This lecture course will provide an introduction into the tools and principles of signal and system analysis. They are important for all fields of quantitative science, which always deals with measuring and analyzing signals. Examples include time-dependent voltages in electric circuits, microscopy images of nanostructures, pressure variations in blood vessels as well as electromagnetic and acoustic waves in matter.

Important questions that will be addressed are for instance: How can we measure a small signal that is buried in a large noise background? How does a lock-in amplifier work? How can we reconstruct a continuous signal that was sampled only at discrete times? What are aliasing and undersampling? How can we characterize as diverse systems as electrical filters, light detectors and optical lenses by a single formalism? What is a feedback loop? Why is Fourier analysis such a powerful tool here? In the exercises, the course topics will be illustrated by practical examples, both analytical and numerical using the Python package.


Useful books, but not mandatory:

A.V. Oppenheim: Signals and Systems, Prentice Hall, 1997

R. Bracewell: The Fourier Transform and Its Applications, McGraw Hill, 2000