Introduction to Communication, Control, and Signal Processing
Signals, systems, and inference form the unifying themes of this MIT OpenCourseWare course from Electrical Engineering and Computer Science. Topics span 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, group delay, state feedback and observers, probabilistic models, stochastic processes, correlation functions, and power spectra with spectral factorization. The course also covers least-mean square error estimation, Wiener filtering, hypothesis testing, detection, and matched filters. Materials follow MIT OCW's standard format of lecture notes, problem sets, and exams drawn from an actual MIT course, free to access under a Creative Commons license with no certificate offered. It suits students who already have a footing in linear systems and probability and want the connections between communication, control, and signal processing made explicit.