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.

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.

Gives various Fourier transform properties

As with analog linear systems, we need to find the frequency response of discrete-time systems.

Compares the efficiency of frequency-domain and time-domain filtering.

Compares the efficiency of frequency domain and time domain filtering.

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.

The FFT, an efficient way to compute the DFT, is introduced and derived throughout this module.

An example of using a Finite Impulse Response filter.

Explains the complication of dealing with both continuous and discrete frequency.

An algorithm for modifying the pitch of a solo human voice in the frequency domain.

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.

Describes five ideal filters.

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

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.

First of two-term sequence on modeling, analysis and control of dynamic systems. Mechanical translation, uniaxial rotation, electrical circuits and their coupling via levers, gears and electro-mechanical devices. Analytical and computational solution of linear differential equations and state-determined systems. Laplace transforms, transfer functions. Frequency response, Bode plots. Vibrations, modal analysis. Open- and closed-loop control, instability. Time-domain controller design, introduction to frequency-domain control design techniques. Case studies of engineering applications.

The Fast Fourier transform (FFT) is an efficient algorithm for computing the Discrete Fourier Transform. The FFT is a block-based algorithm.

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

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