Abstract: This course develops the fundamentals of feedback control using linear transfer function system models. It covers analysis in time and frequency domains; design in the s-plane (root locus) and in the frequency domain (loop shaping); describing functions for stability of certain non-linear systems; extension to state variable systems and multivariable control with observers; discrete and digital hybrid systems and the use of z-plane design. Assignments include extended design case studies and capstone group projects. Graduate students are expected to complete additional assignments.
Abstract: This module gives an analysis of the time and frequency domain characteristics of a laugh track. It is part of a larger series discussing the implementation of a real-time laugh track removal filter.
Abstract: This course focuses on the design of control systems. Topics covered include: frequency domain and state space techniques; control law design using Nyquist diagrams and Bode plots; state feedback, state estimation, and the design of dynamic control laws; and elementary analysis of nonlinearities and their impact on control design. There is extensive use of computer-aided control design tools. Applications to various aerospace systems, including navigation, guidance, and control of vehicles, are also discussed.
Abstract: The course focuses on the creation, manipulation, transmission, and reception of information by electronic means. Elementary signal theory; time- and frequency-domain analysis; Sampling Theorem. Digital information theory; digital transmission of analog signals; error-correcting codes. A complete course with over 90 modules.
Abstract: This module contains 55 online signal processing simulations created in National Instruments LabVIEW. These simulations provide examples to textbook signal processing concepts. These simulations include aliasing, convolution, effects of windowing in the
Abstract: This module serves as an introduction to working in the frequency domain and thinking of signals in terms of their spectral components. The Fourier transform can be used to represent any signal in terms of frequency instead of time and facilitates the computation of the transfer function of a system.
Abstract: This module describes a processor exercise in which students implement a spectrum analyzer using mixed C and assembly code. Students are to acquire a block of 1024 samples, apply a Hamming window, compute a length-1024 Discrete Fourier Transform using pr
Abstract: This is a processor exercise in which students implement a spectrum analyzer using mixed C and assembly code. Students are to acquire a block of 1024 samples, apply a Hamming window, compute a length-1024 Discrete Fourier Transform using provided Fast Fo
Abstract: This module describes a processor exercise in which students implement a spectrum analyzer using mixed C and assembly code. Students are to acquire a block of 1024 samples, apply a Hamming window, compute a length-1024 Discrete Fourier Transform using provided Fast Fourier Transform code, and display the magnitude-squared spectrum on an oscilloscope.
Abstract: This is a processor exercise in which students implement a spectrum analyzer using mixed C and assembly code. Students are to acquire a block of 1024 samples, apply a Hamming window, compute a length-1024 Discrete Fourier Transform using provided Fast Fo