Team Signal Processing with Efficient Algorithms and Learning

Focus

Practical Software Solutions

Our research team focuses on creating practical software solutions for analyzing radio channels and other more general signal processing tasks. We work on robust, modular, and adaptable, software to address real-world signal processing needs in a reliable way.

Improving Reliability

Our work in estimation theory helps improve the reliability of channel models, and their underlying data. We also consider signal processing issues that can arise in measurement campaign planning, helping to avoid mistakes that could affect the quality of collected data.

Use of Machine Learning

We use machine learning for tasks like parameter estimation and classification, which aids in analyzing radio channels more effectively. We also develop highly efficient low-level implementations of our signal processing techniques and use GPU computing to handle large data sets and speed up computations.

Objectives

Overall, we aim to develop solutions that are practical and helpful for signal processing in radio channels and beyond, balancing theoretical approaches with real-world applications.