EE 1. Introduction to Electrical Engineering Seminar. 1 unit; second term. Required for EE undergraduates. Weekly seminar given by faculty in the department broadly describing different areas of electrical engineering: circuits and VLSI, communications, control, devices, images and vision, information theory, learning and pattern recognition, MEMS and micromachining, networks, electromagnetics and opto-electronics, RF and microwave circuits and antennas, robotics and signal processing, and specifically, research going on at Caltech. Instructor: Hajimiri.

EE 5. Introduction to Embedded Systems. 6 units (2-3-1); third term. This course is intended to give the student a basic understanding of the major hardware and software principles involved in the specification and design of embedded systems. Topics include basic digital logic, CPU and embedded system architecture, and embedded systems programming principles (events, user interfaces, and multitasking). The class is intended for students who wish to gain a basic understanding of embedded systems or for those who would like an introduction to the material before taking EE/CS 51/52. Graded pass/fail. Instructor: George. Not Offered 2017–18.

EE/ME 7. Introduction to Mechatronics. 6 units (2-3-1); first term. Mechatronics is the multi-disciplinary design of electro-mechanical systems. This course is intended to give the student a basic introduction to such systems. The course will focus on the implementations of sensor and actuator systems, the mechanical devices involved and the electrical circuits needed to interface with them. The class will consist of lectures and short labs where the student will be able to investigate the concepts discussed in lecture. Topics covered include motors, piezoelectric devices, light sensors, ultrasonic transducers, and navigational sensors such as accelerometers and gyroscopes. Graded pass/fail. Instructor: George.

APh/EE 9 ab. Solid-State Electronics for Integrated Circuits. 6 units (2-2-2); first, third terms; six units credit for the freshman laboratory requirement. Prerequisite: Successful completion of APh/EE 9 a is a prerequisite for enrollment in APh/EE 9 b. Introduction to solid-state electronics, including physical modeling and device fabrication. Topics: semiconductor crystal growth and device fabrication technology, carrier modeling, doping, generation and recombination, pn junction diodes, MOS capacitor and MOS transistor operation, and deviations from ideal behavior. Laboratory includes computer-aided layout, and fabrication and testing of light-emitting diodes, transistors, and inverters. Students learn photolithography, and use of vacuum systems, furnaces, and device-testing equipment. Instructor: Scherer.

EE 10 ab. Introduction to Digital Logic and Embedded Systems. 6 units (2-3-1); second, third terms. This course is intended to give the student a basic understanding of the major hardware and software principles involved in the specification and design of embedded systems. The course will cover basic digital logic, programmable logic devices, CPU and embedded system architecture, and embedded systems programming principles (interfacing to hardware, events, user interfaces, and multi-tasking). Instructor: George.

EE 40. Introduction to Semiconductors Devices. 9 units (3-0-6); third term. Prerequisites: APh/EE 9 ab, Ma 2, Ph 2. This course provides an introduction to semiconductors and semiconductor sensors. The fundamental physics of semiconductor electronics and devices will be emphasized, together with their applications. Overview of electronic properties of semiconductor that are significant to device operation for integrated circuits. Silicon device fabrication technology. Metal-semiconductor contacts, p-n junctions, bipolar transistors, photoconductors, diodes, transistors, CCDs, MOS/MOSFET/CMOS imagers, temperature sensors, magnetic sensors, thermoelectricity, piezoresistivity, piezoelectrics, etc. Instructor: Choo.

EE 44. Circuits and Systems. 12 units (4-0-8); first term. Prerequisites: Ph1 abc, should be taken concurrently with Ma 2 and Ph 2 a. Fundamentals of circuits and network theory, circuit elements, linear circuits, terminals and port presentation, nodal and mesh analysis, time-domain analysis of circuits and systems, sinusoidal response, introductory frequency domain analysis, transfer functions, poles and zeros, time and transfer constants, network theorems, transformers. Instructor: Hajimiri.

EE 45. Electronics Laboratory. 12 units (3-3-6); second term. Prerequisites: EE 44. Fundamentals of electronic circuits and systems. Lectures on diodes, transistors, small-signal analysis, frequency- domain analysis, application of Laplace transform, gain stages, differential signaling, operational amplifiers, introduction to radio and analog communication systems. Laboratory sessions on transient response, steady-state sinusoidal response and phasors, diodes, transistors, amplifiers. Instructor: Emami.

EE/CS 51. Principles of Microprocessor Systems. 12 units (4-5-3); first term. The principles and design of microprocessor-based computer systems. Lectures cover both hardware and software aspects of microprocessor system design such as interfacing to input and output devices, user interface design, real-time systems, and table-driven software. The homework emphasis is on software development, especially interfacing with hardware, in assembly language. Instructor: George. Not Offered 2017–18.

EE/CS 52 ab. Microprocessor Systems Laboratory. 9 units (3-6-0) second term; 6 units (1-5-0) third term; second, third terms. The student will design, build, and program a specified microprocessor-based system. This structured laboratory is organized to familiarize the student with electronic circuit construction techniques, modern development facilities, and standard design techniques. The lectures cover topics in microprocessor system design such as display technologies, interfacing with analog systems, and programming microprocessors in high-level languages. Instructor: George. Not Offered 2017–18

EE/CS 53. Microprocessor Project Laboratory. 12 units (0-12-0); first, second, third terms. Prerequisites: EE/CS 52 ab or equivalent. A project laboratory to permit the student to select, design, and build a microprocessor-based system. The student is expected to take a project from proposal through design and implementation (possibly including PCB fabrication) to final review and documentation. May be repeated for credit. Instructor: George.

CS/EE/ME 75 abc. Multidisciplinary Systems Engineering. 3 units (2-0-1), 6 units (2-0-4), or 9 units (2-0-7) first term; 6 units (2-3-1), 9 units (2-6-1), or 12 units (2-9-1) second and third terms; units according to project selected. This course presents the fundamentals of modern multidisciplinary systems engineering in the context of a substantial design project. Students from a variety of disciplines will conceive, design, implement, and operate a system involving electrical, information, and mechanical engineering components. Specific tools will be provided for setting project goals and objectives, managing interfaces between component subsystems, working in design teams, and tracking progress against tasks. Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. During the first two terms of the course, students will attend project meetings and learn some basic tools for project design, while taking courses in CS, EE, and ME that are related to the course project. During the third term, the entire team will build, document, and demonstrate the course design project, which will differ from year to year. Freshmen must receive permission from the lead instructor to enroll. Instructor: Not offered 2017–18.

EE 80 abc. Senior Thesis. 9 units; first, second, third terms. Prerequisite: instructor’s permission, which should be obtained during the junior year to allow sufficient time for planning the research. Individual research project, carried out under the supervision of a member of the electrical engineering or computer science faculty. Project must include significant design effort. Written report required. Open only to senior electrical engineering, computer science, or electrical and computer engineering majors. Not offered on a pass/fail basis. Instructor: Staff.

EE 90. Analog Electronics Project Laboratory. 9 units (1-8-0); third term. Prerequisites: EE 40 and EE 45. A structured laboratory course that gives the student the opportunity to design and build a simple analog electronics project. The goal is to gain familiarity with circuit design and construction, component selection, CAD support, and debugging techniques. Instructor: Megdal.

EE 91 ab. Experimental Projects in Electronic Circuits. Units by arrangement; first, second terms. 12 units minimum each term. Prerequisite: EE 45. Recommended: EE/CS 51 and 52, and EE 114 ab (may be taken concurrently). Open to seniors; others only with instructor’s permission. An opportunity to do advanced original projects in analog or digital electronics and electronic circuits. Selection of significant projects, the engineering approach, modern electronic techniques, demonstration and review of a finished product. DSP/microprocessor development support and analog/digital CAD facilities available. Text: literature references. Instructor: Megdal.

EE 99. Advanced Work in Electrical Engineering. Units to be arranged. Special problems relating to electrical engineering will be arranged. For undergraduates; students should consult with their advisers. Graded pass/fail.

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: Hajimiri.

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: Lam.

EST/EE/ME 109. 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: Hunt.

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; not offered 2017-2018. Instructors: 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 Digital Signal Processing. 9 units (3-0-6); second term. Prerequisites: EE 111 or equivalent. Math 3 recommended. Fundamentals of digital signal processing, digital filtering, recursive and non recursive filters, linear phase and minimum phase systems, digital filter structures, allpass filters and applications, quantization and stability analysis, round-off noise calculations, Nyquist and sub-Nyquist sampling, elements of multrirate signal processing, reconstruction of sparsely sampled signals, statistical signal processing and sensor array signal processing, and applications in various areas. Offered 2017–18. 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. Instructor: Staff. Not Offered 2017–18.

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 2017–18.

ACM/EE 116. Introduction to Probability Models. 9 units (3-1-5); first term. Prerequisites: Ma 2, Ma 3. 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.

CMS/ACM/EE 117. Probability and Random Processes. 2 units (3-0-9); first term. Prerequisites: ACM 104 and ACM/EE 116. The course will start with a quick reminder on probability spaces, discrete and continuous random variables. It will cover the following core topics: branching processes, Poisson processes, limit theorems, Gaussian variables, vectors, spaces, processes and measures, the Brownian motion, Gaussian learning, game theory and decision theory (finite state space), martingales (concentration, convergence, Doob’s inequalities, optional/optimal stopping, Snell’s envelope), large deviations (introduction, if time permits). Instructor: Owhadi.

Ph/APh/EE/BE 118 abc. Physics of Measurement. 9 units (3-0-6); first, second, third terms. Prerequisites: Ph127, 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. Instructor: Roukes.

EE 119 abc. Advanced Digital Systems Design. 9 units (3-3-3) first, second term; 9 units (1-8-0) third term; first, second, third terms. Prerequisite: EE/CS 52 ab or CS/EE 181 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. Instructor: George. Not Offered 2017–18.

EE 120. Topics in Information Theory. 9 units (3-0-6); third term. 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. Prerequisites: undergraduate calculus and probability; desirable but not required: EE126a. Instructor: Kostina. Offered 2017–18.

EE/MedE 124. Mixed-mode Integrated Circuits. 9 units (3-0-6); first 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 125. Digital Electronics and Design with FPGAs and VHDL. 9 units (3-6-0); second term. Prerequisite: 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. Instructor: Staff. Offered 2017–18.

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 127, EE/CS 161, EE 167, and/or EE 226 should prepare the student for research in information theory, coding theory, wireless communications, and/or data compression. Instructor: Effros.

EE/Ma/CS 127. Error-Correcting Codes. 9 units (3-0-6); first 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. Instructor: Kostina.

EE 128 ab. Selected Topics in Digital Signal Processing. 9 units (3-0-6); second, third terms. Prerequisites: EE 111 and EE 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 2017–18.

CS/EE/Ma 129 abc. Information and Complexity. 9 units (3-0-6), first and second terms; (1-4-4) third term. Prerequisite: basic knowledge of probability and discrete mathematics. A basic course in information theory and computational complexity with emphasis on fundamental concepts and tools that equip the student for research and provide a foundation for pattern recognition and learning theory. First term: what information is and what computation is; entropy, source coding, Turing machines, uncomputability. Second term: topics in information and complexity; Kolmogorov complexity, channel coding, circuit complexity, NP-completeness. Third term: theoretical and experimental projects on current research topics. Not offered 2017–18.

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 2017–2018.

EE/APh 131. Light Interaction with Atomic Systems - Lasers. 9 units (3-0-6); second term. 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. Instructor: Yariv. Not offered 2017–18.

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 2017-2018.

CS/EE/ME 134. Autonomy. 9 units (3-0-6); third term. This course covers the basics of autonomy at the intersection of computer vision, machine learning and robotics. It includes selected topics from each of these domains, and their integration points. The lectures will be accompanied by a project that will integrate these ideas on hardware and result in a final demonstration of the concepts studied in the course. Instructor: Staff.

EE/CS/EST 135. Power System Analysis. 9 units (3-3-3); second 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. Instructors: Low.

CS/EE 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. Not offered 2017–18.

CMS/CS/EE 144. Networks: Structure Economics. 12 units (3-3-6); second term. Prerequisites: Ma 2, Ma 3, Ma/CS 6a, and CS 38, or instructor permission. Social networks, the web, and the internet are essential parts of our lives and we all depend on them every day, but do you really know what makes them work? This course studies the “big” ideas behind our networked lives. Things like, 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 assumes students are 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 144 or CS 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. Advanced Networking. 9 units (3-3-3); third term. Prerequisites: CS/EE 143 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 topics in the design, analysis, control, and optimization of networks, protocols, and Internet applications. Usually offered in alternate years. Instructor: Low.

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. Instructor: Lahouti.

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 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. Prerequisite: 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); first 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: Staff.

EE 153. Microwave Circuits and Antennas. 12 units (3-2-7); third term. Prerequisite: 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.

CMS/CS/CNS/EE 155. Machine Learning Data Mining. 12 units (3-3-6); second term. Prerequisites: background in algorithms and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156 a or instructor’s permission). 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. This course will also cover core foundational concepts underpinning and motivating modern machine learning and data mining approaches. This course will be research-oriented, and will cover recent research developments. Instructor: Yue.

CS/CNS/EE 156 ab. Learning Systems. 9 units (3-0-6); 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. Prerequisite: 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.

ACM/CS/EE 158. Mathematical Statistics. 9 units (3-0-6); third term. Prerequisites: CMS/ACM 113, ACM/EE 116 and ACM/CS 157. Fundamentals of estimation theory and hypothesis testing; minimax analysis, Cramer-Rao bounds, Rao-Blackwell theory, shrinkage in high dimensions; Neyman-Pearson theory, multiple testing, false discovery rate; exponential families; maximum entropy modeling; other advanced topics may include graphical models, statistical model selection, etc. Throughout the course, a computational viewpoint will be emphasized. Not offered 2017–18.

CS/CNS/EE 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 2017–18..

EE 160. Random Variables and Stochastic Processes. 9 units (3-0-6); first term. Prerequisites: Math 2, Math 3. Introduction to fundamental ideas and techniques of stochastic analysis. Random variables, expectation and conditional expectation, joint distributions, covariance, moment generating functions, central limit theorem, weak and strong laws of large numbers, discrete time stochastic processes, stationarity, power spectral densities, Gaussian processes, Poisson processes. The course develops applications in areas such as communication, signal processing, networks and queues. Not offered 2017–18. Instructor: Hassibi.

EE/CS 161. Big Data Networks. 9 units (3-0-6); third term. Prerequisites: Linear Algebra ACM 104 and Probability and Random Processes ACM/EE 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. Instructor: Hassibi. Given in alternate years. Offered 2017–18.

EE 163 ab. Communication Theory. 9 units (3-0-6); second, third terms. Prerequisites: EE 111; ACM/EE 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. Instructor: Staff

EE 164. Stochastic and Adaptive Signal Processing. 9 units (3-0-6); third term. Prerequisite: ACM/EE 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 2017–18. Instructor: Hassibi.

EE/BE/MedE 166. Optical Methods for Biomedical Imaging and Diagnosis. 9 units (3-1-5); third term. Prerequisite: EE 151 or equivalent. Topics include Fourier optics, scattering theories, shot noise limit, energy transitions associated with fluorescence, phosphorescence, and Raman emissions. Study of coherent anti-Stokes Raman spectroscopy (CARS), second harmonic generation and near-field excitation. Scattering, absorption, fluorescence, and other optical properties of biological tissues and the changes in these properties during cancer progression, burn injury, etc. Specific optical technologies employed for biomedical research and clinical applications: optical coherence tomography, Raman spectroscopy, photon migration, acousto-optics (and opto-acoustics) imaging, two-photon fluorescence microscopy, and second- and third-harmonic microscopy. Given in alternate years; Not Offered 2017–18. Instructor: Yang

EE/CS 167. Introduction to Data Compression and Storage. 9 units (3-0-6); third term. Prerequisites: Ma 3 or ACM/EE 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 2017–18. Instructor: Kostina.

ACM/EE 170. Mathematics of Signal Processing. 12 units (3-0-9); third term. Prerequisites: ACM 104, CMS/ACM 113, and ACM/EE 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. Not offered 2017–18.

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.

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.

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. Prerequisite: 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 2017–18.

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; Offered 2017–18. Instructors: Meister, Perona, Shimojo.

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 2017–18.

BE/EE/MedE 189 ab. Design and Construction of Biodevices. 12 units (3-6-3) a = first and third terms; 9 units (0-9-0) b = third term. Prerequisites: ACM 95/100 ab (for BE/EE/MedE 189 a); BE/EE/MedE 189 a (for BE/ EE/MedE 189 b). Part a, students will design and implement biosensing systems, including a pulse monitor, a pulse oximeter, and a real-time polymerase-chain-reaction incubator. Students will learn to program in LABVIEW. Part b is a student-initiated design project requiring instructor’s permission for enrollment. Enrollment is limited to 24 students. BE/ 453 EE/MedE 189 a is an option requirement; BE/EE/MedE 189 b is not. Instructors: Bois, Yang.

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.