Graduate

Basic teaching techniques for TAs: responsibilities and rights, resource materials, computer security, leading discussion or lab sessions, presentations techniques, maintaining class records, electronic handling of homework, and grading. Examines research and professional training: use of library and online databases, technical typesetting, writing journal and conference papers, publishing, giving talks, and ethical issues. (Formerly EE 200.)

Introduction to underlying principles of nanoscience and nanotechnology. Intended for multidisciplinary audience with a variety of backgrounds. Introduces scientific principles and laws relevant on the nanoscale. Discusses applications in engineering, physics, chemistry, and biology. (Formerly EE 211.)

Covers the many characterization techniques used to characterize materials from volumes less than one cubic micrometer, including the basic physics of each method, the methodology used to get quantitative results, and the advantages and limitations of each technique. (Formerly EE 213.)

Covers selected case studies in interfacing electronic devices with biological systems from Galvani to neuronal stimulation and electroceuticals. Studies include: the squid giant axon, the pacemaker, deep-brain stimulation, organic bioelectronics, bionanoelectronics and optogenetics, bioenergetics, and bioprotonics electroceuticals. Students are assessed through weekly papers on case studies and through a final presentation. (Formerly EE 204.)

Covers microscopic theory of electron transport in nanoelectronic devices and transistors. Topics include: ballistic transport; quantum conductance, NEGF-Landauer formalisms; molecular conductors; graphene and carbon nanotubes, quantum resonant tunneling devices; nanotransistors; and spintronics. (Formerly EE 218.)

Materials controlled at nanometer-scale will revolutionize existing technologies. Course offers opportunities of learning materials that exhibit peculiar physical characteristics at the nanometer scales. Course also includes discussions of unique device architecture based on materials crafted at the nanometer scale. (Formerly EE 216.)

Studies how the computational principles of the brain can be applied to build efficient machine learning models in software and hardware. Topics include the neuroscience of deep learning, spiking neural networks, hardware accelerators, and memory circuit design. Taught in conjunction with ECE 110. Students cannot receive credit for this course and ECE 110.

Theory and application of mathematical models to analyze, design, and program serial kinematic chains (robot arms). Covers models of arbitrary articulated robotic or biological arms and their application to realistic arms and tasks, including the homogeneous coordinate model of positioning tasks; the forward and inverse kinematic models; the Jacobian matrix; trajectory generation;and dynamic models, including Newton-Euler and Lagrangian formulations. (Formerly CMPE 215.)

Presents the principles of biological locomotion and application to robotics problems. Students learn about effective movements in the biological world (slithering, walking, climbing, and flying); extract their underlying principles; and apply them creatively to robotics design. (Formerly CMPE 216.)

 

Examines technologies involved in mechatronics (intelligent electro-mechanical systems)and techniques necessary to integrate these technologies. Topics include electronics (A/D, D/A converters, opamps, filters, power devices), software program design (event-driven programming, state machine-based design), DC and stepper motors, basic sensing, and basic mechanical design (machine elements and mechanical CAD). Students learn how to solve engineering problems using C Programming Language. Combines lab component of structured assignments with a large and open-ended team project. Students cannot receive credit for this course and ECE 118.

Lectures covering technologies for different imaging modalities, detectors, and instrumentations with discussion of properties of the signal generation, image reconstruction, and image data quantification.

Analog integrated circuit design with emphasis on fundamentals of designing linear circuits using CMOS. Covers MOS devices and device modeling, current mirrors, op-amp design, op-amp compensation, comparators, multipliers, voltage references, sample-and-holds, noise, and an introduction to more complicated systems using these building blocks, such as phase locked loops and analog-to-digital converters. If time permits, integrated circuit layout issues and device/circuit fabrication. Students cannot receive credit for this course and ECE 172. (Formerly EE 221.)

Digital integrated circuit design covered with an emphasis on high-speed and low-power applications. Covers signaling techniques and circuits including transmitters and receivers, with emphasis on on-chip interconnect, timing fundamentals and timing circuits. Theoretical fundamentals of phase locked loops and design issues of implementation addressed. Course has a project design component. Interview to assess technical skills of student. Enrollment is restricted to electrical engineering and computer engineering graduate students. (Formerly EE 222.)

Solid-state devices advance rapidly by employing new materials, new architecture, and new functional principles. Class offers opportunities to learn the latest advancements in solid-state devices (e.g., electronic, optoelectronic, photonic devices, and smart sensors) viewed from various scientific, technological, and engineering aspects, such as energy conversion and computation. (Formerly EE 223.)

Reviews the fundamentals of semiconductors and then explores the structure, design, and operation of the most important and widely used semiconductor devices. Topics include the motion of charge carriers in solids, equilibrium statistics, the electronic structure of solids, doping, the pn junction, the junction transistor, the Schottky diode, field-effect transistor, the light-emitting diode, and the photodiode.

Addresses principles of semiconductor processing with applications for semiconductor materials engineering, research, and development. The materials fabrication and processing topics include preparation of silicon, III-V compounds, and dielectric thin films, including thin film deposition techniques, diffusion, ion implantation, and standard device fabrication sequences. Applications of these processing principles for semiconductor materials engineering and bandgap engineering in semiconductor heterostructures are discussed for devices, such as LEDs, lasers, photoreceptors, modulators, and high-speed transistors.

Covers narrowband and high-frequency techniques, noise, distortion, nonlinearities, low-noise amplifiers, power amplifiers, mixers, receivers, and transmitters for wireless communications. Topics are presented in the context of integrated designs in CMOS, but topics are fundamental and widely applicable. (Formerly EE 226.)

Semiconductor physics is examined for advanced new materials and devices. Discusses how familiar concepts are extended to new electronics. Intended for students interested in electrical engineering, physics, and materials science applications. Good familiarity with basic electromagnetism and quantum physics is assumed. (Formerly EE 227.)

Covers key processes to build a coherent picture of the deposition of thin films. Offers an opportunity to implement general computing resources in describing the formation of thin films. The deposition of thin films plays a key role in technology due to their unprecedented physical properties. Their deposition depends on such factors as thermodynamics in the deposition environment and kinetics on the solid surfaces where atoms are assembled; therefore, understanding the fundamental processes involved is important. (Formerly EE 217.)

Covers basic theory of interaction of electromagnetic radiation with resonant atomic transitions and density matrix treatment; and applications including Rabi oscillations, slow light; nonlinear optics; coherent radiation, and noise in photodetectors and lasers. (Formerly EE 232.)

Components and system design of optical fiber communication. Topics include step-index fibers, graded-index fibers, fiber modes, single-mode fibers, multimode fibers, dispersion, loss mechanics, fiber fabrication, light-emission processes in semiconductors, light-emitting diodes, laser diodes, modulation response, source-fiber coupling, photodetectors, receivers, receiver noise and sensitivity, system design, power budget and rise-time budget, fiber-optic networks (FDDI, SONET, etc.), wavelength division multiplexing (WDM). Students cannot receive credit for this course and ECE 130. (Formerly EE 230.)

Introduction to phenomena, devices, and applications of optoelectronics. Main emphasis is on optical properties of semiconductors and semiconductor lasers. (Formerly EE 231.)

Covers use of integrated optics for study of biological material; fluorescence spectroscopy, single molecule detection, optical tweezers, layered dielectric media, hollow-core waveguides, photonic crystals, optofluidics, biophotonic systems, and applications.

Covers the basic principles of optics and microscopy. Topics include geometrical optics, simple ray tracing, diffraction, Fourier optics, image formation in the human eye, the photographic camera, and different types of microscopes. Hands-on experience is provided in laboratories. Requires basic mathematics. (Formerly EE 266.)

Fundamental concepts in digital image processing and reconstruction. Continuous and discrete images; image acquisition, sampling. Linear transformations of images, convolution and superposition. Image enhancement and restoration, spatial and spectral filtering. Temporal image processing: change detection, image registration, motion estimation. Image reconstruction from incomplete data. Applications. (Formerly EE 264.)

Introduction to applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics include the following: Least-squares approximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, state transfer, and least-norm inputs. Observability and least-squares state estimation. (Formerly CMPE 240.)

Graduate-level introduction to control of continuous linear systems using classical feedback techniques. Design of feedback controllers for command-following error, disturbance rejection, stability, and dynamic response specifications. Root locus and frequency response design techniques. Extensive use of Matlab for computer-aided controller design. Course has concurrent lectures with ECE 141.

Sequel to ECE 141 and ECE 241. After reviewing control design techniques examined in ECE 141 and ECE 241, this course explores state space control, discrete time control, and two case studies in control design. Students design and implement feedback controllers on an inverted pendulum experiment.

Course provides introduction to the construction of linear dynamical models from experimental data using parametric and non-parametric identification techniques. Theoretical and practical aspects of these techniques addressed. (Formerly CMPE 243.)

Teaches the design and analysis of digital control systems. The topics covered are discrete-time system modeling; z-transform; stability, controllability, and observability of discrete-time systems; various design approaches to control design in which sensor, computer hardware, actuation, communication, and user interface are part of the design. Note: knowledge of linear algebra, calculus, basic differential equations, Laplace transform, signals and systems, linear-system control theory, MATLAB, and the use of word-processing software are assumed. (Formerly CMPE 244.)

Provides practical knowledge of Kalman filtering and introduces control theory for stochastic processes. Selected topics include: state-space modeling; discrete- and continuous-time Kalman filter; smoothing; and applications in feedback control. Students learn through hands-on experience. Students cannot receive credit for this course and course 145. (Formerly CMPE 245.)

Examines the modeling and analysis of hybrid dynamical systems, including the modeling of hybrid systems, the concept of solutions, Zeno behavior, equilibrium sets, stability, convergence, Lyapunov-based conditions, robustness, and simulation. Students are guided on methods for simulation and encouraged to apply them to several applications. (Formerly CMPE 246.)

Presents the basic concepts and tools for the study of cyber-physical systems, including modeling and analysis tools for continuous-time and discrete-time systems, finite state machines, stateflow, timed and hybrid automata, concurrency, invariants, linear temporal logic, verification, and numerical simulation. Students are guided on methods for simulation and encouraged to apply them to several applications. The course is self-contained. Students are expected to have a basic background in logic circuits, programming, the mathematical modeling of dynamical systems (ECE 8 is recommended), differential equations, linear algebra, and basic calculus. Knowledge of MATLAB/Simulink is useful. Students cannot receive credit for this course and ECE 149.

In-depth study of signal processing techniques, including discrete-time signals and systems, the z-transform, sampling of continuous-time signals, transform analysis of linear time-invariant systems, structures for discrete-time systems, the discrete Fourier transform, computation of the discrete Fourier transform, filter design techniques. Students cannot receive credit for this course and course 153. (Formerly EE 250.)

A core course on digital communications theory. Provides an introduction to digital communication, including source coding, characterization of communication signals and systems, modulation and demodulation for the additive Gaussian channel, digital signaling, and over bandwidth constrained linear filter channels and over fading multipath channels. (Formerly EE 251.)

In-depth study of the physical layer of wireless communications. Wireless propagation channels and their impact on digital communications. Modulation techniques for wireless systems and their performance. Multi-antenna systems and diversity. Multicarrier and spread spectrum. Multi-access methods: FDMA, TDMA, CDMA. The structure of cellular systems. Students cannot receive credit for this course and course 152. (Formerly EE 252.)

An introduction to information theory including topics such as entropy, relative entropy, mutual information, asymptotic equipartition property, channel capacity, differential entropy, rate distortion theory, and universal source coding. (Formerly EE 253 and CMPS 250.)

Introduces radar signal processing, synthetic aperture radar (SAR), and inverse SAR (ISAR). Focuses on the fundamentals and design principles of modern radar systems. Students use hands-on computer simulations to build a strong background in radar sensor systems that can be applied to a variety of problems, such as medical imaging, ground-penetrating radar imaging for geophysical exploration, and the use of radar sensor systems for satellite-based SAR. (Formerly EE 288.)

Covers the following topics: introduction to algebra; linear block code; cyclic codes; BCH code; RS codes; spectral domain study of codes; CRC; and product codes. (Formerly EE 261.)

Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Will review the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course. Note: While a review of probability and statistics is provided, this is not a basic course on this material. (Formerly EE 262.)

Fundamental approaches and techniques in solving inverse problems in engineering and applied sciences, particularly in imaging. Initial emphasis on fundamental mathematical, numerical, and statistical formulations and known solution methods. Sampling of applications presented from diverse set of areas (astronomical, medical and optical imaging, and geophysical exploration). (Formerly EE 265.)

Technologies involved in the modeling and simulation of small-scale unmanned aerial vehicles (UAVs) with an emphasis on control applications, from low-level flight stabilization to higher level path planning and vision-based control. Topics include coordinate frames, aerodynamics, equations of motion, full non-linear simulation, linearized dynamics models and trim states, force and moment balances for steady flight, flight controls by successive loop closure, state space control, path planning and guidance, sensors and estimation. Students enrolled in this class learn how to use the Python programming language to solve engineering problems. Students gain team leadership and project management skills. Taught in conjunction with ECE 163. Students cannot receive credit for this course and ECE 163.

Advanced modeling and analysis of synchronous generators, permanent-magnet ac machines, and dual-fed induction machines. Machine control techniques including droop control, field weakening, multiple reference frame control, and indirect vector control. Taught in conjunction with ECE 169. Students cannot receive credit for this course and ECE 169.

Advanced topics in power electronics including SCR circuits, modulation techniques, multilevel power converters, active and current-source rectifiers, magnetic circuit design, state-space averaging, power converter controller design and stability. Taught in conjunction with ECE 170. Students cannot receive credit for this course and ECE 170.

The power industry is responsible for a large fraction of the U.S.'s emissions of SO2, NOx, and CO2, as well as much other water, solid waste, and land impacts. Its large environmental as well as economic footprint make it a target of many environmental policies, as well as an inherently interesting sector to study. The recent development of energy and climate policy at the global, the U.S. federal, and state-level has made Energy & Climate Policy an intriguing and dynamic topic for both industries and academia. This course will present fundamental analytical tools (optimization and simulation) for modeling firm and market behavior for the energy sector, with a focus on electric power. However, these models can be used for planning investments in generation, transmission, and energy conservation, and for analysis of public policy.

Provides a comprehensive overview of power systems. Students learn how mathematical tools are used for the system planning and operation. Advanced topics include smart grids, electric vehicles and energy data analytics.

A weekly seminar to discuss current topics in applied microscopy and neuronal imaging. (Formerly EE 280A.)

Weekly seminar covering current research in integrated bioelectronics. Enrollment is by permission of the instructor and is restricted to students who have research in bioelectronics. (Formerly EE 280B.)

Weekly seminar series covering topics of current research in theory and application of control to engineering systems. Current research work and literature in these areas discussed. (Formerly CMPE 280C.)

Weekly seminar series in topics of current research in information systems and technology management. Enrollment by permission of instructor. (Formerly TIM 280A.)

Weekly series covering current research in nanophotonics and lab-on-chip systems including nanoplasmonic biosensors; nanospectroscopy (Raman and vibrational mid-infrared spectroscopy); nanofabrication; nanophotonics devices for energy conversion and thermoplasmonics; acoustic fluids; and microfluidic integration. Current research work and recent literature are discussed. Enrollment is by permission of the instructor and restricted to graduate students. Sophomores, juniors, and seniors may enroll by permission of instructor. (Formerly EE 280N.)

Weekly seminar series covering topics of current research in applied optics, including integrated, quantum, nonlinear, and nano-optics. Current research work and literature in these areas are discussed. Enrollment by permission of instructor. (Formerly EE 280O.)

Weekly series covering state-of-the-art research in smart power grids, machine learning, communications, and signal processing. Current research works and recent literature are discussed. Enrollment is by permission of the instructor and is restricted to graduate students. Undergraduates may enroll by permission of the instructor. (Formerly EE 280Z.)

Graduate seminar on a research topic in electrical engineering that varies with the particular instructor. Topics may include, but are not limited to, electromagnetics, antennas, electronics biotechnology, nanotechnology, signal processing, communications, VLSI, MEMS, and radio frequency. Enrollment is restricted to graduate students and consent of instructor. (Formerly EE 283.)

Leading speakers from academia and industry present their latest research.

The aim of this course is two-fold: (1) inform, motivate, and prepare graduate students for possible careers in academia and industry; (2) expose graduate students to the professional skills required for possible career options in engineering and science. Course is for Satisfactory/Unsatisfactory grade only. (Formerly EE 291, Tomorrow's Professors, Engineers, and Entrepreneurs.)

Graduate seminar on a research topic in electrical engineering that varies with the particular instructor. Typical topics include, but are not limited to, electromagnetics, antennas, electronics biotechnology, nanotechnology, signal processing, communications, VLSI, and MEMS. Prerequisite(s):Enrollment is by permission of the instructor and is restricted to graduate students. In some quarters course will be taught in conjunction with ECE 183. (Formerly EE 293.)

Master project conducted under faculty supervision. Petition on file with sponsor faculty.

Independent study or research under faculty supervision. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Students submit petition to sponsoring agency.