printer friendly pageEE 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
ENGR 108 or
MATH 104; ordinary differential equations and Laplace transforms as in
EE 102B or
CME 102.
Terms: Aut, Sum
| Units: 3
EE 264: Digital Signal Processing
Digital signal processing (DSP) techniques and design of DSP applications. 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. The course also covers applications of DSP in areas such as speech, audio and communication systems. The optional lab section (Section 02) provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See
ee264.stanford.edu for more information. Register in Section 02 to take the lab. Undergraduate students taking the lab should register for 4 units to meet the EE design requirement. The optional lab section is not available to remote SCPD students. Prerequisites:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win
| Units: 3-4
EE 264P: Digital Signal Processing Projects
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.
Terms: Spr
| Units: 1-3
Instructors:
Mujica, F. (PI)
;
Schafer, R. (PI)
EE 264W: Digital Signal Processing (WIM)
Writing in the Major (WIM) version of the 4-unit
EE 264 theory + lab course. Digital signal processing (DSP) techniques and design of DSP applications. 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. The course also covers applications of DSP in areas such as speech, audio and communication systems. The lab component provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See
ee264.stanford.edu for more information. Prerequisites:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win
| Units: 5
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,
ENGR 108 or equivalent. Helpful: basic computer graphics / OpenGL.
Terms: Spr
| Units: 3-4
EE 267W: Virtual Reality (WIM)
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.
Terms: Spr
| Units: 5
Instructors:
Wetzstein, G. (PI)
EE 268: The Engineering Economics of Electricity Markets (ECON 261)
This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performanc
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This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performance of electricity supply industries will be studied. A detailed treatment of electricity retailing will focus on the importance of active demand-side participation in a low carbon energy sector. This course uses knowledge of probability at the level of
Stats 116, optimization at the level of MS&E 111, statistical analysis at the level of Economics 102B, microeconomics at the level of Economics 51 and computer programming in R.
Terms: Aut
| Units: 3
Instructors:
Wolak, F. (PI)
;
Naumann, R. (TA)
EE 269: Signal Processing for Machine Learning
This course will introduce you to fundamental signal processing concepts and tools needed to apply machine learning to discrete signals. You will learn about commonly used techniques for capturing, processing, manipulating, learning and classifying signals. 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. This class will culminate in a final project. Prerequisites:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab).
ENGR 108 and
EE 178 are recommended.
Terms: Aut
| Units: 3
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 and 108; familiarity with transistors, logic design, Verilog and digital system organization
Terms: Aut
| Units: 3
EE 272: Design Projects in VLSI Systems I
This course will introduce you to mixed signal design and the electronic design automation (EDA) tools used for it. Working in teams, you will create a chip with a digital deep neural network (DNN) accelerator and a small analog block using a modern design flow and EDA tools. The project involves writing a synthesizable C++ and a Verilog model of your chip, creating a testing/debug strategy for your chip, wrapping custom layout to fit into a standard 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-requisites: EE271 and experience in digital/analog circuit design.
Terms: Win
| Units: 3-4
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