data deluge, how can we efficiently construct reliable images?

- Fri, 7 Dec 2018
- 10:00
- HB 17.150

### Signal Processing Seminar

#### Per Christian Hansen

#### Image Reconstruction Using Training Images

Using techniques from machine learning to form a dictionary, followed by sparse reconstruction. How to construct this dictionary?

- Fri, 21 Dec 2018
- 12:00
- Aula Senaatszaal

### PhD Thesis Defence

#### Andreas Koutrouvelis

#### Multi-Microphone Noise Reduction for Hearing Assistive Devices

How can we suppress all unwanted acoustic sources in the acoustic scene, while keeping the target source undistorted?