Master's Internship Project: Information-aware signal processing for software-defined EIS

Description

This is a master's internship project on information-aware signal processing for software-defined EIS.

The central question is how to keep the useful content of EIS measurements while storing less data and doing less computation.

Instead of saving every raw sample or weighting all samples the same way, the student studies how to select and weight data and build compact signal representations, either before the DFT or alongside it.

The aim is a flexible software-defined EIS design that separates data acquisition, DFT updates and information extraction.

That separation would allow methods such as sliding DFTs, processing only selected samples, and adding low-cost statistics or bounds.

Each method is compared on estimation accuracy, robustness, information preservation, memory use and computational cost.

Conditions

The internship is located in the Netherlands.