Master's Internship Project: Information-aware signal processing for software-defined EIS
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This proposal investigates how EIS data can be processed and selectively retained to maximize useful information while minimizing data storage and computational effort.
Rather than storing all raw samples or treating every sample equally, the student will explore information-aware selection, weighting, and compact signal representations before or alongside the DFT.
The goal is to develop a flexible software-defined EIS architecture in which data acquisition, DFT updating, and information extrac…
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