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Introduction to Communication, Control, and Signal Processing
Conditional Remix & Share Permitted
CC BY-NC-SA
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This course examines signals, systems and inference as unifying themes 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; 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.

Subject:
Applied Science
Career and Technical Education
Electronic Technology
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Oppenheim, Alan
Verghese, George
Date Added:
02/01/2010
Introduction to EECS II: Digital Communication Systems
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CC BY-NC-SA
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An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course—bits, signals, and packets—cover three corresponding layers of abstraction that form the basis of communication systems like the Internet.
The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.

Subject:
Applied Science
Career and Technical Education
Computer Science
Electronic Technology
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Balakrishnan, Hari
Verghese, George
Date Added:
09/01/2012
Signals, Systems and Inference
Conditional Remix & Share Permitted
CC BY-NC-SA
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0.0 stars

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.

Subject:
Applied Science
Career and Technical Education
Electronic Technology
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Hagelstein, Peter
Oppenheim, Alan
Verghese, George
Date Added:
02/01/2018