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ECE 101 Programming I

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Math 112, 120R, 122B, or 125, or placement at the level of Math 120R. 

Course Texts

Required Text: Programming in C, by Roman Lysecky and Frank Vahid, available at: zybooks.com.

References: C: From Theory to Practice, George Tselikis and Nikolaos Tselikas, CRC Press, 2nd edition.  Available through the University of Arizona Library (lib.arizona.edu): arizona-primo.hosted.exlibrisgroup.com/permalink/f/evot53/01UA_ALMA51686677810003843.

Schedule

Weekly Lecture: 2 hours and 30 minutes
Weekly Lab: 2 hours and 50 minutes

Course Description

ECE 101 is an introduction to the basic principles of programming and the C programming language. It introduces students to fundamental software design principles and commonly used techniques to solve computational problems. The course provides principal knowledge in programming concepts such as program flow control, memory management, and elementary data structures. This course also prepares students for more advanced programming courses. 

Course Objectives

Upon the completion of this course, students should have achieved the following objectives:
•    Conceptualize real-world problems as computational problems,
•    Decompose problems into simpler sub-problems,
•    Design code for solving computing problems using the C programming language,
•    Use simple data structures to store and manipulate data, and
•    Apply fundamental software design principles and commonly used techniques.

Learning Outcomes

  • CAC 1: Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
  • CAC 6: Apply computer science theory and software development fundamentals to produce computing-based solutions.
  • EAC 1: an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics. 

Course Topics

A brief list of topics to be covered:

Introduction to programming basics

Variables and data types

  • Variables , assignments and identifiers
  • Arithmetic expressions
  • Basic I/O

Branches

  • If-else statements
  • Relational and logical operators
  • Switch statements
  • Conditional expressions

Loops

  • While loops
  • For loops
  • Nested loops

File I/O, user-defined functions

  • File I/O
  • Function prototypes
  • Return statement

User-defined functions, pointers

  • Functions with branches
  • Pointers and Functions

Introduction to arrays

  • Array iteration
  • Problems modeled with arrays

Multi-dimensional arrays

  • Arrays and functions
  • Arrays in multiple dimensions

Searching and Sorting

  • Linear and binary search
  • Sorting algorithms

Strings

  • String library functions
  • Arrays and strings
  • Functions and strings

User-defined data types

  • Structs, functions, and pointers
  • Arrays of structs

Dynamic memory allocation

  • Malloc and free functions
  • Calloc and realloc
  • Pointers in dynamic allocation

Syllabus Prepared By

Syllabus updated on 8/5/2024
 

Course Units
4
ECE 201 Programming II

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

ECE 101

Course Texts

Required Text: Programming in C++,, by Roman Lysecky and Frank Vahid, available at: zybooks.com.

References:

  • Stroustrup, B, Programming--Principles and Practice Using C++. Second Edition. ISBN 978-0321-992789
  • Stroustrup B, The C++ Programming Language. Fourth Edition. Edition. ISBN 978-0321-563842
  • Meyers S, Effective Modern C++. ISBN 978-1491903995
  • Wikibooks C++ Programming (open source)
  • Wikibooks Object Oriented Programming (open source)
  • cplusplus.com reference website
  • cppreference.com reference website

Schedule

Weekly Lecture: 2 hours and 30 minutes

Course Description

ECE 201 focuses on fundamental concepts of Object-Oriented Programming and data abstraction. Topics include classes, encapsulation, inheritance, polymorphism, exceptions, abstract data types, linked lists, stacks, queues, and binary trees, using the C++ programming language. The course also introduces the concepts of algorithmic complexity and examines basic algorithms such as traversal, searching, and sorting in data structures such as linked lists, stacks, queues, and binary trees.

Course Objectives

Upon the completion of this course, students should have achieved the following objectives:

  • Develop software to solve complex engineering problems using commercial integrated development environments (IDEs)
  • Understand the C++ program memory organization and differentiate the location in which variables are stored within memory.
  • Use object-oriented programming constructs, including classes, constructors and destructors, streams, references, operator overloading, file I/O, command line arguments, pointers, and dynamic memory allocation
  • Understand the role of encapsulation, abstraction, and code organization in the software design process
  • Use classes and algorithms defined within the standard template library (STL) in developing programs
  • Perform data abstraction and employ classical algorithms to manipulate data in linked lists, queues, stacks, and binary trees
  • Analyze the tradeoff between algorithmic complexity and code performance and determine the asymptotic runtime

Learning Outcomes

  • CAC 1: Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
  • CAC 2: Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
  • CAC 6: Apply computer science theory and software development fundamentals to produce computing-based solutions
  • EAC 1: an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
  • EAC 2: an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factor.
  • EAC 7: an ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Course Topics

A brief list of topics to be covered:

Review of C and Introduction to C++

  • Basic C++ Syntax
  • File I/O
  • Vectors/arrays
  • Functions and pass by reference

Introduction to Object-Oriented Programing 

  • Classes and objects introduction
  • Mutators and accessors

Classes and Objects

  • Constructors
  • Operator overloading
  • Vector ADTs

Pointers

  • Destructors
  • Memory regions and leaks

Inheritance

  • Derived classes
  • Polymorphism and virtual member functions
  • Abstract classes

Exception and templates

  • Exception basics and with functions
  • Multiple handlers
  • Function and Class templates

Containers

  • List, pair, map, set
  • Queue, deque

Introduction to data structures and algorithms

  • Data Structures
  • Intro to algorithms
  • Abstract data types (ADTs) and applications

Lists

  • Singly-linked lists
  • Doubly-linked lists

Stacks and queues

  • Stacks
  • Queues and dequeues

Graphs and trees

  • Graphs and their applications
  • Binary trees and their applications

Searching and sorting algorithms

  • O notation
  • Algorithmic analysis

Hash Tables

Syllabus Prepared By

Syllabus updated on 8/5/2024

Course Units
3
ECE 207 Elements of Electrical Engineering

Required Course: No

Course Level

Undergraduate

Enrollment Requirements

PHYS 241 or PHYS 251 or PHYS 261H. Prerequisite or concurrently enrolled in: MATH 254 or MATH 250B or MATH 355.

Course Texts

Seventh Edition of Electrical Engineering: Principles & Applications, by Allan R. Hambley, Pearson, 2017.

Schedule

Two 75-minute lectures per week, TTH 03:30 PM-04:45 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data: Current and voltage dividers. Resistors, capacitors, inductors. Node voltage and mesh current analysis of circuits. Thevenin and Norton circuit equivalents. AC circuits, phasors, impedance. Electromagnetic fields, electric power, transformers, magnetic materials. Operational amplifiers, Elements of digital circuits. Sensors and measurements of physical quantities.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

1. Solve a resistive network that is excited by an AC or a DC source.
2. Solve first-order circuits involving resistors and a capacitor or an inductor.
3. Derive the differential equations associated with a circuit containing one or two energy storage elements.
4. Derive the complex impedance associated with a resistive, inductive, and capacitive load.
5. Use the ideal op-amp properties to derive the transfer function of an op-amp circuit.
6. Select a current limiting resistor in an LED circuit.
7. Create a transistor-based circuit to supply the necessary current to power a DC motor.
8. Analyze a circuit containing one or more diodes.
9. Design a collection of transistors to create logic gates.
10. Analyze an AC circuit containing resistors, inductors, and capacitors.
11. State the current/voltage relationships of resistors, inductors, and capacitors.
12. Explain the concept of circuit loading.

Course Topics

Brief list of topics to be covered:

Course Description and Introduction (Chapter 1)
Circuits, Currents, and Voltages
Power and Energy
Kirchhoff’s Current Law and Voltage Law

Resistive Circuits (Chapter 2)
Voltage-Divider and Current-Divider
Node-Voltage Analysis
Mesh-Current Analysis
Thevenin and Norton Equivalents

Inductance and Capacitance (Chapter 3)

First-Order Transients (Chapters 4)
RC Circuits
DC Steady-state
RL Circuits

Sinusoidal Steady-State Analysis (Chapter 5)
Sinusoidal Currents and Voltages
Phasors
Complex Impedances
Power in AC Circuits

Operational Amplifiers (Chapter 14)
Ideal Operational Amplifiers
Amplifier Circuits
Filters

Diodes (Chapter 10)
Basic Diode Concepts
Rectifier Circuits

Computer-based Instrumentation (Chapter 9)
Sampling Frequency
Signal Conditioning
Filtering  

If Time permits, the following:
Transistors (Chapters 12 & 13)
Transistors as switches
Creating Logic Gates with Transistors
Driving High Current Loads with Transistors

Relationship to Student Outcomes

ECE 207 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

Syllabus Prepared By

Syllabus updated on 3/29/2022

Course Units
3
ECE 220 Basic Circuits
Usually offered: Fall, Spring

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

MATH 129 and (PHYS 241 or PHYS 251 or PHYS 261H)

Course Texts

NILSSON & REIDEL, Electric Circuits, 11th Edition
Publisher: Pearson Learning Solutions
ISBN: 9780134743851 - Digital Inclusive Access for Mastering with Etext for Nilsson 11e Electric Circuits

An electronic version of this text (including Mastering Engineering) has been preloaded in your D2L account. The cost will be billed to your Bursar account.

Schedule

Five 50-minute lectures per week. Five-three-hour lab sections spread throughout the semester, MWF 9:00AM – 9:50 AM and TTH 9:30-10:20 (lecture).

Course Description

Specific Course Information:
2021-2022 Catalog Data: Elementary, transient and sinusoidal analysis of linear circuits with laboratory. Topics include: passive sign convention, mesh and node analysis, Thevenin equivalents, op-amps, capacitance, inductance, first and second order circuits, phasors, impedance, transformers, PSpice simulation software.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Apply knowledge of mathematics, science and engineering.
  2. Design and conduct experiments, as well as to analyze and interpret data.
  3. Identify, formulate, and solve engineering problems.
  4. Communicate effectively (in writing).
  5. Use the techniques, skills, and modern engineering tools necessary for engineering practice.

Course Topics

Brief list of topics to be covered:

Chapter 1 - Circuit Variables (2) - Overview of electrical engineering and circuit analysis, voltage and current, the ideal basic circuit element, reference directions, power and energy.  

Chapter 2 - Circuit Elements (3) - Voltage and current sources, electrical resistance and Ohm's law, construction of a circuit model, Kirchhoff's laws, and dependent sources.   

Chapter 3 - Simple Resistive Circuits (4) - Resistors in series and in parallel, the voltage-divider circuit, the current-divider circuit, measuring voltage and current, the Wheatstone bridge, Delta-Wye equivalent circuits.

Chapter 4 - Techniques of Circuit Analysis (13) - Introduction to the node-voltage method, node-voltage analysis with dependent sources, some special cases; introduction to mesh currents, mesh current analysis with dependent sources, some special cases; the node-voltage method versus the mesh current method; source transformations, Thevenin and Norton equivalent circuits; maximum power transfer; superposition.

Chapter 5 - The Operational Amplifier (8) - Operational amplifier terminals; terminal voltages and currents; inverting, summing, non-inverting, difference, comparators, and integrating amplifier circuits.

Chapter 6 - Inductance, Capacitance, Mutual Inductance (4) - Properties of the inductor, properties of the capacitor, series and parallel combinations of inductance and capacitance, mutual inductance.       

Chapter 7 - Response of First-Order RL and RC Circuits (6) - Natural response of RL and RC circuits, step response of RL and RC circuits, a general solution for step and natural responses, sequential switching, unbounded response.      

Chapter 8 - Natural and Step Responses of RLC Circuits (6) - Natural and step responses of a parallel RLC circuit, natural and step responses of a series RLC circuit.

Chapter 9 - Sinusoidal Steady-State Analysis (10) - Sinusoidal sources and response, phasors, impedance and admittance, series-parallel and Delta-Wye simplifications, source transformations and Thevenin-Norton equivalents, node and mesh analysis, transfer functions, ideal transformers, impedance matching, phasor diagrams.

Relationship to Student Outcomes

ECE 220 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
5
ECE 274A Digital Logic

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

ECE 101. Prerequisite or concurrent enrollment in MATH 129.

Course Texts

zyBooks Digital Logic Design interactive textbook (zybooks.com)

Schedule

Three 50-minute lecture sessions per week, MWF 12:00 PM – 12:50 PM (lecture). One 170-minute laboratory session per week.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: Number systems and coding, logic design, sequential systems, register transfer language.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Represent any given integer number in different bases (such as base 2, 8, 10, and 16).
  2. Explain the different binary representations of signed integers (sign magnitude, 1’s complement, 2’s complement) and use the 2’s complement format to represent any given integer numbers.
  3. Analyze combinational logic circuits using appropriate tools such as Boolean Algebra properties, Karnaugh map.
  4. Design combinational logic circuits using combination logic design process.
  5. Analyze sequential logic circuits using appropriate tools.
  6. Design sequential logic circuits using sequential logic design process.
  7. Describe the structure and operation of Datapath components such as adder, comparator, ALU, multi-function register.
  8. Use the principles of register-transfer level (RTL) design and high-level state machines to analyze and design digital systems.
  9. Design digital circuits using Hardware Description Language (Verilog).
  10. Use industry standard software design suite and programmable devices such as FPGAs to implement digital circuits.

Course Topics

Brief list of topics to be covered:

  • Number systems and signed numbers
  • Hardware Description Language (Verilog)
  • Combinational Logic: Boolean algebra, combinational logic design process, basic combinational components
  • Sequential Logic: basic storage elements, sequential logic design process
  • Datapath Components: adders, subtractors, multipliers, comparators, multiplexors, ALUs, multifunction registers, shifters, counters, timers, register files
  • Register-transfer level (RTL) design
  • Tradeoff or Optimization of digital circuits
  • Physical Implementation, FPGA Overview

Relationship to Student Outcomes

ECE 274A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
4
ECE 304A Design of Electronic Circuits

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 351C.

Course Texts

Required Texts

Required Special Materials

  • PSPICE software – See D2L Notes on how to download PSPICE Designer Lite. PSPICE will be used as part of your lab assignments. We will cover the use of PSPICE during the lectures. A tutorial will also be listed on D2L.
  • ADAML2000 module - The department has these units available to borrow for the semester. www.analog.com/en/design-center/evaluation-hardware-and-oftware/evaluationboards-kits/adalm2000.html
  • Parts: You will need to purchase a parts kit for the lab experiments and project. ($50). vakits.com/ua304a-kit

Additional Recommended Materials for your lab work
The following is a list of recommended material for your lab work:

  • 5-3/4 in Needle nose pliers – These are extremely handy for inserting components (resistors, capacitors, diodes, chips, etc.) into your breadboard.
  • Digital multimeter and electrostatic discharge (ESD) mat/wristband
  • A soldering iron may be useful for the class project. Again, these items are not required but are very handy to have when doing any kind of electronics works.
  • ADAML2000 module - The department has these units available to borrow for the semester. www.analog.com/en/design-center/evaluation-hardware-and-oftware/evaluationboards-kits/adalm2000.html
  • Parts: You will need to purchase a parts kit for the lab experiments and project. ($50). vakits.com/ua304a-kit

Schedule

Three 50-minute lecture sessions per week, MWF 9:00 AM - 9:50 AM. One 170-minute laboratory session per week, MWF 9:00 AM – 9:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Integrated theory and design laboratory course. Current mirrors, active loads, multi-stage amplifiers, output stages, frequency response, and feedback with emphasis on design, simulations of design and laboratory verification, measurement techniques, and technical communications.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Design and use basic analog building blocks and understand how they interact using the operational amplifier as an example.
  • Design differential amplifiers using active or resistive loads to meet large-signal swing and small-signal gain specifications.
  • Relate capacitance in devices to the frequency performance of circuits, including the Miller effect.
  • Understand why and how negative feedback-amplifiers become unstable and how to design to ensure stability.
  • Perform an intuitive approach for analyzing practical feedback-amplifier circuits.
  • Apply circuit techniques used in the design of power amplifiers.

Course Topics

Brief list of topics to be covered:
There are 4 labs during the semester. A list of the lab topics is given below:

  • LAB 1: Review of the ADALM unit. Current mirror circuits and active loads.
  • LAB 2: Differential amplifier circuits
  • LAB 3: Negative and Positive Feedback Amplifiers
  • LAB 4: Multistage amplifiers

Relationship to Student Outcomes

ECE 304A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
4
ECE 304C Theory of Computation

Course Level

Undergraduate

Enrollment Requirements

Engineering. ECE 201

Course Description

This is a foundational course in computer science and engineering that provides an introduction to the theory of computation. This field of study revolves around understanding the capabilities and limitations of computation, including factors such as computational power, storage requirements, and the types of computational models employed. The course primarily focuses on addressing two fundamental questions: first, whether a problem can be solved using a given abstract machine (computability), and second, determining the time and space resources necessary to solve the problem (complexity). The theory of computation has significant relevance to various engineering practices, and its principles underlie the development of efficient algorithms and the design of computer systems. Some of the key topics covered in the course include regular and context-free languages, Turing machines and their variants, decidable and undecidable problems, concepts of reducibility, computational time and space complexity, completeness, as well as hierarchy theorems. By studying these topics, students gain a deeper understanding of the fundamental limits of computation, enabling them to reason about the feasibility and efficiency of solving various computational problems. This knowledge forms a solid foundation for further exploration in computer science and engineering and related disciplines.

Course Units
3
ECE 310 Applications of Engineering Mathematics

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 101 and MATH 254 (ECE 207 or 220).

Course Texts

  • Advanced Engineering Mathematics, Erwin Kreyszig, Wiley, John & Sons, Incorporated, 2011.ISBN: 978-0-470-45836-5.
  • MATLAB Zybooks: Programming in MATLAB

Schedule

Four 50-minute lectures, MWF 9:00 AM – 9:50 AM and M 12:00 PM – 12:50 PM.

Course Description

Specific Course Information: 
2021-2022 Catalog Data:  This course is approximately one-half linear algebra and one-half probability and statistics. Linear algebra topics include: matrix operations, systems of linear equations, determinants, Gauss-Jordan elimination, vector spaces, basis and dimension, projections, determinants, eigenvalues and eigenvectors. Probability and statistics topics include: probability, random variables, density and distribution functions, sample mean and variance, estimation and confidence intervals. An introduction to Matlab and Matlab projects.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. An ability to perform matrix algebra.
  2. An understanding of rank, basis, linear transformations, vector spaces, eigenvalues.
  3. An ability to solve systems of linear equations.
  4. An ability to describe basic principles of probability theory.
  5. An understanding of random variables, probability distributions, means and variances.
  6. An understanding of statistical principles for point estimation, confidence intervals, and hypothesis testing.
  7. Practical experience of using MATLAB to solve linear algebra and probability problems.

Course Topics

Brief list of topics to be covered:

  • Linear Algebra: Matrices, Vectors: Addition and Scalar Multiplication Matrix Multiplication Linear Systems of Equations, Gauss Elimination Linear Independence, Rank of a Matrix, Vector Space Solutions of Linear Systems: Existence, Uniqueness Determinants, Cramer’s Rule Inverse of a Matrix, Gauss-Jordan Elimination Vector Spaces, Inner Product Spaces, Linear Transformations Eigenvalues, Eigenvectors Some Applications of Eigenvalue Problems Symmetric, Skew-Symmetric, and Orthogonal Matrices Eigenbases, Diagonalization, Quadratic Forms Complex Matrices and Forms.
  • Probability, Statistics: Data Representation, Average, Spread Experiments, Outcomes, Events Probability Permutations and Combinations Random Variables, Probability Distributions Mean and Variance of a Distribution Binomial, Poisson, and Hypergeometric Distributions Normal Distribution Distributions of Several Random Variables Introduction to Statistics Random Sampling Point Estimation of Parameters Confidence Intervals Testing Hypotheses.
  • MATLAB: Command window, Basic MATLAB syntax, Executing Expressions, MATLAB Editor, Debugging in MATLAB, MATLAB Help, Creating of Vectors and Matrices, Dot Operations, Matrix operations: Addition, Subtraction, Multiplication, Determinants, Matrix Inverse, Solution of a System of Linear Equations, Control of Program Flow: If and Switch Statements, For and While Loops, 2D Plotting Commands, Graph Annotation and Enhancement: Labels, Mathematical Symbols, Attributes of Axes, Curves, and Legends, Descriptive Statistics, Probability Distributions.

Relationship to Student Outcomes

ECE 310 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
4
ECE 311 Engineering Ethics and Contemporary Issues

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. ECE majors only

Course Texts

C. H. Harris et al., “Engineering Ethics: Concepts and Cases,” 5th Edition, Cengage Learning.

Schedule

One 50-minute lecture sessions per week, Wed 5:00 – 5:50 PM.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: This one (1) credit course is required of all Electrical and Computer Engineering students. No specific prior classes or prerequisites are assumed. The course is an introductory exposition of ethics and its principles in the engineering profession, and discussion of contemporary issues that stem from the impact of high technology on our daily lives. Nowadays, engineered systems are ubiquitous in almost all realms of our activity and therefore it is of paramount importance to be cognizant of how design and use of such systems may bring about profound ethical dilemmas and consequences.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to

  1. Understand the moral and ethical challenges that they will face in the engineering profession.
  2. Understand the consequences of decisions that stem from design, business, and career choices.
  3. Better introspection into the impact of technology on society.
  4. Have written and oral communication and presentation skills.

Course Topics

Brief list of topics to be covered:

  • Fundamentals of ethics - Personal vs. professional ethics
  • Ethical issues faced by engineering professionals
  • Ethics and law
  • Doing things right: ingenuity, diligence and responsibility - Integrity in design, development, and research domains
  • Protection of human subjects
  • Data management and intellectual property
  • Technology and digital revolution
    • Data, information, and knowledge
    • Your “digital DNA” and its footprints
    • Cybertrust and cybersecurity
  • Government, private sector, academe: data collection, management, and fusion
  • Social, legal, and ethical impacts of ubiquitous technologies (e.g., on our rights to privacy).
  • Outsourcing and the 24/7 “knowledge factory” --- following the sun
  • (Electronic) Globalization
  • High technologies: connecting people and places: accessibility, social impacts, political impacts
  • Our role and place in the global technological realm

Topical areas will be supported by selected case studies (e.g., Challenger space shuttle explosion).

Relationship to Student Outcomes

ECE 311 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences.
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
1
ECE 320A Circuit Theory

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major or minor: ECE. MATH 254 and ECE 220.

Course Texts

Electric Circuits, James W. Nilsson and Susan A. Riedel, Pearson Prentice Hall.

Schedule

Two 75-minute lectures per week (Tuesday & Thursday).

Course Description

Steady-state power calculations, single-phase & three-phase circuits, Laplace transform, circuit analysis in the frequency domain, pole/zero analysis, convolution, impulse response, transfer function, frequency response, passive & active filter circuits, Bode plots, and Fourier series for circuit analysis.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Calculate the complex power, in terms of real and reactive components, in single-phase sinusoidal, steady-state systems.
  2. Design a reactive load that improves a system’s power factor.
  3. Convert wye-connected reactive loads to delta-connected reactive loads and vice versa.
  4. Solve for line currents, line voltages, phase currents, and phase voltages in arbitrarily interconnected balanced, three-phase circuits.
  5. Convert a given electrical circuit into its s-domain equivalent representation.
  6. Apply the Laplace Transform operator to generic waveforms and calculate the Inverse Laplace Transform of a given s-domain expression.
  7. Solve for currents and voltages in generic RLC circuits.
  8. Model RLC circuits with transfer functions.
  9. Calculate the output waveform from an input waveform and a system’s transfer function.
  10. Apply the Initial Value Theorem and the Final Value Theorem.
  11. Convolve two waveforms.
  12. Design simple passive frequency selective filters.
  13. Sketch the Bode diagrams associated with a transfer function.
  14. Design active frequency selective filters.
  15. Develop a Fourier Series expansion for a periodic waveform.
  16. Calculate, using the Fourier Series concept, a linear system’s output response when a periodic input waveform is applied to the linear system, if time permits

Course Topics

  1. Sinusoidal steady-state power calculations
  2. Balanced three phase circuits
  3. Introduction to the Laplace transform
  4. Circuit analysis in the frequency domain
  5. Convolution and impulse response
  6. Bode plots and frequency response
  7. Introduction to frequency-selective circuits
  8. Active filter circuits
  9. Fourier analysis of circuits with periodic inputs

Relationship to Student Outcomes

ECE 320A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated August 2023
Course Units
3
ECE 330B Computational Techniques

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. MATH 223 and MATH 254 and PHYS 143 and PHYS 241 and ECE 101.

Course Texts

WH Press, BP Flannery, SA Teukolsky, and WT Vetterling "Numerical Recipes in C: The Art of Scientific Computing", Cambridge University Press, Cambridge, NY.

Schedule

Two 75-minute lecture sessions per week, Tu/Th 11:30AM - 12:15PM.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: This course provides students with the fundamentals of computational techniques for solving numerical problems. In particular, students will become familiar with techniques for numerical differentiation, numerical integration, solving differential equations (e.g., Runge-Kutta method), root finding (e.g., Newton-Raphson method), and numerical optimization (least squares method, linear programming, and stochastic optimization techniques such as simulated annealing and genetic algorithms). In addition, students will be provided with a basic working knowledge of the Matlab environment: They will learn how to create, edit, compile, and run programs in Matlab. Moreover, students will be provided with a basic working knowledge of Gnuplot: They will become familiar with 2D and 3D plotting techniques. Furthermore, students will be introduced to Numerical Recipes and the GNU Scientific Library.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Use MATLAB for data manipulation, data plotting, and basic programming.
  2. Use Gnuplot to generate 2D and 3D plots.
  3. Work with Numerical Recipes and the GNU Scientific Library.
  4. Numerically differentiate and integrate functions with several techniques of different accuracy and efficiency.
  5. Transform systems of differential equations and solve them numerically with several techniques of increasing numerical accuracy.
  6. Determine the roots of functions numerically with several methods.
  7. Perform least-squares optimization.
  8. Perform linear, polynomial, and general curve fits.
  9. Solve optimization problems amenable to linear programming.
  10. Solve high-dimensional,  multivariate optimization problems in the presence of multiple/infinite local minima that cannot be solved with deterministic, gradient-descent-based optimization techniques.

Course Topics

A brief list of topics to be covered:

  • Introduction to Course; Overview of Research Activities in the Visual and Autonomous Exploration Systems Research Laboratory
  • Introduction to Numerical Recipes and GNU Scientific Library; Introduction to Matlab & Gnuplot
  • Numerical  differentiation  (two-point formula, improved two-point  formula, three-point  formula); Numerical  integration: Trapezoidal Rule, Simpson Rule, Introduction to Gauss-Legendre
  • Numerical integration: Gauss-Legendre
  • Transformation of differential equations; Euler Method; Improved Euler Method; Runge-Kutta Method
  • Root  finding: Newton-Raphson method, Regula Falsi, Secant method, Bisection method
  • Numerical optimization: Least Squares; Introduction to Linear Programming
  • Numerical optimization: Linear Programming
  • Numerical optimization: Multi-dimensional Newton Raphson method; Introduction to stochastic optimization
  • Numerical optimization: Simulated Annealing
  • Numerical optimization: Simulated Annealing; Genetic Algorithms
  • Numerical optimization: Genetic Algorithms; Multi-dimensional optimization scenarios.

Relationship to Student Outcomes

ECE 330B contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 340A Introduction to Communications

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 320A.

Course Texts

B. P. Lathi and Zhi Ding, Modern Digital and Analog Communication Systems, 5th Edition, Oxford University Press. (ISBN 978-0-19-533145-5, inclusive access on D2L).

Schedule

Two 75-minute lecture sessions per week, Tu/Th 12:30PM - 1:45PM.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: Analysis and design of analog and digital communication systems based on Fourier analysis. Topics include linear systems and filtering, power and energy spectral density, basic analog modulation techniques, quantization of analog signals, line coding, pulse shaping, AM and FM modulation, digital carrier modulation, and transmitter and receiver design concepts. Applications include AM and FM radio, television, digital communications, and frequency-division and time-division multiplexing.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Identify the major signal types and obtain their key properties such as energy, power, correlation, cross-correlation, auto-correlation.
  2. Obtain Fourier Series for periodic signals.
  3. Sketch the magnitude and phase spectra for periodic signals and identify the discrete frequency components.
  4. Obtain Fourier Transform for aperiodic signals and use it to sketch magnitude and phase spectra.
  5. Use Fourier Transform theorems to describe frequency-domain effects of specific operations in the time-domain (such as time-shift, scaling, convolution, etc.).
  6. Calculate the bandwidth and the signal-to-noise ratio of a signal at the output of a linear time-invariant system.
  7. Explain the operation of amplitude and angle modulation systems in both time and frequency domains.
  8. Sketch the magnitude spectra and compute the bandwidth and power requirements of signals.
  9. Design a basic analog or digital communication system which can include a) the selection of a digital or analog modulation format, b) the block diagram design of a transmitter for the system, c) the block diagram design of a superheterodyne receiver for the system, d) the design of a time or frequency division multiplexing scheme, as appropriate, and e) the choice of an appropriate pulse shape and A/D converter to meet the performance requirements.
  10. Evaluate a given analog or digital communication system in terms of the complexity of the transmitters and receivers and the power and bandwidth requirements of the system.

Course Topics

A brief list of topics to be covered:

1. Signals and Signal Space (Chapter 2, Lathi/Ding) 

  • Signal Classification and operations
  • Signal Correlation, Orthogonal signal sets
  • Fourier Series, examples, properties 

2. Analysis and Transmission of Signals (Chapter 3, Lathi/Ding)

  • Fourier Transforms
  • Signal Transmission through Linear Systems
  • Ideal vs Practical Filters
  • Signal distortion over communication channels
  • Signal power, energy, spectral density 

3. Amplitude Modulation and Demodulation (Chapter 4, Lathi/Ding)

  • Baseband vs Carrier Communication
  • Amplitude Modulation
  • Frequency Division Multiplexing

4. Angle Modulation and Demodulation (Chapter 4, Lathi/Ding)

  • Nonlinear Modulation
  • FM modulation
  • Superheterodyne analog AM/FM receivers
  • FM broadcasting system 

5. Sampling and A/D Conversion (Chapter 5, Lathi/Ding)

  • Sampling Theorem
  • PCM
  • Digital Multiplexing
  • Differential PCM
  • Delta modulation, VOCoders and video compression 

6. Principles of Digital Data Transmission (Chapter 6, Lathi/Ding)

  • Digital communication systems
  • Line and Pulse coding
  • Scrambling
  • PAM
  • Digital Carrier Systems and M-ary Digital carrier modulation

Relationship to Student Outcomes

ECE 340A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 351C Electronic Circuits

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 320A.

Course Texts

Schedule

Three 50-minute lecture sessions per week. MWF 10:00 AM – 10:50 AM (lecture). One 170-minute laboratory session per week.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: ECE 351C is a 4 unit course covering Operational amplifiers, diode circuits, circuit characteristics of bipolar and MOS transistors, MOS and bipolar digital circuits, and simulation software. The purpose of ECE 351C is to get experience with the fundamental nonlinear devices for circuit design: diodes and transistors. We'll learn how to analyze simple linear amplifier circuits with these devices, how to use small-signal models, and spend a relatively small amount of time on how to build digital logic gates. More complex linear amplifier circuits are left for ECE 304.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Design and analyze simple circuits involving diodes and transistors both analytically (by hand) to meet given specifications, and to verify and evaluate such designs using a computer simulation program, such as PSPICE.
  2. Design and analyze Operational amplifier circuits such as active filters.
  3. Design and analyze simple circuits involving diodes, such as rectifiers.
  4. Design and analyze simple linear amplifier circuits using bipolar junction transistors.
  5. Design and analyze simple linear amplifier circuits using MOS transistors.
  6. Design and analyze a multistage audio amplifier circuit.

Course Topics

A brief list of topics to be covered:

  • Signals and Amplifiers
  • Operational Amplifiers
  • Diodes
  • Bipolar Junction Transistors
  • Amplifiers
  • MOSFETs
  • MOSFET Amplifiers

Relationship to Student Outcomes

ECE 351C contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

3. An ability to communicate effectively with a range of audiences.
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
4
ECE 352 Device Electronics

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. Prerequisite or concurrent enrollment in ECE 351C.

Course Texts

Required Texts:

  • Solid State Electronic Devices, 7th Edition by B. Streetman and S. K. Banerjee, Pearson.
  • The book is immediately available on the D2L course site and costs $30 for 180 days rent. You can OPT OUT if you want but you need to decide within 2 weeks from the start of the course, otherwise, you will be automatically charged $30 from the UofA Bursars office. You will receive a notice about the deadline to OPT OUT.

Schedule

Two 75-minute lecture sessions per week, Tu/Th 11:00AM - 12:45PM.

Course Description

Required/elective: ElectIve CE; Required EE

Specific Course Information:
2021-2022 Catalog Data:  Electronic properties of semiconductors; carrier transport phenomena; P-N junctions; bipolar, unipolar, microwave and photonic devices.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Understand basic cubic crystal structure and origin of semiconductor characteristics: conduction band, valence band, energy gap, dopant atoms, host atoms, intrinsic and extrinsic materials, fixed charges and mobile carriers.
  • Understand density of states and Fermi-Dirac distribution functions, effective mass, semiconductor bandgap and carrier statistics, kinetic and potential carrier energies.
  • Calculate properties of intrinsic and extrinsic semiconductor materials, e.g., Fermi levels, carrier concentrations.
  • Apply principles of carrier drift to determine field-dependent transport, conductivity, resistivity, resistance, and sheet resistance.
  • Apply principles of carrier diffusion to determine carrier gradient-dependent transport.
  • Understand band diagrams and determine carrier potential and kinetic energies.
  • Utilize defect densities and carrier recombination processes to calculate generation and recombination rates in semiconductor devices and materials.
  • Apply continuity equation to solve dynamics of carrier transport and recombination in semiconductor devices.
  • Calculate carrier densities, quasi-Fermi levels, and currents in biased PN junctions.
  • Determine device/material parameters given an experimental energy diagram characteristic for p-n junction and MOS capacitor structures.
  • Determine MOS transistor parameters from device process variables such as substrate doping, channel length, gate oxide thickness.
  • Understand the basic current mechanisms and device operation in a Bipolar Junction Transistor. Calculate current gain and base transport factor and emitter injection efficiency.
  • Identify deviations in ideal and real device characteristics.

Course Topics

A brief list of topics to be covered:

  • Chapter 1: Crystal Properties and Growth of Semiconductors – Cubic Lattices, planes and directions, Miller indices, reciprocal lattice. Translating crystal planes to Miller indices, how reciprocal is related to physical lattice points.
  • Chapter 2: Atoms and Electrons – wave-particle duality, Bohr model, electronic structure of atoms and the periodic table, and energy levels in a Silicon atom.
  • Chapter 3: Energy Band and Charge Carriers in Semiconductors – conduction and valence bands, understand band gaps, doping in semiconductors, the density of states and Fermi-Dirac statistics, carrier concentration calculations, drift current mechanism and calculations, carrier mobility and effective mass.
  • Chapter 4: Excess Carriers in Semiconductors – photon interaction with direct and indirect bandgap semiconductors, generation-recombination of excess carriers.
  • Chapter 5: P-N Junctions – fabrication process to form a p-n junction using ion-implantation, equilibrium, forward and reverse bias energy band diagrams, depletion region, current flow derivation, junction capacitance.
  • Chapter 6: Field-Effect Transistors – basic structure, the fabrication process for MOSFET, MOSFET band diagram, MOS capacitor, device operation for enhancement mode MOSFET, threshold voltage calculation, Current-voltage in linear and saturation regions, small-signal model, second-order effects.
  • Chapter 7: Bipolar Junction Transistors – basic structure, energy band diagrams, device operation, current gain, base transport factor, emitter injection efficiency, I-V characteristics, Ebers-Mohl model, small-signal model, second-order effects.

Relationship to Student Outcomes

ECE 352 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 369A Fundamentals of Computer Organization

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 101 and ECE 274A.

Course Texts

Computer Organization and Design: The Hardware/Software Interface by D. A. Patterson and J.L. Hennessy. Accessing the book: zyBooks - Online version of the "Computer Organization and Design."

Schedule

Three 50-minute lecture sessions per week, MWF 10:00 AM -10:50 AM, and Two 75-minute laboratory sessions MW 02:00 PM-03:25 PM.

Course Description

Required/elective:  Required CE; Elective EE

Specific Course Information: 
2021-2022 Catalog Data: Computer architecture is the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. This course qualitatively and quantitatively examines computer design trade-offs, teaches the fundamentals of computer architecture and organization, including CPU, memory, registers, arithmetic unit, control unit, and input/output components. Topics include a reduced instruction set computer architectures (RISC), using the MIPS central processor as an example, the interface between assembly and high-level programming constructs and hardware, instruction and memory cache systems, performance evaluation, benchmarks, and use of the SPIM/WinDLX/Verilog Simulators for the MIPS architecture. ECE 369A serves students in two ways. For those who will continue in computer architecture, it lays the foundation of state-of-the-art techniques implemented in current and future high-performance computing platforms. For those students not continuing in computer architecture, it gives an overview of the kind of techniques used in today's microprocessors.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Understand the fundamentals of the computer architecture world.
  2. Explore computer architecture paradigms on their own.
  3. Articulate the design issues involved in the computer architecture both at theoretical and application levels.
  4. Design and implement single-cycle and pipelined datapaths for a given instruction set architecture.
  5. Evaluate the close relationship between the instruction set architecture design, processor design, and algorithm design.
  6. Understand the performance trade-offs involved in designing the memory subsystem including cache, main memory and virtual memory.
  7. Discuss the state of the art multicore architectures such as the NVIDIA Graphics Processing Unit.
  8. Evaluate the performance of a hypothetical architecture analytically.

Course Topics

A brief list of topics to be covered:

  • Computer Abstractions and Technology [4 lectures]
  • Instruction Sets and Software Systems [9 lectures]
  • MIPS CPU and Control Unit Organization [8 lectures]
  • Pipelining in MIPS CPU [9 lectures]
  • Multicores, Multiprocessors, and Clusters [5 lectures]
  • Exploiting Memory Hierarchy [6 lectures]

Relationship to Student Outcomes

ECE 369A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
4
ECE 372A Microprocessor Organization

Required course: Yes

Course Level

Undergraduate
 

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 201 (ECE 207 or 220) and ECE 274A (concurrent enrollment in ECE 201 okay).

Course Texts

Required Texts:

  • There are no required textbooks. We will be using datasheets for the microcontroller. You will also be using datasheets for components such as the LCD, Motor Driver and Accelerometer and Matrix LED display. Links to the datasheets will be provided in the lectures and lab.

Required Special Materials:

  • You will need to order and purchase an ATMEGA 2560 development board and kit of parts. Source 1: REXQualis Mega 2560 Kit - Complete Starter Kit for Arduino Mega2560. ($56).

Schedule

Two 75-minute lecture sessions per week, T/TH 8:00 AM - 9:30 AM. One 170-minute laboratory session per week.

Course Description

Specific Course Information: 
2021-2022 Catalog Data: This course is an introduction to microcontroller organization, hardware interfacing, and system design. Topics include, but are not limited to C Programming for Microcontrollers, Memory Organization and Addressing Modes, Interrupts, Timers, Parallel and Serial Interfacing, Analog-to-Digital Conversion, Overview of Common Peripheral Components, Event-driven Software Development, and Motor Control. In addition to lectures, students will have hands-on lab assignments that provide them with the opportunity to build and utilize the PIC24F platform. Students will also have a course project in which they will propose, design/implement, and present a self-selected project, subject to approval by the instructor.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Explain the basics of an embedded computer system.
  2. Design C programs for microcontrollers using state machines, memory manipulation with special function registers and bit operations, and common microcontroller peripherals.
  3. Evaluate a microcontroller-based embedded system design while considering many types of realistic constraints and requirements.
  4. Interface with hardware components using a microcontroller.
  5. Have knowledge of common hardware serial communication protocols.
  6. Design and implement laboratory-assigned microcontroller-based systems projects in a small team with different skills and abilities.

Course Topics

A brief list of topics to be covered:

  1. LEDs - Use SFR Macros and bit operations to manipulate several LEDs as well as use a switch to change the rate at which the LEDs blink.
  2. Timer - Control the rate at which several LEDs blink. Switch uses interrupts.
  3. LCD - Interface with an LCD to display various characters.
  4. PWM-ADC (Group Lab) - Control DC motor using PWM, ADC, Potentiometer and External Interrupt.
  5. I2C (Group Lab) - Use I2C to interface with the MPU 6050 accelerometer. Program a threshold piezo alarm and push button interrupt alarm silence. Use SPI to interface with LED matrix array.
  6. Group Project – Develop your unique project idea and solution using concepts learned in class. 

Relationship to Student Outcomes

ECE 372A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
4
ECE 373 Object-Oriented Software Design

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 201.

Course Texts

Required Texts:

  • Tony Gaddis, Starting out with Java: Early Objects, 6th edition, Pearson, 2017.This book is available as Ebook, it costs much less as an Ebook.
  • Michael Blaha, James Rumbaugh, Object-Oriented Modeling and Design, latest edition, Pearson Prentice Hall, 2005.

Schedule

Two 75-minute lecture sessions per week, T/TH 3:30 PM - 4:45 PM.

Course Description

Required/elective: Required CE; Elective EE

Specific Course Information: 
2021-2022 Catalog Data: Object-oriented computing concepts, abstract data types, classes, methods, message passing, inheritance, object-oriented design and architectures, class hierarchies, use case development, sequence diagrams, introduction to unified modeling language, object-oriented programming languages and environments, polymorphism, dynamic binding, OO software implementation projects.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Introduce principles of object-oriented design.
  2. UML to capture design descriptions.
  3. Exploration of alternative designs in complex software, what makes a good design?
  4. Software development in a high level 00 programming Language: Java.
  5. Working with established 00 frameworks and rapid software development/prototyping.
  6. Team Development of complex software.

Course Topics

Brief list of topics to be covered:

  • Object-oriented computing concepts
    • Abstract data types, classes, methods
    • Message passing
    • Inheritance
    • Polymorphism
    • Dynamic binding
  • Object-oriented design and architectures
    • Class hierarchies
    • State modeling
    • Object models
  • Introduction to the Unified Modeling Language
    • Use case development
    • Sequence models
    • Activity models
  • Design tradeoffs for interfaces and implementation
  • Advanced software architectures
    • Generic programming
    • Interface definition languages
    • Multiple inheritance

Relationship to Student Outcomes

ECE 373 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 381A Introductory Electromagnetics

Required course: Yes

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. MATH 223 and ECE 220.

Course Texts

Fundamentals of Applied Electromagnetics by F. T. Ulaby, et al., Ed. 8 (Pearson Education, Inc., Upper Saddle River, New Jersey, 2020).

Schedule

Four 50-minute lectures, MWF 03:00-3:50 PM and W 12:00-12:50PM.

Course Description

Required/elective: Required EE, Elective CE

Specific Course Information:
2021-2022 Catalog Data:  Electrostatic and magnetostatic fields; Maxwell's equations; introduction to plane waves, transmission lines, and sources.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Calculate the reflection and transmission coefficients of uniform plane waves at planar interfaces.
  2. Explain the propagation of signals along lossless and lossy transmission lines in the frequency and time domains.
  3. Calculate the solutions of the one-dimensional transmission line equations and the propagation characteristics of basic transmission line configurations.
  4. Plot the voltage distribution vs. distance and time along a loaded transmission line.
  5. Calculate the input impedance and standing wave pattern of a loaded transmission line.
  6. Describe techniques for matching a loaded transmission line.
  7. Design single-stub matching networks.
  8. Describe the operation/principles of quarter-wave transformers.
  9. Perform vector calculus operations such as the gradient, the divergence and the curl.
  10. Identify and list Maxwell’s equations in time domain, as well as associated boundary conditions.
  11. Apply Coulomb’s law to find the force on a charge caused by other charges.
  12. Apply Gauss’ law to determine the electric field caused by a simple charge distribution.
  13. Calculate the electrostatic potential of simple charge distributions.
  14. Explain the effects of conducting and dielectric materials on field quantities.
  15. List the boundary conditions for the electric field vectors on the interface of two different materials.
  16. Calculate the capacitance for basic configurations that reduce to one-dimensional systems.
  17. Describe the conservation of charge and Ohm’s laws and write them in vector calculus format.
  18. Apply Ampere’s force law to calculate the force between constant currents of simple configurations.
  19. Apply the Biot-Savart law to calculate the magnetic flux density caused by a simple current configuration.
  20. Apply Ampere’s law to calculate the magnetic field produced by simple current configurations.
  21. Identify the magnetostatic potential and flux.
  22. List the boundary conditions for the magnetic field vectors on the interface of two different materials.
  23. Calculate the inductance and resistance for simple actual physical devices.
  24. Identify the time-varying Faraday and Ampere laws (quasi-statics).
  25. Calculate the induction effects from time-varying magnetic fields.
  26. Identify the Poynting vector and use it to calculate the power flow produced by electromagnetic fields.
  27. Identify Maxwell’s equations in the frequency domain.
  28. Identify the wave equation.
  29. Explain the propagation of one-dimensional plane waves in lossless and lossy materials.
  30. List the various polarizations of uniform plane waves.

Course Topics

Brief list of topics to be covered:

  • Transmission lines: Signals propagating along transmission lines with and without loads, impedance matching and maximum power transfer to a load
  • Electrostatics
  • Magnetostatics
  • Quasi-statics
  • Time-varying fields and Maxwell's equations
  • Uniform plane waves in lossless and lossy media
  • Antennas
  • Radiated waves

Relationship to Student Outcomes

ECE 381A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
4
ECE 404 Optical Spectroscopy of Materials

Required course: No

Concurrent with MSE 404

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering.

Course Texts

  • An Introduction to the Optical Spectroscopy of Inorganic Solids; J. Sole, L. Bausa, and D. Jaque (Wiley Interscience, 2005). In-class notes and handouts. See below for additional reading options.
  • Supplementary Reading Materials:
    • +U24- Optical Spectroscopy of Inorganic Solids; B. Henderson and G.F. Imbusch (Oxford University Press, 1985; reprinted: 2006).
    • Symmetry and Spectroscopy: An Introduction to Vibrational and Electronic Spectroscopy; D.C. Harris and M.D. Bertolucci (Oxford University Press, Inc., 1978; reprinted by Dover Publications, Inc. 1989).
    • Molecular Quantum Mechanics (5th Edition); P. Atkins and R. Friedman (Oxford University Press, Inc., 2011).

Schedule

Two 75-minute lectures per week, T/TH 11:00AM-12:15PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  The course provides a survey of Optical Spectroscopic Methods and underlying phenomena for the study of materials.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Develop a familiarity with the physics of spectroscopic analysis and light-matter interaction in this context, including wave propagation and diffraction, quantum mechanical development of allowed electronic and vibrational energy states, and selection rules.
  2. Develop an understanding of the basic operational principles of optical spectrometers/spectrographs.
  3. Demonstrate an ability to compare and contrast different spectroscopic material probes in terms of the material properties characterized and instrumental or methodology requirements.

Course Topics

A brief list of topics to be covered:

  • Optical properties of materials/light-matter interactions
  • Energy levels and optical transitions
  • Instrumentation Concerns
  • Spectroscopic Methods
    • Absorption/Reflection
    • Emission
    • Scattering
  • Signal analysis 

Relationship to Student Outcomes

ECE/MSE 404 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 407 Digital VLSI Systems Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 274A and ECE 351C.

Course Texts

Rabaey, Jan, et al. Digital Integrated Circuits: A Design Perspective. 2nd ed. Pearson, 2003.

Schedule

Three 50-minute lectures per week, MWF 02:00 PM - 02:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  This course covers the fundamental techniques for the design, analysis and layout of digital CMOS circuits and systems. Major topics include: MOSFET basics (structure and behavior of a MOSFET, CMOS fabrication, and design rules), detailed analysis of the CMOS inverter (static behavior, ratioed vs. ratioless design, noise margins, computing rise and fall times, delay models, resistance and capacitance estimation, design and layout of static CMOS logic gates, dynamic CMOS logic design, sequential circuit design (static and dynamic sequential circuit elements, clocking schemes and clock optimization), CMOS data path design.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Use circuit simulator (i.e. SPICE) and layout editor (i.e. Cadence tool) to design inverters, adders, and latches.
  2. Apply static and dynamic design styles to implement combinational and sequential circuits.
  3. Understand Moore’s law, yield, process variations, design robustness, leakage and time to market.
  4. Understand the tradeoffs among system performance, area consumption, and cost.
  5. Compare and evaluate different designs and understand the technology scaling issues.
  6. Formulate problems or model systems in device physics, signal processing, and related disciplines such as X2information, biology and biomedical engineering.
  7. Evaluate timing, reliability and flexibility of circuits and systems with different models.

Course Topics

Brief list of topics to be covered:

  • Basic designs of Static and Dynamic CMOS inverters.
  • Ratioed Logic and Pass Transistor Logic.
  • Performance of Dynamic Logic and Noise Considerations in Dynamic Design.
  • Static Sequential Circuits: Flip-Flop Classification, Master-Slave and Edge-Triggered FF's.
  • Dynamic Sequential Circuits: the Pseudo-Static Latch, and the Dynamic 2-phase Flip- Flop.
  • Datapaths in Digital Processor Architectures: the Full Adder: Circuit Design Considerations, and the Array Multiplier.
  • Interconnect issues with capacitive parasitics and reliability: crosstalk.
  • Timing issues in sequential circuit designs.
  • Memory Classification, the memory core, memory architectures and building blocks.

Relationship to Student Outcomes

ECE 407 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 411 Numeric Modeling of Physics & Biological Systems

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE.

Course Texts

Required Books:

  • Wolfgang Fink, ECE 411/511 "Numeric Modeling of Physics & Biological Systems" Class Notes @ Bookstore.
  • WH Press, BP Flannery, SA Teukolsky, and WT Vetterling "Numerical Recipes in C: The Art of Scientific Computing" Cambridge University Press, Cambridge, NY (2nd Edition freely available).
  • Holly Moore, "MATLAB for Engineers" 3rd Edition, Pearson (potentially accessible per "Inclusive Access" via D2L).           

Recommended:

  • EW Schmid, G Spitz, W Loesch, "Theoretical Physics on the Personal Computer" 2nd Edition, Springer.
  • lSBN-10: 3540522433; ISBN-13: 978-3540522430  (available as EBook free-of-charge via D2L).
  • B. Mueller, J Reinhardt, "Neural Networks: An Introduction" Berlin: Springer (available as EBook free-of-charge via D2L).
  • J Hertz, A Krogh, RG Palmer, "Introduction to the Theory of Neural Computation (Lecture Notes vol 1)" Reading, MA: Addison-Wesley  (available as EBook free-of-charge via D2L).

Schedule

Two 75-minute lectures per week, T/TH 09:30AM-10:45 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  This course combines themes from mechanics, electromagnetics, thermal physics, and neural networks with an introduction to numerical methods as well as the use of MATLAB. Students will become familiar with the underlying theory for a variety of systems in physics and biology (e.g., harmonic, anharmonic, and coupled oscillators; electric fields of electric lenses; geothermal power stations; and artificial neural networks), derive the necessary mathematical equations describing these systems, learn the necessary numerical methods to solve the underlying equations, and implement the system equations and numerical methods in MATLAB to simulate these systems. As a result, students will be prepared to formulate problems or model systems in physics, biology, and related disciplines, and to solve them numerically or in simulation.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Use MATLAB for data manipulation, data plotting, and programming (411: basic; 511: advanced).
  2. Numerically differentiate and integrate functions with several techniques of different accuracy and efficiency (411: intermediate; 511: intermediate).
  3. Transform systems of differential equations and solve them numerically with several techniques of increasing numerical accuracy (411: intermediate; 511: intermediate).
  4. Solve systems of linear equations efficiently (411: basic; 511: advanced).
  5. Understand the underlying theory for a variety of systems in physics and biology, model these systems by deriving the necessary mathematical equations describing these systems, understand and apply the necessary numerical methods to solve the underlying equations, and program the system equations and numerical methods in MATLAB to simulate the systems (411: intermediate; 511: advanced).
  6. Formulate problems or model systems in physics, biology, and related disciplines, and solve them numerically or in simulation (411: intermediate; 511: advanced).
  7. Know and assess the validity, limits, and pitfalls of numerical simulations (411: basic; 511: intermediate).

Course Topics

Brief list of topics to be covered:

  • Basic introduction to Matlab
  • Numerical differentiation
  • Numerical integration
  • Harmonic Oscillations with Sliding and Static Friction.
  • Anharmonic Free and Forced Oscillations
  • Coupled Harmonic Oscillations
  • Artificial Neural Networks
  • Computation of Electric Fields
  • Geo-Thermal Power Station (may potentially be replaced with modeling COVID-19 pandemic

Relationship to Student Outcomes

ECE 411 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
3
ECE 413 Web Development and the Internet of Things

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. ECE 201.

Course Texts

zyBook: learn.zybooks.com/zybook/ARIZONAECE413513HongFall2021

Schedule

Three 50-minute lectures per week, MWF 03:00 PM - 03:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data: This course focuses on the design, integration, and programming of web applications for the Internet of Things (IoT). Course topics include client-side dynamic web page development with HTML, CSS, JavaScript, and Ajax; server-side web application development with Node.js, MongoDB, and RESTful interfaces; and IoT device-side development using formal state-based programming and publish-subscribe interfacing. Additional topics include token-based user authentication, password hashing, responsive design, and relational databases. IoT applications covered in this course include connected cars, connected health, wearables, smart grids, smart homes, and remote measurement, among others.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Understand web programming (HTML, CSS, and JavaScript) techniques for front-end applications.
  2. Understand fundaments of RESTful interfaces and a database for back-end applications.
  3. Understand techniques of the connections between the front-end and the back-end applications.
  4. Understand embedded programming techniques to develop IoT system firmware.
  5. Design and implement full-stack web applications with IoT devices

In addition to the above outcomes, graduate students enrolled in ECE 513 will be able to develop more advanced web applications as well as more complex embedded system firmware.

Course Topics

Brief list of topics to be covered:

  • HTML, CSS, and JavaScript
  • Forms, dynamic webpages, and Event-driven programming
  • Ajax and third-party web APIs
  • jQuery and third-party JavaScript libraries
  • Node.js and Express
  • MongoDB and RESTful APIs
  • Token-based user authentication and password hashing
  • Embedded programming
  • Responsive Design
  • Using the above techniques, design and implementation of a full-stack system

Relationship to Student Outcomes

ECE 413 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
4. An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 414A Photovoltaic Solar Energy Systems

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering.

Course Texts

Recommended Texts:

  • Solar Energy, Arno Smets, et al,,UIT, ISBN 978 1 906860 73 8 (free e-book).
  • Photovoltaics: Fundamentals, Technology, and Practice, Konrad Mertens, Wiley, ISBN 978-1-118-63416-5 (2014).
  • Applied Photovoltaics 2nd Ed., S.R. Wenham, M. A. Green, M. E. Watt, and R. Corkish, Earthscan, ISBN-13 978-84407-401-3 (2007). (Not Required).
  • The Physics of Solar Cells, by Jenny Nelson, Imperial College Press, 2006.
  • Physics of Solar Cells,2nd Ed., Peter Wurfel, Wiley-VCH, ISBN: 978-3-527-40857-6 (2009).

Schedule

Three 50-minute lectures per week, MWF 04:00 PM - 04:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Computer architecture is the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. This course qualitatively and quantitatively examines computer design trade-offs and teaches the fundamentals of computer architecture and organization, including CPU, memory, registers, arithmetic unit, control unit, and input/output components. Topics include a reduced instruction set computer architectures (RISC), using the MIPS central processor as an example, the interface between assembly and high-level programming constructs and hardware, instruction and memory cache systems, performance evaluation, benchmarks, and use of the SPIM/WinDLX/Verilog Simulators for the MIPS architecture. ECE 369A serves students in two ways. For those who will continue in computer architecture, it lays the foundation of state-of-the-art techniques implemented in current and future high-performance computing platforms. For those students not continuing in computer architecture, it gives an overview of the kind of techniques used in today's microprocessors.

Learning Outcomes

Outcomes of Instruction: By the end of this course the student will be able to:

  1. Compute basic solar irradiance characteristics;
  2. Understand circuit properties of photovoltaic cells;
  3. Understand the physical parameters of solar cell operation;
  4. Understand the properties of solar cells and modules and the basic design properties affecting their performance;
  5. Design a photovoltaic system to meet specific requirements;
  6. Have a basic understanding of thin film and multi-junction PV cells and;
  7. Have a basic understanding of solar concentrators.

Course Topics

Brief list of topics to be covered:

  1. Introduction: Energy needs of the planet/US; Energy available from solar radiation; Greenhouse effect; Different types of PV systems; examples from manufacturers; CdTe; CIGS; Si, a-Si, organic PV, concentrator systems; Basic properties of solar radiation – sun movement, AM1.5; Spectrum; Problems with PV energy systems – efficiency, intermittency, storage;
  2. Economics and metrics of PV systems: Cost of different energy sources; Cost per area; $/Wp;Performance ratio; Normalized performance metric;Levelized cost of energy (LCOE); Feed in tariffs (FITs); Energy payback time (EPBT);
  3. Radiometric properties of solar radiation (3 lectures): Spectral content of solar illumination; Air mass conditions; solar constant; Radiometric parameters – measuring illumination on a collector; Black body characteristics; Modeling the sun as a blackbody;
  4. Limits to solar energy conversion: Thermal equilibrium considerations; Carnot efficiency, Landsberg, and Black Body limit;
  5. PV cell operating characteristics;
  6. PV Cell Physics;
  7. PV Cell Design;
  8. Modules and arrays;
  9. System design issues;
  10. Solar concentrators and concentrator systems;
  11. Testing and characterization Methods;
  12. Thin Film Materials: Amorphous silicon; CIGS; CdTe; Light trapping techniques and structures;
  13. Storage Systems: Batteries; Hydrogen production systems; Compressed gas storage systems;
  14. Third generation systems and future prospects: Plasmonic enhancement of PV cell energy yield; Refinement of silicon processing; Optical techniques to increase PV system energy yield.

Relationship to Student Outcomes

ECE 414A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 417 Measurement and Data Analysis in Biomedical Engineering

Required course: No

Concurrent with BME 417

Course Level

Undergraduate

Enrollment Requirements

Senior status only.

Schedule

One 75 minute lectures per week, T 09:00 AM - 10:45 AM and TH 08:00 AM - 08:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Topics in biomedical instrumentation, sensors, physiological measurements, analog and digital signal processing, data acquisition, data reduction, statistical treatment of data, and safety issues. The course includes both lecture and structured laboratory components.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Use circuit components and chip components to design accelerometer.
  2. Use circuit components and chip components to design ECG.
  3. Use circuit components and chip components to design pulse oximeter.
  4. Use materials to design masks, wearable sensors and wearable robots.
  5. Develop sensor firmware for biomedical applications.

Course Topics

Brief list of topics to be covered:
Measurement theory, data analysis,  system design for ECG, accelerometer, EEG, thermometer, body sensor network, pulse oximeter, firmware, materials for medical applications.

Relationship to Student Outcomes

ECE 417 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 429 Digital Signal Processing

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 340A.

Course Texts

Alan V. Oppenheim and Ronald W. Schafer, Discrete-Time Signal Processing, Third Edition, Prentice Hall, 2010. (ISBN: 0131988425)

Schedule

Two 75 minute lectures per week, TTH 11:00 AM -12:15 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Discrete-time signals and systems, z-transforms, discrete Fourier transform, fast Fourier transform, digital filter design.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. State and apply the definitions of the following system properties: linearity, time invariance, causality, and BIBO stability.
  2. Describe the distinctions between analog, continuous-time, discrete-time and digital signals, and describe the basic operations involved in analog-to-digital (A/D) and digital-to-analog (D/A) conversion.
  3. State and apply the definition of a periodic discrete-time signal.
  4. State the sampling theorem and explain aliasing.
  5. Apply simple discrete-time signals to filters to obtain the output response.
  6. Convolve discrete-time signals.
  7. Calculate the z-transform X(z) of a simple signal x(n) (such as an exponential or sinusoid) and specify the region of convergence (ROC) of X(z).
  8. Apply z-transform theorems.
  9. Given the transfer function H(z) and ROC of an LTI system, find the system poles (and zeros) and state whether or not the system is BIBO stable.
  10. Compute the discrete-time Fourier transform (DTFT) of a signal.
  11. Use the frequency response of a discrete-time system.
  12. Knowing the poles and zeros of a transfer function, make a rough sketch of the gain response.
  13. Design digital filters.
  14. Apply DFT properties to compute the DFT and IDFT of simple signals.
  15. Design the parameters associated with DFT implementation (sampling rate and record length) to provide an accurate analysis of the frequency and strength of dominant frequency components present in some given, unknown signal (e.g.,  for spectral analysis of a signal).
  16. Explain the need for zero padding and tapered windows when doing spectral analysis of real-world signals. Explain the tradeoff between reduced resolution and spectral leakage.
  17. Compare the characteristics (advantages & disadvantages) of IIR and FIR filters.
  18. Explain (using frequency domain sketches) the application of oversampling and subsequent decimation for recording in digital audio systems.

Course Topics

Brief list of topics to be covered:

  • Introduction to DSP, classification of signals, digital frequency, sampling, aliasing, quantization noise, discrete-time system components, system properties, filter realizations, impulse response, convolution [9 lectures].
  • Forward z-transform, time-delay property, DTFT existence, signal type from ROC, inverse z-transform, applying z-transform properties, poles & stability, system analysis using z-transform [5 lectures].
  • Forward discrete-time Fourier transform (DTFT), symmetry, frequency shifting, modulation, filter design from lowpass prototypes, DTFT analysis of downsampling/upsampling and expansion/compression operations, DTFT systems analysis, phase and group delay of filters, frequency response from poles & zeros, minimum-phase filters, forward DFT and inverse DFT, relationship to DTFT, applying DFT properties, convolution using DFT, DFT symmetry, sinusoidal analysis and frequency resolution, zero-padding and windowing, spectral analysis [16 lectures].
  • Linear-phase FIR filter types, FIR design by windowing, IIR design using bilinear transformation, decimation-in-time FFT, decimation-in-frequency FFT, filter architectures (if time permits) [9 lectures].

Relationship to Student Outcomes

ECE 429 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 430 Optical Communication Systems

Required course: No

Concurrent with OPTI 430

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering.

Course Texts

Required textbook:

  • Kumar, S. and Deen, M J.: Fiber Optic Communication Systems, Wiley 2014. Note: available as an e-book.

Several more advanced and comprehensive texts are recommended:

  • Agrawal, G.: Fiber-Optic Communication Systems, 4th Ed., Wiley 2010

Schedule

Two 75 minute lectures per week, TTH 12:30 PM - 01:45 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Physics of optical communication components and applications to communication systems. Topics include fiber attenuation and dispersion, laser modulation, photo detection and noise, receiver design, bit error rate calculations, and coherent communications.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Describe and mathematically analyze optical components used in communication systems.
  • Understand the principles of optical communication system design.
  • Analytically evaluate the performance and technical merits of an optical communication system.
  • Be conversant in the major application areas for optical communication systems.
  • Design an optical communication system
  • Identify and describe the major sources of noise and signal impairments in an optical communication system.

Course Topics

Brief list of topics to be covered:

Scheduled Topics/Activities (with references to main textbook chapters)

  1. Introduction and Background
  2. Optical Fibers (Chapters 1, 2 and 10)
    Geometrical-Optics Description
    Wave Propagation
    Optical modes in Fiber
    Fiber Losses
    Dispersion
    Pulse propagation
    Nonlinear effect
  3. Light sources (Chapter 3)
    Laser basics
    Light-Emitting Diodes
    Semiconductor Lasers
    Fiber laser
    Laser Characterization
  4. Optical modulation (Chapter 4)
    Intensity Modulation – Direct Detection Systems
    Coherent modulation – detection detection
    Modulation formats: ASK, FSK, PSK, QAM
    Bi-error rates and receiver sensitivity
    Sensitivity degradation
    System performance
  5. Optical Receivers (Chapter 5)
    Common Photodetectors
    Receiver Design
    Receiver Noise & Sensitivity
    Receiver Performance
  6. Optical Amplifiers (Chapter 6)
    Semiconductor Optical Amplifiers
    Raman Amplifiers
    Erbium-Doped Fiber Amplifiers
  7. Performance analysis of optical links (Chapter 8)
    Bit error rate
    Q function
    Rate limit    

Note that material may be drawn from chapters not listed above and/or other readings and material provided in class.

Relationship to Student Outcomes

ECE 430 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 434 Electrical and Optical Properties of Materials

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering.

Course Texts

Simmons and Potter, Optical Materials, Academic Press, 2000 (e-text).

Schedule

Two 75-minute lectures per week, MW 04:00 PM – 05:15 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Properties of conductors, insulators, and semiconducting materials as related to crystal structure, interatomic bonding and defect structures. The course is designed to cover the electrical and optical properties of materials including all three materials classifications (conductors, insulators, semiconductors).  The course content has covered all of these subject areas for at least 13 years and it recently came to our attention that the course catalog entry was truncated and incorrectly suggests that only one of these discussion areas is covered in the course.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Classify optical properties and electrical properties of materials according to material type, structure and physical properties.

Course Topics

Brief list of topics to be covered:

Chapter 1: Introduction to Waves and Wave Propagation Waves

  • Electromagnetic Spectrum
  • Wave Propagation
  • Dispersion and Material Polarizability
  • Kramers-Kronig relations
  • Phonons
  • Measurement Techniques

Chapter 2: Conductors

  • Drude Model
  • Band Structure
  • Coloration
  • Measurement Techniques
  • Select Special Topics Lectures

Chapter 3: Insulators

  • Harmonic Oscillator
  • Refractive Index and Dispersion
  • Reflection and Transmission
  • Attenuation
  • Scattering
  • Measurement Techniques

Chapter 4: Select Special Topics - Insulators

Chapter 5: Semiconductors

  • Free-electron Models
  • Band Structure
  • Impurities
  • Optical Response
  • Measurement Techniques

Special Topic selected by students (Lasers, PV, NLO,…)

Relationship to Student Outcomes

ECE 434 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 435A Digital Communications Systems

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 340A.

Course Texts

None required.

Schedule

Three 50-minute lectures per week, MWF 02:00 PM - 02:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data: The purpose of the course is to give students a comprehensive introduction to digital communication principles. The major part of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information back from the received signal in the presence of noise and intersymbol interference (ISI). Various digital modulation schemes are discussed through the concept of signal space. Analytical and simulation models for digital modulation systems are designed and implemented in the presence of noise and ISI. Optimal receiver models for digital base-band and band-pass modulation schemes are covered in detail.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • An ability to apply knowledge of mathematics, science, and engineering.
  • An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability.
  • An ability to identify, formulate, and solve engineering problems.
  • An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Course Topics

Brief list of topics to be covered:

  • Review of mathematical tools: Orthogonal functions, probability theory, random processes, Markov processes.
  • Information theory: Information measures (self-information, mutual information, channel capacity), looseless source coding, Huffman codes, channel coding, Shannon coding theorems.
  • Representation of band-pass signals and systems: Band-pass signals and noise representation (Hilbert transform); signal space representation.
  • Digital modulation schemes: Memoryless digital modulation methods (ASK, PSK, FSK, QPSK), modulation with memory (base-band and band-pass), spectra of digitally modulated signals.
  • Optimum receivers for additive white Gaussian noise (AWGN) channel: Maximum a posteriori and maximum likelihood detection, matched filter demodulation, sequence detectors, symbol by symbol MAP detector for channels with memory, receiver performance.
  • Error control coding fundamentals: Finite fields, generator and parity check matrices block and convolutional codes and their decoders, Hamming codes, syndrome decoding, iterative decoders.

Relationship to Student Outcomes

ECE 435A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
3
ECE 441A Automatic Control

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 320A.

Course Texts

Required Textbooks:

Software

  • You will be required to use Matlab to work on assignments throughout the course. We will NOT cover "how to program in Matlab," rather, you are expected to know it or pick it up.

Schedule

Three 50-minute lectures per week, MWF 09:00 AM - 09:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Linear control system representation in time and frequency domains, feedback control system characteristics, performance analysis and stability, and design of control.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Model, via differential equations or transfer functions, electrical, mechanical, and electromechanical dynamical systems. (Exam 1)
  2. Linearize a set of nonlinear dynamical equations. (Exam 1)
  3. Create a second-order model from a system's step response. (Exam 1)
  4. Construct all-integrator block diagrams from a transfer function, a set of differential equations, or a state-space representation and vice-versa. (Exam 1)
  5. Construct and interpret the Routh Array. (Exam 1)
  6. Sketch the root locrn, associated with a transfer function. (Exam 2)
  7. Determine the stability of a closed-loop system. (Exam 2)
  8. Calculate the phase margin and gain margin of a system from its frequency response (Bode plots). (Exam 2)
  9. Compute a state transition matrix from a system matrix. (Exam 2)
  10. Describe in terms of percent overshoot, settling time, steady-state error, rise-time, or peak­ time how the poles of a second-order continuous-time system influence the transient response. (Exam 2)
  11. Translate design specifications into allowable dominant pole locations in the s-plane (Exam2)
  12. Calculate a system's steady-state error and how the steady-state error can be influenced via system parameter changes. (Exam 2)
  13. Analyze stability using state-space techniques (Exam 3)
  14. Calculate a system's sensitivity with respect to different parameters.
  15. Design analog controllers using root locus techniques. (Exam 3)
  16. Design a system utilizing the observable canonical form. (Exam 3)
  17. Design an analog PID controller to meet design specifications. (Exam 3)
  18. Design analog controllers using Bode plot techniques. (Exam 3)
  19. Design full-state feedback gains to achieve acceptable closed-loop behavior.

Course Topics

Brief list of topics to be covered:

  • Course Description and Introduction
  • System Modeling: Electrical & Mechanical Components, Electromechanical Systems;
    Current-Force Analogy, Gears and Levers; Linearization
  • System Descriptions and Manipulation
    Transfer Function Descriptions; Simulations of Systems; Block Diagram Algebra
    System Identification and Frequency Response; State-Space Representation; State Transition Matrix; Mason's Gain Formula
  • Feedback System Characteristics
    Sensitivity; Initial Value Theorem; Tracking; Steady-state Error
  • System Performance and Stability
    Specifications (rise time, overshoot, steady-state error, and settling time); Pole locations and Time Response (2nd Order Systems); Routh-Hurwitz Test; Reative Stability; Time-domain Stability
  • Root Locus Analysis and Controller Design
    Root Locus Construction Rules; Root Locus Phase Lead Design and Lag Design
  • Bode Analysis and Controller Design
    Bode Plot Construction Rules; Frequency Response Measurements and Performance
    Stability Margins; Phase Lead and Lag Bode Design
  • PID Controller Design
  • State Feedback Design
    Pull State Feedback Internal Model Design; Observer Design and Observer-based Compensator Design

Relationship to Student Outcomes

ECE 414A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
5. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

Syllabus Prepared By

Syllabus updated on 3/29/2022
ECE 442 Digital Control Systems

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 320A.

Course Texts

“Digital Control Engineering: Analysis and Design,” by M. Fadali and A. Visioli, Second Edition, Academic Press, Waltham, MA, 2013. ISBN: 978-0-12-394391-0. Available as an e-Book or Download through the University of Arizona Library.

Schedule

Two 75-minute lectures, MW 05:30 PM - 6:45 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Modeling, analysis, and design of digital control systems; A/D and D/A conversions, Z-transforms, time and frequency domain representations, stability, microprocessor-based designs.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Design a pure, two-pole system that satisfies specified performance specifications like percent overshoot, peak time, settling time, and DC gain.
  2. Calculate the z-plane location of a pair of dominant poles given time-domain performance information like percent overshoot, settling time, and peak time.
  3. Create discrete equivalents from given continuous-time systems, e.g., a. create a zero-order hold equivalent discrete-time state space representation from continuous-time, state space representation, b. create a discrete-time transfer function from a continuous-time transfer function using a numerical integration strategy (Numerical Integration Strategies: Forward Rectangular, Backward Rectangular, and Tustin/Trapezoidal) or using a pole-zero mapping technique, c. create a zero-order hold equivalent discrete-time transfer function of a system given a continuous-time transfer function preceded by a zero-order hold.
  4. Construct a discrete-time difference equation containing input variables and output variables at particular time instances from a system’s discrete-time transfer function.
  5. Produce a closed-form expression for the output of a system given a system description and an applied input waveform. The system description could be in the form of a block diagram, transfer function, difference equation, or state-space representation.
  6. Numerically compute the value of any system variable (e.g., state variable or output variable) at any discrete, time instant given initial conditions and input waveforms.
  7. Compute the settling time, peak time, and percent overshoot for a discrete-time system.  The discrete-time system could be represented in the form of a difference equation, a state-space representation, a block diagram, or a transfer function.
  8. Compute the z-Transform of a given discrete-time waveform.
  9. Compute the transfer function of a given system given a system representation in difference equation form, state-space form, or block diagram form.
  10. Compute the Inverse z-Transform given a rational expression in the frequency domain and the Region of Convergence (ROC).
  11. Correlate the different Region of Convergence (ROC) shapes with when the time domain waveform is defined, e.g., as a right-sided time sequence (one-sided), a left-sided time sequence (one-sided), or when the sequence is defined for all time indices (two-sided).
  12. Find the steady-state error in a given system.
  13. Determine if a discrete-time system is Bounded-Input, Bounded-Output (BIBO) stable. The system could be described in the form of a difference equation, a block diagram, a transfer function, or a state-space representation.
  14. Create a state-space representation of a system from a given system description. The system description could be in the form of a difference equation, a block diagram, or a transfer function.
  15. Apply the Final Value Theorem for discrete-time systems to find the limiting values of given system variables, i.e., errors, state variables, or output variables.
  16. Design stabilizing controllers for unstable systems using classical control design strategies, i.e., design strategies based on root locus design techniques.
  17. Sketch (by hand) a root locus diagram corresponding to a given system model that is interconnected in a negative, unity output-feedback configuration.
  18. Perform each step in the root locus diagram construction process.
  19. Design controllers based on the Ragazzini controller design approach.
  20. Identify a range of gain values that would provide a stable, closed-loop system if such a range of gain values exists.
  21. Design full-state feedback controllers to locate closed-loop poles at particular locations in the complex z-plane.
  22. Utilize the phase-angle condition in the root locus design approach to locate pole and zero locations in 1st-order or 2nd-order controllers.
  23. Design full-state feedback controllers with non-zero reference inputs to produce desired closed-loop pole locations and the desired DC gain in the closed-loop system

Course Topics

Brief list of topics to be covered:

This course provides an introduction to the fundamental concepts and mathematics of control systems engineering. Throughout the semester we will cover linear control system representation in time and frequency domains, feedback control system characteristics, performance analysis and stability, and design of control.

Relationship to Student Outcomes

ECE 442 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 450 Analog Integrated Circuits

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 351C.

Schedule

Three 50-minute lectures per week, MWF 01:00 PM - 01:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data: Nonswitching aspects of analog integrated circuits using bipolar or CMOS technologies.  Biasing, DC signal behavior, small behavior. Emphasis on the use of physical reasoning, identification of circuit functions, and use of suitable approximations to facilitate understanding and analysis.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Design basic OpAmp.
  2. Design differential pairs
  3. Design current sources
  4. Investigate cascoded amplifiers
  5. Design high-frequency amplifiers and active load
  6. Design feedback loops
  7. Investigate stability issues in designs

Course Topics

Brief list of topics to be covered:

  • Cascode concept: cascode current source; Cascode amplifiers
    Cascode amplifiers with PMOS input; Cascode applications
  • Biasing
  • Current Mirrors
  • Differential pairs
  • Active load
  • Frequency response (basics)
  • Inspection method
  • Miller effect
  • High frequency circuit
  • CE and CS Freq response
  • Pole approximation
  • CB and CG Freq response
  • Emitter and source follower
  • Input and output impedances
  • Applications and examples of frequency response
  • Feedback concept
  • Feedback system
  • Feedback design
  • Feedback circuit

Relationship to Student Outcomes

ECE 450 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 455 Introduction To Quantum Mechanics and Quantum Information Processing

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing in Engineering. MATH 254 and ECE 275 and (ECE 207 or ECE 220) and ECE 310.

Course Description

This course is a self-contained introduction to quantum mechanics, quantum information, and quantum computing. The course starts with basics of linear spaces, including basis vectors, Gram-Schmidt procedure, Dirac algebra, Hermitian conjugation, eigenvalues and eigenvectors, and commutator. It continues with the principles of quantum mechanics including photon polarization, state vectors, operators, density operators, measurements, and dynamics of a quantum system, spin-1/2 systems and entanglement. The next chapter is devoted fundamentals of quantum computing, including single qubit gates, multiple qubit gates, controlled operations and universal quantum gates. Further, after introduction of quantum parallelism, important quantum algorithms are studied such as Deutsch's and Deutsch-Jozsa algorithms, Grover search algorithm and quantum Fourier transform. The next chapter will be devoted to physical realization of quantum information processing including nuclear magnetic resonance, ion traps, photonic realization, cavity quantum electrodynamics, and quantum dots. We then study various applications of quantum information processing including quantum teleportation, superdense coding and quantum cryptography. Course concludes with various quantum channel models and basics of quantum error correction. As a dual numbered course, the graduate level version will include more challenging homework problem sets and exam problems, as well as a comprehensive course project.

Course Units
3
ECE 456 Optoelectronics

Required course: No

Course Level

Undergraduate

Enrollment Requirements

ECE 381A

Course Texts

Instructor will provide course notes.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Properties and applications of optoelectronic devices and systems. Topics include radiation sources, detectors and detector circuits, fiber optics, and electro-optical components.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Understand the electromagnetic spectrum, wave equation and wave propagation in linear, isotropic media and in anisotropic media.
  2. Understand basic radiometric quantities and perform analyses of basic radiometric designs.
  3. Utilize matrix methods to model and interpret image formation and beam propagation (both plane wave and Gaussian beam) in an optical system.
  4. Understand the function and design of an optical resonator.
  5. Calculate resonator mode characteristics and determine mode stability in an optical resonator
  6. Understand cavity Q, constructive and destructive interference.
  7. Interpret resonator output spectra including calculating and interpreting cavity finesse, resolution, mode spacing, etc.
  8. Understand the design and function of a Fabry-Perot etalon optical spectrum analyzer.
  9. Design an optical resonator (mirror curvature, size, separation, Gaussian beam characteristics in resonator) and understand sources of loss.
  10. Understand the function and design of laser gain media – gas, liquid, solid-state.
  11. Understand energy band diagrams, band gap, excited states, spontaneous emission, stimulated emission, state lifetimes, lineshape broadening: homogeneous and inhomogeneous, Einstein A and B coefficients, rate equations, exponential gain coefficient, population inversion, intensity.
  12. Design three and 4 level laser gain media based on desired design constraints.
  13. Understand the impact of placing the gain medium inside a resonator to produce a laser: laser gain profile, spectrum, threshold, population inversion, critical fluorescence power, stimulated emission power, output power.
  14. Understand the function and design of Q switch devices and methods and mode locking devices and methods.
  15. Discuss a variety of laser types (descriptions, pros, cons).

Course Topics

Brief list of topics to be covered:

  • Waves - electromagnetic, acoustic, traveling, standing. Electromagnetic spectrum. Review of basic terminology (homogeneous, linear, isotropic media, etc.) Maxwell’s equations and the wave equation.
  • Polarization – linear, elliptical, circular, Jones vectors and matrices. Basic Radiometry.
  • Image Formation – use of the refraction and translation (transfer) matrices to describe optical systems. Stability – beam path ray trace through an optical system to determine the stability of the system. Gaussian Beam Propagation – concepts and equations to determine the characteristics of a Gaussian beam. Imaging Gaussian Beams – combine the ABCD matrix method with Gaussian beams to propagate through an optical system.
  • Resonator: mirrors separated by an air space.  Propagation described by ABCD matrix with Gaussian beams.
  • Cavity Q. Cavity modes, mode spacing, constructive and destructive interference Fabry-Perot etalon (optical spectrum analyzer), finesse, resolution.
  • Resonator design (spherical mirrors), beam waist size and location in the cavity, confocal resonator properties, general resonator properties, matrix methods for resonator, resonator stability, sources of loss, output of resonator.
  • Gain medium – gas, liquid, solid-state. Energy bands, band gap, ground state, excited states. Spontaneous emission, stimulated emission.
  • Lineshape broadening: homogeneous and inhomogeneous.
  • Einstein A and B coefficients, rate equations, exponential gain coefficient, population inversion, intensity.
  • Three and 4 level lasers. Laser gain profile spectrum, impact of gain medium on resonator output. Gain medium inside the resonator. Threshold, population inversion.
  • Critical fluorescence power, stimulated emission power, output power. Q switching and mode locking.
  • Laser types (descriptions, pros, cons). Laser output: spectral, beam characteristics, etc.

Relationship to Student Outcomes

ECE 456 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Syllabus Prepared By

Syllabus updated on 3/29/2022
 
Course Units
3
ECE 459 Fundamentals Of Optics For Electrical Engineers

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 381A.

Course Texts

Robert Guenther, Modern Optics, 2nd Ed., John Wiley, 2015.

Schedule

Three 50-minute lectures per week, MWF 01:00 PM - 01:50 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Introduction to diffraction and 2D Fourier optics, geometrical optics, paraxial systems, third-order aberrations, Gaussian beam propagation, optical resonators, polarization, temporal and spatial coherence, optical materials and nonlinear effects, electro-optic modulators. Applications to holography, optical data storage, optical processing, neural nets, associative memory optical interconnects.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Provide students with a basic understanding of optical principles that are used in a variety of engineering applications.
  • Provide students with a background for taking more advanced courses in optics.

Course Topics

Brief list of topics to be covered:

  1. Electromagnetic wave propagation
  2. Polarization
  3. Fresnel reflection coefficients, Brewster angle, TIR
  4. Radiometry
  5. Optical System Analysis/Geometrical Optics
  6. Diffraction theory (Chapters 3-5 from Goodman’s Book)
  7. Gaussian beam propagation
  8. Optical Waveguides
  9. Coherence Theory and Interference
  10. Holography
  11. Examples of Optical Systems

Relationship to Student Outcomes

ECE 459 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 462 Computer Architecture and Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 369A.

Course Texts

The recommended textbook is "Computer Architecture: A Quantitative Approach" (Sixth Edition) by John Hennessy and David Patterson. However, you are not required to purchase this textbook to get the most out of the class. Given the rapidly evolving nature of computer architecture, a lot of the class contents will also be based on freely accessible research papers.

Schedule

Three 50-minute lectures per week, MWF 09:00 AM - 09:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. It provides basic knowledge, fundamental concepts, design techniques and trade-offs, machine structures, technology factors, software implications, and evaluation methods and tools required for understanding and designing modern computer architectures including multicores, embedded systems, and parallel systems. The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks. The first part of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control. The second part goes into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern computing system. Topics include superscalar execution, branch prediction, out-of-order execution, register renaming and memory disambiguation; VLIW, vector, and multithreaded processors; memory protection, translation, and virtualization; and memory synchronization, consistency, and coherence. The third part addresses parallel computing, including multicore architectures, data enters and cloud computing.d others.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Understand the fundamentals of computer architecture and system design.
  • Appreciate and understand the various design issues and tradeoffs of computer design.
  • Apply this knowledge to new computer architecture design problems with the context of balancing application requirements against technology constraints.
  • Understand current trends and future directions of computer architecture.

Course Topics

Brief list of topics to be covered:

  • Fundamentals of computer design (Week 1)
  • Pipelining overview (Week 2)
  • Memory hierarchy design (Week 3 -4)
  • Instruction-level parallelism (Week 5 - 7)
  • Thread-level parallelism/multiprocessor systems (Week 8 - 9)
  • Heterogeneous multiprocessing (Week 10)
  • Data-level parallelism (Week 11)
  • Storage systems (Week 12 -13)
  • Warehouse-scale computers (Week 14)
  • Emerging computer architectures (Week 15)

Relationship to Student Outcomes

ECE 462 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
3. An ability to communicate effectively with a range of audiences
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 466 Knowledge-System Engineering

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Major: ECE. Advanced Standing: Engineering.

Schedule

Three 50-minute lectures per week, MWF 11:00 AM - 11:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data: Offered every two years. Knowledge systems are intelligent systems that totally or partially involve computational representation and processing of knowledge. This class introduces the principles and techniques for engineering and development of knowledge systems. Alternative computational structures for knowledge representation, procedures and algorithms for computational processing, automated reasoning and inference from knowledge, learning new knowledge, handling uncertainty in information, knowledge-based decision networks, distributed knowledge systems, alternative system architectures and engines.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instructions: By the end of this course, the student will be able to:

  1. Understand propositional and first-order logic.
  2. Represent information in first-order logical formulas, and perform formula unification/matching.
  3. Understand forward and backward automated inference.
  4. Formulate problems as state-space search.
  5. Develop programs for breadth-first, depth-first, heuristic, and hill-climbing searches.
  6. Represent information in semantic networks.
  7. Understand constraint networks, constraint satisfaction, and develop programs for constraint satisfaction.
  8. Understand genetic operators, genetic optimization and genetic learning, and write programs for this purpose.
  9. Understand Bayes rules, Bayesian belief networks, and evidence accumulation.

Course Topics

Brief list of topics to be covered:

  • Knowledge representation in first-order logic, matching and unification of first-order logic formulae (2 lectures).
  • Rule-based expert systems, rule firing, forward and backward chaining (2 lectures).
  • Automated planning and problem-solving, total-order problem solvers, least-commitment planning, hierarchical problem solving (4 lectures).
  • Search methods, depth-first search, breadth-first search, heuristic search, greedy search, A* algorithms, hill climbing (2 lectures).
  • Structured knowledge representation; representing knowledge using frames, objects and semantic networks; first-order logic correspondence; matching; inheritance; defaults; and automated inference (2 lectures).
  • Constraints, constraint networks, constraint satisfaction, node and arc consistency, compound labeling, constraint satisfaction algorithms, problem reduction, back jumping, interval constraints, interval calculus, algorithms for interval constraint satisfaction (4 lectures.
  • Genetic algorithms, genetic representation of knowledge, fitness functions, genetic operators, genetic search and optimization, genetic learning (2 lectures).
  • Bayesian probabilistic networks, fundamentals of probability theory, likelihood vectors and conditional probability matrices, hierarchical propagation of evidence, computational algorithms for general networks (5 lectures).
  • Dempster-Shafer theory of evidence, belief interval representations for uncertainty, evidence accumulation and propagation (2 lectures).
  • Knowledge-based decision systems, utility theory, utility functions, decision networks, decision-theoretic knowledge systems, sequential decision problems, value iteration (3 lectures).

Relationship to Student Outcomes

ECE 466 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 474A Computer-Aided Logic Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 274A and ECE 201.

Course Texts

There is no required textbook. The class is designed so that you do not have to purchase any textbooks. However, the lecture notes are based on the following textbooks:

  • Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, Third Edition, MIT Press.
  • Frank Vahid and Roman Lysecky, Verilog for Digital Design, John Wiley & Sons.
  • Frank Vahid, Digital Design, John Wiley & Sons.
  • Robert K. Brayton, Gary D. Hatchel, C. McMullen, and Alberto L. Sangiovanni-Vincentelli, Logic Minimization Algorithms. for VLSI Synthesis, Kluwer Academic Publishers.
  • Giovanni De Micheli, Synthesis and Optimization of Digital Circuits, McGraw-Hill.

Schedule

Two 75-minute lectures per week, TTh 11:00 AM - 12:15 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Tabular minimization of single and multiple output Boolean functions, NMOS and CMOS realizations, synthesis of sequential circuits, RTL description, laboratory exercises.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Understand the basics of high-level synthesis (HLS) and the benefits of HLS in improving productivity in the design of application-specific integrated circuits (or accelerators).
  2. Understand the importance of scheduling in HLS and learn how to use a variety of scheduling algorithms, including ASAP, ALAP, Hu, LIST_L, LIST_R, and force-directed.
  3. Understand a variety of methods used for resource sharing and binding.
  4. Understand the difference between heuristic and exact optimization methods, and be able to classify a variety of algorithms into these two categories.
  5. Use advanced techniques for logic minimization, including the Quine-McCluskey tabular minimization technique for identifying all the prime implicants, and solve the covering problem using Petrick’s method to find an optimal two-level implementation for specified logic functions.
  6. Use Quine-McCluskey with iterative and recursive consensus methods for identifying the complete sum and solve the covering problem using row/column dominance to find a minimal gate, two-level implementation for specified logic functions.
  7. Understand the role of verification in computer-aided design along with different testing methods.
  8. Design a simple high-level synthesis tool in C/C++ to output the resulting Verilog circuit implementation, given a behavioral netlist specification.

Course Topics

Brief list of topics to be covered:

  • Hardware description languages (HDL)
  • High-level synthesis (HLS): scheduling algorithms, resource sharing and binding, etc.
  • Design and implementation of sequential circuits
  • Register-transfer level (RTL) design
  • Optimization and tradeoffs of combinational and sequential circuits
  • Exact and heuristic minimization of two-level circuits

Relationship to Student Outcomes

ECE 474A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 478 Fundamentals of Computer Networks

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 201 and ECE 310.

Course Texts

Text:

  • Computer Networks, A Systems Approach, 5th edition, Larry L. Peterson and Bruce S. Davie, Morgan Kaufmann, 2011.

References:

  • Computer Networks, 6th edition, A S. Tanenbaum, and D. Wetherall, Prentice Hall, 2020.
    Available online via the library
  • Computer Networking, A Top-down Approach, 7th edition, J. Kurose and K. Ross, Addison Welsey, 2017

Schedule

Two 75-minute lectures per week, TTh 12:30 PM - 01:45 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Introduction to computer networks and protocols. Study of the ISO open systems interconnection model, with emphasis on the physical, data link, network, and transport layers. Discussion of IEEE 802, OSI, and Internet protocols.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Outline wired and wireless technologies for interconnecting computer networks.
  • Define metrics for evaluating the network performance.
  • Specify the operational details of reliable link-layer transmission protocols and multiple access protocols.
  • Explain the global addressing system.
  • Analyze various network topology models and compute optimal routing policies.
  • Describe the autonomous system hierarchy and the Interdomain routing policies.
  • Evaluate the performance of end-to-end network protocols.
  • Contrast resource congestion control and resource allocation protocols.
  • Describe the fundamental principles of network security.
  • Explain the operation of fundamental computer applications deployed over the Internet infrastructure.

Course Topics

Brief list of topics to be covered:

Introduction to computer systems

  • Engineering problems as computational problems
  • Overview of computer systems
  • Software design

Introduction to C

  • Code build process (editing, compiling, linking, executing)
  • Elements of a C program; preprocessor directives; statements and expressions; functions; coding formatting style
  • Simple data types; constants and variables; conversion between different data types; binary arithmetic representations
  • The IDE environment

Program flow control

  • Conditions; relational operators; logical operators; precedence rules; selection structures
  • Repetition and loop statements; while statements; for statements; increment and decrement operators; loop termination; nested loops; do-while statements
  • Debugging

Modular programming

  • User functions; library functions; function declaration and definition; function calls; pass by value; scope rules; programs with multiple functions
  • Pointers and addresses; pass by reference; pointer arithmetic
  • File input/output

Simple data structures

  • Arrays; declaration and initialization; multi-dimensional arrays; searching and sorting arrays; pointers and arrays
  • String arrays; string library functions; substrings; concatenation; strings vs. characters
  • Engineering applications; matrix algebra; numerical integration and differentiation; quadratic equations
  • Recursion
  • Structures; structures and functions; arrays of structures; dynamic data structures

Relationship to Student Outcomes

ECE 478 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 479 Principles of Artificial Intelligence

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 373.

Course Texts

Recommended/Optional:

  • Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig. (Not mandatory)

Additional materials will be handed out in class.

Schedule

Two 75-minute lectures per week, TTh 08:00 AM - 09:15 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Provides an introduction to problems and techniques of artificial intelligence (AI). Automated problem solving, methods and techniques; search and game strategies, knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; implementing AI systems.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • The goal of this course is to provide students with an introduction to problems and techniques of artificial intelligence (AI). Computer Engineering majors and students pursuing degrees in other fields benefit by learning how to use problem-solving, knowledge representation, and other AI-based techniques.

Course Topics

Brief list of topics to be covered:

  1. Introduction: What is Artificial Intelligence?
  2. Problem Solving
    2.1 Problems and Problem Spaces
    2.1.1 State space search
    2.1.2 Production systems
    2.1.3. Control Strategies
    2.1.4. Heuristic Search
    2.2 Basic Problem Solving Methods
    2.2.1 Forward and backward reasoning
    2.2.2 Problem trees and graphs
    2.2.3. The role of representation
    2.2.4 Search methods
    2.3 Game Strategies
    2.3.1 Minimax
    2.3.2 Alpha-Beta Search
  3. Knowledge Representation (KR)
    3.1 Principles of KR using predicate logic
    3.2 Overview of KR using other logics
    2.2 Structured representations of knowledge
  4. Planning
    4.1 Blocks world problems
    4.2 Representation for planning
    4.3 Plan generating systems
  5. Introduction to selected advanced topics, including but not limited to computer-guided surgery, intelligent sensing systems, co-evolution, game theory, and big data science.

Relationship to Student Outcomes

ECE 479 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
7. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 484 Antenna Theory and Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 381A

Course Texts

Antenna Theory and Design, Third Edition, with multimedia CD, Constantine Balanis (Wiley-Interscience).

Students log into secured D2L site for more course information.

Schedule

Two 75-minute lectures per week, TTh 11:00 AM - 12:15 PM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Introduction to the fundamentals of radiation, antenna theory and antenna array design. Design considerations for wire, aperture, reflector and printed circuit antennas.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  1. Have practice with foundational aspects of antenna engineering through homework and problem analysis. The foundations are not extensive, yet the student will develop quality and critical thinking checks necessary for extended study and mastery of selected subjects in antenna engineering well beyond the extent of the semester-long class.
  2. Have 10 or more hours of hands-on experience in antenna engineering (engineering, design, analysis) EDA tools. This experience will be demonstrated in homework assignments and a design project. The experience is often valuable skills to put on resumes.
  3. Be exposed to the historical aspects that relate to the current state of the art and future technology advances in antenna engineering.
  4. Become mindful of some non-antenna engineering aspects (manufacturability, reliability, consumer demand, constraints in materials) that influence future technology advances and contributions in research and industry.

Course Topics

Brief list of topics to be covered:

I. Introduction/Review Of Maxwell’s Equations
- Course Overview, How an antenna works, Different Types of Antennas
- Maxwell’s Equations, Boundary Conditions
- The wave equation and its solution

II. Fundamental Parameters Of Antennas (Chapter 2)
- Radiation pattern, Directivity, Gain, Beamwidth, Bandwidth (Sec. 2.2-2.11)
-Polarization, Input impedance, Radiation efficiency, Friis Transmission formula, Sec. 2.12-2.17)

III. Radiation Integrals and Auxiliary Potential Functions (Chapter 3)
- Vector Potential Function and Equivalent Sources, Solution to the Vector Potential Wave Equation, “Recipe” for Computing Antenna Fields from Current Distributions  (3.1-3.5)
- Far-field Radiation Integrals, Duality, Reciprocity and Reaction Theorem (3.6- 3.8)

IV. Linear Wire Antennas (Chapter 4)
- Infinitesimal Dipole, Small Dipole, Region Separation (4.1-4.4)
- Finite-length Dipole, Half-wave Dipole ( 4.5-4.6)
- Linear Elements Near or On Infinite Perfect Conductors (4.7)
- HFSS Tutorial

V.  Array Fundamentals and Array Synthesis (Chapters 6 and 7)
- Array factor, Uniform Array, Directivity, Sidelobes, Tapered Array (6.1-6.4)
- Array scanning, End-fire array, Planar array (6.5-6.8, 6.10)
- Schelkunoff Unit Circle Representation (7.3)
- Dolph-Chebyshev Synthesis (6.8 + supplemental notes)
- Continuous Line Sources and Space Factor, Discretization of Continuous Line Sources (7.2)
- Taylor Line Source (7.6)

VI. Microstrip Antennas (Chapter 14)
- Patch Antennas

VII. Aperture Antennas (Chapter 12)
- Equivalence Principle and Radiation Equations (12.1-12.4)
- Rectangular Apertures (12.5)
- Circular Apertures, Babinet’s Principle (12.6, 12.8)

VIII. Reflector Antennas (Chapter 15)
- Parabolic Reflectors and Reflector Optics
- Antenna Measurements

Relationship to Student Outcomes

ECE 484 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 486 Microwave Engineering I: Passive Circuit Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 381A.

Course Texts

MICROWAVE ENGINEERING, 4th edition: https://ebookcentral.proquest.com/lib/uaz/detail.action?docID=2064708

Schedule

Two 75-minute lectures per week, TTh 09:30 AM - 10:45 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Review of transmission line theory; microstrip lines and planar circuits; RF/microwave network analysis; scattering parameters; impedance transformer design; filter design; hybrids and resonators; RF/microwave amplifier design; RF transceiver design; RF/microwave integrated circuits.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Identify the wave equation and basic plane wave solutions; TEM, TEM, and TM waves; the parallel plate waveguide and its associated electromagnetic fields and current. distributions; the rectangular waveguide, explain its operation and list the electromagnetic field distributions of its dominant modes.
  • Calculate the attenuation in a parallel waveguide and a rectangular waveguide.
  • Identify the coaxial line, explain its operation and list the electromagnetic field distributions of its dominant modes. Find the attenuation and characteristic impedance of a coaxial line.
  • Identify the stripline or the microstrip, explain its operation and list the electromagnetic field distribution of its dominant modes. Interpret the effective dielectric constant of a microstrip line. Apply the effective dielectric constant, attenuation and impedance formulas for a microstrip line design.
  • Identify the different wave velocities and explain the dispersion effect.
  • Describe the lumped element circuit model for a transmission line.
  • Describe the different transmission line parameters and Identify the Telegrapher equations.
  • Calculate the current and voltage distribution of a terminated lossless transmission line line.
  • Calculate the input impedance, reflection coefficient and standing-wave ratio of a terminated lossless transmission line.
  • Explain how the Smith Chart works.
  • Design single stub matching networks and double stub matching networks.
  • Calculate the input impedance, reflection coefficient, voltages, current and delivered power in a transmission line with generator and/or load mismatches.
  • Distinguish between the different types of impedance in transmission lines.
  • Formulate the impedance and/or admittance matrix of an arbitrary microwave network.
  • Describe the properties of a lossless and/or reciprocal microwave network.
  • Describe the scattering matrix. Apply the scattering matrix to characterize various passive microwave circuits. Distinguish between regular and generalized scattering matrix.
  • Explain how the s-parameters of a 2-port microwave network can be measured.
  • Identify the transmission matrix and apply it to characterize various microwave circuits.
  • Apply the appropriate relationships to transform from one type of matrix to another.
  • Design lumped element matching networks; quarter-wave transformers and their operation and theory of small reflections.
  • Apply the theory of small reflections to design.
  • List the basic properties of dividers and couplers; Design a Wilkinson power divider and list its properties, a quadrature hybrid and list its properties, coupled-line couplers and describe their operation.
  • Describe the basic operation of a vector network analyzer and the insertion loss method technique for designing filters; Identify the different filter transformations and Design a low-pass filter using stubs, a stepped impedance low-pass filter, band-pass filter.
  • Identify potential limitations in the circuit fabrication process.
  • Perform microwave measurements for the passive circuit of the design project.
  • Propose solutions to meet specific design goals if those were not achieved at first design iteration.

Course Topics

Brief list of topics to be covered:
Review of Maxwell's equations, boundary conditions; Wave equation and solutions, energy & power; Plane Wave Reflections from Interface; TEM, TE and TM waves, Parallel plate waveguide; Rectangular waveguide; Microstrip line, wave velocities and dispersion; Coaxial line, coax connections and stripline; Transmission line parameters, Telegrapher’s Equations, Equivalent Ckt. Models, Terminated Transmission Lines; The Smith Chart 1- single stub matching and single stub, double; Stub tuners; Impedance and equiv. Voltages & currents, Z&Y matrices; Scattering Matrices and transmission matrix; Lumped element matching, quarter-wave transformer; Theory of small reflections, multi-section transformers; Filter design by insertion loss method; Filter Transformation; Filter implementation- stepped impedance LPF; Microwave Layout/Ckt. Fab/Safety/ 2 port measurements; Coupled Line/Coupled Resonator Filters; Basic Properties of dividers and couplers; Wilkinson power divider; Quadrature hybrid, coupled line couplers.

Relationship to Student Outcomes

ECE 486 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 488 Microwave Engineering II: Active Circuit Design

Required course: No

Course Level

Undergraduate

Enrollment Requirements

Advanced Standing: Engineering. Major: ECE. ECE 486.

Course Texts

Microwave and RF Design – A System Approach, Michael Steer, SciTech Publishing.

Suggested Readings:

  • Microwave Transistor Amplifiers 2nd Edition, Guillermo Gonzales, Prentice-Hall, 1997.
  • Nonlinear Microwave Circuits, Stephan Maas, IEEE Press.

Class Web-site: D2L

Schedule

Three 50-minute lectures per week, MWF 11:00 AM - 11:50 AM.

Course Description

Specific Course Information:
2021-2022 Catalog Data:  Planar active microwave circuits, diode and transistor characteristics, mixers, amps, oscillators, and frequency multipliers. Students will design circuits with CAD tools, fabricate in clean room, and measure performance in the lab.

Learning Outcomes

Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:

  • Understand various modulation schemes; basics of wireless transmitters & receivers; basics of Antennas & wireless link; fundamentals of RF systems.
  • Design lumped element matching networks; Design single and double stub matching networks for various loads; Create all active circuit designs in microstrip form.
  • Apply single / double stub matching network designs for circuits in microstrip form.
  • Identify the diode equation and the small signal model and equivalent circuit.
  • Explain the role and operation of the depletion and diffusion capacitance.
  • Describe the operation of a Schottky barrier diode and Explain how diodes can be used for RF/microwave signal detection and mixing.
  • Identify the various types of microwave mixers, as well as parameters used for the evaluation of their performance. Design a single diode microwave mixer, a balanced microwave mixer,  a sub- harmonic microwave mixer, and a microstrip mixer.
  • Determine the type of diode needs to be used for a specific microwave mixer design; Describe the characteristics of Bipolar and FET microwave transistors.
  • Identify the small-signal electric models of microwave transistors; Apply the transistor model to evaluate its S-parameters; Explain how the S-parameters of a transistor can be measured.
  • Apply signal flow graphs to evaluate scattering and other parameters of microwave circuits.
  • Identify the different power gain expressions of microwave amplifier circuits.
  • Calculate power gain expressions of a microwave amplifier from S-parameters.
  • Calculate the input and output VSWR of a microwave amplifier.
  • Determine the stability of an amplifier from the transistor, matching networks, and terminations; Explain when a two-part network is unilateral.
  • Outline the procedure for drawing the constant G circles for the unconditionally stable & potentially unstable cases; Identify & evaluate the unilateral figure of merit.
  • Design a microwave amplifier with a) maximum transducer power gain and b) for a specific operating power gain both for an unconditionally stable and potentially unstable cases and c) for a specific available power gain and with a specific gain and input/output VSWR.
  • Plot power gain circles for a two-port network.
  • Design a DC bias network for a microwave amplifier.
  • Calculate noise parameters in microwave circuits and systems; Design a low-noise microwave amplifier using constant noise figure circles.
  • Design a microwave amplifier with good ac performance (noise figure, available power gain, power output and input/output VSWR) and a broadband microwave amplifier and a feedback microwave amplifier.
  • Distinguish between class A, B, and C microwave amplifiers.
  • Design a microwave power amplifier; Identify intermodulation distortion.
  • Evaluate the dynamic range of a microwave amplifier; Design a two-stage microwave amplifier; List and describe oscillation conditions.
  • Describe and Design the operation of one-port and two-port negative resistance oscillators.
  • Apply the Nyquist test to determine conditions for unstable operations of a given circuit;
  • Identify different commonly used oscillator configurations; Explain the operation of varactor frequency multiplier; Design a balanced microwave multiplier.
  • Determine which active microwave circuit to use depending on the application.
  • Perform microwave measurements for the active circuit of the design project.
  • Propose solutions to meet specific design goals if those were not met at first design iteration.

Course Topics

Brief list of topics to be covered:

Introduction of Modulation Techniques; Digital Modulation I and II; Receivers, Modulators and Demodulators; Antennas; Radio Link and Systems; Cellular Radio: 1G – 3G and Beyond 3G and Radar; Matching Networks; Microstrip Matching Networks; Microwave Transistors; Scattering Parameters and Signal Flow Graphs; Power Gain Expressions and VSWR Calculations; Stability Considerations; Constant Gain Circles; Simultaneous Conjugate Match; Operating Power Gain Circles; Available Power Gain Circles; VSWR Circles and DC Bias Networks; Noise in Microwave Systems; Constant Noise Figure Circles; Design of Low-Noise Amplifier; Broad- Band Amplifier Design; High-Power Amplifier Design; Two-Stage Amplifier Design; Oscillation Condition.

Relationship to Student Outcomes

ECE 488 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
3. An ability to communicate effectively with a range of audiences.
6. An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

Syllabus Prepared By

Syllabus updated on 3/29/2022
Course Units
3
ECE 503A Mathematical Methods for Optics & Photonics

Required course: No

Course Level

Graduate

Course Description

This course covers the basic mathematics needed for an in-depth understanding of the science and technology of fiber-optical communication systems. Every mathematical tool/technique developed in this course will first be motivated by the relevant application. The students are not expected to have a broad-based prior knowledge of the topics covered in this course, but they should generally be familiar with the basics of algebra, Euclidean geometry, trigonometry, integral and differential calculus, simple differential equations, and the rudiments of complex number analysis. The course will cover Complex Analysis, Fourier transform theory, and method of stationary phase (in the context of optical diffraction), vector algebra, linear algebra, ordinary and partial differential equations (e.g., Maxwell's electrodynamics, wave equation, diffusion equation), special functions (e.g., Bessel functions needed to study the guided modes of optical fibers), and probability theory (needed for understanding various sources of noise in communication systems, photodetection theory, digital communication via noisy channels, Information theory, etc.). Graduate-level requirements include completion of additional readings and additional problems on various homework assignments.

Enrollment Requirements

Familiarity with basic calculus, Euclidean geometry, algebra, trigonometry and the complex number system.

Course Units
3
ECE 506 Reconfigurable Computing

Required course: No

Course Level

Graduate

Course Description

In this class, we investigate the state-of-the-art in reconfigurable computing both from a hardware and software perspective; understand both how to architect reconfigurable systems and how to apply them to solving challenging computational problems. The purpose of this course is to prepare students for engaging in research on reconfigurable computing. Initially, we review in detail the basic building blocks of most reconfigurable computers. Characteristics of FPGA architecture such as the organization of device logic and interconnection resources are examined to quantify hardware limitations. These physical limitations are then contrasted with computer-aided design issues such as the selection of circuit component locations in devices (the placement problem) and subsequent circuit interconnection between components (the routing problem). We then focus on the architecture for existing multi-FPGA systems and on compilation techniques for mapping applications described in a hardware description language to reconfigurable hardware. We will explore the question of “What makes an application suitable for reconfigurable computing?” with case studies in bioinformatics, image processing, video Processing, cryptography, molecular dynamics and computational fluid dynamics. We evaluate the FPGA based application acceleration with the emerging multicore architectures from the perspectives of price/performance and performance/watt. Specific contemporary reconfigurable computing systems are examined to identify existing system limitations and to highlight opportunities for research in dynamic and partial configuration areas.

Enrollment Requirements

ECE 562 and ECE 574A

Course Texts

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation, Scott Hauck, André DeHon, Morgan Kaufman, 2007.

Other reading material will be either presented in the class or available as online papers.

Schedule

150 minutes lecture per week

Assessment

  • Homework: 3-5 assignments
  • Project: 1 project
  • Exams: 1 midterm exam
  • Typical grading policy: 20% midterm, 50% project, 20% homework, 10% participation
Course Units
3
ECE 511 Numeric Modeling of Physics & Biological Systems

Required course: No

Course Level

Graduate

Course Description

This course combines themes from mechanics, electromagnetics, thermal physics, and neural networks with an introduction to numerical methods as well as the use of MATLAB. Students will become familiar with the underlying theory for a variety of systems in physics and biology (e.g., harmonic, anharmonic and coupled oscillators; electric fields of electric lenses; geo-thermal power station; and artificial neural networks), derive the necessary mathematical equations describing these systems, learn the necessary numerical methods to solve the underlying equations, and implement the system equations and numerical methods in MATLAB to simulate these systems. As a result, students will be prepared to formulate problems or model systems in physics, biology, and related disciplines, and to solve them numerically or in simulation.

Enrollment Requirements

ECE 381, MATH 254, PHYS 141, PHYS 143, ECE 175, or consult with instructor

Course Texts

  • Numeric Modeling of Physics & Biological Systems, W. Fink (class notes)
  • Numerical Recipes in C: The Art of Scientific Computing, W.H. Press, B.P. Flannery, S.A. Teukolsky, et al., Cambridge University Press, Cambridge, NY
  • MATLAB for Engineers, H. Moore, 3rd Edition, Pearson
  • Theoretical Physics on the Personal Computer, E.W. Schmid, G. Spitz, W. Loesch, 2nd Ed., Springer, ISBN-10: 3540522433, ISBN-13: 978-3540522430
  • Neural Networks: An Introduction, B. Mueller, J. Reinhardt, Berlin: Springer
  • Introduction to the Theory of Neural Computation (Lecture Notes Vol. 1), J. Hertz, A. Krogh, R.G. Palmer, Reading, MA: Addison-Wesley
Course Units
3
ECE 514A Photovoltaic Solar Energy Systems

Required course: No

Course Level

Graduate

Course Description

This course is intended to provide an introduction to the theory and operation of different types of photovoltaic devices, the characteristics of solar illumination, and the advantages and characteristics of concentrating and light management optics. The physical limits on photovoltaic cell performance and practical device operation will be analyzed. The main device emphasis will focus on different types of silicon photovoltaic cells including crystalline, amorphous, multi-crystalline, and thin film solar cells. An overview of other types of photovoltaic cells including multi-junction III-V, CdTe, CuIn(Ga)Se2, and organics will also be given. A discussion of radiometric and spectral properties of solar illumination will be presented and the impact of these factors on solar cell design will be explored. Techniques for increasing the performance of solar cells by light trapping, photon recycling, and anti-reflection coatings will be covered. The design and operation of imaging and non-imaging concentrators will also be discussed. Basic experiments related to PV cell measurements and the optical properties of concentrators are also planned for the course.

Enrollment Requirements

Graduate standing

Course Texts

Honsberg, Christiana, and Stuart Bowden. PVCDROM. Solar Power Labs at ASU. Online.

Class text (not required): Applied Photovoltaics 2nd Ed., S.R. Wenham, M.A. Green, M.E. Watt, and R. Corkish, Earthscan, ISBN-13 978-84407-401-3 (2007). 

Recommended:

  • The Physics of Solar Cells, Jenny Nelson, Imperial College Press, 2006.
  • Physics of Solar Cells, 2nd Ed., Peter Wurfel, Wiley-VCH, ISBN: 978-3-527-40857-6 (2009).

Schedule

150 minutes lecture per week; four laboratories per semester

Assessment

  • Homework: 6-7 assignments
  • Laboratory: 4 lab experiments
  • Class Paper: Research paper review
  • Exams: 1 midterm exam, 1 final exam
  • Grading policy: 20% midterm exam, 15% homework, 10% research paper review, 10% lab experiments, 10% system design project, 35% final exam
Course Units
3
ECE 542 Digital Control Systems

Required course: No

Course Level

Graduate

Course Description

Modeling, analysis and design of digital control systems. A/D and D/A conversions. Z-transforms. Time and frequency domain representations. Stability. Microprocessor-based designs.

May be convened with ECE 442.

Enrollment Requirements

ECE 340

Course Texts

Schedule

150 minutes lecture per week

Assessment

  • Homework: 10 problem sets during semester
  • Exams: 3 in-class examinations, 1 final exam
  • Graduate-level requirements include additional homework and a term project
Course Units
3
ECE 555 Introduction To Quantum Mechanics and Quantum Information Processing

Course Level

Graduate

Course Description

This course is a self-contained introduction to quantum mechanics, quantum information, and quantum computing. The course starts with basics of linear spaces, including basis vectors, Gram-Schmidt procedure, Dirac algebra, Hermitian conjugation, eigenvalues and eigenvectors, and commutator. It continues with the principles of quantum mechanics including photon polarization, state vectors, operators, density operators, measurements, and dynamics of a quantum system, spin-1/2 systems and entanglement. The next chapter is devoted fundamentals of quantum computing, including single qubit gates, multiple qubit gates, controlled operations and universal quantum gates. Further, after introduction of quantum parallelism, important quantum algorithms are studied such as Deutsch's and Deutsch-Jozsa algorithms, Grover search algorithm and quantum Fourier transform. The next chapter will be devoted to physical realization of quantum information processing including nuclear magnetic resonance, ion traps, photonic realization, cavity quantum electrodynamics, and quantum dots. We then study various applications of quantum information processing including quantum teleportation, superdense coding and quantum cryptography. Course concludes with various quantum channel models and basics of quantum error correction. As a dual numbered course, the graduate level version will include more challenging homework problem sets and exam problems, as well as a comprehensive course project.

Course Units
3
ECE 562 Computer Architecture And Design

Required course: No

Course Level

Graduate

Course Description

This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. It provides basic knowledge, fundamental concepts, design techniques and trade-offs, machine structures, technology factors, software implications, and evaluation methods and tools required for understanding and designing modern computer architectures, including multicores, embedded systems and parallel systems.

The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks.

The first part of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control.

The second part goes into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern computing system. Topics include superscalar execution; branch prediction; out-of-order execution; register renaming and memory disambiguation; VLIW, vector and multithreaded processors; memory protection, translation and virtualization; and memory synchronization, consistency and coherence.

The third part addresses parallel computing, including multicore architectures, datacenters and cloud computing, and others.

Graduate-level students will be required to complete a term paper and extra homework.

Enrollment Requirements

ECE 175, ECE 274, ECE 369A or consent of instructor

Course Texts

Computer Architecture: A Quantitative Approach, J.L. Hennessy and D.A. Patterson, 5th Edition. Morgan Kaufmann Publishers, 2011.

Other reading material will be either presented in class or made available online.

Schedule

150 minutes lecture per week

Summary

Intended to provide students with an in-depth study of computer architecture and design. Provides a basic knowledge and ability required for understanding and designing standard and novel computer architectures. Topics include design methodologies at various levels, instruction set design, ALU design, memory organization and design, cache design, virtual memories, interleaved memories, associative memories, control organization and design, hardwired control, micro-programmed control, pipelining, superscalar and super-pipelining, RISC design, vector processing, and others.

Assessment

  • Homework: 4-6 homework problem sets
  • Exams: 2 in-class exams
  • Project: 1 semester-long project completed in 3 phases
  • Computer usage: Assembly and C programming exercises
Course Units
3
ECE 564 Advanced Topics In Computer Networks

Required course: No

Course Level

Graduate

Course Description

Current state of the Internet; multimedia requirements; quality of service in IP networks; RSVP; real-time protocol (RTP); differentiated-services (Diffserv) architecture; traffic control; traffic policing and admission control; burstiness and traffic characterization; flow control; TCP enhancements; fairness and protection; packet scheduling and buffer management; inter-domain routing (BGP protocol); intra-domain routing (OSPF protocol); hierarchical routing; web caching; medium access control in wireless LANs; mobile ad hoc networking (routing and MAC protocols, power control, topology control); addressing schemes and MAC design for sensor networks; and others.

Enrollment Requirements

Introductory course on computer networks

Course Texts

Class notes will be provided in several parts, which can be purchased from the EES Copy Center in Room 137 of the Harvill Building. Occasionally, notes, supplemental material, homework assignments, quizzes, etc., will be sent by email or will be posted on the class website.

Several technical articles from the literature will be assigned throughout the semester. Their titles will be announced in class and posted on the class site. Electronic copies of such articles can often be obtained from the UA Digital Library. Material not available in electronic form can be purchased from the EES Copy Center. Papers will be continually assigned throughout the semester.

Other references include:

  • IETF RFCs and IEEE standards.
  • Selected chapters from various books (copies can be purchased from the EES Copy Center).

Schedule

150 minutes lecture per week

Course Links

Summary

The goal of this course is to expose students to recent advances in wired and wireless networks, with focus on the architectural and protocol aspects underlying the design and operation of such networks. These aspects include medium access protocols, routing protocols, quality-of-service provisioning, traffic control, flow control, protocols for wireless LANs, ad hoc networks, sensor networks, etc. In the process of learning network architectures and protocols, students will be exposed to various analytical methods that are used in the design and engineering of next-generation networks. They will also use simulations to evaluate the performance of various design concepts.

Assessment

  • Homework (mini-projects): 4-6 assignments
  • Exams: 1 midterm exam, 1 final exam
  • Quizzes: 4-5
  • Class participation
  • Typical grading policy: 20% midterms, 25% final exam, 25% homework, 20% quizzes, 10% class participation. 
Course Units
3
ECE 568 Introduction To Parallel Processing

Required course: No

Course Level

Graduate

Course Description

This course is intended to introduce graduate students to the field of modern computer architecture design stressing speedup and parallel processing techniques. The course is a comprehensive study of parallel processing techniques and their applications from basic concepts to state-of-the-art parallel computer systems. Topics to be covered in this course include the following: First, the need for parallel processing and the limitations of uniprocessors are introduced. Next, a substantial overview and basic concepts of parallel processing and their impact on computer architecture are introduced. This will include major parallel processing paradigms such as pipelining, superscalar, superpipeline, vector processing, multithreading, multi-core, multiprocessing, multicomputing, and massively parallel processing. We then address the architectural support for parallel processing such as 1) parallel memory organization and design; 2) cache design; 3) cache coherence strategies; 4) shared-memory versus distributed memory systems; 5) symmetric multiprocessors (SMPs), distributed-shared memory (DSM) multiprocessors, multicomputers, and distributed systems; 6) processor design (RISC, superscalar, superpipeline, multithreading, multi-core processors, and speculative computing designs); 7) communication subsystem; 8) computer networks, routing algorithms and protocols, flow control, reliable communication; 9) emerging technologies (such as optical computing, optical interconnection networks, optical memories); 10) parallel algorithm design and parallel programming and software requirements,; and 11) case studies of several commercial parallel computers from the TOP500 list of supercomputers.

Enrollment Requirements

ECE 369

Schedule

150 minutes lecture per week

Assessment

  • Homework: 3-5 assignments
  • Project: 1 term paper
  • Exams: 2 midterm exams
  • Typical grading policy: 50% midterms, 20% project, 25% homework, 5% participation
Course Units
3
ECE 574A Computer-Aided Logic Design

Required course: No

Course Level

Graduate

Course Description

This course is an introduction to computer-aided logic design. This is a highly active research area, enabling the design of increasingly complex digital systems. In this course we will mainly focus on three areas: specification, synthesis and optimization. We will look at how to specify functionality at a variety of abstractions, use industry-standard tools to simulate these designs, investigate some of the underlying optimization techniques utilized, as well as develop your own tools. Topics include, but are not limited to: 1) Register-Transfer Level, or RTL, Design, 2) Behavioral Synthesis, 3) Optimization and Tradeoffs of Combinational and Sequential Circuits, 4) Exact and Heuristic Minimization of Two-Level Circuits.

Students will be expected to implement a variety of Verilog and C/C++ projects throughout the semester. While specific programming assignments may change with the course offering, projects typically focus on the implementation of optimization and synthesis methods discussed in class, as well as the RTL design. 

Enrollment Requirements

ECE 275

Course Texts

No textbook is required. The class notes and slides are sourced from the following materials:

  • Digital Design, Frank Vahid, John Wiley & Sons, ISBN 0470044373
  • Verilog for Digital Design, Frank Vahid and Roman Lysecky, John Wiley & Sons, ISBN 9780470052624
  • Logic Synthesis and Verification Algorithms, Gary D. Hachtel and Fabio Somenzi, Springer, ISBN 0387310045
  • Logic Minimization Algorithms for VLSI Synthesis, Robert K. Brayton, Gary D. Hathtel, C. McMullen, and Alberto L. Sangiovanni-Vincentelli, Kluwer Academic Publishers, ISBN 0898381649
  • Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest, McGraw-Hill, 0070131430
  • Synthesis and Optimization of Digital Circuits, Giovanni De Micheli, McGraw-Hill, ISBN 0070163332

Schedule

150 minutes lecture per week

Assessment

  • Exam: 4 (lowest score dropped)
  • Project: 4 programming projects
  • Participation: 12-15 participation activities (1 dropped)
  • Typical grading policy: 55% exams, 40% programming assignments, 5% participation/in-class exercises
Course Units
3
ECE 581B Electromagnetic Field Theory

Required course: No

Course Level

Graduate

Course Description

This course is structured as a sequential, second course that follows ECE 581A. In ECE 581A, the fundamental concepts and analytical techniques associated with engineering electromagnetics were introduced. These concepts and the associated analytical tools were then used to investigate a variety of canonical problems in the rectangular coordinate system. In ECE 581B, these concepts will be extended to the analysis of propagation, scattering, and diffraction problems in the cylindrical and spherical coordinate systems. These problems include metallic and dielectric waveguides, closed and open guiding structures, plane wave scattering from cylinders, wedges, and spheres; line source scattering from cylinders and wedges; and dipole scattering from spheres. Integral equation techniques and the method of moments will also be discussed.

As with ECE 581A, ECE 581B class material will emphasize understanding and analysis tools. The material is a complete exposure at an advanced graduate level. This theoretical study provides the student with the basis to deal with a wide range of practical topics including microwave engineering, millimeter wave engineering, optical engineering, antennas, sensors remote sensing, electromagnetic interference and electromagnetic compatibility. Understanding the fundamentals of electromagnetics is intrinsic to understanding how to analyze and design various types of components, devices, and systems for all of these applications and more.

Enrollment Requirements

ECE 581A

Course Texts

Advanced Engineering Electromagnetics, by C.A. Balanis, John Wiley and Sons Inc., New York, 1989.

Schedule

150 minutes lecture per week

Assessment

  • Homework: 10-13 assignments
  • Project
  • Midterm exam
  • Final exam
  • Typical grading policy: 25% midterms, 35% final exam, 25% project/report, 15% homework
Course Units
3
ECE 632 Advanced Optical Communication Systems

Course Description

the trade-offs related to the system engineering process. Topics include advanced chromatic dispersion compensation, PMD compensation and the nonlinearity management. The spectral efficiency limits will be described and techniques to achieve it, such as turbo equalization, forward error correction (FEC), and coded modulation. Advanced modulation formats, such as various multilevel modulations and OFDM, and constrained coding techniques suitable to deal with fiber nonlinearities will be presented. Further, the spatial-domain based multiplexing and modulation will be studied. The physics behind parametric amplification will be presented as well as its application to all-optical regeneration, wavelength conversion, and multibanded switching. Other topics include soliton and dispersion-managed soliton transmission.

Each chapter from course syllabus will be followed with a comprehensive homework. A semester-long project in which students will be able to design a high-speed optical transmission system using the concepts introduced in this course is predicted.

Enrollment Requirements

ECE 430/530 or equivalent

Course Texts

M. Cvijetic, I.B. Djordjevic, Advanced Optical Communication Systems and Networks. Artech House, January 2013.

Optional references:

Assessment

Homework will be project-oriented and given after every chapter from course syllabus. One semester-long project will be given, which will have theoretical part, simulation part and experimental demonstration component.

Typical grading policy: 20% homework, 30% project, 20% midterm, 30% final exam.

Course Units
3
ECE 633 Quantum Information Processing and Quantum Error Correction

Required course: No

Course Level

Graduate

Course Description

This course is a self-contained introduction to quantum information, quantum computation, and quantum error-correction. The course starts with basic principles of quantum mechanics including state vectors, operators, density operators, measurements, and dynamics of a quantum system. The course continues with fundamental principles of quantum computation, quantum gates, quantum algorithms, and quantum teleportation. A significant amount of time has been spent on quantum error correction codes (QECCs), in particular on stabilizer codes, Calderbank-Shor-Steane (CSS) codes, quantum low-density parity-check (LDPC) codes, subsystem codes (also known as operator-QECCs), topological codes and entanglement-assisted QECCs. The next topic in the course is devoted to the fault-tolerant QECC and fault-tolerant quantum computing. The course continues with quantum information theory. The next part of the course is spent investigating physical realizations of quantum computers, encoders and decoders; including photonic quantum realization, cavity quantum electrodynamics, and ion traps. The course concludes with quantum key distribution (QKD).

The course should alternate with ECE 638: Wireless Communications.

Enrollment Requirements

ECE 501B or equivalent; typically, basic linear algebra is sufficient

Course Texts

I.B. Djordjevic, Quantum Information Processing and Quantum Error Correction. Elsevier/Academic Press, 2012.

Summary

This course offers in-depth exposition on the design and realization of a quantum information processing and quantum error correction. The successful student will be ready for further study in this area, and will be prepared to perform independent research. The student completed the course will be able design the information processing circuits, stabilizer codes, CSS codes, subsystem codes, topological codes and entanglement-assisted quantum error correction codes; and propose corresponding physical implementation. The student completed the course will be proficient in fault-tolerant design as well.

Assessment

Homework will be assigned approximately every two weeks.  

Typical grading policy: 20% homework, 30% project, 15% midterm exam, 35% final exam.

Course Units
3
ECE 635 Error Correction

Required course: No

Course Level

Graduate

Course Description

This graduate course provides an in-depth treatment of modern error correction codes and decoding algorithms.

Error correcting codes (ECC) are an integral part of modern day communications, computer and data storage systems and play a vital role in ensuring he integrity of data in the presence of errors. In the most general terms, the purpose of error correcting code is to protect user data, and this is achieved by appending redundant, so called parity bits, along with the data bits. Low-density parity-check (LDPC) codes are a class of error-correction codes that have revolutionized communications and data storage industry. They have been the focus of intense research over more than a decade because they can approach theoretical limits of reliable transmission over various communications and storage channels even when decoded by sub-optimal low complexity iterative algorithms. The past decade in information theory has been marked by the quest for low complexity decoders, and the emergence of iterative message passing decoders.

Efficient and high-speed implementations coupled with recent advances in integrated circuit technologies, have made LDPC codes de-facto industry standards in a number of systems. With emerging technologies requiring much faster processing speeds with stricter energy utilization constraints while still requiring very low target error-rates, there has been an increasing need for reduced-complexity iterative decoders that provide improved performance.

Wireless networks, satellite communications, deep-space communications, power line communications are among applications where the LDPC codes are the standardized ECC scheme. More specifically LDPC codes are used as an error correcting scheme in: digital video broadcast over satellite (DVB-S2 Standard) and over cable (DVB-C2 Standard), terrestrial television broadcasting (DVB-T2, DVB-T2-Lite Standards), GEO-Mobile Radio (GMR) satellite telephony (GMR-1 Standard), local and metropolitan area networks (LAN/MAN) (IEEE 802.11 (WiFi)), wireless personal area networks (WPAN) (IEEE 802.15.3c (60 GHz PHY)), wireless local and metropolitan area networks (WLAN/WMAN) (IEEE 802.16 (Mobile WiMAX), near-earth and deep space communications (CCSDS), wire and power line communications ( ITU-T G.hn (G.9960)), ultra-wide band technologies (WiMedia 1.5 UWB), etc. [11]. Very recently LDPC codes have found their way in magnetic hard disk drives and optical communications, and they are the main candidates for ECC system in ash memories.

Enrollment Requirements

Graduate standing

Course Texts

  • Tom Richardson and Ruediger Urbanke, Modern Coding Theory
  • S. Lin and W. Ryan, Channel Codes: Classical and Modern
  • D.J.C. Mackay, Information Theory, Inference & Learning Algorithms
  • M.I. Jordan, An Introduction to Probabilistic Graphical Models

Course Links

Assessment

  • Homework: assigned but not graded
  • 1-2 projects
  • Exams: 2 midterm exams, 1 final exam
  • Typical grading policy: 30% midterms, 35% final exam, 15% homework, 20% project
Course Units
3
ECE 638 Wireless Communications

Required course: No

Course Level

Graduate

Course Description

This course will cover advanced topics in wireless communications for voice, data, and multimedia. It will also cover optical wireless communications, both indoor and free-space optical communications, and medical wireless communications. The course begins with a brief overview of current wireless systems and standards. It then characterizes the wireless channel, including path loss for different environments, random log-normal shadowing due to signal attenuation, and the flat and frequency-selective properties of multipath fading. Next it examines the fundamental capacity limits of wireless channels and the characteristics of the capacity-achieving transmission strategies. The next focus will be on practical digital modulation techniques and their performance under wireless channel impairments. A significant amount of time will be spent on multiple antenna techniques: MIMO channel model, MIMO channel capacity, and space-time coding. The section on multicarrier modulation provides comprehensive treatment of orthogonal frequency division multiplexing (OFDM). We will further study ultra wideband (UWB) communications, software defined radio and cognitive radio. Next section is related to optical wireless communications (OWC), in particular infrared OWC, visible light communications and free-space optical (FSO) communications. The section on wireless medical communications will cover implanted antennas inside biological tissue, antennas inside a human head, and antennas inside a human body. The course concludes with coding for wireless channels, adaptive modulation, adaptive coding and multiuser detection.

Course Texts

  • A. Goldsmith, Wireless Communications. Cambridge: Cambridge University Press, 2005.
  • R.A. Carrasco and M. Johnston, Non-Binary Error Control Coding for Wireless Communication and Data Storage. John Wiley & Sons, Ltd., 2005.
  • M. Ghavami, L.B. Michael and R. Kohno, Ultra Wideband Signals and Systems in Communication Engineering. John Wiley & Sons, Ltd., 2007.
  • D. Tse, and P. Viswanath, Fundamentals of  Wireless Communication. Cambridge University Press, 2005.
  • T.M. Duman and A. Ghrayeb, Coding for MIMO Communication Systems. John Wiley & Sons, Ltd., 2007.
  • E. Biglieri, R. Calderbank, A. Constantinides, A. Goldsmith, A. Paulraj and H.V. Poor, MIMO Wireless Communications. Cambridge University Press, 2007.

Schedule

150 minutes lecture per week
Course Units
3
ECE 678 Wireless Protocols

Required course: No

Course Level

Graduate

Course Description

In recent years, we have witnessed significant advances in wireless communications and networks. On the access side, 802.11-based wireless LANs, or WLANs, have been deployed in virtually all university campuses, corporations, airports, and hotels, forming many wireless clouds at the edge of the Internet. Wireless mesh and regional-area networks is on the rise. Through advanced beam-forming antennas and MIMO capabilities, they promise to bridge the connectivity between WLAN clouds and enable ubiquitous and seamless wireless communications in metropolitan areas. Wireless sensor networks have been deployed for various civilian and military applications, including environment monitoring, detection of chemical hazards, border crossing, weather forecasting, etc. High-bandwidth wireless communications using ultra-wide band (UWB) technology is gaining momentum, and will soon revolutionize home networking and bring to light a new generation of consumer electronics. Smart radios with spectrum-adaptive capabilities (aka cognitive radios) are emerging as a new paradigm for radio communications. Office and personal area networks using Bluetooth are becoming commonplace.

The purpose of this seminar course is to expose students to recent advances in wireless networks, focusing on the theoretical underpinnings, protocol design, and architectural concepts. Various topics will be covered through representative papers from top-tier conferences (e.g., MobiCom, MobiHoc, Sigcomm, INFOCOM, etc.), IEEE and ACM journals, magazines, and regulatory documents and standards (including FCC specifications). The class will emphasize discussion and debate, with the goal of strengthening students’ critical and analytical thinking.

Enrollment Requirements

Graduate standing

Course Texts

No required text.

Material will consist of assigned research papers, tutorial/survey articles and standards documents (including FCC specifications). In addition, the slides of presentations given by the instructor and students will be made available to the class, and will constitute part of the class material. In each lecture, 1-2 papers will typically be assigned as "required." Additional papers may be provided as "recommended reading."

Schedule

150 minutes lecture per week

Course Links

Assessment

  • Presentations: 1 per student
  • Quizzes: 12-15
  • Class participation
  • Final exam
  • Typical grading policy: 30% presentations, 20% final exam, 30% quizzes, 20% class participation
Course Units
3
OPTI 595B Information in a Photon

This course will develop the mathematical theory of noise in optical detection from first principles, with the goal of understanding the fundamental limits of efficiency with which one can extract information encoded in light. We will explore how optical-domain interferometric manipulations of the information bearing light, i.e., prior to the actual detection, and the use of detection-induced electro-optic feedback during the detection process can alter the post-detection noise statistics in a favorable manner, thereby facilitating improved efficiency in information extraction. Throughout the course, we will evaluate applications of such novel optical detection methods in optical communications and sensing, and compare their performance with those with conventional ways of detecting light. We will also compare the performance of these novel detection methods to the best performance achievable---in the given problem context---as governed by the laws of (quantum) physics, without showing explicit derivations of those fundamental quantum limits. The primary goal behind this course is to equip students (as well as interested postdocs and faculty) coming from a broad background who are considering taking on theoretical or experimental research in quantum enhanced photonic information processing, with intuitions on a deeper way to think of optical detection, and to develop an appreciation of: (1) the value of a full quantum treatment of light to find fundamental limits of encoding information in the photon, and (2) how pre-detection manipulation of the information-bearing light can help dispose it information favorably with respect to the inevitable detection noise.

Course Units
3
OPTI 613 Introduction to Infrared Systems

This courses provides the background, theory, and practice of how to design, analyze, and test high performance infrared imaging systems. The course is presented in three sections. The first section provides a brief review of the basic mathematics, radiometry, and diffraction theory needed to be successful in imaging system performance calculations. The second section includes a detailed look at all the components that make up an electro-optical or infrared imaging system to include targets, atmospherics, optics, detectors, electronics, signal and image processing, displays and the human visual system. The student is taught how to calculate the component resolution (modulation transfer function) and sensitivity for each of the components. Modulation Transfer Functions and optical throughput along with signal-to-noise is determined for each imaging system component. The student is taught how to determine whether a system is turbulence-limited, detector-limited, diffraction or aberration-limited, display-limited, or human vision system limited. The third section teaches the student how to combine all the component transfer functions and throughput (with infrared radiation) to determine the imaging system contrast threshold function. This system CTF is used in the design of imaging systems to accomplish some object discrimination task (e.g., detection, recognition, or identification). System theory, laboratory performance, and field performance are covered. These concepts apply to both infrared and electro-optical imaging system performance.

Course Units
3
SIE 571 Systems Cyber Security Engineering

The purpose of this course is to introduce selected topics, issues, problems, and techniques in the area of System Cyber Security Engineering (SCSE), early in the development of a large system. Students will explore various techniques for eliminating security vulnerabilities, defining security specifications / plans, and incorporating countermeasures in order to achieve overall system assurance. SCSE is an element of system engineering that applies scientific and engineering principles to identify, evaluate, and contain or eliminate system vulnerabilities to known or postulated security threats in the operational environment. SCSE manages and balances system security risk across all protection domains spanning the entire system engineering life-cycle. The fundamental elements of cyber security will be explored including: human cyber engineering techniques, penetration testing, mobile and wireless vulnerabilities, network mapping and security tools, embedded system security, reverse engineering, software assurance and secure coding, cryptography, vulnerability analysis, and cyber forensics. After a fundamental understanding of the various cyber threats and technologies are understood, the course will expand upon the basic principles, and demonstrate how to develop a threat / vulnerability assessment on a representative system using threat modeling techniques (i.e. modeling threats for a financial banking system, autonomous automobile, or a power distribution system). With a cyber resilience focus, students will learn how to identify critical use cases or critical mission threads for the system under investigation, and how to decompose and map those elements to various architectural elements of the system for further analysis. Supply chain risk management (SCRM) will be employed to enumerate potential cyber threats that could be introduced to the system either unintentionally or maliciously throughout the supply chain. The course culminates with the conduct of a realistic Red Team / Blue Team simulation to demonstrate and explore both the attack and defend perspectives of a cyber threat. The Red Team will perform a vulnerability assessment of the prospective system, with the intention of attacking its vulnerabilities. The Blue Team will perform a vulnerability of the same system with the intention of defending it against cyber threats. A comparison will be made between the outcomes of both teams in order to better understand the overarching solutions to addressing the threats identified. Upon completion of the course, students will be proficient with various elements of cyber security and how to identify system vulnerabilities early on in the system engineering lifecycle. They will be exposed to various tools and processes to identify and protect a system against those vulnerabilities, and how to develop program protection plans to defend against and prevent malicious attacks on large complex systems. Graduate students will be given an additional assignment to write a draft Program Protection Plan (PPP) for the system that the class performed the threat analysis for. Program protection planning employs a step-by-step analytical process to identify the critical technologies to be protected; analyze the threats; determine program vulnerabilities; assess the risks; and apply countermeasures. A PPP describes the analysis, decisions and plan to mitigate risks to any advanced technology and mission-critical system functionality.

Course Units
3
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