About This Professional Master

The mission of the electrical engineering program, consistent with the Missouri S&T campus mission statements, is the education of students to fully prepare them to provide leadership in the recognition and solution of society’s problems in the area of electrical engineering. Most graduate programs in electrical engineering normally include some specialization in one or more of the following six emphasis areas of electrical engineering: circuits and electronics, communications and signal processing, controls and systems, electromagnetics, devices and optics, and power. 

Term: Typically about 3 years

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  • Requirements
  • Course Information

Requirements

A Master of Science non-thesis program consists of:

  • A minimum of 30 credit hours of coursework.

Course Information

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Courses

Description

An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science, and statistics. Clustering in sequential data, massive data, and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with the student's degree program.

Learning Objective

  1. Understand the fundamental concepts of clustering in data analysis.
  2. Evaluate and compare various clustering algorithms.
  3. Apply clustering techniques to real-world systems engineering problems.
  4. Analyze and interpret clustering results.
  5. Implement clustering algorithms in Python or another programming language.

Course Content

  • Introduction to Clustering
  • Data Preprocessing
  • Distance Metrics
  • Partitional Clustering
  • Hierarchical Clustering
  • Density-Based Clustering
  • Advanced Topics in Clustering
  • Clustering in Systems Engineering

Course Evaluation Criteria

  • Research Assignments and Presentations
  • Final Project

Description

Analysis, design, modeling, and control of switching mode power converter circuits for ac-dc, dc-dc, dc-ac, and ac-ac conversion. Power semiconductor devices, passive components, and non-ideal sources and loads. Applications to industry, consumer goods, electric vehicles, and alternative energy.

Learning Objective

  1. Understand the fundamental principles of power electronics and its applications in various domains.
  2. Analyze, design, and model switching mode power converter circuits for different types of power conversion.
  3. Demonstrate knowledge of power semiconductor devices, passive components, and their characteristics.
  4. Evaluate and mitigate the impact of non-ideal sources and loads in power electronic systems.
  5. Apply power electronics concepts to real-world applications in industry, consumer goods, electric vehicles, and alternative energy.
  6. Implement control strategies for power converters to achieve desired performance and efficiency.

Course Content

  • Power Semiconductor Devices
  • Passive Components in Power Electronics
  • AC-DC Power Conversion; DC-DC Power Conversion; DC-AC Power Conversion; AC-AC
  • Power Conversion
  • Control of Power Electronic Converters
  • Non-Ideal Sources and Loads

Course Evaluation Criteria

  • Assignments
  • Midterm Exam
  • Final Project

Description

The physical phenomena associated with high voltage dielectric breakdown are presented. Methods of generating and measuring high voltages and currents are explained. Type-tests performed on a variety of high voltage equipment are introduced.

Learning Objective

  1. Explain the role of power electronics in extra high voltage engineering.
  2. Analyze and design power electronic converters for high voltage applications.
  3. Understand the operation and characteristics of high-voltage semiconductor devices.
  4. Evaluate the performance of power electronic systems in terms of efficiency and power factor.
  5. Apply control strategies for power electronic converters in high voltage systems.
  6. Recognize safety considerations and standards for high voltage power electronics.

Course Content

  • Power Semiconductor Devices for High Voltage
  • Power Electronic Converters
  • Control of Power Electronic Converters
  • Efficiency and Power Factor Improvement
  • Protection and Safety in High Voltage Power Electronics

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

Principles of high-frequency effects in PCBs and components, generation of unwanted radio-frequency (RF) signals by ICs, RF radiation mechanisms, shielding, and immunity against electrostatic discharge and RF signals.

Learning Objective

  1. Explain the fundamental principles of high-frequency effects in printed circuit boards (PCBs) and electronic components.
  2. Analyze how integrated circuits (ICs) can generate unwanted radio-frequency (RF) signals and understand the mechanisms behind it
  3. Describe the various mechanisms responsible for the radiation of RF signals from electronic systems. 
  4. Explore different shielding techniques used to mitigate RF interference in electronic systems.
  5. Discuss methods to enhance the immunity of electronic systems against external RF signals and interference.

Course Content

  • High-Frequency Effects in PCBs and Components
  • Generation of Unwanted RF Signals by ICs
  • RF Radiation Mechanisms
  • Electromagnetic Compatibility (EMC) Standards
  • Shielding Techniques
  • Immunity Against Electrostatic Discharge (ESD)
  • Immunity Against RF Signals

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

Signal integrity ensures signals transmitted over a propagation path maintain sufficient fidelity for proper receiver operation. Compromised signal integrity is often associated with parasitics (e.g., unintentional inductance, capacitance). Theory and CAD tools used for signal integrity analysis of functioning designs.

Learning Objective

  1. Understand the importance of signal integrity in high-speed digital and mixed-signal systems.
  2. Analyze the behavior of signals on transmission lines and the effects of impedance mismatch.
  3. Identify and mitigate common signal integrity issues, including reflections and ringing.
  4. Use simulation tools to model and optimize signal integrity in electronic designs.
  5. Apply knowledge of signal integrity principles to real-world design projects.
  6. Interpret measurement results from signal integrity tests and apply corrective actions.

Course Content

  • Parasitics and Impedance
  • Transmission Lines and Reflections
  • CAD Tools for Signal Integrity

Course Evaluation Criteria

  • Assignments
  • Midterm Exam
  • Final Project

Description

The materials in this course are intended to provide a) follow-up electromagnetics-related courses, b) electromagnetics-related career including RF design, and c) a graduate degree in electromagnetic-related fields and an in-depth understanding of the basics of wave propagation and transmission lines.

Learning Objective

  1. Understand the behavior of waves at boundaries and interfaces, including reflection, transmission, and standing waves.
  2. Analyze and solve problems related to transmission lines, including impedance matching, voltage and current distribution, and signal distortion.
  3. Design and analyze various types of transmission lines, such as coaxial cables, microstrip lines, and optical fibers.
  4. Understand the concepts of signal integrity, distortion, and attenuation in transmission lines.
  5. Explore the practical applications of transmission lines in power distribution, RF communication, and microwave engineering.

Course Content

  • Wave Behavior and Reflection
  • Transmission Line Fundamentals
  • Signal Integrity in Transmission Lines
  • Transients in Transmission Lines

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

Propagated fields of elemental dipole, directivity and gain, radiation resistance, the half-wave dipole, wire antennas, arrays, broadband antennas, aperture antennas, horn antennas, and antenna temperature.

Learning Objective

  1. Explain the fundamental concepts of electromagnetic wave propagation and antenna theory.
  2. Understand the principles of radiation and reception of electromagnetic waves by antennas.
  3. Analyze and design various types of antennas for specific applications.
  4. Describe the characteristics of different propagation environments and their effects on signal propagation.
  5. Use software tools and simulations for antenna design and propagation analysis.
  6. Understand the challenges and solutions related to antenna integration in modern communication systems.
  7. Analyze the impact of polarization, frequency, and directionality on antenna performance.

Course Content

  • Antenna Fundamentals
  • Antenna Design and Analysis
  • Propagation Models
  • Specialized Antennas and Applications

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

The course provides an introduction to basic neural network architectures and their applications. Students learn to construct neural networks and train them to solve engineering problems, specifically pattern recognition and function approximation. Mathematical analysis of network architectures, training algorithms, and practical applications of neural nets. 

Learning Objective

  1. Understand the basic concepts of artificial neural networks and their role in machine learning.
  2. Analyze the structure and components of neural network models.
  3. Implement neural networks using popular programming frameworks and libraries.
  4. Explore different activation functions, loss functions, and optimization algorithms.
  5. Train and fine-tune neural networks for specific tasks and datasets.
  6. Apply neural networks to solve problems in electrical engineering domains.
  7. Evaluate the performance of neural network models using appropriate metrics.
  8. Complete practical projects that demonstrate the application of neural networks.

Course Content

  • Neural Network Architecture
  • Applications in Electrical Engineering
  • Optimization and Regularization

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

Review of linear quadratic regulators, LQR extensions; constrained optimization (Pontragin's minimum principle); review of probability theory and random processes; optimal prediction and filters; frequency domain properties of LQR and Kalman filters; linear quadratic Gaussian (LQG) control; model uncertainties, frequency shaping, LQG/LTR design methodology.

Learning Objective

  1. Understand the principles and importance of optimal control and estimation in electrical engineering applications.
  2. Design and implement LQR controllers for dynamic systems.
  3. Develop and apply optimal prediction and filtering algorithms.
  4. Analyze the frequency domain properties of LQR controllers and Kalman filters.
  5. Design and implement LQG controllers for systems with model uncertainties.
  6. Apply frequency shaping techniques for control system design.
  7. Employ LQG/LTR design methodology to optimize control system performance.

Course Content

  • Linear Quadratic Regulators (LQR)
  • Probability Theory and Random Processes
  • Optimal Prediction and Filters
  • Frequency Domain Analysis of LQR and Kalman Filters
  • Linear Quadratic Gaussian (LQG) Control
  • Frequency Shaping and Design

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam

Description

Performance and robustness of multivariable systems, linear fractional transformations, LQG/LTR advanced loop shaping, Youla parameterization, H (subscript infinity) optimal control, mixed H (subscript 2) and H (subscript infinity) control, controller synthesis for multiple objective optimal control, linear matrix inequalities theory and case studies. 

Learning Objective

  1. Understand the concept of robustness in control systems and its significance in electrical engineering applications.
  2. Design robust controllers using various techniques, including H-infinity control
  3. Analyze the stability and performance of robust control systems.
  4. Implement robust control strategies for practical electrical engineering problems.
  5. Evaluate trade-offs between performance and robustness in control system design.
  6. Apply robust control methods to address real-world engineering challenges.

Course Content

  • Uncertainty Modeling
  • Robust Stability Analysis
  • H-infinity Control
  • Robust Performance Analysis
  • Robust Control Design Techniques

Course Evaluation Criteria

  • Assignments
  • Term Project
  • Final Exam