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Compressive Sampling

EI7638Examination Performance5 ECTSGerman/Englishsummer semesterDepartment Computer Engineering
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You learn the fundamentals of Compressive Sampling (Compressed Sensing): how to efficiently measure and reconstruct signals with sparse parametrization. By the end you will be able to apply the theoretical concepts and the treated algorithms for parameter estimation in the design and analysis of signal processing and estimation methods.

What you will be able to do

  • Understand the fundamentals of Compressive-Sampling theory
  • Apply sparse parametrization of signals
  • Use algorithms for parameter estimation
  • Utilize methods for measurement reduction compared to classical sampling

What the module consists of

  • VorlesungIntroduction to theory and further connections to geometry and approximation
  • ÜbungConsolidation through exercises and numerical examples; implementation of small algorithms in MATLAB

Teaching method

  • Tafelvortrag/Präsentation an der TafelDelivery of lecture content
  • Übungsaufgaben und RechenbeispieleDeepening and application of the lecture material
  • MATLAB-Implementationen in den ÜbungenPractical implementation and testing of small algorithms
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Official page in TUMonline · Details are not binding.