6.011 | Spring 2018 | Undergraduate
Signals, Systems and Inference
Course Description
This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback …
This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.
Learning Resource Types
assignment Problem Sets
grading Exams
notes Lecture Notes
co_present Instructor Insights
One noisy image of astronaut on the moon with flag above a fixed version of the same image.
Comparison of a noisy image of an astronaut (above) with the image after a Wiener filter is applied (below). Original image courtesy of NASA and is in the public domain. Noisy and filtered images courtesy of OCW.