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.
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 Certificates: Add Specialized Credentials to Your Degree
Enhance your master’s with a graduate certificate by strategically selecting your electives. These specialized credentials demonstrate your expertise in high-demand technical areas and give you a competitive edge in the job market.
- No Additional Coursework: Certificates can be earned within existing degree requirements
- Flexible Options: Choose the certificate that best aligns with your career goals
Secure and Connected Cyber-Physical Systems
Prepare for advanced roles in cybersecurity, network optimization and secure system design. Complete all three courses below (9 credits total):
- ECE 816: Cryptography and Network Security (3 credits)
- ECE 830: Embedded Cyber-Physical Systems (3 credits)
- ECE 842: Performance Modeling of Communication Networks (3 credits)
Semiconductor Manufacturing, Processing, and Devices
Position yourself at the forefront of semiconductor technology. Complete any three courses from the list below (9 credits total):
- ECE 813: Advanced VLSI Design (3 credits)
- ECE 850: Electrodynamics of Plasmas (3 credits)
- ECE 870: Introduction to Micro-Electro-Mechanical Systems (3 credits)
- ECE 874: Physical Electronics (3 credits)
- ECE 875: Electronic Devices (3 credits)
- ECE 931C: Properties of Semiconductors (3 credits)
- ECE 989: Advanced Topics in Plasma (3 credits)
Graduate Electrical and Computer Engineering Course Descriptions
Note: Not all courses are available every semester. Contact an enrollment specialist for more information on course availability and to discuss your desired academic plan.
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
Personalize Your Program Through Electives
If you follow a general track, you can choose electives based on your interests and goals. However, this program also offers seven focus areas that allow you to customize your degree with specific electives.
Focus Areas
Choose from the following focus areas:
- Computer Networking
- Electromagnetics
- Energy and Power Systems
- Materials and Devices
- Micro and Nanoelectronics and VLSI
- Robotics and Controls
- Signal Processing and Communications
If you choose to pursue a focus area, you will need to pass three classes within that area.
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.
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.
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.
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.
Radar fundamentals in propagation and antennas, radar transceiver design, and radar signal processing.
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.
This is a refresher course designed for students entering our graduate program after being away from school for some time, and/or those students with undergraduate degrees outside of electrical and computer engineering who wish to have a rapid review of fundamental concepts expected to be known by incoming graduate students. A circuit analysis module is to be taken by all students in the course followed by two additional modules that students have a choice of topics to pick from.
This graduate course provides an in-depth exploration of the technologies, methodologies and challenges involved in the operation and control of modern electric power systems. It will cover advanced generation technologies, including conventional power plants, microgrid-scale systems, renewable energy sources and energy storage solutions, with a focus on their technical characteristics and economic impacts. It also examines cutting-edge transmission and distribution technologies, such as flexible AC transmission systems (FACTS), flow control devices, phasor measurement units (PMUs) and the communication and protection technologies that ensure system reliability and stability.
Students will gain practical and theoretical insights into power system operations, including market mechanisms, auction strategies, optimal power flow, state estimation, demand forecasting, unit commitment and generation dispatch. The course will also explore the role of Supervisory Control and Data Acquisition (SCADA) systems, Energy Management Systems (EMS) and Independent System Operators (ISOs) in managing modern grids. A special emphasis will be placed on cybersecurity challenges and solutions to protect power grids from emerging threats. Students will leave equipped with the skills and knowledge necessary to tackle the complexities of evolving power systems.
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.
Transceiver architecture designs with emphasis on hardware building blocks. Integrated radio frequency designs for various communication standards. Basic building blocks including low noise and power amplifiers, mixers, voltage control oscillators, and frequency synthesizers. Integrated circuit designs of basic building blocks.
Prerequisite: ECE 410
Advanced topics in digital integrated circuit design. Design specifications: functionality, performance, reliability, manufacturability, testability, cost. Standard cells. Design-rule checking. Circuit extraction, simulation, verification. Team-based design.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Major security techniques, including authenticity, confidentiality, message integrity, non-repudiation, and the mechanisms to achieve them. Network security and system security practices, including authentication practice, email security, IP security, Web security, and firewalls.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
Modeling and control of AC motors.
Robot modeling, kinematics, dynamics, trajectory planning, sensors, controller design, motion planning.
Fundamentals of piezoelectric materials, magnetostrictive materials, shape memory alloys, electroactive polymers, and other emerging smart materials. Sensing and actuation mechanisms. Physics-based, control-oriented modeling of transducer dynamics. Modeling and control of hysteresis. Device and system applications in sensing, actuation, and energy harvesting.
Power semiconductor devices, circuits, control, and applications. Converter and inverter analysis and design, DSP (Digital Signal Processor) control and implementation. Automotive and utility applications.
Synchronous machines. Models and measurements of power components. Symmetrical components.
Analysis and simulation of small and large disturbance stability of power systems. Generator, exciter, voltage regulator models. Design of excitation systems and power system stabilizers.
Fundamentals of stochastic processes. Modeling of components and systems for reliability analysis of engineering systems.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
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.
Theory of guided transmission system. Microstrip lines, metallic and dielectric waveguides. EM cavities. Excitation and discontinuities of waveguides. Surface wave and radiation modes. Integrated optics. Scattering of EM waves.
Numerical methods and linear spaces. Finite difference time domain methods. Yee Algorithm. Boundary truncation methods. Perfectly matched layers (PMLs). Finite element method (time and frequency). Scalar basis functions. Vector basis functions. Boundary truncation using PMLs. Integral equation methods. Surface and volume integral equations.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
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.
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.
Plasma kinetic and macroscopic plasma transport theory. Electromagnetic wave propagation and charged particle diffusion processes in plasma. Electromagnetic energy absorption via elastic and inelastic collisions. Dc, rf, and microwave discharges.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
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.
Elements of a game, zero-sum games, non-zero-sum games, dynamic games, stochastic games.
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
Second-order systems and fundamental properties of solutions. Lyapunov stability, input-output stability, passivity, absolute stability and linearization. Design of feedback controllers using integral control, feedback linearization, sliding mode control, Lyapunov redesign, passivity-based control and recursive methods. Applications to electrical and mechanical systems.
Advanced topics in random variable theory. Stochastic processes and stochastic calculus. Optimal systems for filtering and detection.
Analysis and implementation of statistical estimation and detection methods used in signal processing, communications and control applications. Bayesian, Neyman-Pearson, and minimax detection schemes. Bayesian, mean-square-error and maximum-likelihood estimation methods.
Optimum signal design in noisy channels, matched filters, quadrature sampling of band-pass signals in noise. Coherent and non-coherent binary modulation such as PSK, FSK, DPSK, M-ary modulation, intersymbol interference, spread spectrum.
Basis functions. Orthonormal Signal Expansion. Short-Time Fourier Transform (STFT). Gabor Decomposition. Wigner Distribution. Cohen’s Class of Time-Frequency Distributions. Multiresolution Analysis. Discrete Wavelet Transform (DWT). Quadrature Mirror Filters (QMF). Biorthogonal Wavelets. Two-Dimensional Wavelets. Wavelet Packets. Overcomplete dictionaries and sparse representations. Applications in signal and image denoising and compression.
Cellular system design, characterization of wireless channels, signaling and receiver design for mobile radio, multiple access techniques and mobility management.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Applications of quantum mechanics and statistical mechanics in solids. Band theory of semiconductors. Electrical transport phenomena. Pn junctions.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Operating properties of semiconductor devices including DC, AC, transient and noise models of FET, BJT, metal-semiconductor contact, heterostructure, microwave and photonic devices.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Carrier scattering, single particle and collective transport, quantum effects, hot electron effects, electron-photon and electron-phonon interactions.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
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.
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.
Topics vary each semester.
Topics vary each semester.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Matrices and linear algebra. General linear systems of equations. Least squares minimization techniques. Eigenvalues and eigenvectors, spectral decompositions, and exponentials.
Continuation of MTH 320. Riemann integral. Metric spaces. Differentiation in higher dimensions. Inverse and implicit function theorems.
Analytic functions of a complex variable, line integrals and harmonic functions, Cauchy’s theorem and integral formula, power series, Laurent series, isolated singularities, residue calculus, Rouche’s theorem, automorphisms of the disk, the Riemann mapping theorem.
Numerical solution of linear and nonlinear algebraic equations and eigenvalue problems. Curve fitting. Interpolation theory. Numerical integration, differentiation, and solution of differential equations. Algorithms implementation with a programming language like Fortran, C/C++ or MATLAB.
Set theory, metric spaces, topological spaces, maps, product and quotient topologies. Connected and compact spaces, separation axioms, pointwise and uniform convergence.
Block codes, maximum likelihood decoding, Shannon’s theorem. Generalized Reed-Solomon codes, modification of codes, subfield codes. Alterant and Goppa codes, cyclic codes and BCH codes.
Lebesgue measure on real line, general measure theory. Convergence theorems, Lusin’s theorem, Egorov’s theorem, Lp-spaces, Fubini’s theorem. Functions of bounded variation, absolutely continuous functions, Lebesgue differentiation theorem.
Methods for solving boundary and initial value problems for ordinary and partial differential equations.
Continuation of MTH 841.
Sobolev spaces and embedding theorems, weak solutions of second order elliptic equations in divergence form (existence, uniqueness, and regularity), Fredholm alternative, maximum principle, calculus of variations, Euler-Lagrange equations.
Convergence and error analysis of numerical methods in applied mathematics.
Linear multi-step methods and single step nonlinear methods for initial value problems. Consistency, stability and convergence. Finite difference, finite element, shooting methods for boundary value problems.
Basic concepts in graph theory, connectivity, algebraic and topological methods. Networks, graph algorithms, Hamiltonian and Eulerian graphs, extremal graph theory, random graphs.
Schroedinger equation, hydrogen atom, harmonic oscillator, and other one-dimensional systems.
Matrix formulation of quantum mechanics, perturbation theory, scattering.
Theoretical methods used in classical mechanics, quantum mechanics, electrodynamics, and statistical mechanics.
Electrostatics, magnetostatics, time-varying fields and Maxwell’s equations. Gauge transformations. Poynting’s theorem and conservation laws.
Plane electromagnetic waves, polarization states, reflection, refraction. Wave guides and resonant cavities. Radiating systems, dipole fields, radiated power. Special theory of relativity.
Axioms of quantum and wave mechanics, applications to spherically symmetric potentials. Hydrogen atom, harmonic oscillator, matrix mechanics, angular momentum theory, rotations.
Approximation methods, perturbation theory, atomic physics applications, scattering theory, identical particles, Pauli principle, Bose and Einstein statistics, Hartree-Fock approximation, collisions of identical particles, radiation.
Parameter estimation, sampling distributions, confidence intervals, hypothesis testing, simple and multiple regression, analysis of variance. Time series models, data analysis and forecasting.
Stationary time series. Autocorrelation and spectra. ARMA and ARIMA processes: estimation and forecasting. Seasonal ARIMA models. Identification and diagnostic techniques. Multivariate time series. Time series software.
Probability models, random variables and vectors. Special distributions including exponential family. Expected values, covariance matrices, moment generating functions. Convergence in probability and distribution. Weak Law of Large Numbers and Lyapunov Central Limit Theorem.
Statistical inference: sufficiency, estimation, confidence intervals and testing of hypotheses. One and two sample nonparametric tests. Linear models and Gauss-Markov Theorem.
Programming in R and use of associated open source tools. Addressing practical issues in documenting workflow, data management, and scientific computing.
Focus Areas
As part of this degree, students may pursue an optional focus area. To complete a focus area, they must pass at least three courses in that focus area.
Focus Area: Computer Networking
To complete this focus area, choose three courses from the following.
Major security techniques, including authenticity, confidentiality, message integrity, non-repudiation, and the mechanisms to achieve them. Network security and system security practices, including authentication practice, email security, IP security, Web security, and firewalls.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
Optimum signal design in noisy channels, matched filters, quadrature sampling of band-pass signals in noise. Coherent and non-coherent binary modulation such as PSK, FSK, DPSK, M-ary modulation, intersymbol interference, spread spectrum.
Cellular system design, characterization of wireless channels, signaling and receiver design for mobile radio, multiple access techniques and mobility management.
Focus Area: Electromagnetics
To complete this focus area, choose three courses from the following.
Transceiver architecture designs with emphasis on hardware building blocks. Integrated radio frequency designs for various communication standards. Basic building blocks including low noise and power amplifiers, mixers, voltage control oscillators, and frequency synthesizers. Integrated circuit designs of basic building blocks.
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.
Theory of guided transmission system. Microstrip lines, metallic and dielectric waveguides. EM cavities. Excitation and discontinuities of waveguides. Surface wave and radiation modes. Integrated optics. Scattering of EM waves.
Numerical methods and linear spaces. Finite difference time domain methods. Yee Algorithm. Boundary truncation methods. Perfectly matched layers (PMLs). Finite element method (time and frequency). Scalar basis functions. Vector basis functions. Boundary truncation using PMLs. Integral equation methods. Surface and volume integral equations.
Plasma kinetic and macroscopic plasma transport theory. Electromagnetic wave propagation and charged particle diffusion processes in plasma. Electromagnetic energy absorption via elastic and inelastic collisions. Dc, rf, and microwave discharges.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Basis functions. Orthonormal Signal Expansion. Short-Time Fourier Transform (STFT). Gabor Decomposition. Wigner Distribution. Cohen’s Class of Time-Frequency Distributions. Multiresolution Analysis. Discrete Wavelet Transform (DWT). Quadrature Mirror Filters (QMF). Biorthogonal Wavelets. Two-Dimensional Wavelets. Wavelet Packets. Overcomplete dictionaries and sparse representations. Applications in signal and image denoising and compression.
Topics vary each semester.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Focus Area: Energy and Power Systems
To complete this focus area, choose three courses from the following.
Modeling and control of AC motors.
Power semiconductor devices, circuits, control, and applications. Converter and inverter analysis and design, DSP (Digital Signal Processor) control and implementation. Automotive and utility applications.
Synchronous machines. Models and measurements of power components. Symmetrical components.
Analysis and simulation of small and large disturbance stability of power systems. Generator, exciter, voltage regulator models. Design of excitation systems and power system stabilizers.
Fundamentals of stochastic processes. Modeling of components and systems for reliability analysis of engineering systems.
Focus Area: Materials and Devices
To complete this focus area, choose three courses from the following.
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.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
Operating properties of semiconductor devices including DC, AC, transient and noise models of FET, BJT, metal-semiconductor contact, heterostructure, microwave and photonic devices.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Carrier scattering, single particle and collective transport, quantum effects, hot electron effects, electron-photon and electron-phonon interactions.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Focus Area: Micro and Nanoelectronics and VLSI
To complete this focus area, you must complete the following three courses.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Secure and Connected Cyber-Physical Systems through the electives option.
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.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Applications of quantum mechanics and statistical mechanics in solids. Band theory of semiconductors. Electrical transport phenomena. Pn junctions.
*Note: This elective course counts towards obtaining the Graduate Certificate in Semiconductor Manufacturing, Processing, and Devices through the electives option.
Focus Area: Robotics and Controls
To complete this focus area, choose three courses from the following.
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.
Robot modeling, kinematics, dynamics, trajectory planning, sensors, controller design, motion planning.
Fundamentals of piezoelectric materials, magnetostrictive materials, shape memory alloys, electroactive polymers, and other emerging smart materials. Sensing and actuation mechanisms. Physics-based, control-oriented modeling of transducer dynamics. Modeling and control of hysteresis. Device and system applications in sensing, actuation, and energy harvesting.
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.
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
Second-order systems and fundamental properties of solutions. Lyapunov stability, input-output stability, passivity, absolute stability and linearization. Design of feedback controllers using integral control, feedback linearization, sliding mode control, Lyapunov redesign, passivity-based control and recursive methods. Applications to electrical and mechanical systems.
Focus Area: Signal Processing and Communications
To complete this focus area, choose three courses from the following.
Elements of a game, zero-sum games, non-zero-sum games, dynamic games, stochastic games.
Advanced topics in random variable theory. Stochastic processes and stochastic calculus. Optimal systems for filtering and detection.
Analysis and implementation of statistical estimation and detection methods used in signal processing, communications and control applications. Bayesian, Neyman-Pearson, and minimax detection schemes. Bayesian, mean-square-error and maximum-likelihood estimation methods.
Topics vary each semester.
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