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Linear systems: IDS, LTI, Markovian and Non-Markovian stochastic systems, estimation and detection of Markovian systems, states and simulation via causal systems. Noise and Non-Markovian systems: estimation, non-linear filters, applications to communications.
Applications of the above to electrical engineering: communication systems, digital imaging, wireless communications, detection, synchronization, radar and sonar. Applications of filters to communications: DSM, DPD, FIR filters.
This course aims at understanding the basic and standard stochastic processes, such as Markov processes, their representations, their characterizations, deriving and analyzing their transition probabilities. To introduce and understand processes and their applications in engineering and other areas of science. Topics include: Markov chains, random walk, random telegraph, random walk with drift.
A user-friendly introduction to basic, elementary probability theory, proofs of some central results in the theory. Graduate course; for students with no mathematical background or at least with a first-approach mathematical background.
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