Twin Cities campus

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Twin Cities Campus

Electrical Engineering Minor

Electrical and Computer Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Director of Graduate Studies, Department of Electrical and Computer Engineering, University of Minnesota, 3-166 Keller Hall, 200 Union Street SE, Minneapolis, MN 55455 (612-625-3564; fax: 612-625-4583)
Email: newgrad@umn.edu
Website: https://cse.umn.edu/ece
  • Program Type: Graduate minor related to major
  • Requirements for this program are current for Fall 2022
  • Length of program in credits (master's): 6
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
The Department of Electrical and Computer Engineering offers diverse educational programs that encompass nearly all aspects of modern electrical and computer engineering, ranging from the very theoretical system and information theory, to highly experimental work in novel device research and microelectronics. Emphases in the major are solid state and physical electronics, surface physics, thin films, sputtering, noise and fluctuation phenomena, quantum electronics, plasma physics, automation, power systems and power electronics theory, wave propagation, communication systems and theory, optics, lasers, fiber optics, magnetism, semiconductor properties and devices, VLSI and WSI engineering in theory and practice, network theory, signal and image processing, and computer and systems engineering. Interdisciplinary work is also available in bioelectrical sciences, control sciences, computer sciences, solar energy, applications of systems theory to urban transportation and economic planning, and biological modeling.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
  • partially online (between 50% to 80% of instruction is online)
Prerequisites for Admission
Special Application Requirements:
Students interested in the minor are strongly encouraged to confer with their major field advisor and director of graduate studies, and the Electrical Engineering director of graduate studies regarding feasibility and requirements.
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
Courses offered on both the A-F and S/N grading basis must be taken A-F, with a minimum grade of C earned for each course. The minimum cumulative GPA for the minor is 3.00.
Minor Coursework (6-12 credits)
Master's students select a minimum of 6 credits, and doctoral students select a minimum of 12 credits from the following in consultation with the Electrical Engineering director of graduate studies:
EE 5121 - Transistor Device Modeling for Circuit Simulation (3.0 cr)
EE 5141 - Introduction to Microsystem Technology (4.0 cr)
EE 5163 - Semiconductor Properties and Devices I (3.0 cr)
EE 5164 - Semiconductor Properties and Devices II (3.0 cr)
EE 5171 - Microelectronic Fabrication (3.0 cr)
EE 5173 - Basic Microelectronics Laboratory (1.0 cr)
EE 5181 - Micro and Nanotechnology by Self Assembly (3.0 cr)
EE 5231 - Linear Systems and Control (3.0 cr)
EE 5235 - Robust Control System Design (3.0 cr)
EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
EE 5251 - Optimal Filtering and Estimation (3.0 cr)
EE 5301 - VLSI Design Automation I (3.0 cr)
EE 5302 - VLSI Design Automation II (3.0 cr)
EE 5323 - VLSI Design I (3.0 cr)
EE 5324 - VLSI Design II (3.0 cr)
EE 5327 - VLSI Design Laboratory (3.0 cr)
EE 5329 - VLSI Digital Signal Processing Systems (3.0 cr)
EE 5333 - Analog Integrated Circuit Design (3.0 cr)
EE 5351 - Applied Parallel Programming (3.0 cr)
EE 5364 - Advanced Computer Architecture (3.0 cr)
EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
EE 5393 - Circuits, Computation, and Biology (3.0 cr)
EE 5501 - Digital Communication (3.0 cr)
EE 5505 - Wireless Communication (3.0 cr)
EE 5531 - Probability and Stochastic Processes (3.0 cr)
EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
EE 5545 - Digital Signal Processing Design (3.0 cr)
EE 5549 - Digital Signal Processing Structures for VLSI (3.0 cr)
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0 cr)
EE 5581 - Information Theory and Coding (3.0 cr)
EE 5583 - Error Control Coding (3.0 cr)
EE 5585 - Data Compression (3.0 cr)
EE 5601 - Introduction to RF/Microwave Engineering (3.0 cr)
EE 5602 - RF/Microwave Circuit Design (3.0 cr)
EE 5611 - Plasma-Aided Manufacturing (4.0 cr)
EE 5613 - RF/Microwave Circuit Design Laboratory (2.0 cr)
EE 5616 - Antennas: Theory, Analysis, and Design (3.0 cr)
EE 5621 - Physical Optics (3.0 cr)
EE 5622 - Physical Optics Laboratory (1.0 cr)
EE 5624 - Optical Electronics (4.0 cr)
EE 5627 - Optical Fiber Communication (3.0 cr)
EE 5653 - Physical Principles of Magnetic Materials (3.0 cr)
EE 5655 - Magnetic Recording (3.0 cr)
EE 5657 - Physical Principles of Thin Film Technology (4.0 cr)
EE 5705 - Electric Drives in Sustainable Energy Systems (3.0 cr)
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory (1.0 cr)
EE 5721 - Power Generation Operation and Control (3.0 cr)
EE 5741 - Advanced Power Electronics (3.0 cr)
EE 5745 - Wind Energy Essentials (2.0 cr)
EE 8141 - Advanced Heterojunction Transistors (3.0 cr)
EE 8161 - Physics of Semiconductors (3.0 cr)
EE 8163 - Quantum Electronics (3.0 cr)
EE 8213 - Advanced System Theory (3.0 cr)
EE 8215 - Nonlinear Systems (3.0 cr)
EE 8231 - Optimization Theory (3.0 cr)
EE 8331 - CMOS Data Converters: A/D and D/A (3.0 cr)
EE 8337 - Analog Circuits for Wire/Wireless Communications (3.0 cr)
EE 8367 - Parallel Computer Organization (3.0 cr)
EE 8551 - Multirate Signal Processing and Applications (3.0 cr)
EE 5571 - Statistical Learning and Inference (3.0 cr)
EE 8591 - Predictive Learning from Data (3.0 cr)
EE 8601 - Advanced Electromagnetic Theory (3.0 cr)
EE 8611 - Plasma Physics (3.0 cr)
EE 8725 - Advanced Power System Analysis and Economics (3.0 cr)
EE 8741 - Power Electronics in Power Systems (3.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Masters
Doctoral
 
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EE 5121 - Transistor Device Modeling for Circuit Simulation
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Basics of MOS, bipolar theory. Evolution of popular device models from early SPICE models to current industry standards. prereq: [3115, 3161, CSE grad student] or dept consent
EE 5141 - Introduction to Microsystem Technology
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Microelectromechanical systems composed of microsensors, microactuators, and electronics integrated onto common substrate. Design, fabrication, and operation principles. Labs on micromachining, photolithography, etching, thin film deposition, metallization, packaging, and device characterization. prereq: [3161, 3601, CSE grad student] or dept consent
EE 5163 - Semiconductor Properties and Devices I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Principles/properties of semiconductor devices. Selected topics in semiconductor materials, statistics, and transport. Aspects of transport in p-n junctions, heterojunctions. prereq: [3161, 3601, CSE grad student] or dept consent
EE 5164 - Semiconductor Properties and Devices II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Principles/properties of semiconductor devices. Charge control in different FETs, transport, modeling. Bipolar transistor models (Ebers-Moll, Gummel-Poon), heterostructure bipolar transistors. Special devices. prereq: 5163 or instr consent
EE 5171 - Microelectronic Fabrication
Credits: 3.0 [max 4.0]
Typically offered: Every Fall
Fabrication of microelectronic devices. Silicon integrated circuits, GaAs devices. Lithography, oxidation, diffusion. Process integration of various technologies, including CMOS, double poly bipolar, and GaAs MESFET. prereq: CSE grad student or dept consent
EE 5173 - Basic Microelectronics Laboratory
Credits: 1.0 [max 1.0]
Typically offered: Every Fall
Students fabricate a polysilicon gate, single-layer metal, NMOS chip, performing 80 percent of processing, including photolithography, diffusion, oxidation, and etching. In-process measurement results are compared with final electrical test results. Simple circuits are used to estimate technology performance. prereq: [[5171 or concurrent registration is required (or allowed) in 5171], CSE grad student] or dept consent
EE 5181 - Micro and Nanotechnology by Self Assembly
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Self-assembly process of micro and nano structures for realization of 1-, 2-, 3-dimensional micro- and nano-devices. Micro and nanoscale fabrication by electrostatic, magnetic, surface tension, Capillary, intrinsic and extrinsic forces. Nanoscale lithographic patterning. Devices packaging, Self-healing process. prereq: EE 3161, Phys 1302
EE 5231 - Linear Systems and Control
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent
EE 5235 - Robust Control System Design
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Development of control system design ideas; frequency response techniques in design of single-input/single-output (and MI/MO) systems. Robust control concepts. CAD tools. prereq: CSE grad, 3015, 5231 or instr consent
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
EE 5301 - VLSI Design Automation I
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms. prereq: [2301, CSE grad student] or dept consent
EE 5302 - VLSI Design Automation II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Basic algorithms, computational complexity. High-level synthesis. Test generation. Power estimation. Timing optimization. Current topics. prereq: [5301, CSE grad student] or dept consent
EE 5323 - VLSI Design I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing. prereq: [2301, 3115, CSE grad student] or dept consent
EE 5324 - VLSI Design II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
CMOS arithmetic logic units, high-speed carry chains, fast CMOS multipliers. High-speed performance parallel shifters. CMOS memory cells, array structures, read/write circuits. Design for testability, including scan design and built-in self test. VLSI case studies. prereq: [5323, CSE grad student] or dept consent
EE 5327 - VLSI Design Laboratory
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Complete design of an integrated circuit. Designs evaluated by computer simulation. prereq: [4301, [5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5329 - VLSI Digital Signal Processing Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis. prereq: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent
EE 5333 - Analog Integrated Circuit Design
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter). prereq: [3115, CSE grad student] or dept consent
EE 5351 - Applied Parallel Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies. prereq: [4363 or equivalent], programming experience (C/C++ preferred)
EE 5364 - Advanced Computer Architecture
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5204/EE 5364
Typically offered: Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design. prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5371/5863
Typically offered: Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis. prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5393 - Circuits, Computation, and Biology
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
EE 5501 - Digital Communication
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theory/techniques of modern digital communications. Communication limits. Modulation/detection. Data transmission over channels with intersymbol interference. Optimal/suboptimal sequence detection. Equalization. Error correction coding. Trellis-coded modulation. Multiple access. prereq: [3025, 4501, CSE grad student] or dept consent
EE 5505 - Wireless Communication
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to wireless communication systems. Propagation modeling, digital communication over fading channels, diversity and spread spectrum techniques, radio mobile cellular systems design, performance evaluation. Current European, North American, and Japanese wireless networks. prereq: [4501, CSE grad student] or dept consent; 5501 recommended
EE 5531 - Probability and Stochastic Processes
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks. prereq: [3025, CSE grad student] or dept consent
EE 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, CSE grad student] or dept consent
EE 5545 - Digital Signal Processing Design
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Real-time implementation of digital signal processing (DSP) algorithms, including filtering, sample-rate conversion, and FFT-based spectral analysis. Implementation on a modern DSP Platform. Processor architecture. Arithmetic operations. Real-time processing issues. Processor limitations. Integral laboratory. prereq: [4541, CSE grad student] or dept consent
EE 5549 - Digital Signal Processing Structures for VLSI
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Pipelining. Parallel processing. Fast convolution. FIR, rank-order, IIR, lattice, adaptive digital filters. Scaling and roundoff noise. DCT. Viterbi coders. Lossless coders, video compression. prereq: [4541, CSE grad student] or dept consent
EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5561/EE 8541
Typically offered: Every Spring
Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks. prereq: [4541, 5581, CSE grad student] or instr consent
EE 5581 - Information Theory and Coding
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Source/channel models, codes for sources/channels. Entropy, mutual information, capacity, rate-distortion functions. Coding theorems. prereq: [5531, CSE grad student] or dept consent
EE 5583 - Error Control Coding
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Error-correcting codes. Concepts, properties, polynomial representation. BCH, Golay, Reed-Muller/Reed-Solomon codes. Convolutional codes. Iterative codes. prereq: [[3025, Math 2373] or equiv], [CSE grad student or dept consent]
EE 5585 - Data Compression
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Source coding in digital communications and recording. Codes for lossless compression. Universal lossless codes. Lossless image compression. Scalar and vector quantizer design. Loss source coding theory. Differential coding, trellis codes, transform/subband coding. Analysis/synthesis schemes. prereq: CSE grad student or dept consent
EE 5601 - Introduction to RF/Microwave Engineering
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Fundamentals of EM theory and transmission lines concepts. Transmission lines and network analysis. CAD tool. Lumped circuit component designs. Passive circuit components. Connectivity to central communication theme. prereq: [3601, CSE grad student] or dept consent
EE 5602 - RF/Microwave Circuit Design
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Transmission lines, network analysis concepts. CAD tools for passive/active designs. Diode based circuit designs (detectors, frequency multipliers, mixers). Transistor based circuit design (amplifiers, oscillators, mixer/doubler). prereq: [5601 or equiv], [CSE grad student or instr consent]
EE 5611 - Plasma-Aided Manufacturing
Credits: 4.0 [max 4.0]
Course Equivalencies: EE 5611/ME 5361
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Manufacturing using plasma processes. Plasma properties as a processing medium. Plasma spraying, welding and microelectronics processing. Process control and system design; industrial speakers. Cross-disciplinary experience between heat transfer design issues and manufacturing technology. prereq: [[[ME 3321, ME 3322] or equiv], [upper div CSE or grad student]] or dept consent
EE 5613 - RF/Microwave Circuit Design Laboratory
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Scattering parameters, planar lumped circuits, transmission lines, RF/microwave substrate materials, matching networks/tuning elements, resonators, filters, combiners/dividers, couplers. Integral lab. prereq: [[5601 or concurrent registration is required (or allowed) in 5601], CSE grad student] or dept consent
EE 5616 - Antennas: Theory, Analysis, and Design
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 4616/EE 5616
Typically offered: Every Fall
With the widespread use of cell phones autonomous vehicles, and the coming of the Internet of Things, there is an increasing need to understand wireless communications and radar sensors. A key component of these systems is the antenna. The purpose of this course is to help the student develop knowledge in the area of antennas. This involves understanding the parameters that are used to characterize antennas and how these effect system performance. An important aspect of the course is to provide the student with an understanding of the operating principles behind the most commonly used antennas. This is followed with exposure to basic design principles. These can be used to perform antenna design or can be used as starting points for design using an electromagnetic simulator. As part of the course, students will be exposed to simulator use through homework assignments, and possibly, course project work. prereq: EE 3601 or equivalent
EE 5621 - Physical Optics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Physical optics principles, including Fourier analysis of optical systems/images, scalar diffraction theory, interferometry, and coherence theory. Diffractive optical elements, holography, astronomical imaging, optical information processing, microoptics. prereq: [3015, CSE grad student] or dept consent
EE 5622 - Physical Optics Laboratory
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Fundamental optical techniques. Diffraction and optical pattern recognition. Spatial/temporal coherence. Interferometry. Speckle. Coherent/incoherent imaging. Coherent image processing. Fiber Optics. prereq: [[5621 or concurrent registration is required (or allowed) in 5621], CSE grad student] or dept consent
EE 5624 - Optical Electronics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Fundamentals of lasers, including propagation of Gaussian beams, optical resonators, and theory of laser oscillation. Polarization optics, electro-optic, acousto-optic modulation, nonlinear optics, phase conjugation. prereq: [[3601 or Phys 3002], CSE grad student] or dept consent
EE 5627 - Optical Fiber Communication
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Components/systems aspects of optical fiber communication. Modes of optical fibers. Signal degradation/dispersion. Optical sources/detectors. Digital/analog transmissions systems. Direct/coherent detection. Optical amplifiers. Optical soliton propagation. prereq: [3015, 3601, CSE grad student] or dept consent
EE 5653 - Physical Principles of Magnetic Materials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Physics of diamagnetism, paramagnetism, ferromagnetism, antiferromagnetism, ferrimagnetism. Ferromagnetic phenomena. Static/dynamic theory of micromagnetics, magneto-optics, and magnetization dynamics. Magnetic material applications. prereq: CSE grad student or dept consent
EE 5655 - Magnetic Recording
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Magnetic fundamentals, recording materials, idealized models of magnetic records/reproduction, analytic models of magnetic record heads, sinusoidal magnetic recording, digital magnetic recording, magnetic recording heads/media, digital recording systems. prereq: CSE grad student or dept consent
EE 5657 - Physical Principles of Thin Film Technology
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Fabrication, characterization, and application of thin film and nanostructured materials and devices. Focuses on vacuum deposition. Materials science. Hands-on, team-based labs.
EE 5705 - Electric Drives in Sustainable Energy Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Role of electric drives in wind-electric systems, inertial storage, elec/hybrid vehicles. AC machines for energy-efficient operation using d-q axis modeling. Vector-/direct-torque-controlled induction motor drives. Permanent-magnet and interior-permanent magnet ac motor drives. Sensorless drives. Voltage space-vector modulation technology. prereq: [4701, CSE grad student] or dept consent
EE 5707 - Electric Drives in Sustainable Energy Systems Laboratory
Credits: 1.0 [max 1.0]
Typically offered: Periodic Spring
Lab to accompany 5705. prereq: 5705 or concurrent registration is required (or allowed) in 5705
EE 5721 - Power Generation Operation and Control
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Engineering aspects of power system operation. Economic analysis of generation plants & scheduling to minimize total cost of operation. Scheduling of hydro resources and thermal plants with limited fuel supplies. Loss analysis, secure operation. State estimation, optimal power flow. Power system organizations. prereq: [4721, CSE grad student] or dept consent
EE 5741 - Advanced Power Electronics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Physics of solid-state power devices, passive components, magnetic optimization, advanced topologies. Unity power factor correction circuits, EMI issues, snubbers, soft switching in dc/ac converters. Practical considerations. Very low voltage output converters. Integrated computer simulations. prereq: CSE grad student] or dept consent
EE 5745 - Wind Energy Essentials
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Design, planning, development/operation of wind energy facilities. Wind turbine generator types, wind forecasting/assessment, wind farm project development, grid integration, wind turbine controls, blade aerodynamics/acoustics, mechanical/hydrostatic transmissions, materials/structural reliability, wind turbine foundations, radar interference, role of public policy in wind energy. prereq: CSE grad student or dept consent
EE 8141 - Advanced Heterojunction Transistors
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Recent developments in device modeling with emphasis on bipolar junction transistors. High-level effects in base and collector regions and their interrelationship. prereq: 5664 or instr consent
EE 8161 - Physics of Semiconductors
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Modern solid-state theory applied to specific semiconductor materials. Influence of band structure and scattering mechanisms upon semiconductor properties. Plasma effects in semiconductors. Mathematical treatments of generation-recombination kinetics, carrier injection, drift, and diffusion. Use of semiconductor properties in devices of current importance. prereq: instr consent
EE 8163 - Quantum Electronics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Quantum theory of light/laser systems. Planck's radiation law, Einstein's coefficients. Quantum mechanics of atom-radiation interaction. Quantized radiation field. Interaction of quantized field with atoms. Generation/amplification of light. Nonlinear optics. Specific laser systems. Semiconductor lasers. prereq: instr consent
EE 8213 - Advanced System Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Generalized linear systems; applications, structural properties, computational approaches, classification, functional behavior, and synthesis. prereq: IT grad student, instr consent
EE 8215 - Nonlinear Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Current topics in stability analysis of nonlinear systems, design of controllers for nonlinear systems, discrete-time and stochastic nonlinear systems. prereq: instr consent
EE 8231 - Optimization Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Introduction to optimization in engineering; approximation theory. Least squares estimation, optimal control theory, and computational approaches. prereq: instr consent
EE 8331 - CMOS Data Converters: A/D and D/A
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Data converters, low power low voltage analog circuits. Basic background in design of CMOS analog-to-digital and digital-to-analog converters. Special circuit design techniques for low power design. Students design/test several design problems. prereq: 5333 or instr consent
EE 8337 - Analog Circuits for Wire/Wireless Communications
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Basic background, advanced design concepts necessary to design integrated CMOS RF circuits. Emphasizes CMOS and RF. Where appropriate, mention is made of bipolar circuits and applications to other communications areas. prereq: 5333
EE 8367 - Parallel Computer Organization
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 8205/EE 8367
Typically offered: Every Spring
Design/implementation of multiprocessor systems. Parallel machine organization, system design. Differences between parallel, uniprocessor machines. Programming models. Synchronization/communication. Topologies, message routing strategies. Performance optimization techniques. Compiler, system software issues. prereq: 5364 or CSci 5204
EE 8551 - Multirate Signal Processing and Applications
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Multirate discrete-time systems with applications in modern signal and data processing problems. Hilbert Spaces and Linear Operators; Reisz Bases and Frames; Vector Space Representation of Sampling, Interpolation, Time-frequency analysis and wavelets; Filterbanks and Polyphase Structures; Sparsity and redundancy with applications in linear and nonlinear approximation, super-resolution, blind-source separation. prereq: [CSE grad student] or dept consent
EE 5571 - Statistical Learning and Inference
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Deterministic and random approaches to learning and inference from data, with applications to statistical models for estimation, detection, and classification. Algorithms and their performance include minimum-variance unbiased estimators, sufficient statistics, fundamental bounds, (non)linear least-squares, maximum-likelihood, expectation-maximization, nonparametric density estimators, mean-square error and Bayesian estimators, importance sampling, Kalman and particle filtering, sequential probability ratio test, bootstrap, Monte Carlo Markov Chains, and graphical models. prereq: courses in Stochastic Processes (EE 5531) and Digital Signal Processing (EE 4541)
EE 8591 - Predictive Learning from Data
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of ‘new’ algorithms. prereq: CSE grad student or instr consent
EE 8601 - Advanced Electromagnetic Theory
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Aspects of electromagnetic theory. Review of introductory material. Scattering theory, geometric theory of diffraction, integral equation methods, Green's functions. prereq: 4601 or equiv
EE 8611 - Plasma Physics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Plasma theory and charged particle transport phenomena: collision processes, orbit theory, kinetic theory, Boltzmann transport equation, moment (continuity) equations, magnetohydrodynamics, transport properties. Applications of plasma theory to modeling of dc, rf, and microwave discharges. prereq: instr consent
EE 8725 - Advanced Power System Analysis and Economics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Solving sets of equations that involve large sparse matrices. Sparse matrix storage, ordering schemes, application to power flow, short circuit calculation, optimal power flow, and state estimation. prereq: 4721, CSE grad student or instr consent
EE 8741 - Power Electronics in Power Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Impact of power electronics loads on power quality. Passive and active filters. Active input current wave shaping. HVDC transmission. Static VAR control, energy storage systems. Interconnecting photovoltaic and wind generators. Static phase shifters and circuit breakers for flexible AC transmission (FACTS). prereq: 4741, IT grad student or instr consent