Results for EE

Electricity and magnetism and its essential role in modern electrical engineering devices and systems, such as sensors, displays, DVD players, and optical communication systems. The topics that will be covered include electrostatics, magnetostatics, Maxwell's equations, one-dimensional wave equation, electromagnetic waves, transmission lines, and one-dimensional resonators. Pre-requisites: none.

This course introduces the core ideas of modern physics that enable applications ranging from solar energy and efficient lighting to the modern electronic and optical devices and nanotechnologies that sense, process, store, communicate and display all our information. Though the ideas have broad impact, the course is widely accessible to engineering and science students with only basic linear algebra and calculus through simple ordinary differential equations as mathematics background. Topics include the quantum mechanics of electrons and photons (Schr¿dinger's equation, atoms, electrons, energy levels and energy bands; absorption and emission of photons; quantum confinement in nanostructures), the statistical mechanics of particles (entropy, the Boltzmann factor, thermal distributions), the thermodynamics of light (thermal radiation, limits to light concentration, spontaneous and stimulated emission), and the physics of information (Maxwell's demon, reversibility, entropy and noise in physics and information theory). Pre-requisite: Physics 41. Pre- or co-requisite: Math 53 or CME 102.

Continuation of EE101A. Introduction to circuit design for modern electronic systems. Modeling and analysis of analog gain stages, frequency response, feedback. Filtering and analog to digital conversion. Fundamentals of circuit simulation. Prerequisites: EE101A, EE102A. Recommended: MATH 53 or CME102.

Continuation of EE 102A. Concepts and tools for continuous- and discrete-time signal and system analysis with applications in communications, signal processing and control. Analog and digital modulation and demodulation. Sampling, reconstruction, decimation and interpolation. Finite impulse response filter design. Discrete Fourier transforms, applications in convolution and spectral analysis. Laplace transforms, applications in circuits and feedback control. Z transforms, applications in infinite impulse response filter design. Prerequisite: EE 102A.

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites: ENGR 108; EE 178 or CS 109; CS106A or equivalent.

The design of integrated digital systems encompassing both customized software and hardware. Software/hardware design tradeoffs. Algorithm design for pipelining and parallelism. System latency and throughput tradeoffs. FPGA optimization techniques. Integration with external systems and smart devices. Firmware configuration and embedded system considerations. Enrollment limited to 25; preference to graduating seniors. Prerequisites: 108B, and CS 106B or X.

The underpinnings of modern technology are the transistor (circuits), the capacitor (memory), and the solar cell (energy). EE 116 introduces the physics of their operation, their historical origins (including Nobel prize breakthroughs), and how they can be optimized for future applications. The class covers physical principles of semiconductors, including silicon and new material discoveries, quantum effects, band theory, operating principles, and device equations. Recommended (but not required) co-requisite: EE 65 or equivalent.

Formally EE 292Q. Introduction to operation principles and key performance aspects of 3D+ imaging sensors used widely in industry. Concepts include imaging physics, data acquisition and image formation methods, and signal and image quality metrics that are broadly applicable across sensor types. Practical examples and demonstrations of various sensors such as radar, acoustic, LIDAR, and ToF modules will be presented in class as well as through structured labs. Invited speakers will highlight emerging 3D+ imaging applications that these sensors are enabling today. Prerequisites: EE 101A or equivalent. EE 102A or equivalent.

Introduction to electromagnetism and Maxwell's equations in static and dynamic regimes. Electrostatics and magnetostatics: Gauss's, Coulomb's, Faraday's, Ampere's, Biot-Savart's laws. Electric and magnetic potentials. Boundary conditions. Electric and magnetic field energy. Electrodynamics: Wave equation; Electromagnetic waves; Phasor form of Maxwell's equations.Solution of the wave equation in 1D free space: Wavelength, wave-vector, forward and backward propagating plane waves.Poynting's theorem. Propagation in lossy media, skin depth. Reflection and refraction at planar boundaries, total internal reflection. Solutions of wave equation for various 1D-3D problems: Electromagnetic resonators, waveguides periodic media, transmission lines. Formerly EE 141. Prerequisites: an introductory course in electromagnetics (PHYSICS 43, PHYSICS 63, PHYSICS 81, or EE 42) and a solid background in vector calculus (CME 100, CME 102, or MATH 52, with MATH 52 being an ideal prerequisite)

Addressing the energy challenges of today and the environmental challenges of the future will require efficient energy conversion techniques. This course will discuss the circuits used to efficiently convert ac power to dc power, dc power from one voltage level to another, and dc power to ac power. The components used in these circuits (e.g., diodes, transistors, capacitors, inductors) will also be covered in detail to highlight their behavior in a practical implementation. A lab will be held with the class where students will obtain hands on experience with power electronic circuits. For WIM credit, students must enroll in EE 153 for 4 units. No exceptions. Formerly EE 292J. Prerequisite: EE 101A. Strongly recommended EE 101B.

An introduction to electric motors and the principles of electromechanical energy conversion. Students will learn about, design, and build an electric motor system, choosing from one of three application areas: renewable energy (wind turbines), robotics (drones and precision manufacturing), or electric vehicles (cars, ships, and airplanes). Topics covered include ac and dc rotating machines, power electronics inverters and drives, and control techniques. Prerequisite: EE 42, Physics 43, ENGR 40M or equivalent.

Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals. Prerequisites: Calculus at the level of MATH 51, CME 100 or equivalent and basic knowledge of computing at the level of CS106A.

Independent work under the direction of a faculty member. Individual or team activities involve lab experimentation, design of devices or systems, or directed reading. Course may be repeated for credit.

Independent work under the direction of a faculty member given for a letter grade only. If a letter grade given on the basis of required written report or examination is not appropriate, enroll in 190. Course may be repeated for credit.

EE191A is part of the Accelerated Calculus for Engineers program. Independent work under the direction of a faculty member given for a letter grade only. EE 191A counts as a Math one unit seminar course: it is this unit that constitutes the ACE program.

WIM-version of EE 191. For EE students using special studies (e.g., honors project, independent research project) to satisfy the writing-in-major requirement. A written report that has gone through revision with an adviser is required. An adviser from the Technical Communication Program is recommended.

Students receive training from faculty or graduate student mentors to prepare them to assist in instruction of Electrical Engineering courses. The specific training and units of credit received are to be defined in consultation with one of the official instructors of EE 195. Note that University regulations prohibit students from being paid for the training while receiving academic credit for it. Enrollment limited.

While traversing through the natural world, you effortlessly perceive and react to a rich stream of stimuli. This constantly changing stream evokes spatiotemporal patterns of spikes that propagate through your brain from one ensemble of neurons to another. An ensemble may memorize a spatiotemporal pattern at the speed of life and recall it at the speed of thought. In the first half of this course, we will discuss and model how a neural ensemble memorizes and recalls such a spatiotemporal pattern. In the second half, we will explore how neuromorphic hardware could exploit these neurobiological mechanisms to run AI not with megawatts in the cloud but rather with watts on a smartphone. Prerequisites: Either computational modeling (BIOE 101, BIOE 300B) or circuit analysis (EE 101A).

Formally EE 292Q. Introduction to operation principles and key performance aspects of 3D+ imaging sensors used widely in industry. Concepts include imaging physics, data acquisition and image formation methods, and signal and image quality metrics that are broadly applicable across sensor types. Practical examples and demonstrations of various sensors such as radar, acoustic, LIDAR, and ToF modules will be presented in class as well as through structured labs. Invited speakers will highlight emerging 3D+ imaging applications that these sensors are enabling today. Prerequisites: EE 101A or equivalent. EE 102A or equivalent.

Introduction to quantum control, dynamics, and information processing, aimed at graduate students and advanced undergraduate students. Prerequisites include knowledge of quantum mechanics, linear algebra, and statistical analysis. The course will provide an overview of both the fundamentals and state-of-the-art techniques in the area of quantum engineering. Topics include qubits and operators, modeling and numerical analysis of open quantum systems, quantum control protocols, average Hamiltonian theory, dynamical decoupling, quantum device benchmarking, different quantum platforms and their applications.

Atomic systems, spontaneous emission, stimulated emission, amplification. Three- and four-level systems, rate equations, pumping schemes. Laser principles, conditions for steady-state oscillation. Transverse and longitudinal mode control and tuning. Exemplary laser systems: gas (HeNe), solid state (Nd:YAG, Ti:sapphire) and semiconductors. Elements of laser dynamics and noise. Formerly EE231. Prerequisites: EE 236B and familiarity with modern physics and semiconductor physics. Recommended: EE 216 and EE 223 (either may be taken concurrently).

This course will be an introduction to solar photovoltaics. No prior photovoltaics knowledge is required. Class lectures will be supplemented by guest lectures from distinguished engineers, entrepreneurs and venture capitalists actively engaged in solar industry. Past guest speakers include Richard Swanson (CEO, SunPower), Benjamin Cook (Managing Partner at NextPower Capital) and Shahin Farshchi (Partner, Lux Capital). Topics Include: Economics of solar energy. Solar energy policy. Solar cell device physics: electrical and optical. Different generations of photovoltaic technology: crystalline silicon, thin film, multi-junction solar cells. Perovskite and silicon tandem cells. Advanced energy conversion concepts like photon up-conversion, quantum dot solar cells. Solar system issues including module assembly, inverters, micro-inverters and microgrid. No prior photovoltaics knowledge is required. Recommended: EE116, EE216 or equivalent.

Addressing the energy challenges of today and the environmental challenges of the future will require efficient energy conversion techniques. This course will discuss the circuits used to efficiently convert ac power to dc power, dc power from one voltage level to another, and dc power to ac power. The components used in these circuits (e.g., diodes, transistors, capacitors, inductors) will also be covered in detail to highlight their behavior in a practical implementation. A lab will be held with the class where students will obtain hands on experience with power electronic circuits. For WIM credit, students must enroll in EE 153 for 4 units. No exceptions. Formerly EE 292J. Prerequisite: EE 101A. Strongly recommended EE 101B.

This is a companion course to EE 264 Digital Signal Processing for students interested in developing advanced DSP projects beyond the scope of the one credit hour EE 264 lab option (section 2). Weekly meetings with the instructor to plan the week ahead and to share results from the previous are mandatory and will be scheduled at a mutually convenient time. A final project report, project demonstration, and presentation is required. Instructor will determine appropriate number of units based on the project complexity. Prerequisite: EE 264 and instructor approval.

OpenGL, real-time rendering, 3D display systems, display optics & electronics, IMUs and sensors, tracking, haptics, rendering pipeline, multimodal human perception and depth perception, stereo rendering, presence. Emphasis on VR technology. Hands-on programming assignments. The 3-unit version requires a final programming assignment in which you create your own virtual environment. The 4-unit version requires a final course project and written report in lieu of the final assignment. Prerequisites: Strong programming skills, ENGR 108 or equivalent. Helpful: basic computer graphics / OpenGL.

Writing in the Major (WIM) version of the 4-unit EE 267 theory + lab/project course. This course also meets the EE design requirement. Topics include: OpenGL, real-time rendering, 3D display systems, display optics & electronics, IMUs and sensors, tracking, haptics, rendering pipeline, multimodal human perception and depth perception, stereo rendering, presence. Emphasis on VR technology. Hands-on programming assignments. The 5-unit WIM version requires everything the 4-unit version does, i.e. a final course project and written report in lieu of the final assignment. The 5-unit WIM version additional requires participation in 2 writing in the major workshops, and weekly writing assignments. Prerequisites: Strong programming skills, ENGR 108 or equivalent. Helpful: basic computer graphics / OpenGL.

Course focuses on how to build modern computing systems, namely notebooks, smartphones, and data centers, covering primarily their hardware architecture and certain system software aspects. For each system class, we cover the system architecture, processor technology, advanced memory hierarchy and I/O organization, power and energy management, and reliability. We will also cover topics such as interactions with system software, virtualization, solid state storage, and security. The programming assignments allow students to explore performance/energy tradeoffs when using heterogeneous hardware resources on smartphone devices. Prerequisite: EE180. Recommended: CS 140.

Internet of Things (IoT) origin, vision and definition. Application domains, use case scenarios and value propositions. Functional blocks of IoT systems: devices, communications, services, management, security, and application. Architectural reference model and design methodology. IoT Devices: sensors, actuators and embedded systems. Communications aspects of IoT systems: Internet infrastructure; wireless local area networks; radio access networks; wireless personal area networks; wireless sensor networks; wireless communication in vehicular environments; 5G. Current IoT frameworks and underlying architectures. Data storage and analytics. Web services. IoT system management tools. Security aspects of IoT systems. Open issues.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent; for 290E, EE PhD degree candidacy, an "S" grade in EE 290D and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290E and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290F and instructor consent.

The class teaches cutting-edge optimization and analysis algorithms for the design of complex digital integrated circuits and their use in designing machine learning hardware. It provides working knowledge of the key technologies in Electronic Design Automation (EDA), focusing on synthesis, placement and routing algorithms that perform the major transformations between levels of abstraction and get a design ready to be fabricated. As an example, the design of a convolutional neural network (CNN) for basic image recognition illustrates the interaction between hardware and software for machine learning. It will be implemented on a state-of-the-art FPGA board. Prerequisite: EE 108.

Fundamental aspects of CVD are initially considered, first focusing on processes occurring in the gas phase and then on those occurring on the surface. Qualitative understanding is emphasized, with minimal use of equations. Adding energy both thermally and by using a plasma is discussed; atomic-layer deposition is briefly considered. Examples of CVD equipment are examined. The second portion of the tutorial examines layers deposited by CVD. The focus is on group IV semiconductors especially epitaxial and heteroepitaxial deposition, in which the crystal structure of the depositing layer is related to that of the substrate. Polycrystalline silicon and the IC interconnect system are then discussed. Finally, the use of high-density plasmas for rapid gap filling is contrasted with alternative CVD dielectric deposition processes.

This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. In this class students will learn about techniques to optimize machine learning models and deploy them on a device such as a Arduino, Raspberry PI, Jetson, or Edge TPUs. The class has a significant project component. Prerequisites: CS 107(required), CS 229 (recommended), CS 230 (recommended).

Seminar. For engineering students interested in camera and display engineering, computer vision, and computational imaging. Speakers include Stanford faculty and research scientists as well as industry professionals, mostly from consumer electronics companies. This course is open to graduate and professional students only, or with instructor consent.

This course presents the application of rigorous digital image processing to problems in visualization and understanding of fine paintings, drawings, and other two-dimensional artworks. It builds upon a wealth of techniques but modifies and applies them to cases of interest to the technical art community. Such techniques include transforms such as DCT and wavelets, color quantization, blind source (image) separation, edge detection, super-resolution, visual style learning and transfer, digital in-painting, color transforms, level-set analysis, estimation of region statistics, Affine image transforms, and many others. Students will perform several projects which will involve coding, mathematical/statistical analysis, and explaining the relevance of the work to art scholarship.

Great products are crafted by product teams, commonly composed of engineering, product management, and customer support. We start by identifying unmet market needs and then satisfying those needs through an iterative process of building from functional infancy to market leadership. In this class, we seek to demystify this process through direct conversations with guests who've delivered immensely successful products. We aim to introduce how great hardware and software products are crafted -- in both startups and larger companies. Students will learn why pursuing areas of interest and curiosity is critical to building world-class solutions to problems. Previous companies profiled: Apple, HP, Microsoft, VMWare, Genentech, Blue Bottle Coffee, Pixar, and Pivotal Labs -- to name a few. Previous guests include Ted Hoff (Inventor of the microprocessor and employee #12 at Intel), Diane Greene (Co-founder and CEO of VMware, former President of Google Cloud, and former Chair of The MIT Corporation), Rob Mee (Co-Founder of Pivotal Labs and Founder of Mechanical Orchard), Evans Hankey (former VP of Design at Apple), Matt Kraning (EE292i Alumnus, Co-Founder Expanse, acquired by Palo Alto Networks where Matt now serves as CTO Cortex), and Jon Rubinstein (NeXT, Apple, Palm). Pre-requisites: None

This course introduces the set of skills and philosophies (beyond technical expertise) that will help students become world-class product professionals early in their careers. The legendary guests from EE292i mastered many such capabilities, ultimately yielding historic successes. While there are no guarantees of such historic accomplishment, we understand well many of the skills and practices required to "build" world-class professionals. Doing so dramatically increases your probability of success. Topics include: Identifying great job opportunities, interviewing to win; cultivating empathy -- strengthening teamwork, understanding customer needs, and captivating others with your vision; negotiating for yourself, your team, and your ideas; integrity -- why honesty, integrity, and decency remain the "coins of the realm" in the product world; why iteration always beats perfection; embracing failure to learn; recognizing your strengths and passions -- how to "double down" on strengths and leverage teammates to compensate for weaknesses; identifying emerging technical and business opportunities; building the emotional and physical stamina required for success in product development; learning how to maximize your economic outcomes; and much more. Prerequisites: None.

A series of seminar and lectures focused on power engineering. Renowned researchers from universities and national labs will deliver bi-weekly seminars on the state of the art of power system engineering. Seminar topics may include: power system analysis and simulation, control and stability, new market mechanisms, computation challenges and solutions, detection and estimation, and the role of communications in the grid. The instructors will cover relevant background materials in the in-between weeks. The seminars are planned to continue throughout the next academic year, so the course may be repeated for credit.

Independent work under the direction of a department faculty. Written thesis required for final letter grade. The continuing grade 'N' is given in quarters prior to thesis submission. See 390 if a letter grade is not appropriate. Course may be repeated for credit.

Integrating electronics with sensing, stimulation, and locomotion capabilities into the body will allow us to restore or enhance physiological functions. In order to be able to insert these electronics into the body, energy source is a major obstacle. This course focuses on the analysis and design of wirelessly powered catheter-deliverable electronics. Emphases will be on the interaction between human and electromagnetic fields in order to transfer power to the embedded electronics via electromagnetic fields, power harvesting circuitry, electrical-tissue interface, and sensing and actuating frontend designs.

Design of advanced analog circuits at the system level, including switching power converters, amplitude-stabilized and frequency-stabilized oscillators, voltage references and regulators, power amplifiers and buffers, sample-and-hold circuits, and application-specific op-amp compensation. Approaches for finding creative design solutions to problems with difficult specifications and hard requirements. Emphasis on feedback circuit techniques, design-oriented thinking, and hands-on experience with modern analog building blocks. Several designs will be built and evaluated, along with associated laboratory projects. Prerequisite: EE 251 or EE 314A.

This is a seminar course with invited speakers. Sponsored by Stanford's SystemX Alliance, the talks will cover emerging topics in contemporary hardware/software systems design. Special focus will be given to the key building blocks of sensors, processing elements and wired/wireless communications, as well as their foundations in semiconductor technology, SoC construction, and physical assembly as informed by the SystemX Focus Areas. The seminar will draw upon distinguished engineering speakers from both industry and academia who are involved at all levels of the technology stack and the applications that are now becoming possible. May be repeat for credit

What are the practical and fundamental limits to the evolution of the technology of modern MOS devices and interconnects? How are modern devices and circuits fabricated and what future changes are likely? Advanced techniques and models of MOS devices and back-end (interconnect and contact) processing. What are future device structures and materials to maintain progress in integrated electronics? MOS front-end and back-end process integration. Prerequisites: EE 216 or equivalent. Recommended: EE 212.

Design of RF integrated circuits for communications systems, primarily in CMOS. Topics: the design of matching networks and low-noise amplifiers at RF, mixers, modulators, and demodulators; review of classical control concepts necessary for oscillator design including PLLs and PLL-based frequency synthesizers. Design of low phase noise oscillators. Design of high-efficiency (e.g., class E, F) RF power amplifiers, coupling networks. Behavior and modeling of passive and active components at RF. Narrowband and broadband amplifiers; noise and distortion measures and mitigation methods. Overview of transceiver architectures. Prerequisite: EE214B.

Introduction to quantum photonics - generation and manipulation of quantum light on a chip. Classical (coherent) and quantum (Fock, squeezed, entangled, cluster) states of light. Cavity quantum electrodynamics: strong and weak-coupling regime (Purcell factor, spontaneous emission control). Light-matter entanglement in solid state. Measurements of photon statistics and photon indistinguishability; quantum state tomography. Platforms for quantum photonics. Quantum networks; photonics in quantum simulation and computing. Prerequisites: undergraduate/ introductory graduate electromagnetics and quantum mechanics

Wave propagation in anisotropic, nonlinear, and time-varying media. Microscopic and macroscopic description of electric-dipole susceptibilities. Free and forced waves; phase matching; slowly varying envelope approximation; dispersion, diffraction, space-time analogy. Harmonic generation; frequency conversion; parametric amplification and oscillation; electro-optic light modulation. Raman and Brillouin scattering; nonlinear processes in optical fibers. Prerequisites: 242, 236C.

Continuation of 364A. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Course requirements include project. Prerequisite: 364A.

Imaging internal structures within the body using magnetic resonance studied from a systems viewpoint. Analysis of magnetic resonance imaging systems including physics, Fourier properties of image formation, effects of system imperfections, image contrast, and noise. Pre- or corequisite: EE 261 or equivalent

EE 379B follows 379A and focuses on state-of-the-art data communication system theory and design, particularly systems with multiple users and dimensions (MIMO over parallel antennas or wires). The focus is on multi-user physical-layer channels like multiple access, broadcast, and interference channels, their capacity regions and designs to achieve any points therein. Examples include the latest cellular, Wi-Fi, wireline, cable, and other systems that stress fundamental transmission limits. Topics include system design, particularly physical-layer modulation/coding analysis and optimization through various artificial intelligence and optimization methods for multi-dimensional channels. Included are methods to design and adapt both transmitter and receiver to variable channels. Prerequisites: EE 278, linear algebra, EE 279 or EE 379A (or 379), or instructor consent. Instructor: Cioffi

Modeling and control methodologies for high-performance network engineering, including: Markov chains and stochastic modeling, queueing networks and congestion management, dynamic programming and task/processor scheduling, network dimensioning and optimization, and simulation methods. Applications for design of high-performance architectures for wireline/wireless networks and the Internet, including: traffic modeling, admission and congestion control, quality of service support, power control in wireless networks, packet scheduling in switches, video streaming over wireless links, and virus/worm propagation dynamics and countermeasures. Enrollment limited to 30. Prerequisites: basic networking technologies and probability.

Independent work under the direction of a faculty member. Individual or team activities may involve lab experimentation, design of devices or systems, or directed reading. May be repeated for credit.

Independent work under the direction of a faculty member; written report or written examination required. Letter grade given on the basis of the report; if not appropriate, student should enroll in 390. May be repeated for credit.

The seminar features guest lectures from the industry. The Industrial AI (I-AI) computing applications are at the center of on-going digital transformation. Known as the Fourth Industrial Revolution, or Industry 4.0, this is a multi-trillion-dollar transformation of economy. The I-AI is related to Internet of Things (IoT), where 'things' include man-made systems and business processes: industrial, transportation, operations and support, and supply chains. I-AI applications are mission critical with large cost of error compared to AI apps for the Internet of People. The lecturers from technology (e.g., computing) companies, consultancies, AI vendors, OEMs, and end users of the I-AI will discuss business and 'big picture' technical issues. Example vertical industries are energy, transportation, oil and gas, data centers, and manufacturing.

Limited to candidates for the degree of Engineer or Ph.D.May be repeated for credit.

Distinctive patterns and challenges of entrepreneurship in Asia; update of business and technology issues in the creation and growth of start-up companies in major Asian economies. Distinguished speakers from industry, government, and academia.

May be repeated for credit.

May be repeated for credit.