Analysis of Signals and Systems

Course #EC2410

Start Starts: not available

Clock Est. completion in 3 months

Location pin Offered through Distance Learning

Avg. tuition cost per course: See tuition Info For specific tuition costs of each program or contact information, please contact the NPS Tuition office at tuition@nps.edu .

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Overview

Analysis of digital and analog signals in the frequency domain; properties and applications of the discrete Fourier transform, the Fourier series, and the continuous Fourier transform; analysis of continuous systems using convolution and frequency domain methods; applications to sampling, windowing, and amplitude modulation and demodulation systems.

Prerequisites

  • MA1113

Learning Outcomes

  • Given a continuous signal, determine the effects of sampling in time.
  • Be able to define the minimum sampling frequency to avoid aliasing when discretizing a continuous signal
  • Given a dynamic system, determine if it is Linear Time Invariant (LTI);
  • Given a LTI system, compute the response by convolution;
  • Given a periodic signal, determine its Fourier Series expansion;
  • Given a continuous time signal, compute its Fourier Transform;
  • Given a continuous time signal, compute the Fourier Transform using the tables and the properties;
  • Given a system, characterize it in terms of its frequency response;
  • Apply the properties of the Fourier Transform to basic modulation and demodulation problems;
  • Develop the ability to recognize and characterize simple discrete random processes in the time domain.
  • Learn to be able to fully characterize stationary discrete random processes from a second moment viewpoint in the time domain and frequency domains. Develop an understanding for Gaussian white noise for 1- and N-dimensional random vectors.
  • Learn to characterize random signals from a second moment viewpoint as they are processed through discrete linear systems. Also learn to characterize simple linear transformations for continuous signals in time and frequency domains.
  • Learn how to deal with uncertainty in estimated parameters via confidence intervals.

Other Information

Ability to program in MATLAB is required.