Duluth campus
 
Duluth Campus

Electrical Engineering M.S.E.E.

Electrical Engineering
Swenson College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
EE Graduate Program, 271 MWAH, 1023 University Drive, Duluth, MN 55812 (218-726-6830; fax: 218-726-7267)
  • Program Type: Master's
  • Requirements for this program are current for Fall 2017
  • Length of program in credits: 31
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science in Electrical Engineering
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The master of science in electrical engineering (MSEE) combines scholarship and research in a program oriented toward students and engineering practitioners in the private and public sectors who are interested in advanced coursework and applied research. The program requires 31 credits of graduate coursework and research with focus on the departmental faculty's research areas of control systems, communications, signal processing, VLSI, nanoscale optoelectronics and photovoltaics, biomedical engineering, and intelligent transportation systems.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
An undergraduate degree in electrical engineering, computer engineering, or computer science. Applicants from related majors may apply but may be required to take additional undergraduate courses.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
    • Paper Based - Total Score: 550
  • IELTS
    • Total Score: 6.5
  • MELAB
    • Final score: 80
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan A: Plan A requires 15 to 21 major credits, 0 to 6 credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 25 to 31 major credits and 0 to 6 credits outside the major. The final exam is oral. A capstone project is required.
Capstone Project:The Plan B project is for those students or practicing engineers who wish to have a hands-on learning experience solving technical problems, preferably by teaming up with an industrial counterpart. Plan B students are required to take a minimum of 1 and a maximum of 3 project credits.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 1 semesters must be completed before filing a Degree Program Form.
The master of science in electrical engineering (MSEE) degree requires 31 semester credits. The program offers two degree plans, plan A and plan B. Plan A is research oriented and it requires students to complete a research thesis (10 credits) and additional coursework. Plan B is coursework oriented with a project (1~3 credits) as the research component. For both thesis research and project research, a student is expected to identify a research adviser during the first two semesters in the program. Plan A: Thesis Option Students must complete a minimum of 31 semester credits including 10 thesis credits and 21 coursework credits. Plan A students must register for 10 thesis (EE 8777) credits, and write and defend a thesis on original research. Students may take up to 6 credits from graduate programs in related fields outside of EE. All courses must be 4xxx or above; a maximum of 6 credits in courses at 4xxx level is allowed, a minimum of 3 credits in courses at 8xxx is required; excluding EE 8001 and EE 8777. Plan B: Project Option Students must complete a minimum of 31 semester credits including project credits. Plan B students must register for at least 1 project credit (EE 8222), and write and defend a project report. Students may take up to 6 credits from graduate programs in related fields outside of EE. All courses must be 4xxx or above; a maximum of 6 credits in courses at 4xxx level is allowed, a minimum of 3 credits in courses at 8xxx is required, excluding EE 8001 and EE 8222.
Plan A or Plan B
Plan A
Take exactly 1 credit(s) from the following:
· EE 8001 - Graduate Professional Communication Seminar (1.0 cr)
Take exactly 10 credit(s) from the following:
· EE 8777 - Thesis Credits: Master's (1.0-18.0 cr)
Take 20 or more credit(s) from the following:
· EE 4305 - Computer Architecture (4.0 cr)
· EE 4311 - Design of Very Large-Scale Integrated Circuits (3.0 cr)
· EE 4321 - Computer Networks (3.0 cr)
· EE 4341 - Digital Systems (4.0 cr)
· EE 4501 - Power Systems (4.0 cr)
· EE 4611 - Introduction to Solid-State Semiconductors (3.0 cr)
· EE 4896 - Co-op in Electrical Engineering (1.0 cr)
· EE 5151 - Digital Control System Design (3.0 cr)
· EE 5211 - Advanced Analog Integrated Circuit Design (3.0 cr)
· EE 5315 - Multiprocessor-Based System Design (3.0 cr)
· EE 5477 - Antennas and Transmission Lines (3.0 cr)
· EE 5479 - Antennas and Transmission Lines Laboratory (1.0 cr)
· EE 5501 - Energy Conversion System (3.0 cr)
· EE 5522 - Power Electronics I (3.0 cr)
· EE 5533 - Grid- Resiliency, Efficiency and Technology (3.0 cr)
· EE 5611 - Microelectronics Technology (3.0 cr)
· EE 5741 - Digital Signal Processing (3.0 cr)
· EE 5742 - Pattern Recognition and Machine Learning (4.0 cr)
· EE 5745 - Medical Imaging (3.0 cr)
· EE 5765 - Modern Communication (4.0 cr)
· EE 5801 - Introduction to Artificial Neural Networks (3.0 cr)
· EE 5831 - Fuzzy Set Theory and Its Application (3.0 cr)
· EE 5995 - Special Topics: (Various Titles to be Assigned) (1.0-3.0 cr)
· EE 8151 - Linear Systems and Optimal Control (3.0 cr)
· EE 8741 - Digital Image Processing (4.0 cr)
· EE 8742 - Fundamentals of Signal Detection and Estimation (3.0 cr)
· EE 8765 - Digital Communications (3.0 cr)
· EE 8831 - Soft Computing (3.0 cr)
or Plan B
Take exactly 1 credit(s) from the following:
· EE 8001 - Graduate Professional Communication Seminar (1.0 cr)
Take 1 - 3 credit(s) from the following:
· EE 8222 - Master's Plan B Research and Design Project (1.0-3.0 cr)
Take 27 - 29 credit(s) from the following:
· EE 4305 - Computer Architecture (4.0 cr)
· EE 4311 - Design of Very Large-Scale Integrated Circuits (3.0 cr)
· EE 4321 - Computer Networks (3.0 cr)
· EE 4341 - Digital Systems (4.0 cr)
· EE 4501 - Power Systems (4.0 cr)
· EE 4611 - Introduction to Solid-State Semiconductors (3.0 cr)
· EE 4896 - Co-op in Electrical Engineering (1.0 cr)
· EE 5151 - Digital Control System Design (3.0 cr)
· EE 5211 - Advanced Analog Integrated Circuit Design (3.0 cr)
· EE 5315 - Multiprocessor-Based System Design (3.0 cr)
· EE 5351 - Introduction to Robotics and Mobile Robot Control Architectures (3.0 cr)
· EE 5477 - Antennas and Transmission Lines (3.0 cr)
· EE 5501 - Energy Conversion System (3.0 cr)
· EE 5522 - Power Electronics I (3.0 cr)
· EE 5533 - Grid- Resiliency, Efficiency and Technology (3.0 cr)
· EE 5611 - Microelectronics Technology (3.0 cr)
· EE 5741 - Digital Signal Processing (3.0 cr)
· EE 5742 - Pattern Recognition and Machine Learning (4.0 cr)
· EE 5745 - Medical Imaging (3.0 cr)
· EE 5765 - Modern Communication (4.0 cr)
· EE 5801 - Introduction to Artificial Neural Networks (3.0 cr)
· EE 5831 - Fuzzy Set Theory and Its Application (3.0 cr)
· EE 5995 - Special Topics: (Various Titles to be Assigned) (1.0-3.0 cr)
· EE 8151 - Linear Systems and Optimal Control (3.0 cr)
· EE 8741 - Digital Image Processing (4.0 cr)
· EE 8742 - Fundamentals of Signal Detection and Estimation (3.0 cr)
· EE 8765 - Digital Communications (3.0 cr)
 
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EE 8001 - Graduate Professional Communication Seminar
Credits: 1.0 [max 1.0]
Prerequisites: graduate student
Grading Basis: S-N or Aud
Typically offered: Every Fall
The course will help students to improve oral and written technical communication skills needed by electrical engineering professionals. The course is a required course for MSEE degree. The course includes lectures on oral and written professional communications, instructions on resume writing, attending graduate seminars and giving technical presentations. During the course, the student will submit a written and oral technical report and receive feedback from the instructor and/or an instructor from the Communication and/or Writing departments at UMD. prereq: graduate student
EE 8777 - Thesis Credits: Master's
Credits: 1.0 -18.0 [max 50.0]
Grading Basis: No Grade
Typically offered: Every Fall & Spring
(No description) prereq: Max 18 cr per semester or summer; 10 cr total required (Plan A only)
EE 4305 - Computer Architecture
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Advanced assembly language programming techniques. Memory design principles. Virtual memory. Cache memory. Processor design. Pipelined and Reduced Instruction Set Computers (RISC). Advanced microprocessor features. (3 hrs lect, 3 hrs lab) prereq: 2325
EE 4311 - Design of Very Large-Scale Integrated Circuits
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Philosophy of and techniques for designing VLSI circuits in CMOS technology. Full- and semi-custom design techniques. Digital, analog, and hybrid CMOS circuits and systems. Substantial design project required. (3 hrs lect) prereq: 3235 or instructor consent
EE 4321 - Computer Networks
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Network classification and services. Protocol and communication architectures. Hardware components: multiplexers, concentrators, bridges, routers, access servers. (3 hrs lect) prereq: 2325
EE 4341 - Digital Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Digital logic family characteristics. Medium Scale Integration (MSI) components and applications. Programmable Logic Devices (PLDs). Alternative clocking techniques. Computer arithmetic circuits and memory design. Fundamental mode asynchronous finite-state machine design. (3 hrs lect, 3 hrs lab) prereq: 2325, no graduate credit; credit will not be granted if already received for ECE 3341
EE 4501 - Power Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Fundamentals of rotating machines: DC, synchronous, and induction machines. Transformers. Power system representation. Transmission lines. Power system analysis: stability and dynamic performance. Balanced and unbalanced faults. Power system protection. (3 hrs lect, 3 hrs lab) prereq: 2006; no grad credit
EE 4611 - Introduction to Solid-State Semiconductors
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Fundamentals of solid-state semiconductors and devices. Quantum mechanical concepts and atomic states, solid-state structure, band structure, semiconductor statistics, and transport. (3 hrs lect) prereq: Phys 2012 or 2015; credit will not be granted if already received for ECE 3611
EE 4896 - Co-op in Electrical Engineering
Credits: 1.0 [max 6.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Career-related work experience with employer closely associated with student's academic area. Students must have department approval for the course prior to starting the Co-Op. Midterm status report and final written report with employer survey must be submitted to the EE department. This course cannot be counted towards EE degree requirements or EE technical electives. prereq: BSEE or MSEE standing in Electrical Engineering, department consent
EE 5151 - Digital Control System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Digital control system characteristics: transient and steady-state responses, frequency response, stability. Digital control system design using transform techniques. Controllability and observability. Design of digital control systems using state-space methods: pole placement and observer design, multivariable optimal control. Implementation issues in digital control prereq: 3151; credit will not be granted if already received for 4151
EE 5211 - Advanced Analog Integrated Circuit Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Provides ECE students with fundamental analysis and design skills for transistor-level analog integrated circuits, such as operational amplifiers, transconductance amplifiers, bandgap references, amplifier-based filters, analog-to-digital converters, digital-to analog converters and phase-locked loop. Project-oriented with a focus on transistor-level design of analog circuits from transistor sizing to layout in an integrated circuit environment such as Cadence tool sets. The expected outcomes are that students are able to design an analog system of medium complexity at transistor-level. prereq: 3235 or equiv
EE 5315 - Multiprocessor-Based System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Parallelism, interconnection networks, shared memory architecture, principles of scalable performance, vector computers, multiprocessors, multicomputers, dataflow architectures, and supercomputers. prereq: 2325; credit will not be granted if already received for 4315
EE 5477 - Antennas and Transmission Lines
Credits: 3.0 [max 3.0]
Prerequisites: 3445; credit will not be granted if already received for 4477
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Theory and performance of antennas and transmission lines. Topics: Allocation of RF spectrum, radiation theory, EM wave propagation, ground effects, interference, antenna performance metrics, transient and sinusoidal transmission line behavior, bounce diagrams, Smith chart, waveguide theory, modeling with the numerical electromagnetics code (NEC), unlicensed wireless applications, specific antenna designs and applications, class demonstrations. prereq: 3445; credit will not be granted if already received for 4477
EE 5479 - Antennas and Transmission Lines Laboratory
Credits: 1.0 [max 1.0]
Prerequisites: 5477 pre or co-req
Grading Basis: A-F or Aud
Typically offered: Every Spring
This laboratory course provides hands-on experience with designing, constructing, and measuring the performance of radio frequency (RF) antennas and transmission lines. Concepts include velocity factor, propagation, factors, characteristic impedance, tuning stubs and matching sections, resonance, parasitic elements, gain, directivity, return loss and RF safety. This course supports the theory presented in EE 5477 (Antennas and Transmission Lines) and is optional for those enrolled in or having completed EE 5477. prereq: 5477 pre or co-req
EE 5501 - Energy Conversion System
Credits: 3.0 [max 3.0]
Course Equivalencies: 02012 - EE 5501/ME 5325
Grading Basis: A-F or Aud
Typically offered: Every Fall
Theory, design and operation of conventional and alternative electrical energy conversion systems. Carbon dioxide cycle, Earth/Sun radiation balance, and environmental impacts. Power delivery systems and integration of conversion systems with the grid. Development of generation portfolios. Impact of energy policies and current energy issues. Case studies. prereq: Chem 1151 or 1153 and 1154
EE 5522 - Power Electronics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Power semiconductor devices; traditional power converters; ac-dc converters: half-wave and full-wave rectifiers; dc-dc converters: traditional and transformer derived choppers; dc-ac converters: single-phase and three-phase inverters; ac-ac converters; pulse-width modulation; applications. prereq: 3235
EE 5533 - Grid- Resiliency, Efficiency and Technology
Credits: 3.0 [max 3.0]
Prerequisites: 2006 or instructor consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Concepts and architecture of grid, smart grid and microgrid; resiliency under physical and cyber attacks; grid efficiency via sensors, networks and control; technology including standards and protocols for cybersecurity and protection of the grid; case studies and testbeds. prereq: 2006 or instructor consent
EE 5611 - Microelectronics Technology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Various fabrication processes in silicon-based microelectronic circuits and devices: lithography, oxidation, diffusion, thin film deposition, etching and integration of various technologies; material defects analysis and device characterization skills; design of fabrication process with SUPREME IV simulator; fabrication technologies involved in other devices: optical devices, MEMS and semiconductor nanostructures. prereq: 3235
EE 5741 - Digital Signal Processing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Discrete linear shift-invariant systems, z- & Fourier transform, sampling, discrete-time processing of signals, reconstruction of analog signals, filters and filter structures in direct, parallel, and cascaded forms, FIR & IIR digital filter design, impulse-invariant, bi-linear transform & window functions, FFT, introduction to image processing. prereq: 2111; credit will not be granted if already received for 4741
EE 5742 - Pattern Recognition and Machine Learning
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even, Spring Odd Year
Various methods of pattern recognition, non-parametric techniques, linear discriminant functions, support vector machines, statistical classification, min-max procedures, maximum likelihood decisions and case studies. prereq: STAT 3611, senior or graduate standing in science or engineering or instructor consent; some basic concepts in linear algebra and probability theory.
EE 5745 - Medical Imaging
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Introduction to the methods and devices for medical imaging, including x-ray imaging, x-ray computer tomography (CT), nuclear medicine (single photon planar imaging, single photon emission computer tomography (SPECT), and positron emission tomography (PET), magnetic resonance imaging (MRI), and ultrasound imaging. The physics and design of systems, typical applications, medical image processing, and tomographic reconstruction. prereq: EE (ECE) 2111, Math 3298 or instructor permission
EE 5765 - Modern Communication
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Design and analysis of modern communication systems; evaluation of analog and digital modulation techniques. (3 hrs lect, 3 hrs lab) prereq: 2111; credit will not be granted if already received for 4765
EE 5801 - Introduction to Artificial Neural Networks
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
General techniques and theory of neural networks, their applications and limitations. The course particularly addresses the design issues and learning algorithms for diverse areas of applications. prereq: CS 1521, Math 3280, Stat 3611 or instructor consent; credit will not be granted if already received for 4801
EE 5831 - Fuzzy Set Theory and Its Application
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Fuzzy sets and operations on fuzzy sets. Fuzzy relations and the extension principle. Linguistic variable and fuzzy IF-THEN rules. Fuzzy arithmetic. Fuzzy logic and approximate reasoning. Design of Fuzzy Systems from I/O data. Fuzzy logic--based control. Pattern Classifications. prereq: CS 1521, Math 3280; credit will not be granted if already received for 5831
EE 5995 - Special Topics: (Various Titles to be Assigned)
Credits: 1.0 -3.0 [max 3.0]
Prerequisites: instructor consent
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Current problems and research. Discussions, selected reading, and/or invited speakers. prereq: instructor consent
EE 8151 - Linear Systems and Optimal Control
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Spring Odd Year
State-space representations of dynamic systems. Input-output stability. Lyapunov stability. Controllability and observability. Minimal realizations. State and output feedback. Pole placement design. State observers. Linear quadratic optimal control: fixed and free end point, finite and infinite horizon. Pontryagin's Minimal Principle. Dynamic programming. prereq: 3151
EE 8741 - Digital Image Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Odd Year
Mathematical foundations and practical techniques to process and manipulate images. Students will acquire the ability to analyze two-dimensional images, dealing with mathematical representation of images, image sampling and quantization, Image Transforms, Image Enhancement, Image Restoration, Image Coding, Edge Detection, Texture Analysis, and Compression. prereq: 4741
EE 8742 - Fundamentals of Signal Detection and Estimation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Even Year
Study of detection, estimation, and signal representation, detection of signals in noise, estimation of signal parameters, linear estimation theory. Performance bounds on Estimation and Detection. Kalman-Bucy and Wiener filters. Applications to Multiuser detection, Spectrum estimation, Adaptive Wiener filter, and system identification. prereq: 2111 and Stat 3611 or their equivalents, grad student or instructor consent
EE 8765 - Digital Communications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Even Year
Overview of digital data transmission, performance analysis of digital modulation, quadrature multiplexed signaling schemes, signal-space methods in digital data transmission, information theory and block coding, convolutional coding, repeat-request system, spread-spectrum systems, satellite communications. prereq: 5765
EE 8831 - Soft Computing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Fuzzy set theory, neural networks, genetic algorithms, data clustering techniques, and several stochastic optimization methods that do not require gradient information which is aimed at solving real world decision-making, modeling, and control problem. prereq: Knowledge of linear algebra and computer programming
EE 8001 - Graduate Professional Communication Seminar
Credits: 1.0 [max 1.0]
Prerequisites: graduate student
Grading Basis: S-N or Aud
Typically offered: Every Fall
The course will help students to improve oral and written technical communication skills needed by electrical engineering professionals. The course is a required course for MSEE degree. The course includes lectures on oral and written professional communications, instructions on resume writing, attending graduate seminars and giving technical presentations. During the course, the student will submit a written and oral technical report and receive feedback from the instructor and/or an instructor from the Communication and/or Writing departments at UMD. prereq: graduate student
EE 8222 - Master's Plan B Research and Design Project
Credits: 1.0 -3.0 [max 3.0]
Grading Basis: S-N only
Typically offered: Every Fall & Spring
Provides ECE Plan B graduate students with experience in applying research, analysis, and design skills to a project of current interest to industry. Through the chosen project, the student should demonstrate the ability to achieve results in a fixed time frame and present the results to the department orally and via a technical report. prereq: Graduate student, instructor consent; credit will not be granted if already received for 8777
EE 4305 - Computer Architecture
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Advanced assembly language programming techniques. Memory design principles. Virtual memory. Cache memory. Processor design. Pipelined and Reduced Instruction Set Computers (RISC). Advanced microprocessor features. (3 hrs lect, 3 hrs lab) prereq: 2325
EE 4311 - Design of Very Large-Scale Integrated Circuits
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Philosophy of and techniques for designing VLSI circuits in CMOS technology. Full- and semi-custom design techniques. Digital, analog, and hybrid CMOS circuits and systems. Substantial design project required. (3 hrs lect) prereq: 3235 or instructor consent
EE 4321 - Computer Networks
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Network classification and services. Protocol and communication architectures. Hardware components: multiplexers, concentrators, bridges, routers, access servers. (3 hrs lect) prereq: 2325
EE 4341 - Digital Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Digital logic family characteristics. Medium Scale Integration (MSI) components and applications. Programmable Logic Devices (PLDs). Alternative clocking techniques. Computer arithmetic circuits and memory design. Fundamental mode asynchronous finite-state machine design. (3 hrs lect, 3 hrs lab) prereq: 2325, no graduate credit; credit will not be granted if already received for ECE 3341
EE 4501 - Power Systems
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Fundamentals of rotating machines: DC, synchronous, and induction machines. Transformers. Power system representation. Transmission lines. Power system analysis: stability and dynamic performance. Balanced and unbalanced faults. Power system protection. (3 hrs lect, 3 hrs lab) prereq: 2006; no grad credit
EE 4611 - Introduction to Solid-State Semiconductors
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Fundamentals of solid-state semiconductors and devices. Quantum mechanical concepts and atomic states, solid-state structure, band structure, semiconductor statistics, and transport. (3 hrs lect) prereq: Phys 2012 or 2015; credit will not be granted if already received for ECE 3611
EE 4896 - Co-op in Electrical Engineering
Credits: 1.0 [max 6.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Career-related work experience with employer closely associated with student's academic area. Students must have department approval for the course prior to starting the Co-Op. Midterm status report and final written report with employer survey must be submitted to the EE department. This course cannot be counted towards EE degree requirements or EE technical electives. prereq: BSEE or MSEE standing in Electrical Engineering, department consent
EE 5151 - Digital Control System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Digital control system characteristics: transient and steady-state responses, frequency response, stability. Digital control system design using transform techniques. Controllability and observability. Design of digital control systems using state-space methods: pole placement and observer design, multivariable optimal control. Implementation issues in digital control prereq: 3151; credit will not be granted if already received for 4151
EE 5211 - Advanced Analog Integrated Circuit Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Provides ECE students with fundamental analysis and design skills for transistor-level analog integrated circuits, such as operational amplifiers, transconductance amplifiers, bandgap references, amplifier-based filters, analog-to-digital converters, digital-to analog converters and phase-locked loop. Project-oriented with a focus on transistor-level design of analog circuits from transistor sizing to layout in an integrated circuit environment such as Cadence tool sets. The expected outcomes are that students are able to design an analog system of medium complexity at transistor-level. prereq: 3235 or equiv
EE 5315 - Multiprocessor-Based System Design
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Parallelism, interconnection networks, shared memory architecture, principles of scalable performance, vector computers, multiprocessors, multicomputers, dataflow architectures, and supercomputers. prereq: 2325; credit will not be granted if already received for 4315
EE 5351 - Introduction to Robotics and Mobile Robot Control Architectures
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Basic concepts and tools for the analysis, design, and control of robotic mechanisms. Topics include basic robot architecture and applications to dynamical systems, mobile mechanisms, kinematics, inverse kinematics, trajectory and motion planning, mobile roots, collision avoidance, and control architectures. prereq: 3151
EE 5477 - Antennas and Transmission Lines
Credits: 3.0 [max 3.0]
Prerequisites: 3445; credit will not be granted if already received for 4477
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Theory and performance of antennas and transmission lines. Topics: Allocation of RF spectrum, radiation theory, EM wave propagation, ground effects, interference, antenna performance metrics, transient and sinusoidal transmission line behavior, bounce diagrams, Smith chart, waveguide theory, modeling with the numerical electromagnetics code (NEC), unlicensed wireless applications, specific antenna designs and applications, class demonstrations. prereq: 3445; credit will not be granted if already received for 4477
EE 5501 - Energy Conversion System
Credits: 3.0 [max 3.0]
Course Equivalencies: 02012 - EE 5501/ME 5325
Grading Basis: A-F or Aud
Typically offered: Every Fall
Theory, design and operation of conventional and alternative electrical energy conversion systems. Carbon dioxide cycle, Earth/Sun radiation balance, and environmental impacts. Power delivery systems and integration of conversion systems with the grid. Development of generation portfolios. Impact of energy policies and current energy issues. Case studies. prereq: Chem 1151 or 1153 and 1154
EE 5522 - Power Electronics I
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Power semiconductor devices; traditional power converters; ac-dc converters: half-wave and full-wave rectifiers; dc-dc converters: traditional and transformer derived choppers; dc-ac converters: single-phase and three-phase inverters; ac-ac converters; pulse-width modulation; applications. prereq: 3235
EE 5533 - Grid- Resiliency, Efficiency and Technology
Credits: 3.0 [max 3.0]
Prerequisites: 2006 or instructor consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Concepts and architecture of grid, smart grid and microgrid; resiliency under physical and cyber attacks; grid efficiency via sensors, networks and control; technology including standards and protocols for cybersecurity and protection of the grid; case studies and testbeds. prereq: 2006 or instructor consent
EE 5611 - Microelectronics Technology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Fall
Various fabrication processes in silicon-based microelectronic circuits and devices: lithography, oxidation, diffusion, thin film deposition, etching and integration of various technologies; material defects analysis and device characterization skills; design of fabrication process with SUPREME IV simulator; fabrication technologies involved in other devices: optical devices, MEMS and semiconductor nanostructures. prereq: 3235
EE 5741 - Digital Signal Processing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Discrete linear shift-invariant systems, z- & Fourier transform, sampling, discrete-time processing of signals, reconstruction of analog signals, filters and filter structures in direct, parallel, and cascaded forms, FIR & IIR digital filter design, impulse-invariant, bi-linear transform & window functions, FFT, introduction to image processing. prereq: 2111; credit will not be granted if already received for 4741
EE 5742 - Pattern Recognition and Machine Learning
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Even, Spring Odd Year
Various methods of pattern recognition, non-parametric techniques, linear discriminant functions, support vector machines, statistical classification, min-max procedures, maximum likelihood decisions and case studies. prereq: STAT 3611, senior or graduate standing in science or engineering or instructor consent; some basic concepts in linear algebra and probability theory.
EE 5745 - Medical Imaging
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Odd Year
Introduction to the methods and devices for medical imaging, including x-ray imaging, x-ray computer tomography (CT), nuclear medicine (single photon planar imaging, single photon emission computer tomography (SPECT), and positron emission tomography (PET), magnetic resonance imaging (MRI), and ultrasound imaging. The physics and design of systems, typical applications, medical image processing, and tomographic reconstruction. prereq: EE (ECE) 2111, Math 3298 or instructor permission
EE 5765 - Modern Communication
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Design and analysis of modern communication systems; evaluation of analog and digital modulation techniques. (3 hrs lect, 3 hrs lab) prereq: 2111; credit will not be granted if already received for 4765
EE 5801 - Introduction to Artificial Neural Networks
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
General techniques and theory of neural networks, their applications and limitations. The course particularly addresses the design issues and learning algorithms for diverse areas of applications. prereq: CS 1521, Math 3280, Stat 3611 or instructor consent; credit will not be granted if already received for 4801
EE 5831 - Fuzzy Set Theory and Its Application
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Fuzzy sets and operations on fuzzy sets. Fuzzy relations and the extension principle. Linguistic variable and fuzzy IF-THEN rules. Fuzzy arithmetic. Fuzzy logic and approximate reasoning. Design of Fuzzy Systems from I/O data. Fuzzy logic--based control. Pattern Classifications. prereq: CS 1521, Math 3280; credit will not be granted if already received for 5831
EE 5995 - Special Topics: (Various Titles to be Assigned)
Credits: 1.0 -3.0 [max 3.0]
Prerequisites: instructor consent
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Current problems and research. Discussions, selected reading, and/or invited speakers. prereq: instructor consent
EE 8151 - Linear Systems and Optimal Control
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Spring Odd Year
State-space representations of dynamic systems. Input-output stability. Lyapunov stability. Controllability and observability. Minimal realizations. State and output feedback. Pole placement design. State observers. Linear quadratic optimal control: fixed and free end point, finite and infinite horizon. Pontryagin's Minimal Principle. Dynamic programming. prereq: 3151
EE 8741 - Digital Image Processing
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Fall Odd Year
Mathematical foundations and practical techniques to process and manipulate images. Students will acquire the ability to analyze two-dimensional images, dealing with mathematical representation of images, image sampling and quantization, Image Transforms, Image Enhancement, Image Restoration, Image Coding, Edge Detection, Texture Analysis, and Compression. prereq: 4741
EE 8742 - Fundamentals of Signal Detection and Estimation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Even Year
Study of detection, estimation, and signal representation, detection of signals in noise, estimation of signal parameters, linear estimation theory. Performance bounds on Estimation and Detection. Kalman-Bucy and Wiener filters. Applications to Multiuser detection, Spectrum estimation, Adaptive Wiener filter, and system identification. prereq: 2111 and Stat 3611 or their equivalents, grad student or instructor consent
EE 8765 - Digital Communications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Spring Even Year
Overview of digital data transmission, performance analysis of digital modulation, quadrature multiplexed signaling schemes, signal-space methods in digital data transmission, information theory and block coding, convolutional coding, repeat-request system, spread-spectrum systems, satellite communications. prereq: 5765