1. EE 3417: Linear Systems Continuous Signals and Systems
Lectures: Tue/Thu, 12:30-1:50 pm, NH 229
Instructor: Dan Popa, Ph.D., Associate Professor, EE
Course TAs: Rommel Alonzo, Yathartha Tulhadar
Lab/Recitation Section for EE 3417 students, Tue/Thu 11:00-12:20, 2:00-3:20 – ELAB 256
Instructor Office hours: Tue/Thu 9:30-11 am NH543
Course info:
Grading policy:
Grading criteria: on curve based on class average, generally
>80% will be an A, 60-80% B, 50-60% C, 30-50% D, <30% F.
6 Homeworks – 20%
Midterm 1 (in-class) – 20%
Midterm 2 (take-home) – 20%
6 Quizzes – 20%
Final (in-class) – 20%

2. Syllabus Assignments:
Homeworks: 6 Homeworks contain both written and/or computer simulations using MATLAB. Submit code to TA’s if it is part of the assignments.
Reading Assignments: After each course. The assigned reading material is given out in order to make you better understand the concepts. Materials from the reading assignments may be part of course exams.
Examinations: Midterm 1(in-class), Midterm 2 (take-home), 6 quizzes (at lab for EE 3417) and one final (in-class).
EE 3417 students – LAB session in ELAB 256, Tue/Thu, covers – problems (recitation), MATLAB and SIMULINK, LABVIEW
In rare circumstances (medical emergencies) exams may be retaken and assignments can be resubmitted without penalty.
Missed deadlines for take-home exams and homeworks: Maximum grade drops 25% per late day.

3. Honor Code
Academic Dishonesty will not be tolerated. All homeworks and exams are individual assignments. Discussing homework assignments with your classmates is encouraged, but the turned-in work must be yours. Discussing exams with classmates is not allowed. Your take-home exams and homeworks will be carefully scrutinized to ensure a fair grade for everyone.
Random quizzes on turned-in work: Every student will be required to answer quizzes in person at least twice during the semester for homework and take home exam. You will receive invitations to stop by during office hours. Credit for turned in work may be rescinded for lack of familiarity with your submissions.
Attendance and Drop Policy: Attendance is not mandatory but highly encouraged. If you skip classes, you will find the homework and exams much more difficult. Assignments, lecture notes, and other materials re going to be posted here, however, due to the pace of the lectures, copying someone else's notes may be an unreliable way of making up an absence. You are responsible for all material covered in class regardless of absences.

4. Textbooks & Description
B.P. Lathi, Linear Systems and Signals, 2nd ed. (required), Oxford Press, ISBN-13:
Other materials (on library reserve)
Student Edition of MATLAB Version 5 for Windows by Mathworks, Mathworks Staff, MathWorks Inc.
R.D. Strum, D.E. Kirk, Contemporary Linear Systems using MATLAB, PWS Publishing, 1994, ISBN:
B.W. Dickinson, Systems: Analysis, Design and Computation, Prentice Hall, 1991, ISBN:
G.F. Franklin, J.D. Powell, A. Emami-Naeni, Feedback Control of Dynamic Systems, 5th edition, Prentice Hall, 2006, ISBN:
Catalog description:
EE LINEAR SYSTEMS (3-0) For non-electrical engineering majors. Time-domain transient analysis, convolution, Fourier Series and Transforms, Laplace Transforms and applications, transfer functions, signal flow diagrams, Bode plots, stability criteria, and sampling. Classes meet concurrently with EE 3417.
EE 3417 CONTINUOUS SIGNALS AND SYSTEMS (3-3) Time-domain transient analysis, convolution, state-space analysis, frequency domain analysis, Laplace transforms and transfer functions, signal flow and block diagrams, Bode plots, stability criteria, Fourier series and transforms. Applications from control systems and signal processing. Problems and numerical examples using MATLAB will be covered during recitation and computer laboratory sessions.

5. Description & Prerequisites
This is an introductory signal and systems course. It presents a broad overview of continuous linear systems concepts and techniques, and focuses on fundamentals such as time-domain and frequency domain analysis, stability, and discretization (sampling).
The course material is divided between several areas:
Signals and systems: classification, manipulation, modeling
Continuous time-domain analysis of systems
Continuous frequency domain analysis of systems
Sampling and Fourier analysis of signals
Programming excercises using MATLAB
ME Majors Prerequisite: Grade C or better in MATH 3330, ME Majors Corequisite: EE 2320 or equivalent. BE Majors Prerequisite: Grade C or better in MATH 3319.
EE 3417 prerequisite: Grade C or better in both EE 2347 and EE 2415.

6. Tentative Course Schedule
Part 1: Introduction and Systems Analysis in the time domain
Week 1 - August 27, Lecture 1
Introduction to signals and systems, syllabus and examples.
Week 2 - Sept 1, 3 Lectures 2,3
Review of basics: Matrix and vector algebra, complex numbers, integrals and series. (Background), MATLAB programming
Homework #1 handed out on Sept 1
Week 3 - Sept 8, 10, Lectures 4,5
Signals: classification, operations, standard signals (Chapter 1)
Operations: Time Shifting, Scale, Reversal
Classification: analog, digital, periodic, aperiodic, finite, infinite, causal, anticausal, energy and power signals, deterministic and stochastic.
Measures: Power, Energy
Signal spaces
Week 4 - Sept 15, 17, Lectures 6, 7
Signal Models, step, impulse, exponential, odd, even functions
Quiz Sept 15
Systems: properties and classification (Chapter 1)
LTI/LTV, memory/dynamic, causal/anticausal, invertible/non-invertible
Basic models: electrical/mechanical, internal and external description
Homework #1 due Sept 15, Homework #2 handed out

7. Tentative Course Schedule
Part 1: Introduction and Systems Analysis in the time domain
Week 5 - Sept 22, 24, Lectures 8, 9
Quiz Lab : Systems Sept 22
Time domain analysis of systems: (Chapter 2)
Differential equations and solutions
Response: zero input, impulse response
Week 6 - Sept 29, Oct 1, Lectures 10, 11
Convolution integral
Response: zero state
Stability: internal/external
Intuitive insights into system behavior
Homework #2 due Sept 29, Homework #3 handed out
Week 7 - Oct 6, 8, Lectures 12, 13
Quiz Domain I/O Analysis of Systems, Oct 6
State space analysis of systems: (Chapter 10)
State equations, Time domain and solutions
System realizations
Review list for Midterm 1
Week 8 - Oct 13, 15, Lectures 14, 15
Homework #3 due Oct 13,
In-class Midterm on Oct 13: covers: basic signals, systems, time-domain analysis.

8. Tentative Course Schedule
Part 2: System Analysis in Frequency Domain
Week 8 - Oct 13, 15, Lectures 14, 15
Homework #4 handed out Oct 15
Frequency domain analysis of systems: (Chapter 4)
Laplace transform
Week 9 - Oct 20, 22, Lectures 16, 17
Quiz Laplace transforms Oct 22
Properties and use of Laplace transform
Transfer functions and block diagrams
Frequency response - Bode plots
Week 10 - Oct 27, 29, Lectures 18, 19
Homework #4 due Oct 29 , Homework #5 handed out
Frequency domain analysis of systems:
Application to feedback control
Applications to filter design
Week 11 - Nov. 3, 5 Lectures 20, 21
State space analysis of systems: (Chapter 10)
Frequency Domain Solutions
Midterm II (Take-home) handed out Nov 5, covers frequency domain.
Homework #5 due Nov. 5

9. Tentative Course Schedule
Part 2: System Analysis in Frequency Domain
Week 12 - Nov. 10, 12 Lectures 22, 23
Midterm #2 due Nov. 10 in class. Midterm 2 grades will be returned only by appointment (see instructions).
Homework #6 handed out on Nov. 10
Fourier analysis of signals (Chapter 6)
Fourier series: existence, calculation
Trigonometric and exponential series
Fourier series: convergence
Week 13 - Nov. 17, 19 Lectures 24, 25
Parseval's theorem
LTI system response to periodic inputs
Quiz Lab: Fourier Series, Nov 19
Fourier analysis of systems (Chapter 7)
The Fourier Transform and its properties
Connection between Laplace and Fourier Transform

10. Tentative Course Schedule
Part 2: System Analysis in Frequency Domain
Week 14 – Nov. 24, Lecture 26
Application to signal processing: filters and window functions
Parseval's Theorem
Homework #6 due Nov. 24
Week 15 - Dec 1, 3 Lectures 27, 28
Quiz Fourier Transforms, Dec 1
Sampling (Chapter 8)
Week 16 - Dec 8 Lecture 29
Couse Recap
Week 17- Dec 14
Final exam (in-class) (comprehensive) TBD
Bring a 5-page, double-sided cheat sheet, handwriting only

11. Course Objectives
Ability to analyze systems using time-domain methods including impulse response and convolution.
Ability to analyze systems using Laplace-domain methods including transfer function and related concepts.
Ability to analyze systems using frequency-domain methods including frequency response of a system and Bode plots.
Ability to describe systems using modern state-space approaches.
Ability to analyze signals using Fourier series and Fourier transform.
Ability to appliy systems analysis tools to solve engineering problems.
Ability to use MATLAB as an engineering tool.

12. Textbook Reading and Review
Course Refresher:
Math: complex numbers, matrix algebra, vectors and trigonometry, differential equations.
Programming: MATLAB
For weeks 1,2
Read Preface, and Background section of Textbook
Purpose of weekly assigned textbook readings
To solidify concepts
To go through additional examples
To expose yourselves to different perspectives
Reading is required. Problems or questions on exams might cover reading material not covered in class.

13. Research in Multiscale Robotics and Systems – Next Gen Systems (NGS)
Tools and Fundamentals
Established Technologies
Emerging Technologies
Robotics
Control Systems
Manufacturing & Automation
Microsystems & MEMS
Nanotechnology
Biotechnology
Modeling & Simulation
Control Theory
Algorithms
Sensor networks
Surgical robotics
Human-like robots
Distributed systems
Micromanufacturing
Microrobotics
Microassembly
Micropackaging
Sensors & Actuators
NanoManufacturing
Small-scale
Robotics &
Manufacturing
New applications
for small-scale systems

14. Micro-Robotics at Next Gen Systems (NGS)
IEEE Mobile Micro-Robotics Challenge
Wireless, fully autonomous mobile microrobots.
Mobility
Challenge
Vibration
Actuated
[1] Matmat, M.; Al Ahmad, M.; Escriba, C.; Soulimane, S.; Marty, A.; Fourniols, J.; , "Thermo-electro-mechanical V-shaped actuator design and simulations," Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Micro-Systems, EuroSimE International Conference on , vol., no., pp.1-4, April 2008
[2] [6]. C. Elbuken, L. Gui, C. L. Ren, M. Yavuz, M. B. Khamesee, “Design and analysis of a polymeric photo-thermal microactutor,” Sensors and Act-ors A, vol. 147, pp , 2008.
Micro
Assembly
Event
Laser
Actuated
05/05/11

15. “Manufacturable” microrobot families at NGS
Family of Microrobots made by assembly and 3D die/wafer stacking
Copter
quad rotor
Pede
Microcrawler
Blimp
Microballoon
4/22/2017

16. Human Robot Interaction Research @ NGS
Realistic & Intuitive Human-Robot Interaction
Co-botics w/ Physical Interaction
Real-Time Visual Servoing
Advanced Human-Robot Interfaces

17. Lecture 1: Intro to Linear Signals and Systems
What are linear systems and why is it important to study them?
Signal:
Conventional Electrical or Optical signals
Any time dependent physical quantity
System:
Object in which input signals interact to produce output signals.
Static vs dynamic systems
Fundamental properties that make it predictable:
Sinusoid in, sinusoid out of same frequency (when transients settle)
Double the amplitude in, double the amplitude out (when initial state conditions are zero)

18. System Modeling
Building mathematical models based on observed data, or other insight for the system.
Parametric models (analytical): ODE, PDE
Non-parametric models: ex: graphical models - plots, or look-up tables.
Mental models – Ex. Driving a car and using the cause-effect knowledge
Simulation models – ex: Many interconnect subroutines, objects in video game

19. Types of Models White Box Black Box Gray Box – combination of the two
derived from first principles laws: physical, chemical, biological, economical, etc.
Examples: RLC circuits, MSD mechanical models (electromechanical system models).
Black Box
model is entirely derived from measured data
Example: regression (data fit)
Gray Box – combination of the two

20. White Box vs Black Box Models
White Box Models
Black-Box Models
Information Source
First Principle
Experimentation
Advantages
Good Extrapolation
Good understanding
High reliability, scalability
Short time to develop
Little domain expertise required
Works for not well understood systems
Disadvantages
Time consuming and detailed domain expertise required
Not scalable, data restricts accuracy, no system understanding
Application Areas
Planning, Construction, Design, Analysis, Simple Systems
Complex processes
Existing systems
This course deals with both white and black continuous models which are linear

21. Application Areas for This Course
Classical circuits & systems (1920s – 1960s) (transfer functions, state-space description of systems).
First engineering applications: military - aerospace 1940’s-1960s
Transitioned from specialized topic to ubiquitous in 1980s with applications to:
Electronic circuit design
Signal and image processing
Networks (wired, wireless), imaging, radar, optics.
Control of dynamical systems
Feedback control, prediction/estimation/identification of systems, robotics, micro and nano systems

22. Linear vs. Nonlinear
Why study continuous linear analysis of signals and systems when many systems are nonlinear in practice?
Basis for digital signals and systems
Many dynamical systems are nonlinear but some techniques for analysis of nonlinear systems are based on linear methods
Methods for linear systems often work reasonably well, for nonlinear systems as well
If you don’t understand linear dynamical systems you certainly can’t understand nonlinear systems

23. LTI Models Continuous-time linear dynamical system (LDSC) has the form
dx/dt= A(t)x(t) + B(t)u(t),
y(t) = C(t)x(t) + D(t)u(t)
where:
t  R denotes time
x(t)  Rn is the state (vector)
u(t)  Rm is the input or control
y(t)  Rp is the output

24. Linear Systems in Practice
most linear systems encountered are time-invariant: A, B, C, D are constant, i.e., don’t depend on t
Examples: second-order electromechanical systems with constant coefficients
when there is no input u (hence, no B or D) system is called autonomous
Examples: filters, uncontrolled systems
when u(t) and y(t) are scalar, system is called single-input, single-output (SISO)
when input & output signal dimensions are more than one, MIMO
Example: Aircraft – MIMO

25. Week 1, Lectures 1-2: Review
These lectures cover math concepts related to:
White Box Systems Examples: RLC circuit, MSD mechanical system
1st order ODE equations – solving
Review of Taylor series, derivation, integration
Complex numbers and examples, rings and fields
Rational polynomials fractions and partial fraction expansions
Vectors and matrices, vector spaces, linear mappings
Systems of linear equations

26. Week 1, Lectures 1-2: Review
MATLAB functions:
Simple commands: whos (who), clear, clf, clc, load, save, print, plot (xlabel, ylabel, xlim, ylim, semilogx, semilogy, plot3, contour, legend, )
Complex numbers: real, imag, conj, abs, angle, log, log10, cart2pol, pol2cart
PFE: residue
Vector operations, element by element operations, matrices, matrix operations (inv, det).

27. Week 1, Lectures 1-2: Reading and Practice
Reading for week 1:
Chapter B, Lathi textbook, including:
MATLAB session B
B.7 tables
B.1-2 – complex numbers
B-5 – PFE
B-6, B-4 – Vectors and Matrices
B-2, B-3 – sinusoids and sketching signals
Example exercises:
B.1, B.2: polar to cartesian, cartesian to polar conversion
B.3, B.4: multiplication/division and addition/subtraction of complex numbers
B.5 – Functions of complex variable
B.8, B.9, B.10 – Partial fraction expansions
B.12, B.13 – Matrix operations

28. MATLAB Exercises Run the following MATLAB demos:
Type “demo” at the MATLAB prompt
Watch “Getting Started” Videos
Run as many Simulink Demos as you can. For each one:
Double click all the model boxes and look inside
Try to modify parameters which make sense to you and see their effects by running the model.