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# Electrical Engineering (EE) Graduate Courses (2020-21)

EE 105 abc. Electrical Engineering Seminar. 1 unit: first, second, third terms. All candidates for the M.S. degree in electrical engineering are required to attend any graduate seminar in any division each week of each term. Graded pass/fail. Instructor: Emami.
ACM/EE 106 ab. Introductory Methods of Computational Mathematics. 12 units (3-0-9): first, second terms. Prerequisites: Ma 1 abc, Ma 2, Ma 3, ACM 11, ACM 95/100 ab or equivalent. The sequence covers the introductory methods in both theory and implementation of numerical linear algebra, approximation theory, ordinary differential equations, and partial differential equations. The linear algebra parts covers basic methods such as direct and iterative solution of large linear systems, including LU decomposition, splitting method (Jacobi iteration, Gauss-Seidel iteration); eigenvalue and vector computations including the power method, QR iteration and Lanczos iteration; nonlinear algebraic solvers. The approximation theory includes data fitting; interpolation using Fourier transform, orthogonal polynomials and splines; least square method, and numerical quadrature. The ODE parts include initial and boundary value problems. The PDE parts include finite difference and finite element for elliptic/parabolic/hyperbolic equation. Stability analysis will be covered with numerical PDE. Programming is a significant part of the course. Instructor: Hou.
APh/EE 109. Introduction to the Micro/Nanofabrication Lab. 9 units (0-6-3): first, second, third terms. Introduction to techniques of micro-and nanofabrication, including solid-state, optical, and microfluidic devices. Students will be trained to use fabrication and characterization equipment available in the applied physics micro- and nanofabrication lab. Topics include Schottky diodes, MOS capacitors, light-emitting diodes, microlenses, microfluidic valves and pumps, atomic force microscopy, scanning electron microscopy, and electron-beam writing. Instructors: Troian, Ghaffari.
EE 110 abc. Embedded Systems Design Laboratory. 9 units (3-4-2): first, second, third terms. The student will design, build, and program a specified microprocessor-based embedded system. This structured laboratory is organized to familiarize the student with large-scale digital and embedded system design, electronic circuit construction techniques, modern development facilities, and embedded systems programming. The lectures cover topics in embedded system design such as display technologies, interfacing to analog signals, communication protocols, PCB design, and programming in high-level and assembly languages. Given in alternate years; 110 c Offered 2020-21; 110 ab Not offered 2020-21. Instructor: George.
EE 111. Signal-Processing Systems and Transforms. 9 units (3-0-6): first term. Prerequisites: Ma 1. An introduction to continuous and discrete time signals and systems with emphasis on digital signal processing systems. Study of the Fourier transform, Fourier series, z-transforms, and the fast Fourier transform as applied in electrical engineering. Sampling theorems for continuous to discrete-time conversion. Difference equations for digital signal processing systems, digital system realizations with block diagrams, analysis of transient and steady state responses, and connections to other areas in science and engineering. Instructor: Vaidyanathan.
EE 112. Introduction to Signal Processing from Data. 9 units (3-0-6): second term. Prerequisites: EE 111 or equivalent. Math 3 recommended. Fundamentals of digital signal processing, extracting information from data by linear filtering, recursive and non-recursive filters, structural and flow graph representations for filters, data-adaptive filtering, multrirate sampling, efficient data representations with filter banks, Nyquist and sub-Nyquist sampling, sensor array signal processing, estimating direction of arrival (DOA) information from noisy data, and spectrum estimation. Not Offered 2020-21. Instructor: Vaidyanathan.
EE 113. Feedback and Control Circuits. 9 units (3-3-3): third term. Prerequisites: EE 45 or equivalent. This class studies the design and implementation of feedback and control circuits. The course begins with an introduction to basic feedback circuits, using both op amps and transistors. These circuits are used to study feedback principles, including circuit topologies, stability, and compensation. Following this, basic control techniques and circuits are studied, including PID (Proportional-Integrated-Derivative) control, digital control, and fuzzy control. There is a significant laboratory component to this course, in which the student will be expected to design, build, analyze, test, and measure the circuits and systems discussed in the lectures. Instructor: George.
EE/MedE 114 ab. Analog Circuit Design. 12 units (4-0-8): second, third terms. Prerequisites: EE 44 or equivalent. Analysis and design of analog circuits at the transistor level. Emphasis on design-oriented analysis, quantitative performance measures, and practical circuit limitations. Circuit performance evaluated by hand calculations and computer simulations. Recommended for juniors, seniors, and graduate students. Topics include: review of physics of bipolar and MOS transistors, low-frequency behavior of single-stage and multistage amplifiers, current sources, active loads, differential amplifiers, operational amplifiers, high-frequency circuit analysis using time- and transfer constants, high-frequency response of amplifiers, feedback in electronic circuits, stability of feedback amplifiers, and noise in electronic circuits, and supply and temperature independent biasing. A number of the following topics will be covered each year: trans-linear circuits, switched capacitor circuits, data conversion circuits (A/D and D/A), continuous-time Gm.C filters, phase locked loops, oscillators, and modulators. Offered 2020-21. Instructor: Hajimiri.
EE/MedE 115. Micro-/Nano-scales Electro-Optics. 9 units (3-0-6): first term. Prerequisites: Introductory electromagnetic class and consent of the instructor. The course will cover various electro-optical phenomena and devices in the micro-/nano-scales. We will discuss basic properties of light, imaging, aberrations, eyes, detectors, lasers, micro-optical components and systems, scalar diffraction theory, interference/interferometers, holography, dielectric/plasmonic waveguides, and various Raman techniques. Topics may vary. Not offered 2020-21.
ACM/EE/IDS 116. Introduction to Probability Models. 9 units (3-1-5): first term. Prerequisites: Ma 3, some familiarity with MATLAB, e.g. ACM 11 is desired. This course introduces students to the fundamental concepts, methods, and models of applied probability and stochastic processes. The course is application oriented and focuses on the development of probabilistic thinking and intuitive feel of the subject rather than on a more traditional formal approach based on measure theory. The main goal is to equip science and engineering students with necessary probabilistic tools they can use in future studies and research. Topics covered include sample spaces, events, probabilities of events, discrete and continuous random variables, expectation, variance, correlation, joint and marginal distributions, independence, moment generating functions, law of large numbers, central limit theorem, random vectors and matrices, random graphs, Gaussian vectors, branching, Poisson, and counting processes, general discrete- and continuous-timed processes, auto- and cross-correlation functions, stationary processes, power spectral densities. Instructor: Zuev.
ME/EE/EST 117. Energy Technology and Policy. 9 units (3-0-6): first term. Prerequisites: Ph 1 abc, Ch 1 ab and Ma 1 abc. Energy technologies and the impact of government policy. Fossil fuels, nuclear power, and renewables for electricity production and transportation. Resource models and climate change policies. New and emerging technologies. Instructor: Blanquart.
Ph/APh/EE/BE 118 abc. Physics of Measurement. 9 units (3-0-6): second, third terms. Prerequisites: Ph 127, APh 105, or equivalent, or permission from instructor. This course focuses on exploring the fundamental underpinnings of experimental measurements from the perspectives of responsivity, noise, backaction, and information. Its overarching goal is to enable students to critically evaluate real measurement systems, and to determine the ultimate fundamental and practical limits to information that can be extracted from them. Topics will include physical signal transduction and responsivity, fundamental noise processes, modulation, frequency conversion, synchronous detection, signal-sampling techniques, digitization, signal transforms, spectral analyses, and correlations. The first term will cover the essential fundamental underpinnings, while topics in second term will include examples from optical methods, high-frequency and fast temporal measurements, biological interfaces, signal transduction, biosensing, and measurements at the quantum limit. Part c not offered in 2020-21. Instructor: Roukes.
EE/CS 119 abc. Advanced Digital Systems Design. 9 units (3-3-3): first, second terms. Prerequisites: EE/CS 10 a or CS 24. Advanced digital design as it applies to the design of systems using PLDs and ASICs (in particular, gate arrays and standard cells). The course covers both design and implementation details of various systems and logic device technologies. The emphasis is on the practical aspects of ASIC design, such as timing, testing, and fault grading. Topics include synchronous design, state machine design, ALU and CPU design, application-specific parallel computer design, design for testability, PALs, FPGAs, VHDL, standard cells, timing analysis, fault vectors, and fault grading. Students are expected to design and implement both systems discussed in the class as well as self-proposed systems using a variety of technologies and tools. Given in alternate years; Offered 2020-21. Instructor: George.
EE/APh 120. Physical Optics. 9 units (3-0-6): second term. Prerequisites: Intermediate-level familiarity with Fourier transforms and linear systems analysis. Basic familiarity with Maxwell's electromagnetic theory (EE40 and EE44, or equivalent). Course focuses on applying linear systems analysis on propagation of light waves. Contents begin with a review of Electromagnetic theory of diffraction and transitions to Fourier Optics for a scalar-wave treatment of propagation, diffraction, and image formation with coherent and incoherent light. In addition to problems in imaging, the course makes connections to a selected number of topics in optics where the mathematics of wave phenomena plays a central role. Examples include propagation of light in multilayer films and meta surfaces, Gaussian beams, Fabry-P&#233;rot cavities, and angular momentum of light. Areas of application include modern imaging, display, and beam shaping technologies. Instructor: Mirhosseini.
EE 121. Computational Signal Processing. 12 units (3-0-9): first term. Prerequisites: EE 111, ACM/EE/IDS 116, ACM/IDS 104. The role of computation in the acquisition, representation, and processing of signals. The course develops methodology based on linear algebra and optimization, with an emphasis on the interplay between structure, algorithms, and accuracy in the design and analysis of the methods. Specific topics covered include deterministic and stochastic signal models, statistical signal processing, inverse problems, and regularization. Problems arising in contemporary applications in the sciences and engineering are discussed, although the focus is on the common abstractions and methodological frameworks that are employed in the solution of these problems. Not offered 2020-21. Instructor: Chandrasekaran.
EE/APh 123. Advanced Lasers and Photonics Laboratory. 9 units (1-3-5): first term. Prerequisites: none. This course focuses on hands-on experience with advanced techniques related to lasers, optics, and photonics. Students have the opportunity to build and run several experiments and analyze data. Covered topics include laser-based microscopy, spectroscopy, nonlinear optics, quantum optics, ultrafast optics, adaptive optics, and integrated photonics. Limited enrollment. Not offered 2020-21. Instructor: Marandi.
EE/MedE 124. Mixed-mode Integrated Circuits. 9 units (3-0-6): third term. Prerequisites: EE 45 a or equivalent. Introduction to selected topics in mixed-signal circuits and systems in highly scaled CMOS technologies. Design challenges and limitations in current and future technologies will be discussed through topics such as clocking (PLLs and DLLs), clock distribution networks, sampling circuits, high-speed transceivers, timing recovery techniques, equalization, monitor circuits, power delivery, and converters (A/D and D/A). A design project is an integral part of the course. Instructor: Emami.
EE/CS/MedE 125. Digital Electronics and Design with FPGAs and VHDL. 9 units (3-6-0): third term. Prerequisites: basic knowledge of digital electronics. Study of programmable logic devices (CPLDs and FPGAs). Detailed study of the VHDL language, with basic and advanced applications. Review and discussion of digital design principles for combinational-logic, combinational-arithmetic, sequential, and state-machine circuits. Detailed tutorials for synthesis and simulation tools using FPGAs and VHDL. Wide selection of complete, real-world fundamental advanced projects, including theory, design, simulation, and physical implementation. All designs are implemented using state-of-the-art development boards. Offered 2020-21. Instructor: Pedroni.
EE/Ma/CS 126 ab. Information Theory. 9 units (3-0-6): first, second terms. Prerequisites: Ma 3. Shannon's mathematical theory of communication, 1948-present. Entropy, relative entropy, and mutual information for discrete and continuous random variables. Shannon's source and channel coding theorems. Mathematical models for information sources and communication channels, including memoryless, Markov, ergodic, and Gaussian. Calculation of capacity and rate-distortion functions. Universal source codes. Side information in source coding and communications. Network information theory, including multiuser data compression, multiple access channels, broadcast channels, and multiterminal networks. Discussion of philosophical and practical implications of the theory. This course, when combined with EE 112, EE/Ma/CS/IDS 127, EE/CS 161, and EE/CS/IDS 167, should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression. Instructor: Effros.
EE/Ma/CS/IDS 127. Error-Correcting Codes. 9 units (3-0-6): second term. Prerequisites: Ma 2. This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission or storage systems. Topics include algebraic block codes, e.g., Hamming, BCH, Reed-Solomon (including a self-contained introduction to the theory of finite fields); and the modern theory of sparse graph codes with iterative decoding, e.g. LDPC codes, turbo codes. The students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes. Not Offered 2020-21. Instructor: Kostina.
EE 128 ab. Selected Topics in Digital Signal Processing. 9 units (3-0-6): second, third terms. Prerequisites: EE 111 and EE/CS/IDS 160 or equivalent required, and EE 112 or equivalent recommended. The course focuses on several important topics that are basic to modern signal processing. Topics include multirate signal processing material such as decimation, interpolation, filter banks, polyphase filtering, advanced filtering structures and nonuniform sampling, optimal statistical signal processing material such as linear prediction and antenna array processing, and signal processing for communication including optimal transceivers. Not offered 2020-21.
ME/CS/EE 129. Experimental Robotics. 9 units (3-6-0): first term. This course covers the foundations of experimental realization on robotic systems. This includes software infrastructures, e.g., robotic operating systems (ROS), sensor integration, and implementation on hardware platforms. The ideas developed will be integrated onto robotic systems and tested experimentally in the context of class projects. Not offered 2020-2021.
APh/EE 130. Electromagnetic Theory. 9 units (3-0-6): first term. Electromagnetic fields in vacuum: microscopic Maxwell's equations. Monochromatic fields: Rayleigh diffraction formulae, Huyghens principle, Rayleigh-Sommerfeld formula. The Fresnel-Fraunhofer approximation. Electromagnetic field in the presence of matter, spatial averages, macroscopic Maxwell equations. Helmholtz's equation. Group-velocity and group-velocity dispersion. Confined propagation, optical resonators, optical waveguides. Single mode and multimode waveguides. Nonlinear optics. Nonlinear propagation. Second harmonic generation. Parametric amplification. Not offered 2020-21.
EE/APh 131. Light Interaction with Atomic Systems-Lasers. 9 units (3-0-6): second term. Prerequisites: APh/EE 130. Light-matter interaction, spontaneous and induced transitions in atoms and semiconductors. Absorption, amplification, and dispersion of light in atomic media. Principles of laser oscillation, generic types of lasers including semiconductor lasers, mode-locked lasers. Frequency combs in lasers. The spectral properties and coherence of laser light. Not offered 2020-21. Instructor: Yariv.
APh/EE 132. Special Topics in Photonics and Optoelectronics. 9 units (3-0-6): third term. Interaction of light and matter, spontaneous and stimulated emission, laser rate equations, mode-locking, Q-switching, semiconductor lasers. Optical detectors and amplifiers; noise characterization of optoelectronic devices. Propagation of light in crystals, electro-optic effects and their use in modulation of light; introduction to nonlinear optics. Optical properties of nanostructures. Not offered 2020-21.
ME/CS/EE 133 abc. Robotics. 9 units (3-3-3): first, second, third terms. Prerequisites: ME/CS/EE 129, may be taken concurrently, or with permission of instructor. The course develops the core concepts of robotics. The first quarter focuses on classical robotic manipulation, including topics in rigid body kinematics and dynamics. It develops planar and 3D kinematic formulations and algorithms for forward and inverse computations, Jacobians, and manipulability. The second quarter transitions to planning, navigation, and perception. Topics include configuration space, sample-based planners, A* and D* algorithms, to achieve collision-free motions. The third quarter discusses advanced material, for example grasping and dexterous manipulation using multi-fingered hands, or autonomous behaviors, or human-robot interactions. The lectures will review appropriate analytical techniques and may survey the current research literature. Course work will focus on an independent research project chosen by the student. Instructor: Niemeyer.
ME/CS/EE 134. Robotic Systems. 9 units (3-6-0): second term. Prerequisites: ME/CS/EE 129, may be taken concurrently, or with permission of instructor. This course builds up, and brings to practice, the elements of robotic systems at the intersection of hardware, kinematics and control, computer vision, and autonomous behaviors. It presents selected topics from these domains, focusing on their integration into a full sense-think-act robot. The lectures will drive team-based projects, progressing from building custom robots to writing software and implementing all necessary aspects. Working systems will autonomously operate and complete their tasks during final demonstrations. Instructor: Niemeyer.
EE/CS/EST 135. Power System Analysis. 9 units (3-3-3): first term. Prerequisites: EE 44, Ma 2, or equivalent. Basic power system analysis: phasor representation, 3-phase transmission system, transmission line models, transformer models, per-unit analysis, network matrix, power flow equations, power flow algorithms, optimal powerflow (OPF) problems, swing dynamics and stability. Current research topics such as (may vary each year): convex relaxation of OPF, frequency regulation, energy functions and contraction regions, volt/var control, storage optimization, electric vehicles charging, demand response. Instructor: Low.
EE/Ma/CS/IDS 136. Topics in Information Theory. 9 units (3-0-6): third term. Prerequisites: Ma 3 or ACM/EE/IDS 116 or CMS 117 or Ma/ACM/IDS 140a. This class introduces information measures such as entropy, information divergence, mutual information, information density from a probabilistic point of view, and discusses the relations of those quantities to problems in data compression and transmission, statistical inference, language modeling, game theory and control. Topics include information projection, data processing inequalities, sufficient statistics, hypothesis testing, single-shot approach in information theory, large deviations. Instructor: Kostina.
CS/EE/IDS 143. Communication Networks. 9 units (3-3-3): first term. Prerequisites: Ma 2, Ma 3, CS 24 and CS 38, or instructor permission. This course focuses on the link layer (two) through the transport layer (four) of Internet protocols. It has two distinct components, analytical and systems. In the analytical part, after a quick summary of basic mechanisms on the Internet, we will focus on congestion control and explain: (1) How to model congestion control algorithms? (2) Is the model well defined? (3) How to characterize the equilibrium points of the model? (4) How to prove the stability of the equilibrium points? We will study basic results in ordinary differential equations, convex optimization, Lyapunov stability theorems, passivity theorems, gradient descent, contraction mapping, and Nyquist stability theory. We will apply these results to prove equilibrium and stability properties of the congestion control models and explore their practical implications. In the systems part, the students will build a software simulator of Internet routing and congestion control algorithms. The goal is not only to expose students to basic analytical tools that are applicable beyond congestion control, but also to demonstrate in depth the entire process of understanding a physical system, building mathematical models of the system, analyzing the models, exploring the practical implications of the analysis, and using the insights to improve the design. Instructors: Low, Ralph.
CMS/CS/EE/IDS 144. Networks: Structure & Economics. 12 units (3-4-5): second term. Prerequisites: Ma 2, Ma 3, Ma/CS 6 a, and CS 38, or instructor permission. Social networks, the web, and the internet are essential parts of our lives, and we depend on them every day. This course studies how they work and the "big" ideas behind our networked lives. Questions explored include: What do networks actually look like (and why do they all look the same)?; How do search engines work?; Why do memes spread the way they do?; How does web advertising work? For all these questions and more, the course will provide a mixture of both mathematical analysis and hands-on labs. The course expects students to be comfortable with graph theory, probability, and basic programming. Instructor: Wierman.
CS/EE 145. Projects in Networking. 9 units (0-0-9): third term. Prerequisites: Either CMS/CS/EE/IDS 144 or CS/IDS 142 in the preceding term, or instructor permission. Students are expected to execute a substantial project in networking, write up a report describing their work, and make a presentation. Instructor: Wierman.
CS/EE 146. Control and Optimization of Networks. 9 units (3-3-3): first term. Prerequisites: Ma 2, Ma 3 or instructor's permission. This is a research-oriented course meant for undergraduates and beginning graduate students who want to learn about current research topics in networks such as the Internet, power networks, social networks, etc. The topics covered in the course will vary, but will be pulled from current research in the design, analysis, control, and optimization of networks. Usually offered in odd years. Not offered 2020-21.
EE/CS 147. Digital Ventures Design. 9 units (3-3-3): first term. Prerequisites: none. This course aims to offer the scientific foundations of analysis, design, development, and launching of innovative digital products and study elements of their success and failure. The course provides students with an opportunity to experience combined team-based design, engineering, and entrepreneurship. The lectures present a disciplined step-by-step approach to develop new ventures based on technological innovation in this space, and with invited speakers, cover topics such as market analysis, user/product interaction and design, core competency and competitive position, customer acquisition, business model design, unit economics and viability, and product planning. Throughout the term students will work within an interdisciplinary team of their peers to conceive an innovative digital product concept and produce a business plan and a working prototype. The course project culminates in a public presentation and a final report. Every year the course and projects focus on a particular emerging technology theme. Not offered 2020-21. Instructor: Staff.
EE/CNS/CS 148. Selected Topics in Computational Vision. 9 units (3-0-6): third term. Prerequisites: undergraduate calculus, linear algebra, geometry, statistics, computer programming. The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D reconstruction. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system. Instructor: Perona.
EE/APh 149. Frontiers of Nonlinear Photonics. 9 units (3-0-6): second term. This course overviews recent advances in photonics with emphasis on devices and systems that utilize nonlinearities. A wide range of nonlinearities in the classical and quantum regimes is covered, including but not limited to second- and third-order nonlinear susceptibilities, Kerr, Raman, optomechanical, thermal, and multi-photon nonlinearities. A wide range of photonic platforms is also considered ranging from bulk to ultrafast and integrated photonics. The course includes an overview of the concepts as well as review and discussion of recent literature and advances in the field. Not Offered 2020-21. Instructor: Marandi.
EE 150. Topics in Electrical Engineering. Units to be arranged: terms to be arranged. Content will vary from year to year, at a level suitable for advanced undergraduate or beginning graduate students. Topics will be chosen according to the interests of students and staff. Visiting faculty may present all or portions of this course from time to time. Instructor: Staff.
EE 151. Electromagnetic Engineering. 9 units (3-0-6): third term. Prerequisites: EE 45. Foundations of circuit theory-electric fields, magnetic fields, transmission lines, and Maxwell's equations, with engineering applications. Instructor: Yang.
EE 152. High Frequency Systems Laboratory. 12 units (2-3-7): second term. Prerequisites: EE 45 or equivalent. EE 153 recommended. The student will develop a strong, working knowledge of high-frequency systems covering RF and microwave frequencies. The essential building blocks of these systems will be studied along with the fundamental system concepts employed in their use. The first part of the course will focus on the design and measurement of core system building blocks; such as filters, amplifiers, mixers, and oscillators. Lectures will introduce key concepts followed by weekly laboratory sessions where the student will design and characterize these various system components. During the second part of the course, the student will develop their own high-frequency system, focused on a topic within remote sensing, communications, radar, or one within their own field of research. Instructor: Russell.
EE 153. Microwave Circuits and Antennas. 12 units (3-2-7): third term. Prerequisites: EE 45. High-speed circuits for wireless communications, radar, and broadcasting. Design, fabrication, and measurements of microstrip filters, directional couplers, low-noise amplifiers, oscillators, detectors, and mixers. Design, fabrication, and measurements of wire antennas and arrays. Instructor: Antsos.
EE 154 ab. Practical Electronics for Space Applications. 9 units (2-3-4): second and third terms. Part a: Subsystem Design: Students will be exposed to design for subsystem electronics in the space environment, including an understanding of the space environment, common approaches for low cost spacecraft, atmospheric / analogue testing, and discussions of risk. Emphasis on a practical exposure to early subsystem design for a TRL 3-4 effort. Part b: Subsystems to System Interfacing: Builds upon the first term by extending subsystems to be compatible with "spacecraft", including a near-space "flight" of prototype subsystems on a high-altitude balloon flight. Focus on qualification for the flight environment appropriate to a TRL 4-5 effort. Offered 2020-21. Instructor: Klesh.
CMS/CS/CNS/EE/IDS 155. Machine Learning & Data Mining. 12 units (3-3-6): second term. Prerequisites: CS/CNS/EE 156 a. Having a sufficient background in algorithms, linear algebra, calculus, probability, and statistics, is highly recommended. This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. We will also discuss recent research developments. Instructor: Pachter.
CS/CNS/EE 156 ab. Learning Systems. 9 units (3-1-5): first, third terms. Prerequisites: Ma 2 and CS 2, or equivalent. Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks. Instructor: Abu-Mostafa.
EE/Ae 157 ab. Introduction to the Physics of Remote Sensing. 9 units (3-0-6): first, second terms. Prerequisites: Ph 2 or equivalent. An overview of the physics behind space remote sensing instruments. Topics include the interaction of electromagnetic waves with natural surfaces, including scattering of microwaves, microwave and thermal emission from atmospheres and surfaces, and spectral reflection from natural surfaces and atmospheres in the near-infrared and visible regions of the spectrum. The class also discusses the design of modern space sensors and associated technology, including sensor design, new observation techniques, ongoing developments, and data interpretation. Examples of applications and instrumentation in geology, planetology, oceanography, astronomy, and atmospheric research. Instructor: van Zyl.
Ge/EE/ESE 157 c. Remote Sensing for Environmental and Geological Applications. 9 units (3-3-3): third term. Analysis of electromagnetic radiation at visible, infrared, and radio wavelengths for interpretation of the physical and chemical characteristics of the surfaces of Earth and other planets. Topics: interaction of light with materials, spectroscopy of minerals and vegetation, atmospheric removal, image analysis, classification, and multi-temporal studies. This course does not require but is complementary to EE 157ab with emphasis on applications for geological and environmental problems, using data acquired from airborne and orbiting remote sensing platforms. Students will work with digital remote sensing datasets in the laboratory and there will be one field trip. Instructor: Ehlmann.
EE/APh 158. Quantum Electrical Circuits. 9 units (3-0-6): third term. Prerequisites: advanced-level familiarity with Maxwell's electromagnetic theory and quantum mechanics (EE 151 and Ph 125 abc, or equivalent). Course focuses on superconducting electrical systems for quantum computing. Contents begin with reviewing required concepts in microwave engineering, quantum optics, and superconductivity, and proceeds with deriving quantum mechanical description of superconducting linear circuits, Josephson qubits, and parametric amplifiers. The second part of the course provides an overview of integrated nano-mechanical, piezo-electric and electro-optical systems and their applications in transducing quantum electrical signals in conjunction with superconducting qubits. Instructor: Mirhosseini.
CS/CNS/EE/IDS 159. Advanced Topics in Machine Learning. 9 units (3-0-6): third term. Prerequisites: CS 155; strong background in statistics, probability theory, algorithms, and linear algebra; background in optimization is a plus as well. This course focuses on current topics in machine learning research. This is a paper reading course, and students are expected to understand material directly from research articles. Students are also expected to present in class, and to do a final project. Not offered 2020-21.
EE/CS/IDS 160. Fundamentals of Information Transmission and Storage. 9 units (3-0-6): second term. Basics of information theory: entropy, mutual information, source and channel coding theorems. Basics of coding theory: error-correcting codes for information transmission and storage, block codes, algebraic codes, sparse graph codes. Basics of digital communications: sampling, quantization, digital modulation, matched filters, equalization. Instructor: Kostina.
EE/CS 161. Big Data Networks. 9 units (3-0-6): third term. Prerequisites: Linear Algebra ACM/IDS 104 and Introduction to Probability Models ACM/EE/IDS 116 or their equivalents. Next generation networks will have tens of billions of nodes forming cyber-physical systems and the Internet of Things. A number of fundamental scientific and technological challenges must be overcome to deliver on this vision. This course will focus on (1) How to boost efficiency and reliability in large networks; the role of network coding, distributed storage, and distributed caching; (2) How to manage wireless access on a massive scale; modern random access and topology formation techniques; and (3) New vistas in big data networks, including distributed computing over networks and crowdsourcing. A selected subset of these problems, their mathematical underpinnings, state-of-the-art solutions, and challenges ahead will be covered. Given in alternate years. Not offered 2020-21. Instructor: Hassibi.
EE 163. Communication Theory. 9 units (3-0-6): second term. Prerequisites: EE 111; ACM/EE/IDS 116 or equivalent. Mathematical models of communication processes; signals and noise as random processes; sampling; modulation; spectral occupancy; intersymbol interference; synchronization; optimum demodulation and detection; signal-to-noise ratio and error probability in digital baseband and carrier communication systems; linear and adaptive equalization; maximum likelihood sequence estimation; multipath channels; parameter estimation; hypothesis testing; optical communication systems. Capacity measures; multiple antenna and multiple carrier communication systems; wireless networks; different generations of wireless systems. Not Offered 2020-21. Instructor: Staff.
EE 164. Stochastic and Adaptive Signal Processing. 9 units (3-0-6): third term. Prerequisites: ACM/EE/IDS 116 or equivalent. Fundamentals of linear estimation theory are studied, with applications to stochastic and adaptive signal processing. Topics include deterministic and stochastic least-squares estimation, the innovations process, Wiener filtering and spectral factorization, state-space structure and Kalman filters, array and fast array algorithms, displacement structure and fast algorithms, robust estimation theory and LMS and RLS adaptive fields. Given in alternate years; Offered 2020-21. Instructor: Hassibi.
CS/CNS/EE/IDS 165. Foundations of Machine Learning and Statistical Inference. 12 units (3-3-6): second term. Prerequisites: CMS/ACM/IDS 113, ACM/EE/IDS 116, CS 156 a, ACM/CS/IDS 157 or instructor's permission. The course assumes students are comfortable with analysis, probability, statistics, and basic programming. This course will cover core concepts in machine learning and statistical inference. The ML concepts covered are spectral methods (matrices and tensors), non-convex optimization, probabilistic models, neural networks, representation theory, and generalization. In statistical inference, the topics covered are detection and estimation, sufficient statistics, Cramer-Rao bounds, Rao-Blackwell theory, variational inference, and multiple testing. In addition to covering the core concepts, the course encourages students to ask critical questions such as: How relevant is theory in the age of deep learning? What are the outstanding open problems? Assignments will include exploring failure modes of popular algorithms, in addition to traditional problem-solving type questions. Instructor: Anandkumar.
CMS/CS/EE 166. Computational Cameras. 12 units (3-3-6): third term. Prerequisites: ACM 104 or ACM 107 or equivalent. Computational cameras overcome the limitations of traditional cameras, by moving part of the image formation process from hardware to software. In this course, we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics, and vision. At the start of the course, we will study modern image processing and image editing pipelines, including those encountered on DSLR cameras and mobile phones. Then we will study the physical and computational aspects of tasks such as coded photography, light-field imaging, astronomical imaging, medical imaging, and time-of-flight cameras. The course has a strong hands-on component, in the form of homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class. Example homework assignments include building an end-to-end HDR imaging pipeline, implementing Poisson image editing, refocusing a light-field image, and making your own lensless "scotch-tape" camera. Instructor: Bouman.
EE/CS/IDS 167. Introduction to Data Compression and Storage. 9 units (3-0-6): third term. Prerequisites: Ma 3 or ACM/EE/IDS 116. The course will introduce the students to the basic principles and techniques of codes for data compression and storage. The students will master the basic algorithms used for lossless and lossy compression of digital and analog data and the major ideas behind coding for flash memories. Topics include the Huffman code, the arithmetic code, Lempel-Ziv dictionary techniques, scalar and vector quantizers, transform coding; codes for constrained storage systems. Given in alternate years; Not offered 2020-21. Instructor: Kostina.
MedE/EE/BE 168 abc. Biomedical Optics: Principles and Imaging. 9 units (4-0-5): parts a and b are taught in second and third terms in odd academic years, and part c is taught in second term in even academic years. Prerequisites: instructor's permission. Part a covers the principles of optical photon transport in biological tissue. Topics include a brief introduction to biomedical optics, single-scatterer theories, Monte Carlo modeling of photon transport, convolution for broad-beam responses, radiative transfer equation and diffusion theory, hybrid Monte Carlo method and diffusion theory, and sensing of optical properties and spectroscopy, (absorption, elastic scattering, Raman scattering, and fluorescence). Part b covers established optical imaging technologies. Topics include ballistic imaging (confocal microscopy, two-photon microscopy, super-resolution microscopy, etc.), optical coherence tomography, Mueller optical coherence tomography, and diffuse optical tomography. Part c covers emerging optical imaging technologies. Topics include photoacoustic tomography, ultrasound-modulated optical tomography, optical time reversal (wavefront shaping/engineering), and ultrafast imaging. MedE/EE/BE 168 ab not offered 2020-2021. MedE/EE/BE 168 c offered 2020-2021. Instructor: Wang.
ACM/EE/IDS 170. Mathematics of Signal Processing. 12 units (3-0-9): third term. Prerequisites: ACM/IDS 104, CMS/ACM/IDS 113, and ACM/EE/IDS 116; or instructor's permission. This course covers classical and modern approaches to problems in signal processing. Problems may include denoising, deconvolution, spectral estimation, direction-of-arrival estimation, array processing, independent component analysis, system identification, filter design, and transform coding. Methods rely heavily on linear algebra, convex optimization, and stochastic modeling. In particular, the class will cover techniques based on least-squares and on sparse modeling. Throughout the course, a computational viewpoint will be emphasized. Instructor: Hassibi.
EE/CS/MedE 175. Digital Circuits Analysis and Design with Complete VHDL and RTL Approach. 9 units (3-6-0): third term. Prerequisites: medium to advanced knowledge of digital electronics. A careful balance between synthesis and analysis in the development of digital circuits plus a truly complete coverage of the VHDL language. The RTL (register transfer level) approach. Study of FPGA devices and comparison to ASIC alternatives. Tutorials of software and hardware tools employed in the course. VHDL infrastructure, including lexical elements, data types, operators, attributes, and complex data structures. Detailed review of combinational circuits followed by full VHDL coverage for combinational circuits plus recommended design practices. Detailed review of sequential circuits followed by full VHDL coverage for sequential circuits plus recommended design practices. Detailed review of state machines followed by full VHDL coverage and recommended design practices. Construction of VHDL libraries. Hierarchical design and practice on the hard task of project splitting. Automated simulation using VHDL testbenches. Designs are implemented in state-of-the-art FPGA boards. Not Offered 2020-21. Instructor: Pedroni.
EE/APh 180. Nanotechnology. 6 units (3-0-3): first term. This course will explore the techniques and applications of nanofabrication and miniaturization of devices to the smallest scale. It will be focused on the understanding of the technology of miniaturization, its history and present trends towards building devices and structures on the nanometer scale. Examples of applications of nanotechnology in the electronics, communications, data storage and sensing world will be described, and the underlying physics as well as limitations of the present technology will be discussed. Instructor: Scherer.
APh/EE 183. Physics of Semiconductors and Semiconductor Devices. 9 units (3-0-6): third term. Principles of semiconductor electronic structure, carrier transport properties, and optoelectronic properties relevant to semiconductor device physics. Fundamental performance aspects of basic and advanced semiconductor electronic and optoelectronic devices. Topics include energy band theory, carrier generation and recombination mechanisms, quasi-Fermi levels, carrier drift and diffusion transport, quantum transport. Instructor: Nadj-Perge.
EE/BE/MedE 185. MEMS Technology and Devices. 9 units (3-0-6): third term. Prerequisites: APh/EE 9 ab, or instructor's permission. Micro-electro-mechanical systems (MEMS) have been broadly used for biochemical, medical, RF, and lab-on-a-chip applications. This course will cover both MEMS technologies (e.g., micro- and nanofabrication) and devices. For example, MEMS technologies include anisotropic wet etching, RIE, deep RIE, micro/nano molding and advanced packaging. This course will also cover various MEMS devices used in microsensors and actuators. Examples will include pressure sensors, accelerometers, gyros, FR filters, digital mirrors, microfluidics, micro total-analysis system, biomedical implants, etc. Not offered 2020-21.
CNS/Bi/EE/CS/NB 186. Vision: From Computational Theory to Neuronal Mechanisms. 12 units (4-4-4): second term. Lecture, laboratory, and project course aimed at understanding visual information processing, in both machines and the mammalian visual system. The course will emphasize an interdisciplinary approach aimed at understanding vision at several levels: computational theory, algorithms, psychophysics, and hardware (i.e., neuroanatomy and neurophysiology of the mammalian visual system). The course will focus on early vision processes, in particular motion analysis, binocular stereo, brightness, color and texture analysis, visual attention and boundary detection. Students will be required to hand in approximately three homework assignments as well as complete one project integrating aspects of mathematical analysis, modeling, physiology, psychophysics, and engineering. Given in alternate years; Not Offered 2020-21. Instructors: Meister, Perona, Shimojo, Tsao.
EE/MedE 187. VLSI and ULSI Technology. 9 units (3-0-6): third term. Prerequisites: APh/EE 9 ab, EE/APh 180 or instructor's permission. This course is designed to cover the state-of-the-art micro/nanotechnologies for the fabrication of ULSI including BJT, CMOS, and BiCMOS. Technologies include lithography, diffusion, ion implantation, oxidation, plasma deposition and etching, etc. Topics also include the use of chemistry, thermal dynamics, mechanics, and physics. Not offered 2020-21.
BE/EE/MedE 189 ab. Design and Construction of Biodevices. 189 a, 12 units (3-6-3) offered both first and third terms; 189 b, 9 units (0-9-0) offered only third term: . Prerequisites: BE/EE/MedE 189 a must be taken before BE/EE/MedE 189 b. Part a, students will design and implement computer-controlled biosensing systems, including a pulse monitor, a pulse oximeter, and a real-time polymerase-chain-reaction incubator. Part b is a student-initiated design project requiring instructor's permission for enrollment. Enrollment is limited to 24 students. Instructors: Bois, Yang.
MedE/EE 268. Medical Imaging. 9 units (4-0-5): third term. Medical imaging technologies will be covered. Topics include X-ray radiography, X-ray computed tomography (CT), nuclear imaging (PET & SPECT), ultrasonic imaging, and magnetic resonance imaging (MRI). Instructor: Lihong Wang.
EE 291. Advanced Work in Electrical Engineering. Units to be arranged: . Special problems relating to electrical engineering. Primarily for graduate students; students should consult with their advisers.