Academics |
EE 201. Applied Quantum Mechanics (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): MATH 046, PHYS 040A; or consent of instructor. Covers topics in quantum mechanics including Schroedinger equation, operator formalism, harmonic oscillator, quantum wells, spin, bosons and fermions, solids, perturbation theory, Wentzel-Kramers- Brillouin approximation, tunneling, tight-binding model, quantum measurements, quantum cryptography, and quantum computing. EE 202. Fundamentals of Semiconductors and Nanostructures (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 133, EE 201; or consent of instructor. Examines principles of semiconductor materials and nanostructures. Topics include periodic structures, electron and phonon transport, defects, optical properties, and radiative recombination. Also covers absorption and emission of radiation in nanostructures, and nonlinear optics effects. Emphasizes properties of semiconductor superlattices, quantum wells, wires, and dots. EE 203. Solid-State Devices (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 133 or consent of instructor. Covers electronic devices including p-n junctions, field-effect transistors, heterojunction bipolar transistors, and nanostructure devices. Explores electrical and optical properties of semiconductor heterostructures, superlattices, quantum wires and dots, as well as devices based on these structures. EE 204. Advanced Electromagnetics (4) Lecture, 3 hours; laboratory, 3 hours. Prerequisite(s): EE 117 or consent of instructor. Presents selected topics in electromagnetic theory and antenna design. Topics include power transmission and attenuation in microstrip transmission lines (TL) and waveguides (WG); transient analysis and applications of TL and WG; radiation of electromagnetic waves; antenna design; electromagnetic interference and compatibility; and numerical methods in electromagnetic theory. EE 205. Optoelectronics and Photonic Devices (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 203, 204; or consent of instructor. A study of the physical optical and photonic devices and their use in an optical communication system. Covers silica fibers, light-emitting diodes (LEDs), heterojunction lasers, p-i-n photodiodes, and avalanche photodiodes. EE 206. Nanoscale Characterization Techniques (4) Lecture, 3 hours; laboratory, 3 hours. Prerequisite(s): EE 201, EE 202, EE 203; or consent of instructor. An indepth study of nanoscale materials and device characterization techniques. Laboratory emphasizes atomic force microscopy (AFM) and scanning tunneling microscopy (STM). Topics include semiconductor fabrication fundamentals; metrology requirements; in situ monitoring; interconnects and failure analysis; principles of AFM, STM, and scanning electron microscopy; X-ray methods; optical and infrared techniques; and electrical characterization. EE 207. Noise in Electronic Devices (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 203 or consent of instructor.A study of fluctuation processes in solids and noise in electronic devices. Topics include the theory of random processes and analysis of noise types such as generation-recombination noise, low-frequency noise, random telegraph noise, thermal noise, and short noise. EE 208. Semiconductor Electron, Phonon, and Optical Properties (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 202. Topics include semiconductor electronic band structure theory and methods, phonon dispersion theory and methods, defects in semiconductors, and optical properties of semiconductors. EE 209. Semiclassical Electron Transport (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 201, EE 203, EE 208. Covers the Boltzmann transport equation applied to semiconductor device modeling. Topics include the physics of carrier scattering in common semiconductors, theoretical treatments of low and high field transport, balance equations, and Monte Carlo solutions. EE 210. Advanced Digital Signal Processing (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 110B, EE 141. Provides in-depth coverage of advanced techniques for digital filter and power spectral estimation. Topics include digital filter design, discrete random signals, finite-wordlength effects, nonparametric and parametric power spectrum estimation, multirate digital signal processing, least square methods of digital filter design, and digital filter applications. EE 211. Adaptive Signal Processing (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 210, EE 215, EE 236. Provides an in-depth understanding of adaptive signal processing techniques. Covers Wold decomposition, Yule-Walker equations, spectrum estimation, Weiner filters, linear prediction, Kalman filtering, time-varying system tracking, nonlinear adaptive filtering, and performance analysis of adaptive algorithms and their variations including stochastic gradient, least mean square, least squares, and recursive least squares. EE 212. Quantum Electron Transport (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 208. Covers the theory and methods used to model quantum electron transport in ultrascaled traditional semiconductor devices such as transistors, nanoscaled research semiconductor devices such as quantum dots, and novel electronic material systems such as carbon nanotubes and molecular wires. EE 213. Computer-Aided Electronic Circuit Simulation (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 001A, EE 001B, EE 133. Introduction to numerical algorithms and computer-aided techniques for the simulation of electronic circuits. Covers theoretical and practical aspects of important analyses. Topics include circuit formulation methods; large-signal nonlinear direct current, small-signal alternating current, and moment-matching transient; sensitivity; and noise. Also discusses recent advances in timing analysis, symbolic analysis, and radio frequency circuit analysis. EE 214. Single-Electronics and Quantum Computing (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 201 or equivalent; graduate standing or consent of instructor. Introduces single-electron devices and their potential use in very large-scale integration applications and quantum computing. Topics include Coulomb blockade, “orthodox” theory of single-electron tunneling, single-electron transistor, shot noise theory superconducting and quantum dot single-electron devices, analog applications, single-electron memory and logic, basic principles of quantum computing and quantum cryptography, Shor’s algorithm, quantum error correction, and potential solid-state realizations of a quantum computer. EE 215. Stochastic Processes (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): graduate standing or consent of instructor. A study of probability theory and stochastic processes, with a focus on the most fundamental aspect of modern communication, control, and signal processing systems driven by random signal inputs. Topics include random variables and stochastic processes; spectral analysis; Wiener optimum filter, matched filter, and Karhunen-Loeve expansion; mean square estimation theory including smoothing, filtering, and linear prediction; Levinson’s algorithm, lattice filters, and Kalman filters; and the Markov process. EE 216. Nanoscale Phonon Engineering (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 202. Studies acoustic and optical phonons that affect electrical, thermal, and optical properties of materials. Focuses on the confinement-induced changes of phonon properties in nanostructures and their implications for performance of electronic, thermoelectric, and optoelectronic devices. Explores phonon theory, Raman spectroscopy and other phonon characterization techniques, thermal conductivity, and related measurements. EE 219. Advanced Complementary Metal Oxide Semiconductor (CMOS) Technology (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 203. Introduces advanced complementary metal oxide semiconductor (CMOS) technology. Topics include MOS field effect transistor (MOSFET) scaling, short and narrow channel effects, high field effects, vertical MOSFET transistors, single electron transistors, MOSFET nonvolatile memory devices, and small- and large-signal MOSFET models. Covers CMOS process integration. EE 220. Applied Ferromagnetism (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 116; consent of instructor. Introduces fundamentals of ferromagnetism necessary to develop next-generation nanomagnetic and spintronics-related devices. Includes basics of magnetism, magnetic circuits, ferromagnetic resonance (FMR), nuclear magnetic resonance (NMR), spintronics, and analyses of applications. EE 224. Digital Communication Theory and Systems (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 115; either the MATH 149A and MATH 149B sequence or the STAT 160A and STAT 160B sequence; or equivalents. Provides an overview of basic communication techniques and an introduction to optimum signal detection and correction. Topics include sampling and bandwidth;pulse code modulation; line coding and pulse shaping; delta modulation; stochastic approach to bandwidth and noise corruption; white Gaussian noise; matched filter; optimum signal detection; Shannon theorem; and error correction. EE 225. Error-Correcting Codes (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 215 or consent of instructor. Provides an overview of basic error-correcting techniques used in data transmission and storage. Topics include groups and Galois fields, error-correction capability and code design of Hamming codes, cyclic codes, Bose-Chaudhuri-Hocquengem (BCH) codes, and Reed-Solomon codes. Also considers concatenated design and decoding techniques. EE 226. Wireless Communications (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 215, EE 224. Presentation of fundamental cellular concepts and new techniques in wireless communications. Topics include cellular systems and standards, frequency reuse, system capacity, channel allocation, cellular radio propagation, fading channel modeling and equalization, spread spectrum communications and other multiple access techniques, and wireless networking. EE 227. Spread Spectrum Communications (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 115, EE 215; or consent of instructor. Provides an overview of spread spectrum communication techniques. Topics include direct sequence, frequency hopping and hybrid spread spectrum, pseudorandom sequence generation, modulation and spreading, code tracking, carrier synchronization, coherent and noncoherent data demodulation over fading channels, direct sequence multiple access, and performance evaluation of various multiuser detectors. Xu EE 228. Fundamentals of Data Compression (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 215 (may be taken concurrently). Covers the fundamental theory and tools for designing data and signal compression systems. Topics include lossless coding, scalar quantization, predictive and transform coding techniques, vector quantization, and the general trade-off between the reproduction signal quality and the bit-rate of the digital representation. Provides a foundation for further study and research in speech, audio, image, and video compression. EE 229. Video Processing and Communication (4) Lecture, 3 hours; laboratory, 1 hour; extra reading, 2 hours. Prerequisite(s): EE 150, EE 210. Covers the fundamental principles and technologies in the compression and transmission of coded video streams over wired and wireless networks, including wireless network protocols, compression standards, digital signal processor architectures, network or traffic management, quality of service, rate control schemes, and error resilience. EE 235. Linear System Theory (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 132, MATH 113. Provides a review of linear algebra. Topics include the mathematical description of linear systems; the solution of state-space equations; controllability and observability; canonical and minimal realization; and state feedback, pole placement, observer design, and compensator design. EE 236. State and Parameter Estimation Theory (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 235 or equivalent. Covers autoregressive and moving-average models, state estimation and parameter identification (including least square and maximum likelihood formulations), observability theory, synthesis of optimum inputs, Kalman-prediction (filtering and smoothing), steady-state and frequency domain analysis, on-line estimation, colored noise, and nonlinear filtering algorithms. EE 237. Nonlinear Systems and Control (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 235. Explores nonlinear systems and control. Topics include nonlinear differential equations, second order nonlinear systems, equilibrium and phase portrait, limit cycle, harmonic analysis and describing function, Lyapunov stability theory, absolute stability, Popov and circle criterion, input-output stability, small gain theorem, averaging methods, and feedback linearization. EE 238. Linear Multivariable Control (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 235. Investigates multivariable feedback systems, stability, performance, uncertainty, and robustness. Topics include analysis and synthesis via matrix factorization; Q-parameterization and all stabilizing controllers; frequency domain methods; and H(insert infinity) design and structured singular value analysis. EE 239. Optimal Control (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 215, EE 235. Presents the theory of stochastic optimal control systems and methods for their design and analysis. Covers principles of optimization, Lagrange’s equation, linearquadratic-Gaussian control; certainty-equivalence; the minimum principle; the Hamilton-Jacobi-Bellman equation; and the algebraic Ricatti equation. EE 240. Pattern Recognition (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 141 or consent of instructor. Covers basics of pattern recognition techniques. Topics include hypothesis testing, parametric classifiers, parameter estimation, nonparametric density estimation, nonparametric classifiers, feature selection, discriminant analysis, and clustering. EE 241. Advanced Digital Image Processing (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 152 or consent of instructor. Covers advanced topics in digital image processing. Examines image sampling and quantization, image transforms, stochastic image models, image filtering and restoration, and image data compression. EE 242. Intelligent Systems (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): graduate standing or consent of instructor. Introduces fundamental concepts of design of intelligent systems. Topics include biological versus computational systems, knowledge representation, computational reasoning, computational learning, language and human-machine communication, expert systems, computational vision, and examples of intelligent machines. EE 243. Advanced Computer Vision (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 146 or consent of instructor. A study of three-dimensional computer vision. Topics include projective geometry, modeling and calibrating cameras, representing geometric primitives and their uncertainty, stereo vision, motion analysis and tracking, interpolating and approximating three-dimensional data, and recognition of two-dimensional and three-dimensional objects. EE 244. Computational Learning (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): graduate standing or consent of instructor. Explores fundamental computational learning techniques. Topics include elements of learning systems, inductive learning, analytic learning, case-based learning, genetic learning, connectionist learning, reinforcement learning and integrated learning techniques, and comparison of learning paradigms and applications. EE 245. Advanced Robotics (4) Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 144, EE 235. Topics include robotics, mechatronics, and automation systems; design and analysis; mechanics; sensing and programming; linear and non-linear control; rigid and flexible systems; redundant robots; perceptiondriven action; multiarm cooperation; distributed autonomous robotic systems; programming languages and tools; simulations techniques; and application to mechatronics, manufacturing, and biomorphic systems. EE 246. Intelligent Transportation Systems (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): graduate standing or consent of instructor. EE 115 and EE 132 are recommended. Focuses on the control, communications, and computer aspects of intelligent transportation systems. Topics include traffic flow theory fundamentals, intelligent transportation system user services, travel and traffic management, advanced vehicle safety systems, intelligent transportation system applications, architectures, standards, strategic needs assessment and deployment, and evaluation. EE 247. Current Topics in Computer Vision and Pattern Recognition (4) Lecture, 3 hours; outside research, 3 hours. Prerequisite(s): EE 240 or EE 243 or consent of instructor. Explores advanced mathematical techniques of recent research interest. Topics include particle filters, sampling techniques, stochastic optimization, stochastic approximation algorithms, independent components analysis, energy function techniques, nonlinear discriminant analysis, and support vector machines. EE 250. Information Theory (4) Lecture, 3 hours; extra reading, 3 hours. Prerequisite(s): EE 215. An overview of fundamental limitations imposed on communication systems. Topics include Shannon’s information measures, weak and strong typicality, lossless data compression, source and channel models and Shannon’s coding theorems, channel capacity and the rate-distortion function, Gaussian sources and channels, and limits of communication between multiple terminals. EE 251. Algorithmic and Combinatorial Coding Theory (4) Seminar, 2 hours; lecture, 2 hours. Prerequisite(s): EE 225 or consent of instructor. Explores combinatorial and algorithmic techniques in coding theory. Covers algebraic design of Bose-Chaudhuri-Hocquenghem (BCH) codes and Reed-Muller codes. Algorithmic topics include gradient-like decoding, split-syndrome techniques, and information-set decoding. Introduces decoding with polynomial complexity based on Bayesian estimation, iterative decoding, and codes on graphs. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor. EE 259. Colloquium in Electrical Engineering (1) Colloquium, 1 hour. Prerequisite(s): graduate standing. Lectures on current research topics in electrical engineering presented by faculty members and visiting scientists. Graded Satisfactory (S) or No Credit (NC). Course is repeatable. EE 260. Seminar in Electrical Engineering (4) Seminar, 4 hours. Prerequisite(s): consent of instructor. Seminar on current research topics in electrical engineering, including areas such as signal processing, image processing, control, robotics, intelligent systems, computer vision, and pattern recognition. Course is repeatable to a maximum of 16 units. EE 290. Directed Studies (1-6) Individual study, 3-18 hours. Prerequisite(s): graduate standing; consent of instructor and Graduate Advisor. Individual study, directed by a faculty member, of selected topics in electrical engineering. Graded Satisfactory (S) or No Credit (NC). Course is repeatable to a maximum of 12 units. EE 297. Directed Research (1-6) Outside research, 3-18 hours. Prerequisite(s): graduate standing; consent of instructor. Research conducted under the supervision of a faculty member on selected problems in electrical engineering. Graded Satisfactory (S) or No Credit (NC). Course is repeatable. EE 298-I. Individual Internship in Electrical Engineering (1-12) Internship, 2-24 hours; written work, 1-12 hours. Prerequisite(s): graduate standing; consent of instructor. Provides the Electrical Engineering graduate student with career experience as an electrical engineer in an industry or a research unit. Includes fieldwork with an approved pprofessional individual or organization and academic work under the direction of a faculty member. Requires a final report. Graded Satisfactory (S) or No Credit (NC). Course is repeatable to a maximum of 12 units. EE 299. Research for the Thesis or Dissertation (1-12) Outside research, 3-36 hours. Prerequisite(s): graduate standing; consent of instructor. Research in electrical engineering for the M.S. thesis or Ph.D. dissertation. Graded Satisfactory (S) or No Credit (NC). Course is repeatable. |
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