EE 361 - Signals and Systems II Syllabus
Catalog Data
Stochastic and deterministic signals and linear systems. Analog and discrete Fourier Series, analog and
discrete Fourier transforms, basic probability theory, stochastic processes, stochastic signals and linear
systems. (3 credits)
Corequistes and Prerequisites
Corequisites: None
Prerequisites: EE 360 and MATH 432 or MATH 459. All prerequisites must be completed with a grade
of C or better. Advanced Standing required.
Relevant Textbooks
Linear Systems and Signals, B.P. Lathi, Oxford, 2002, ISBN: 0-19-515129-1.
Signals and Systems, Oppenheim and Willsky, Prentice Hall, 1983, ISBN: 0-13-8097313.
Schaums Outline on Signals and Systems, Hsu, Mc Graw Hill, 1995, ISBN: 0-07-030641-9.
Schaum’s Outlines on Probability, Random Variables, and Random Processes, Hsu, ISBN: 9780071632898.
Probabilistic Methods of Signal and System Analysis, G. R. Cooper and C. D. McGillem, Oxford
University Press, 1999, ISBN 0195123549.
Coordinators
Dr. Peter Stubberud
Dr. Sahjendra Singh
Dr. Pushkin Kachroo
Dr. Ebrahim Saberinia
Dr. Brendan Morris
Course Topics
Signals spaces, orthogonal signal spaces, signal projections, and correlation.
Fourier series analysis and synthesis with trigonometric and exponential basis.
Continuous and discrete time Fourier Transform.
Introduction to probability, random variables, and random processes including probability space,
distribution and density functions, means, variances, correlations and covariances.
Spectral analysis of random process
Analysis of linear systems with random signals.
Course Outcomes (Program Outcomes) [UULOs]
Upon completion of this course, students will be able to:
Determine a linear combination of functions that optimally represents a finite length signal.
(1.1, 1.2, 1.4, 1.6, 1.10) [2.3, 2.4, 2.6]
Determine the similarity of two signals using correlation.
(1.1, 1.2, 1.4, 1.6, 1.10) [2.3, 2.4, 2.6]
Analyze finite length and periodic, discrete and analog signals using the Fourier Series and transform.
(1.1, 1.2, 1.4, 1.6, 1.10) [2.3, 2.4, 2.6]
Determine a linear system’s response to a periodic input signal using the Fourier Series.
(1.1, 1.2, 1.6, 1.8, 1.10)] [2.3, 2.4, 2.6]
Model a random signal by a random process.
(1.1, 1.2, 1.4, 1.5, 1.6, 1.7, 1.8, 1.10, 1.11) [2.3, 2.4, 2.6]
Determine the mean, variance, autocorrelation, cross-correlation, covariance and power spectral density
of a random process.
(1.1, 1.2, 1.4, 1.5, 1.6, 1.7, 1.8, 1.10, 1.11) [2.3, 2.4, 2.6]
Analyze a linear system’s response to a random signal.
(1.1, 1.2, 1.4, 1.5, 1.6, 1.7, 1.8, 1.10, 1.11) [2.3, 2.4, 2.6]
Program Outcomes
Upon graduation, Electrical Engineering students will be able to:
Apply the appropriate technical knowledge and skills
An ability to apply mathematics through differential and integral calculus,
An ability to apply advanced mathematics such as differential equations, linear algebra, complex
variables, and discrete mathematics,
An ability to apply knowledge of basic sciences,
An ability to apply knowledge of computer science,
An ability to apply knowledge of probability and statistics,
An ability to apply knowledge of engineering,
An ability to design a system, component, or process to meet desired needs within realistic
constraints,
An ability to identify, formulate, and solve engineering problems,
An ability to analyze and design complex electrical and electronic devices,
An ability to use the techniques, skills, and modern engineering tools necessary for engineering
practice,
An ability to design and conduct experiments, as well as to analyze and interpret data,
University Undergraduate Learning Outcomes (UULOs)
Inquiry and Critical Thinking
Identify problems, articulate questions or hypotheses, and determine the need for information.
Access and collect the needed information from appropriate primary and secondary sources.
Use quantitative and qualitative methods, including the ability to recognize assumptions, draw
inferences, make deductions, and interpret information to analyze problems in context, and
then draw conclusions.
Recognize the complexity of problems, and identify different perspectives from which problems and
questions can be viewed.
Evaluate and report on conclusions, including discussing the basis for and strength of findings, and
identify areas where further inquiry is needed.
Identify, analyze, and evaluate reasoning, and construct and defend reasonable arguments and
explanations.
Computer Usage
Students write code using a computational programming language (such as Matlab) to create signals and
system and analyze linear systems. Instruction of computational programming language and its
application to signals and linear systems takes place in EE 360D.
Grading
Homework Assignments, Computational Software Assignments (min 10% of final grade), Exam, Project,
Final Exam.
Course Syllabus Preparer and Date
Peter Stubberud, Thursday, January 22, 2015