The online Master of Science in Electrical and Computer Engineering offers two plans of study, a thesis and non-thesis option. The curriculum is built to empower electrical and computer engineers to follow their passions, confront technical challenges and develop powerful new devices. Our courses in high-demand fields like autonomous vehicles, neural networks and diamond technology give professionals the advanced knowledge of systems and platforms that they need to usher in the next wave of revolutionary advances.
The pioneering researchers of MSU’s faculty shaped the electrical and computer engineering curriculum and regularly update courses with the concepts and technologies that graduates need to excel in cutting-edge organizations. You’ll have opportunities to receive individualized mentorship and pursue your own areas of interest as you complete a program that’s built for a constantly evolving world of fast-paced innovation.
Below are the requirements for completion for each study plan. Students in both the thesis and non-thesis option must take at least two courses (six credits) from the following selection of courses:
- ECE 821: Advanced Power Electronics and Applications
- ECE 830: Embedded Cyber-Physical Systems
- ECE 835: Advanced Electromagnetic Fields and Waves I
- ECE 842: Performance Modeling of Communication Networks
- ECE 851: Linear Control Systems
- ECE 863: Analysis of Stochastic Systems
- ECE 874: Physical Electronics
Master’s degree Plan A (thesis option) Requirements
- Total credit requirement: 30 credits (courses below 400 level may not be counted)
- 12 Credits in ECE courses at the 800- or 900-level
- 6 credits in supporting classes from outside the MSU College of Engineering. Note that more courses may be offered online in the future. The current online courses that fulfill the breadth requirement include:
- STT 861, 875
- A minimum of 4 credits and no more than 8 credits of ECE 899 (thesis research)
- A minimum of 20 credits at the 800 level or above (including thesis credits)
- First-year graduate students must attend a minimum of 7 seminars from the graduate seminar series. All live sessions are recorded and are made available for students to watch later if they are unable to attend the live sessions.
- Students who elect the thesis option will also need to work with an ECE faculty member to advise them with their thesis research.
Master’s degree Plan B (non-thesis option) Requirements
- Total credit requirement: 30 credits (courses below 400 level may not be counted)
- 12 Credits in ECE courses at the 800- or 900-level
- 6 credits in supporting classes from outside the MSU College of Engineering. Note that more courses may be offered online in the future. The current online courses that fulfill the breadth requirement include:
- STT 861, 875
- A minimum of 18 credits at the 800 level or above.
- First-year graduate students must attend a minimum of 7 seminars from the graduate seminar series. All live sessions are recorded and are made available for students to watch later if they are unable to attend the live sessions.
Graduate Electrical and Computer Engineering Course Descriptions
Note: Not all courses are available every semester. Contact an Admission Counselor for more information on course availability and to discuss your desired academic plan.
Operational theory, characteristics and applications of optical components, light emitting diodes, lasers, laser diodes, photodetectors, photovoltaics, fiber optics, optical modulators and non-linear optical devices.
The Autonomous Vehicles (AV) class provides both an introduction to AVs and a hands-on programming of real and virtual AVs. Technologies addressed include: sensors, computer vision, artificial intelligence (AI), planning, communication, security and software architectures. Broader impacts of AVs are reviewed including social and legal impacts, along with risks and ethical problems. The hands-on portion teaches the Robot Operating System (ROS) and how to use it to control AVs: from reading sensor data, detecting objects with computer vision and AI, to circumnavigating obstacles. For their final project, students will each program an AV to traverse a real or virtual world. Proficiency in Python is assumed.
Diamond has a host of outstanding properties including highest thermal conductivity, extreme hardness, wide bandgap, large electric field breakdown strength, high hole and electron mobility, high radiation hardness, good electrochemical performance, and good chemical inertness. Diamond has applications and potential applications in electronics, optics, sensors, MEMS, wear/cutting, quantum computing, and thermal management. This course is intended to provide the student with a state-of-the-art knowledge of diamond properties, diamond devices/applications, and diamond growth/processing/manufacturing technology.
An increased interest nationally has emerged in the area of quantum sensors, communication and computing. Important concepts of these systems are quantum states, entanglement, and coherence, as well as the realization of qubits, quantum gates, and precision measurements with quantum sensors. Quantum engineering is understanding these concepts and transforming them into usable devices and systems. Quantum systems are being realized with solid state color centers, superconducting devices/circuits, and trapped ions. This course will provide the students with a graduate level introduction to these topics so that they have a basic understanding of how to follow talks at conferences and read the research literature.
Modeling continuous and discrete dynamics of embedded cyber-physical systems (CPS). Hybrid systems. Composition of state machines. Concurrent models of computation. Design and implementation of CPS including sensors and actuators, embedded processors, Internet of Things (IoT), cloud IoT, multitasking, and scheduling. Analysis and verification of CPS. Emerging topics in CPS. Projects in support of lecture material.
Electrostatics, magnetostatics, electrodynamics and Maxwell’s equations. Potential functions. Eigenfunction expansion. Green’s functions. Radiation of EM waves. EM boundary-value problems. TEM waves. Maxwell’s equations with magnetic sources.
Definitions for Pareto-optimality and systematic computerized algorithms for finding Pareto-optimal solutions. Classical generative and emerging methods using evolutionary optimization methods. Decision-making tasks to choose a final preferred solution.
Vector spaces, representation, system description, solution to the state equations, stability, controllability and observability. Adjoints of linear maps. Eigenstructure assignment. Partial and full order observers. Disturbance decoupling.
Micro-electro-mechanical systems (MEMS). Fundamentals of micromachining and microfabrication techniques. Design and analysis of devices and systems in mechanical, electrical, fluidic, and thermal energy and signal domains. Sensing and transduction mechanisms, including capacitive and piezoresistive techniques. Design and analysis of miniature sensors and actuators. Examples of existing devices and their applications.
Elements of deep neural networks. Adaptive and learning processes. Deep Feedforward and Convolutional Neural Networks. Recurrent Neural Networks and their variants (e.g. LSTMs, GRUs). Formulations of Reinforcement learning. Implementation and Deployment.
An exploration of automated ground vehicles and enabling technologies. Research focused, with critical reading, discussion, and presentation assignments. Teams conduct hands-on research using ROS and simulators or hardware platforms (on-campus only) to prepare a submission-ready conference or journal manuscript. Topics include: AV sensing, connectivity, and control technologies; machine and deep learning; use cases and human factors; tracking, prediction, and routing; localization and navigation; security, reliability, and deployment challenges.
Investigation of evolutionary computation from a historical, theoretical and application viewpoint. Readings from the present literature, experiments with provided software on the application of evolutionary computation principles.
Applications of quantum mechanics and statistical mechanics in solids. Band theory of semiconductors. Electrical transport phenomena. Pn junctions.
Radar fundamentals in propagation and antennas, radar transceiver design, and radar signal processing.
Cellular system design, characterization of wireless channels, signaling and receiver design for mobile radio, multiple access techniques and mobility management.
Fundamental theories and protocols for communication networks, with an emphasis on statistical performance modeling of Medium Access Control, Data Link Control, Routing, and Transport Layer Protocols. Network analysis and design using optimization techniques and statistical tools including Markov Processes, Queueing Theory, and emerging machine learning methodologies such as Reinforcement Learning. Simulation based and application-driven hands on class projects in support of lecture material.
Game theory has fascinated many researchers over the past century. With applications in domains ranging from security, robust control, motion planning and finance to more recent areas such as multi-agent systems, Artificial Intelligence and Machine Learning, game theoretic methods provide a rigorous framework to model problems involving reasoning and decision-making, most typically in the presence of an adversary.
The purpose of this graduate level course is to teach students to formulate problems as mathematical games and provide the basic analysis and computational tools to solve them. The course covers:
The purpose of this graduate level course is to teach students to formulate problems as mathematical games and provide the basic analysis and computational tools to solve them. The course covers:
- Static games: From two player zero sum games to N player non-zero sum games.
- Nash equilibrium and saddle-points.
- Algorithms and computational methods to solve games.
- Dynamic games: discrete and continuous time settings.
The intended audience includes (but is not restricted to) students in control, communications, signal processing, mechanical engineering, computational mathematics science and engineering and computer science. The class will be project-oriented and the students are strongly encouraged to choose a project that is relevant to their own research area.
Power semiconductor devices, circuits, control, and applications. Converter and inverter analysis and design, DSP (Digital Signal Processor) control and implementation. Automotive and utility applications.
Real-time parameter estimation. Design of self-tuning regulators and model reference adaptive controllers. Investigation of robustness and robust adaptive controllers. Extension to nonlinear systems
Advanced topics in random variable theory. Stochastic processes and stochastic calculus. Optimal systems for filtering and detection.
Fundamentals on hardware, software, and networking. Stability and control of hybrid systems. Switched systems. Control with communication constraints. Fundamental limits on bit rate. Multi-agent coordination and control.
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