Prior to Fall 2019, the course catalog subject area was listed as Cen.
Ece 501 Advanced Electronic Circuits (3)
Linear and non-linear applications of operational amplifiers, with an emphasis on circuit design. Non-ideal operational amplifier behavior, including both static and dynamic characteristics. Amplifier stability and frequency compensation techniques. Operational amplifier based oscillators. Circuit noise. Prerequisites: Ece 300 Introduction to Electronics or equivalent. Students who have received credit for ICEN401 cannot receive credit for this course.
Ece 502 Power Electronics (3)
An introduction to fundamentals of power electronic circuits and their role in industrial, residential and power system applications. This course covers the characteristics of power semiconductor devices including diodes, thyristors, GTOs, IGBTs and MOSFETs. Analysis and design of basic dc-dc converters, single phase and multi-phase rectifiers and inverter circuits will be introduced as well as an introduction to the fundamentals of soft switching converters. Industrial applications, such as renewable energy, telecom and computing industry will be discussed. Computer simulation will be used to analyze the detailed operation of switching converters. This course includes a laboratory. Course fee applies. Consult the Schedule of Classes. Prerequisite(s): Ece 300 and Ece 413 (or permission of instructor). Students who have received credit for IECE402 cannot receive credit for this course.
Ece 511 Microwave Engineering (3)
An introduction to radio frequency and microwave analysis and design. Transmission lines and waveguides, microwave network characterization and analysis, impedance matching and tuning. Passive microwave devices such as power dividers, couplers, resonators, filters, and ferrimagnetic components. An introduction to active devices. Prerequisites: Ece 310 Engineering Electromagnetics and Ece 300 Introduction to Electronics or equivalent. Students who have received credit for IECE411 cannot receive credit for this course.
Ece 512 Antenna Engineering (3)
An introduction to the fundamental principles of antenna theory. Basic antenna parameters, including radiation resistance, input impedance, gain and directivity. Antenna radiation properties and Friis transmission formula. Elementary (dipole, linear wire and loop) antennas and their radiation properties. Impedance matching techniques and mutual coupling. Analysis and design of antenna arrays. Introduction to commonly used aperture and microstrip antennas. Prerequisites: Ece 310 Engineering Electromagnetics or equivalent. Students who have received credit for IECE412 cannot receive credit for this course.
Ece 513 Electrical Energy Systems (3)
The course starts with covering three phase circuits and power calculations in three-phase systems. Active and reactive power transfer in an electrical grid are analyzed. Concepts of electromagnetic energy conversion and transformers will be introduced. Different types of energy sources and their interconnection to the grid will be covered such as hydro energy, wind power, solar photovoltaics and energy storage. The course is concluded with an introduction to economics of power generation and an overview of elements of smart grids. Prerequisites: Ece 202 and Ece 310 or permission of instructor. Students who have received credit for ICEN413 cannot receive credit for this course.
Ece 515 Electric Machine Control and Drive Systems (3)
Advanced topics on the modeling and control of electrical machines. Topics covered include induction machine equations, dynamic analysis of induction machines in terms of dq-windings, vector control of induction motor drives, mathematical description of vector control, detuning effects in induction motor vector control, dynamic analysis of doubly-fed induction generators and their vector control, space-vector pulse-width modulated inverters, direct torque control and encoder-less operation of induction motors, vector control of permanent magnet synchronous-motor drives and switched-reluctance motor drives. Prerequisite(s): Ece 414 or equivalent or permission of instructor.
Ece 516 (Csi 516) Computer Communications Networks (3)
This course covers fundamentals in computer communication networks and the principles of distributed systems that leverage these networks. The course will focus on key Internet application architectures, principles and protocols, covering reliable data transfer and transport protocols; routing and forwarding; data link layer communications and principles of shared media access. Students will also be introduced to various physical layer techniques like error correction and bandwidth efficiency; content delivery networks; and software-defined networks. The students will apply their understanding of networking fundamentals while working on hands-on programming assignments, packet trace analysis and Internet measurements. Prerequisites: Recommended - Students should have knowledge of computer systems and probability. Students who have received credit for ICSI/IECE416 cannot receive credit for this course.
Ece 518 Power Systems Analysis (3)
This course covers principles of electric power systems, three-phase transformers, transmission line parameters, admittance model, impedance model, network work calculations, power-flow solution, symmetrical faults, symmetrical components and sequence network, asymmetrical faults, elements of power system protection and power system stability. Prerequisites: Ece 202, Ece 310 and Ece 413 or permission of instructor. Students who have received credit for IECE418 cannot receive credit for this course.
Ece 519 Fundamentals of Electric and Hybrid Electric Transportation Systems (3)
This course presents a comprehensive systems-level perspective of electric, hybrid electric and plug-in hybrid electric vehicles with emphasis on component/subsystem analysis, basic design guidelines and mathematical relationships. Specific topics include electric and hybrid electric drive trains, energy storage, electromechanical energy conversion (induction and permanent magnet motors and generators), power-electronic drives and battery interface converters, vehicle-level modeling and control, and impact of electric vehicle integration on the power grid. Coursework will include design and simulation projects of the various subsystems. Prerequisite: Ece 513 or equivalent, or permission of instructor. Students who received credit for Ece 419 cannot receive credit for this course.
Ece 520 (Csi 522) Introduction to VLSI (3)
An introduction to Very Large Scale Integrated (VLSI) circuit design. The device, circuit, and system aspects of VLSI design are covered in an integrated fashion. Emphasis is placed on NMOS, PMOS and CMOS technology. Using transistors, simple gates such as XOR, AND, OR, AOI, OAI, and flip flops, are constructed and simulated using industry standard layout and simulation tools. Students who have received credit for IECE420 cannot receive credit for this course. Recommended prerequisites: Either Ece 231 and Ece 300 or Csi 404 and Phy 415, or equivalent.
Ece 521 Digital ASIC Design (3)
The design of complex digital Application Specific Integrated Circuits (ASICs). Standard cell libraries and the Verilog language are used to build complex digital synchronous circuits using Cadence Design Systems tools. Interconnect delay estimation, clock tree synthesis, repeater and pipeline stage design and introduced. A synchronous digital system utilizing 100s of flip flops and logic gates is designed as a final project. Students who receive credit for ECE 421 cannot receive credit for this course. Prerequisites: Ece 420/520 Introduction to VLSI.
Ece 522 Integrated Circuit Devices (3)
Modern solid-state devices and their operational principles. Solid state physics fundamentals, such as carriers and their mobility, band structures, doping concentrations and PN junctions. The operation of PN diodes, PIN diodes, and Schottky diodes, as well as three terminal devices, such as BJTs, JFETs, SCRs, MESFETs and MOSFETs. Device modelling and behavior. Prerequisites: Ece 300 Introduction to Electronics or equivalent. Students who have received credit for IECE422 cannot receive credit for this course.
Ece 531 (Csi 534) Reconfigurable Computing (3)
This course provides a study of FPGA architecture with detailed discussion on opportunities and challenges in this flexible platform. Topics include device architecture, programming languages and models for FPGAs including streaming and I/O, Mapping, Placement and Routing in reconfigurable logic, application design, development, verification and application specific optimization techniques. Prerequisites: Ece 231 or Csi 404. Students who have received credit for IECE431 cannot receive credit for this course.
Ece 532 (Csi 504) Advanced Computer Architecture (3)
The organization of the hardware components of computing systems. Logic design theory review. Comparative survey of instruction set architectures. Design, control, communication, and interconnection strategies for major components such as arithmetic-logic units, control units, CPUs, memories, and I/O systems. Prediction and measurement of performance. Introduction to VLSI, parallel processing, and other current architectural trends. Only one of Csi 504 and Ece 532 may be taken for credit. Prerequisites: Recommended - Students should have knowledge of computer architecture and organization.
Ece 541 Parallel Computing (3)
This course introduces students to fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as parallel programming techniques for optimal use in these systems. Topics include parallel programming models, performance optimization and evaluation, cache coherence, memory consistency, interconnection networks, synchronization and latency consideration. Multi-core CPUs and GPUs will be used for assignments. Prerequisites: Ece/Csi 213 Data Structures and Ece/Csi 404 Computer Organization or equivalent. Students who have received credit for IECE441 cannot receive credit for this course.
Ece 551 (Csi 552) Robotics (3)
An introduction to the fundamentals of robotics, including configuration space, transformation matrix, kinematics, motion planning, a brief introduction to robot manipulation, degrees of freedom, implicit and explicit representations of configurations, and holonomic and nonholonomic restrictions will all be covered. This material is fundamental to the study of anything that moves (e.g., robots). Students will use a library of robotics software and a robot simulator to build and test software. Prerequisites: Recommended - Students should have knowledge of linear algebra, discrete math, and data structures. Students who receive credit for Csi 452/Ece 451 cannot receive credit for this course.
Ece 553 (Csi 553) Cyber-Physical Systems (3)
This course is an introduction to the basics of models, analysis tools, and control for embedded systems operating in real time. Topics include models of computation, basic analysis, control, and systems simulation, interfacing with the physical world, mapping to embedded platforms and distributed embedded systems. This course has a lab component. Course fee applies. Consult the Schedule of Classes. Prerequisites: Ece 233 or Csi 404 and either Ece 371 or APhy 415. Students who have received credit for IECE453 cannot receive credit for this course.
Ece 560 Topics in Electrical and Computer Engineering (3)
This course will explore current emerging technologies and related technical management practices on a global basis. The content of this course will vary from semester to semester. Each offering will cover an advanced engineering topic in Electrical and Computer Engineering. May be repeated for credit when content varies. Prerequisites: Permission of instructor. Students who have received credit for IECE494 cannot receive credit for this course.
Ece 562 Digital Signal Processing (3)
The mathematical basis of discrete-time signal analysis. Topics include the theory and implementation of fast Fourier transform algorithms and the design and implementation of digital filters along with advanced techniques such as linear prediction, adaptive filtering, and two-dimensional signal processing. Prerequisites: Ece 371 Signals and Systems or equivalent. Students who have received credit for IECE462 cannot receive credit for this course.
Ece 563 Digital Image Processing (3)
An introduction to the fundamental techniques and algorithms used for acquiring, processing and extracting useful information from digital images. Image sampling and quantization, image transforms, image enhancement and restoration, image encoding, image analysis and pattern recognition. Prerequisites: Ece 462/562 Digital Signal Processing or equivalent.
Ece 564 Brain-Computer Interfaces (3)
An introduction to the theory and design of brain-computer interfaces (BCIs). The content of the course is divided into four modules: (1) An introduction to BCIs and an exploration of the neurophysiology critical to their development; (2) Imaging, software, and signal processing for BCIs; (3) Types of BCIs; and (4) Special topics and directions for future development. Each module includes laboratory activities meant to solidify and reinforce the content covered in the lectures. Students will learn to use EEG to record signals from the body; build a real-time signal analysis system for use in a BCI; and complete a semester project where they design and build their own BCI system. This course is given in collaboration with the National Center for Adaptive Neurotechnologies (NCAN), one of the world's pioneering neuroengineering laboratories. Prerequisites: A background in analog and digital signal processing and permission of the instructor.
Ece 565 Introduction to Machine Learning for Engineers (3)
This course offers a comprehensive introduction to machine learning (ML). Students will gain both theoretical knowledge and practical skills required to utilize ML techniques for building Internet-of-Things (IoT) applications. The theory covers the mathematical methods that are utilized to train and test ML models. The course curriculum is designed to focus on applying ML models to realize various signal processing, communication, and control operations. The course assignments and projects involve coding-based tasks that enable the students to design and implement their own ML models and optimize them for targeted IoT applications. The high-level overview of the topics covered in this course includes the extraction of feature vectors for signal processing, deep learning (DL) for communication, and reinforcement learning (RL) control. Recommended prerequisites: Mat 214, Mat 220, Ece 371 and either Ece 233 or Ece 332. Students who received credit for Ece 465 cannot receive credit for this course.
Ece 566 Deep Learning (3)
This course is an introduction to deep learning, which provides a fundamental understanding of neural networks (NNs). The course will cover different NNs, activation functions, hyperparameter tuning, batch normalization, regularization, optimization, and intuition to create application specific loss functions. Topics include Single and Multi-Layer Perceptron, Convolutional Neural Network (CNN) and sequential models. Recommended prerequisites: A knowledge of Python, Bash/Linux, basic statistics, probability, and optimization is recommended.
Ece 571 Probability and Random Processes (3)
A foundation in the theory and applications of probability and stochastic processes with an emphasis on applications within the broad areas of electrical and computer engineering such as signal processing, detection, estimation, and communications. Fundamental probabilistic results such as the axioms of probability, random variables, distribution functions, functions and sequences of random variables, stochastic processes, and representations of random processes and their application in electrical and computer engineering. Prerequisites: A Mat 370 Probability and Statistics for Engineering and the Sciences or equivalent.
Ece 572 Advanced Digital Communications (3)
An introduction to digital communications, including signal generation, signal detection, synchronization, channel modeling, and coding. Baseband pulse modulation. Signal space representation of signals and optimal receiver structures. Bandpass modulation techniques including PSK, QAM and FSK. Carrier, symbol, and frame synchronization. Channel characterization and modeling, including terrestrial channels. Prerequisites: Ece 471 Communication Systems or equivalent. Students who have received credit for IECE472 cannot receive credit for this course.
Ece 573 Radiowave Propagation and Remote Sensing (3)
An introduction to the basic physical mechanisms of electromagnetic wave propagation in the troposphere and ionosphere, and the fundamentals of microwave remote sensing. Direct transmission. Attenuation due to atmospheric gases and rain. Reflection and refraction of electromagnetic waves, ducting and ray tracing. Path loss and fading models. Groundwave and ionospheric propagation. Active and passive remote sensing systems. Prerequisites: Ece 310 Engineering Electromagnetics and Ece 371 Signals and Systems or equivalent.
Ece 574 Modern Wireless Networks (3)
This course provides a comprehensive study of recent wireless data and telecommunication networks. Topics include fundamentals of wireless coding and modulation, wireless signal propagation, cellular networks: 1G/2G/3G, LTE, LTE-Advanced, and 5G, IEEE 802.11a/b/g/n/ac wireless local area networks, 60 GHz mmWave wireless networks, vehicular wireless networks, and wireless protocols for Internet of Things including Bluetooth, BLE, 802.15.4, Zigbee, LoRA and Sigfox. Prerequisites: Computer Communication Networks Ece 416/Csi 416 and Ece 472/572 Advanced Digital Communication.
Ece 581 Linear Control Theory (3)
An introduction to the analysis and design of linear control systems. Mathematical models, including state-space variable models. Continuous and sampled-data systems. Feedback control, and stability. Root locus and frequency response compensation methods. Uncertain models and robustness. Prerequisites: Ece 371 Signals and Systems or equivalent. Students who have received credit for IECE481 cannot receive credit for this course.
Ece 602 Advanced Power Electronics (3)
Topics on advanced power electronics circuit design including gate drive circuit design, wide bandgap power devices and their applications, snubber circuits, high frequency resonant power conversion including variable frequency control, asymmetrical pulse width modulation and phase shift control. Voltage and current mode control techniques, power factor correction rectification techniques as well as design guidelines for electromagnetic compliance will be covered. This course includes a design project based on real life applications in fields such as renewable energy, transportation electrification, telecom, data centers. Prerequisite(s): Ece 402/Ece 502 or equivalent or permission of instructor.
Ece 620 Mixed-Signal IC Design (3)
The implementation of digital and analog circuits together on a single integrated circuit. The design of analog integrated circuits such as operational amplifiers, operational transconductance amplifiers, and bandgap voltage references. Analog and digital IC design concepts are combined to develop a user-programmable Video Graphics Array (VGA) controller IC that stores user-selected digital values in its internal registers. A final project requires the design of a VGA controller that reads its screen contents from an external SRAM. Prerequisites: Ece 421/521 Digital ASIC Design.
Ece 621 Radio Frequency IC Design (3)
The design, simulation, and implementation of RF/microwave integrated circuit components and devices for applications within the medium frequency (MF) to ultrahigh frequency (UHF) range. System and design concepts are taught through the example of the Radio Frequency Identification (RFID) system. A final project requires the design of an RFID integrated circuit to operate at 433 MHz. Designs are built using the MOSIS 0.5 um process. Prerequisites: Ece 420/520 Introduction to VLSI.
Ece 629 Projects in Electronic Circuits and Systems (3)
Supervised projects in Electronic Circuits and Systems. Students investigate the state-of-the-art in Electronic Circuits and Systems through the study of current publications, class discussions, student presentations, and a major project. Prerequisites: Students must have completed at least 3 courses in the Electronic Circuits and Systems Concentration Area of the Department of Electrical and Computer Engineering.
Ece 659 Projects in Control and Computing Systems (3)
Supervised projects in Control and Computing Systems. Students investigate the state-of-the-art in Control and Computing Systems through the study of current publications, class discussions, student presentations, and a major project. Prerequisites: Students must have completed at least 3 courses in the Control and Computing Systems Concentration Area of the Department of Electrical and Computer Engineering.
Ece 660 Topics in Electrical and Computer Engineering (3)
This course will explore current emerging technologies and related technical management practices on a global basis. The content of this course will vary from semester to semester. Each offering will cover an advanced engineering topic in Electrical and Computer Engineering. May be repeated for credit when content varies. Prerequisite: Permission of Instructor.
Ece 664 Probabilistic Machine Learning (3)
An introduction to the foundations of machine learning from a probabilistic perspective. Topics include linear models (e.g., logistic and linear regression), nonparametric models (exemplar-based and kernel methods, trees), clustering, dimensionality reduction, learning with fewer examples, and basics of neural networks. Prerequisites: Ece 571 Probability and Random Processes.
Ece 672 Foundations of Statistical Inference (3)
An introduction to the tools needed to address modern inference problems in engineering and data science addressing the common methodology needed for applications in areas such as signal processing, communications, control, machine learning and artificial intelligence. Topics include fundamental principles of statistical decision theory and their application to hypothesis testing and estimation, classical optimality criteria for decision rules and their performance, Bayesian and non-Bayesian parameter estimation and properties of estimators. Prerequisites: Ece 571. Recommended prerequisites are Mat 220 and Ece 371.
Ece 673 Information Theory, Inference, and Machine Learning (3)
The mathematics of Information Theory and its relationship to Machine Learning and Bayesian Inference. Topics will include Discrete Probability, Entropy, Mutual Information, Typical Sequences and Sets, Data Compression, Huffman Codes, Universal Source Coding, Discrete Memoryless Channels, Channel Capacity, Arithmetic Coding, Differential Entropy, Gaussian Channels, Rate Distortion Theory, Machine Learning Tree Algorithms, Clustering, and Inference. Special Topics will address Machine Learning and Inference viewed from the lens of Information Theory and an introduction to Quantum Shannon Theory. Prerequisites: Ece 571 Probability and Random Processes or permission of instructor.
Ece 674 Error Control Coding (3)
The course will begin with an introduction to the fundamental problems of the ECC Theory, and their mathematical formulations. Topics covered in class include algebraic codes (cyclic codes, BCH codes, Reed-Solomon codes), convolutional codes, and modern graph based codes (Turbo-Codes and LDPC codes). Prerequisites: Ece 462 Digital Signal Processing, A Mat 370 Probability and Statistics for Engineering and the Sciences or permission of the instructor.
Ece 676 Wireless Communications (3)
This course introduces students to design, analysis and fundamental limits of wireless communication systems. Topics that will be covered in this course include: wireless channel models, fading and diversity, mmWave propagation, capacity of wireless channels, adaptive modulation techniques, multiple-antenna and MIMO systems; multicarrier systems and OFDM and inter-symbol interference countermeasures. Prerequisites: Ece 572 Advanced Digital Communications or permission of the instructor.
Ece 677 Communication Network Analysis (3)
The objective of the course is to develop mathematical models that allow the study of packet arrival, admission control and congestion control used in emerging high-speed and wireless networks. This course is focused on probabilistic modeling and analysis of communication network protocols. Topics covered are Markov Chains, Poisson process, queueing models, network of queues, stability and delay analysis, optimal routing, capacity assignment, rate control, fairness. Prerequisites: Ece/Csi 416 Computer Communication Networks and Ece 571 Probability and Random Processes or equivalent.
Ece 679 Projects in Signal Processing and Communications (3)
Supervised projects in Signal Processing and Communications. Students investigate the state-of-the-art in Signal Processing and Communications through the study of current publications, class discussions, student presentations, and a major project. Prerequisites: Students must have completed at least 3 courses in the Signal Processing and Communications Concentration Area of the Department of Electrical and Computer Engineering.
Ece 680 Advanced Linear Control Theory (3)
An introduction to the analysis and design of control systems. System modeling and analysis. System structural properties such as stability, controllability, observability. Optimization and design to meet specifications. Feedback control systems emphasizing on state space techniques. Optimum feedback control including dynamic programming and numerical methods. Minimum principle. Infinite Horizon LQR problems. Prerequisites: Ece 481/581 Linear Control Theory.
Ece 681 Nonlinear and Adaptive Control (3)
An introduction to the basic concepts of nonlinear and adaptive control. Nonlinear and adaptive control systems. Lyapunov stability and boundedness. Identification and parameter estimation. Bayesian and non-Bayesian adaptive control. Gradient and least squares schemes. Direct and indirect adaptive control. Self-tuning regulators. Model Reference. Pole placement algorithms. Convergence, stability and robustness properties. Prerequisites: Ece 581 Linear Control Theory and Ece 571 Probability and Random Processes.
Ece 695 Master's Project (3)
Independent project at the master's level under the direction of faculty guidance. May be repeated for credit with permission of department. Prerequisites: Consent of department.
Ece 695C Master's Project Continuation (1)
Course grading is Load Only and does not earn credit. Appropriate for master's students engaged in the master's project beyond the level applicable to their degree program. Prerequisites: Consent of department.
Ece 697 Graduate Independent Study and Research (1-3)
Independent study in a particular area of electrical and computer engineering under the supervision of a departmental faculty member. May be repeated for credit with permission of the department. Prerequisites: Consent of department.
Ece 699 Master's Thesis (1-9)
Independent research project at the master's level under the direction of faculty guidance. Students will present their research as appropriate. May be repeated for credit with permission of department. Prerequisites: Consent of department.
Ece 699C Master's Research Continuation (1)
Course grading is Load Only and does not earn credit. Appropriate for master's students engaged in research on the master's project beyond the level applicable to their degree program. Prerequisites: Consent of department.
Ece 890 Doctoral Research (1-15)
Independent research project at the doctoral level under the direction of faculty guidance. Students will present their research as appropriate. May be repeated for credit with permission of department. Prerequisites: Permission of Instructor.
Ece 899 Doctoral Dissertation (1-15)
Independent research at the doctoral level under the direction of faculty guidance. Students will present their research as appropriate. May be repeated for credit with permission of department. Registration for this course is limited to doctoral students who have met all requirements with the exception of the dissertation defense and its official submission to the Graduate School. Requirements include successfully passing all examinations, meeting the minimum GPA requirement, and completing all program requirements as indicated on the approved academic Plan of Study. Prerequisites: Permission of department.