EE421G - SIGNALS AND SYSTEMS I

 

CATALOG DATA:

EE 421G - Signal and Systems I: 3 Credits

An introduction to the modeling and analysis of signals and systems. Topics include convolution, Fourier series, Fourier transform bandwidth, basic filter design, modulation techniques, random variables and random processes and spectral density.  Prereq:  MA 214 and a “C” or better in EE 221.

 

TEXTBOOK:

Charles L. Phillips and John M. Parr, Signals, Systems, and Transforms, Prentice Hall, 1995. (Required).

Alberto Leon-Garcia, Probability and Random Processes for Electrical Engineering, Second Edition, Addison-Wesley, 1993 (required).

 

COORDINATOR:

Dr. L. E. Holloway, Associate Professor of Electrical Engineering

 

GOALS:

This course is designed to give juniors in electrical engineering a fundamental understanding of the theory of signals and linear systems both deterministic and stochastic. Particular emphasis is placed upon electrical engineering applications in the area of signal processing and communications.

 

PREREQUISITES BY TOPIC:

  1. Calculus including multivariable and differential equations
  2. Linear circuit analysis including transfer functions and Bode plots

TOPICS:

  1. Deterministic Signal and System Classification and Analysis:
    1. Signal Models and Classification (1 week)
    2. Time Domain System Analysis (1 week)
    3. Fourier Analysis (3 weeks)
  2. EE Application of Signal and System Analysis:
    1. Filter Design (1 week)
    2. Basic Analog Bandpass Communication Theory (1 week)
  1. Stochastic Signal and System Analysis:
    1. Averages, Probability and Random Signals (4 weeks)
    2. EE Applications of Probabilistic Methods (2 weeks)
  1. Tests (1 week)

 

OUTCOMES:

Upon completion of this course students should demonstrate the ability to:

  1. Classify systems based on properties of their input-output relationship.
  2. Analyze and synthesize signals using the definitions and properties of the Fourier series and Fourier transform.
  3. Apply convolution, Fourier series, and Fourier transform methods to determine the output of linear time-invariant systems.
  4. Analyze and design simple modulation systems and filters.
  5. Define a random experiment; its outcomes, events, and probability distribution.
  6. Apply independence of events, conditional probability, and Bayes rule to random experiments.
  7. Calculate the probability of an event given the cumulative distribution function or probability density function of its random variable.
  8. Determine the mean, variance, and standard deviation of a random variable.
  9. Determine the autocorrelation and power spectral density of a random signal.

 

COMPUTER USAGE:

Students model and analyze signals and systems using MATLAB.

 

 

CLASS SCHEDULE:

Lecture 3 hours per week.

 

PROFESSIONAL CONTRIBUTION:

3 Credits Engineering Science or 100%

 

 RELATION OF COURSE TO PROGRAM OUTCOMES:

These course outcomes fulfill the following program outcomes:

(a)     An ability to apply knowledge of mathematics, science, and engineering.

(c)   An ability to design a system, component, or process to meet desired needs.

(k)  An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

(l)       breadth of knowledge over all areas within electrical engineering (electromagnetics, power, electronics, signals and systems, and computer engineering)

(n)     knowledge of probability and statistics, including applications to electrical and computer systems

(o)     knowledge of mathematics through differential and integral calculus

(p)     knowledge of basic sciences, computer science, and engineering sciences necessary to analyze and design complex electrical and electronic devices, software, and systems containing hardware and software components

(q)     knowledge of advanced mathematics, linear algebra, complex variables, and discrete mathematics.

 

Prepared By: L. E. Holloway Date 5/21/04