printer friendly pageEE 256: Numerical Electromagnetics
Principles and applications of numerical techniques for solving practical problems of electromagnetics. Finite-difference time-domain (FDTD) method and finite-difference frequency-domain (FDFD) method for solving Maxwell's equations. Numerical analysis of stability. Perfectly matched layer (PML) absorbing boundaries. Total-field/scattered-field (TF/SF) method. Waveguide mode analysis. Bloch boundary conditions. The course requires programming and the use of MATLAB or other equivalent tools. Prerequisite:
EE 242 or equivalent.
Terms: Win
| Units: 3
EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)
Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of large-scale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.
Last offered: Winter 2013
EE 261: The Fourier Transform and Its Applications
The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems. Prerequisites: Math through ODEs, basic linear algebra, Comfort with sums and discrete signals, Fourier series at the level of 102A
Terms: Aut, Spr, Sum
| Units: 3
Instructors:
Gill, J. (PI)
;
Osgood, B. (PI)
;
Paik, S. (PI)
;
Barnes, L. (TA)
;
Li, H. (TA)
;
Paik, S. (TA)
;
Siripuram, A. (TA)
EE 262: Two-Dimensional Imaging
Time and frequency representations, two-dimensional auto- and cross-correlation, Fourier spectra, diffraction and antennas, coordinate systems and the Hankel and Abel transforms, line integrals, impulses and sampling, restoration in the presence of noise, reconstruction and tomography, imaging radar. Tomographic reconstruction using projection-slice and layergarm methods. Students create software to form images using these techniques with actual data. Final project consists of design and simulation of an advanced imaging system. Prerequisite:
EE261. Recommended:
EE278,
EE279.
Last offered: Winter 2015
EE 263: Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: linear algebra and matrices as in
MATH104; differential equations and Laplace transforms as in
EE102B.
Terms: Aut, Sum
| Units: 3
Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Bartan, B. (TA)
...
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Instructors:
Nasiri Mahalati, R. (PI)
;
Shah, K. (PI)
;
Bartan, B. (TA)
;
Hemmati, S. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Sharafat, A. (TA)
;
Shen, L. (TA)
;
Zhou, Z. (TA)
EE 264: Digital Signal Processing
This is a course on digital signal processing techniques and their applications. Topics include: review of DSP fundamentals; 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; and parametric signal modeling. The course will also discuss applications of DSP in areas such as speech and audio processing, autonomous vehicles, and software radio. An optional (1 extra credit hour) lab will provide a hands-on opportunity to explore the application of DSP theory to practical real-time applications. For more information, see the course web page at
ee264.stanford.edu. Prerequisite: EE102A and EE102B or equivalent.
Terms: Win, Sum
| Units: 3-4
Instructors:
Krause Perin, J. (PI)
;
Mujica, F. (PI)
;
Schafer, R. (PI)
...
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Instructors:
Krause Perin, J. (PI)
;
Mujica, F. (PI)
;
Schafer, R. (PI)
;
Ahmed, T. (TA)
;
Burkle, M. (TA)
;
Messingher, S. (TA)
;
Patel, A. (TA)
EE 266: Stochastic Control (MS&E 251)
Introduction to stochastic control, with applications taken from a variety of areas including supply-chain optimization, advertising, finance, dynamic resource allocation, caching, and traditional automatic control. Markov decision processes, optimal policy with full state information for finite-horizon case, infinite-horizon discounted, and average stage cost problems. Bellman value function, value iteration, and policy iteration. Approximate dynamic programming. Linear quadratic stochastic control. Formerly
EE365. Prerequisites:
EE 263,
EE 178 or equivalent.
Last offered: Spring 2016
EE 267: Virtual Reality
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,
EE 103 or equivalent. Helpful: basic computer graphics / OpenGL.
Terms: Spr
| Units: 3-4
EE 271: Introduction to VLSI Systems
Provides a quick introduction to MOS transistors and IC fabrication and then creates abstractions to allow you to create and reason about complex digital systems. It uses a switch resistor model of a transistor, uses it to model gates, and then shows how gates and physical layout can be synthesized from Verilog or SystemVerilog descriptions. Most of the class will be spent on providing techniques to create designs that can be validated, are low power, provide good performance, and can be completed in finite time. Prerequisites: 101A, 108A and 108B; familiarity with transistors, logic design, Verilog and digital system organization
Terms: Win
| Units: 3
EE 272: Design Projects in VLSI Systems
An introduction to mixed signal design. Working in teams you will create a small mixed-signal VLSI design using a modern design flow and CAD tools. The project involves writing a Verilog model of the chip, creating a testing/debug strategy for your chip, wrapping custom layout to fit into a std cell system, using synthesis and place and route tools to create the layout of your chip, and understanding all the weird stuff you need to do to tape-out a chip. Useful for anyone who will build a chip in their Ph.D. Pre-requsiites: EE271 and experience in digital/analog circuit design.
Last offered: Spring 2012
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