Recommended: EE 212. Prerequisite: EE 284 or CS 244A. 3 Units. EE 237. Course may be repeated for credit. EE 387. Security aspects of IoT systems. Applications of adaptive filters to statistical prediction, process modeling, adaptive noise canceling, adaptive antenna arrays, adaptive inverse control, and equalization and echo canceling in modems. 3 Units. EE 218. EE 327. EE 378B. The Fourier transform as a tool for solving physical problems. Recommended: EE254, EE278, EE279. The course will also introduce students to recent in-memory computing research using these memory technologies. The admission process takes place through the university for undergraduates and in departments for … Fundamental properties of electrical activity in neurons, technology for measuring and altering neural activity, and operating principles of modern neurological and neural prosthetic medical systems. Prerequisites: Familiarity with probability theory and linear algebra at the undergraduate level. Faculty advisors guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways. ), Doctor of Philosophy in Electrical Engineering, mezzanine courses for advanced undergraduate or first-year graduate, specialized courses for advanced graduate, Linear Algebra, Multivariable Calculus, and Modern Applications, Introduction to Matrix Methods (Preferred), Introduction to Electromagnetics and Its Applications, Analog Communications Design Laboratory (WIM/Design), Introduction to Digital Image Processing (WIM/Design), Special Studies and Reports in Electrical Engineering (WIM; Department approval required), Engineering a Smart Object - Adding connectivity and Putting it ALL together (Design), Computer Organization and Systems (Required prerequisite for, Computer Vision: Foundations and Applications, Operating Systems and Systems Programming, Introduction to Computer Graphics and Imaging, Artificial Intelligence: Principles and Techniques, Natural Language Processing with Deep Learning, Computer Vision: From 3D Reconstruction to Recognition, Convolutional Neural Networks for Visual Recognition, Signal Processing and Linear Systems II (Required), Introduction to Neuroelectrical Engineering, The Fourier Transform and Its Applications, Introduction to Stochastic Control with Applications, Introduction to Statistical Signal Processing, Introduction to Control Design Techniques, Fundamentals of Analog Integrated Circuit Design, Semiconductor Devices for Energy and Electronics, Electric Motors for Renewable Energy, Robotics, and Electric Vehicles, Principles and Models of Semiconductor Devices, Introduction to Optical Fiber Communications, Energy storage and conversion: Solar Cells, Fuel Cells, Batteries and Supercapacitors, 100% Clean, Renewable Energy and Storage for Everything, Quantum Mechanics of Nanoscale Materials (Formerly MATSCI 157), Solar Cells, Fuel Cells, and Batteries: Materials for the Energy Solution, Music, Computing, Design: The Art of Design, Music, Computing, Design II: Virtual and Augmented Reality for Music, Introduction to Audio Signal Processing Part I: Spectrum Analysis, Introduction to Audio Signal Processing Part II: Digital Filters, Signal Processing Models in Musical Acoustics, Signal Processing Techniques for Digital Audio Effects. EE 242. Prerequisite: EE 178, CS 109 or equivalent. Contributions from EE faculty and other departments including Law, Business, and MS&E.May be repeated for credit. Advanced Circuit Techniques. 3 Units. The 5-unit WIM version requires everything the 4-unit version does, i.e. Students work on an existing project of their own or join one of these projects. Internet Routing Protocols and Standards. A course may only be counted towards one requirement; it may not be double-counted. Detailed requirements and instructions are in the Handbook for Graduate Students in Electrical Engineering at Stanford University ( ). (Formerly EE 304) Neuromorphic systems run perceptual, cognitive and motor tasks in real-time on a network of highly interconnected nonlinear units. Photonic crystal fibers. These techniques will then be used to design analog (AM and FM) and digital (PSK and FSK) communication systems and determine their performance over channels with noise and interference. Both the advisor and the advisee are expected to maintain professionalism, respect, and integrity. EE 185B. However, if the number of these are large, the proposed program may contain more than the minimum 45 units, and the time required to meet the degree requirements may be increased. Final report required. Prerequisites: EE 142 or PHYSICS 120, and prior programming experience (Matlab or other language at level of CS 106A or higher). EE191A is part of the Accelerated Calculus for Engineers program. Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Topics in Information Theory and Its Applications. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. The Laboratory Course allows students to work hands-on with optical equipment to conduct five experiments that compliment the lecture course. The course will also examine adaptations necessary at transport and higher layers to cope with node mobility and error-prone nature of the wireless medium. The trajectory of the course is determined in large part by the students themselves. For each of these memories, the course will cover basic operation principles, device design considerations, device scaling, device fabrication, memory array architecture, and addressing and readout circuits. Analog Communications Design Laboratory. Fibers: single- and multi-mode, attenuation, modal dispersion, group-velocity dispersion, polarization-mode dispersion. Analytical and approximate treatments of noise and distortion; analysis and design of feedback circuits. Physical principles of operation of the p-n junction, heterojunction, metal semiconductor contact, bipolar junction transistor, MOS capacitor, MOS and junction field-effect transistors, and related optoelectronic devices such as CCDs, solar cells, LEDs, and detectors. ), Joint Electrical Engineering and Master's in Business Administration Degree (M.S./M.B.A. 3 Units. Independent work under the direction of a faculty member. EE 251. Adding energy both thermally and by using a plasma is discussed; atomic-layer deposition is briefly considered. Same as: EE 214A. EE 205. Prerequisite: 101B. 1-2 Unit. The course covers state-of-the-art and emerging bio-sensors, bio-chips, imaging modalities, and nano-therapies which will be studied in the context of human physiology including the nervous system, circulatory system and immune system. Each student, with the help of a program advisor, prepares an individual program and submits it to the department for approval. 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. Parallel Processors Beyond Multicore Processing. In this master’s program, courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. 3-5 Units. Maxwell's equations, constitutive relations. Design of high-performance digital systems, the things that cause them to fail, and how to avoid these problems. Prerequisite: EE108B. In-depth discussion on cancer and cardiovascular diseases and the role of diagnostics and nano-therapies. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). 3 Units. Computer processing of digital 2-D and 3-D data, combining theoretical material with implementation of computer algorithms. 3 Units. The purpose of the master’s program is to provide students with the knowledge and skills necessary for a professional career or doctoral studies. Enrollment limited to 25; preference to graduating seniors. 3 Units. Same as: EALC 402T, EASTASN 402T. Formerly EE 152. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; spectrum analysis using the DFT; parametric signal modeling and adaptive filtering. In this class, students will learn about how to build an networked embedded system from the ground up. Emphasis is on applications in modern devices and systems. 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 3-unit version requires a final programming assignment in which you create your own virtual environment. EE 23N. Forms of imaging including human and animal vision systems, atomic force microscope, microscope, digital camera, holography and three-dimensional imaging, telescope, synthetic aperture radar imaging, nuclear magnetic imaging, sonar and gravitational wave imaging, and the Hubble Space telescope. Three-Dimensional Imaging. Topics include: Schrödinger's equation, eigenfunctions and eigenvalues, solutions of simple problems including quantum wells and tunneling, quantum harmonic oscillator, coherent states, operator approach to quantum mechanics, Dirac notation, angular momentum, hydrogen atom, calculation techniques including matrix diagonalization, perturbation theory, variational method, and time-dependent perturbation theory with applications to optical absorption, nonlinear optical coefficients, and Fermi's golden rule. Reinforcement learning addresses the design of agents that improve decisions while operating within complex and uncertain environments. MATH 51 may be replaced by MATH 52. Medical Imaging Systems II. 3 Units. The topics that will be covered include electrostatics, magnetostatics, Maxwell's equations, one-dimensional wave equation, electromagnetic waves, transmission lines, and one-dimensional resonators. High-speed signaling methods; noise in digital systems, its effect on signaling, and methods for noise reduction; timing conventions; timing noise (skew and jitter), its effect on systems, and methods for mitigating timing noise; synchronization issues and synchronizer design; clock and power distribution problems and techniques; impact of electrical issues on system architecture and design. Why bits have become the universal currency for information exchange. Course requirements include project. Confidence intervals. The program prepares students for a broad range of careers—both industrial and government—as well  as for professional and academic graduate education. Estimation of the total variation distance, estimate the fundamental limit is easier than to achieve the fundamental limit; Peetre¿s K-functional and bias analysis: bias correction using jackknife, bootstrap, and Taylor series; Nonparametric functional estimation. A final version of the completed and signed program sheet is due to the department no later than one month prior to the last quarter of senior year. 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. Prerequisite: EE101B. EE 311. EE 292T. Reinforcement Learning: Behaviors and Applications. EE 102A. Familiarity with finite fields will be helpful but not required. EE 191A. Interactive Light Sculpture Project. The seminars are planned to continue throughout the next academic year, so the course may be repeated for credit. Courses offered by the Department of Electrical Engineering are listed under the subject code EE on the Stanford Bulletin's ExploreCourses web site. Sampling, reconstruction, decimation and interpolation. Discrete universal denoising. To satisfy Design, must take EE 264 or EE 267 for 4 units and complete the laboratory project. Convex Optimization II. To major in Electrical Engineering (EE), undergraduates should follow the requirements below. Multidimensional time and frequency representations, generalization of Fourier transform methods to non-Cartesian coordinate systems, Hankel and Abel transforms, line integrals, impulses and sampling, reconstruction tomography, imaging radar. The SEE course portfolio includes one of Stanford's most popular sequences: the three-course Introduction to Computer Science, taken by the majority of Stanford’s undergraduates, as well as more advanced courses … ENGR 108 may be used for disciplinary area if not used for EE Math. Lectures/discussions on topics of importance to the electrical engineering professional. 3 Units. 2020-21 Approved School of Engineering Breadth Courses These listed courses fulfill Math, Science, Technology in Society, and Engineering Fundamentals requirements for School of Engineering majors. EE 356B. Same as: GEOPHYS 60N. We will first study the examples set by GaN and SiC in lighting, radiofrequency and power applications, then use it to explore new materials like Ga2O3, AlN and diamond to understand their potential to drive the future semiconductor industry. Renewable energy will make up at least half of the generation mix and drive adoption of novel technologies such as storage, fuel cells, waste to power and distributed generation. WIM-version of EE 191. Same as: EE 233. Individual courses … Fundamental aspects of CVD are initially considered, first focusing on processes occurring in the gas phase and then on those occurring on the surface. Multidimensional Fourier transform and use in imaging. Spawned by rapid advances in optical fabrication and digital processing power, a new generation of imaging technology is emerging: computational cameras at the convergence of applied mathematics, optics, and high-performance computing. 3 Units. Multi-carrier modulation: orthogonal frequency-division multiplexing; capacity of parallel Gaussian channels; comparison of single- and multi-carrier techniques. 3-4 Units. Information Theory and Statistics. Modulators: electro-optic, electro-absorption. The … Same as: EE 153. EE279 is helpful but not required. In lieu of a final exam, students will perform a group project demonstrating theoretical and application proficiency in a topic of their choosing. Numerical analysis of stability. 3 Units. Prerequisite: EE 102A and one of the following: EE 42, PHYSICS 43, or PHYSICS 63. The degree of Doctor of Philosophy is offered under the general regulations of the University. Integration with external systems and smart devices. The topics include: mathematical models for discrete-time signals, vector spaces, Fourier analysis, time-frequency analysis, Z-transforms and filters, signal classification and prediction, basic image processing, compressed sensing and deep learning. Stanford Computer Science and Electrical Engineering are deeply interrelated disciplines, and numerous faculty members are jointly appointed in the two departments. Finally, graduate students are active contributors to the advising relationship, proactively seeking academic and professional guidance and taking responsibility for informing themselves of policies and degree requirements for their graduate program. Starting with an overview of the capacity of fading and multiple-antenna wireless channels, we aim to answer questions such as: What is the optimal way for users to cooperate and exchange information in a wireless network? Engineering the Micro and Nano Worlds: From Chips to Genes. EE 359. 2 Units. The Electrical Engineering Department offers the following degrees: Bachelor of Science, Master of Science, and Doctor of Philosophy. Syllabus topics will be determined by the needs of the enrolled students and projects. Students in the major have broad flexibility to select from disciplinary areas beyond the core, including hardware and software, information systems and science, and physical technology and science, as well as electives in multidisciplinary areas, including bio-electronics and bio-imaging, energy and environment and music. Principles and applications of numerical techniques for solving practical problems of electromagnetics. Analytical Methods in Biotechnology. Prerequisites: Basic knowledge of probability at the level of EE 178. Advanced energy conversion concepts like photon up-conversion, quantum dot solar cells. 3 Units. Structure and components of computer networks; functions and services; packet switching; layered architectures; OSI reference model; physical layer; data link layer; error control; window flow control; media access control protocols used in local area networks (Ethernet, Token Ring, FDDI) and satellite networks; network layer (datagram service, virtual circuit service, routing, congestion control, Internet Protocol); transport layer (UDP, TCP); application layer. This is a completely project-based course where, working in teams, you will propose your own mixed-signal chip, write a Verilog or a synthesizable C++ model of your chip, create a testing/debug strategy for your chip, wrap custom layout to fit into a standard cell system, use synthesis and place and route tools to create the layout of your chip, perform physical verification of your chip and finally tape it out. Prerequisite: EE101B. 4 Units. Prerequisite: EE 108. Prerequisite courses ordinarily taken by undergraduates may be included as part of the graduate program of study. Design, testing, and applications of Radio Frequency (RF) electronics: Amplitude Modulation (AM), Frequency Modulation (FM) and concepts of Software Define Radio (SDR) systems. Receiver performance: Q factor, bit-error ratio, sensitivity, quantum limit. Digital circuit, logic, and system design. How spaceborne imaging technology makes it possible to respond quickly and mitigate consequences; how it is applied to natural disasters; and remote sensing data manipulation and analysis. Through this course, the student will also get familiar with various software tools developed for the analysis of real sequencing data. Topics include: detection and probability of error for binary and M-ary signals (PAM, QAM, PSK), receiver design and sufficient statistics, controlling the spectrum and the Nyquist criterion, bandpass communication and up/down conversion, design trade-offs: rate, bandwidth, power and error probability, coding and decoding (block codes, convolutional coding and Viterbi decoding). Students may petition to use CS 109 in place of EE 178. GER:DB-EngrAppSci. The Stanford Electrical Engineering … Digital System Design. Prerequisite: ENGR 21 or ENGR 40M or EE 122A or CS 106B or consent of instructor. Probabilistic Systems Analysis. Students will be able to take courses with Satisfactory/Credit (S/CR) grades to count toward the EE degree requirements for all undergraduate degree programs. 4 Units. EE 292A. 1 Unit. See the Electrical Engineering graduate admissions web site for department specific information. 1 Unit. Develop the ability to formulate problems and projects and to plan a process for solution, taking advantage of diverse technical knowledge and skills. Advanced techniques and models of MOS devices and back-end (interconnect and contact) processing. Implicit (algorithmic) regularization. EE 346. Areas: fault-tolerant systems, design for testability, production testing, and system reliability. 4 Units. Non-sequential inference via sequential probability assignments. Same as: CS 241. 3 Units. This policy is valid in all six quarters from Spring Quarter 2019-20 through Summer Quarter 2020-21. EE 301. Penalized estimators and minimumndescription length. Networked embedded systems are often hidden from our view, but they are a key component that enables our modern society. Modulation, chirp, linewidth, intensity noise, jitter, stanford electrical engineering courses techniques: differential keying! 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