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ECE Course Announcements

Winter 2018: Organic Electronic Devices and Applications

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Stephen Forrest
Prerequisites: Senior level quantum mechanics, junior level electronic devices

Course Description:
Today, there is a revolution in optoelectronics: OLED displays are used in billions of smart phones, televisions, tablets and smart watches worldwide. They are now coming into use in lighting for both residential and automotive applications. Organic solar cells are achieving 15% efficiencies, bringing them to the cusp of generating a new, ultralow cost renewable energy source. Contemporaneously, the fundamental understanding of organic semiconductors used in these emerging applications has been a subject of intense study for over 70 years, and in many cases is still not fully understood. In this course, we will trace the history, science and modern applications of organic electronic technology. Since some students have taken the first course on this topic in W17, only the first few weeks of the course will provide the fundamental physics of organics primarily as a review. This will include the basics of the optical and electrical properties of organic semiconductors. Next, we will discuss how organics are deposited and patterned to achieve thin film device structures. The bulk of the class material is concerned with device physics, engineering and applications. In particular light emission from OLEDs, their various structures and adaptations for high efficiency displays and lighting will be discussed. This is followed by a treatment of organic thin film transistor physics and applications for sensing, medical applications etc. The course is concluded by a comprehensive treatment of organic solar cells: their status, efficiency limits, reliability, as an energy harvesting technology will be described.
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Winter 2018: Control and Modeling of Power Electronics

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Al Avestruz
Prerequisites: Familiarity with classical control concepts

Course Description:
Transformative technologies in energy conversion will be smarter, faster, and more reliable. This class will address the control and modeling of acdc, dcac, and dcdc power electronic systems. Topics include smallsignal models; digital and analog control; switched, sampleddata, and averaged models; large signal considerations; distributed power conversion; computer modeling in PLECS, MATLAB/Simulink, and LTSpice; and other advanced topics. Design cases may include audio switching power amplifiers, peak power point tracking for renewables and energy scavenging, resonant converters for wireless power transfer, power factor correction, and grid connected converters among others.
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Winter 2018: Multidisciplinary Capstone Design Project - Supplemental Information

Course No.: EECS 498-006 and EECS 498-007
Credit Hours: 3 or 4 credits
Instructor: Jay Guo and Hun Seok Kim
Prerequisites:

Course Description:
See attached PDF
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Winter 2018: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-005
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites:

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects. This pilot course is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for ECE masters students interested in meaningful project experiences.

For WN18, we expect to have several projects with application focus in biomedical, energy, spaceflight, and other areas needing EECS students (e.g. sensor/electronics, embedded systems, controls, and wireless). Please contact Prof. Gilchrist with questions.
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Winter 2018: Mining Large-scale Graph Data

Course No.: EECS 598-008
Credit Hours: 4 credits
Instructor: Danai Koutra
Prerequisites: Basic knowledge of linear algebra, programming, and machine learning

Course Description:
Graphs naturally represent information ranging from linksbetween webpages to friendships in social networks, tocollaborations between coauthors and connections betweenneurons in our brains. These graphs often span billions of nodesand interactions between them. Within this deluge of interconnected data, how can we extract useful knowledge,understand the underlying processes, make interesting discoveries, and contribute to decision-making?

This course will cover recent methods and algorithms foranalyzing large-scale graphs, as well as applications in variousdomains (e.g., neuroscience, web science, social science,computer networks). The focus will be on scalable and practicalmethods, and students will have the chance to analyzelarge-scale datasets. The topics that we will cover includeclustering and community detection, recommendation systems,similarity analysis, deep learning, summarization, and anomalydetection in the graph setting.
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Winter 2018: Social Computing Systems

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: EECS 485 or EECS 493 or permission of instructor

Course Description:
Computation rarely exists in isolation. From social media, to collaboration and coordination tools, to crowdsourcing and collective intelligence, technology has risen from use as an individual tool for focused domains to play a role in or even mediate a majority of social interactions today. Social Computing is the study of this interplay between social processes and the computation that supports and augments them. This course will cover topics including collaborative systems, social media, systems for supporting collective action, data mining and analysis, crowdsourcing, human computation, and peer production.
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Winter 2018: Randomness in Computation

Course No.: EECS 598-010
Credit Hours: 3 credits
Instructor: Christopher Peikert
Prerequisites: EECS 376 or EECS 477

Course Description:
Randomness and the tools or probability theory have proven central in many areas of modern science, and especially in computing and the design and analysis of algorithms. This course will expose students to a wide variety of randomized algorithms and the main techniques (linearity of expectation, the second moment method, Chernoff bounds, martingales, and the probabilistic method) used to analyze them. The course also will explore applications of these tools to analyze random combinatorial objects and deterministic algorithms applied to random inputs drawn from some distribution.

Advanced topics may include: the Lovasz Local Lemma, Talagrands inequality, streaming algorithms, quantum computation, approximation algorithms, semidefinite programs, probabilistic proof systems, cryptographic protocols, and others. (The choice of advanced topics will depend on the interests of the students and instructor.)
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Winter 2018: Optics and Quantum Spectroscopy of Semiconductors

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Mack Kira
Prerequisites: PHYSICS 240 and (EECS 320 or 334 or 434 or 520 or 540)

Course Description:
Rough Syllabus: This lecture will provide a pragmatic and brief introduction to solid-state theory, many-body formalism, and semiconductor quantum optics to explore pragmatic possibilities for nanotechology. As a central theme, the coupling of the quantized light field to electrons is investigated in detail, while the many-body Coulomb interaction of charge carriers is fully included. In this context, we will analyze which quantum effects and quasiparticles optical experiments can detect and control in terms of excitonic effects, plasmonics, quasiparticle accelerators, and ultrafast spectroscopy. To extend the quantum ideas further, we will follow how including quantum fluctuations of light to laser spectroscopy will transform it to quantum spectroscopy, a new realm where dropleton, entanglement, quantum memory etc. effects can be explored.
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Winter 2018: Motion Planning

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: Linear algebra (e.g. MATH 214) and significant programming experience (e.g. EECS 281)

Course Description:
Motion planning is the study of algorithms that reason about the movement of physical or virtual entities. These algorithms can be used to generate sequences of motions for many kinds of robots, robot teams, animated characters, and even molecules. This course will cover the major topics of motion planning including (but not limited to) planning for manipulation with robot arms and hands, mobile robot path planning for non-holonomic constraints, multi-robot path planning, high-dimensional sampling-based planning, and planning on constraint manifolds. Students will implement motion planning algorithms in open-source frameworks, read recent literature in the field, and complete a project that draws on the course material.
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Winter 2018: Patent Fundamentals

Course No.: EECS/ENGR 410
Credit Hours: 4 credits
Instructor: Mohammed Islam
Prerequisites: Open to all students

Course Description:
Have you ever had a great idea, then discovered that someone else was using it? Do you wish you could protect your inventions? Learn how to get a patent and protect your rights. In this course, you will write your own patent application and learn how to shepherd it through the Patent Office. The basics of Patent Law are covered, including patentable subject matter, novelty, obviousness, specification and claims of a patent, and claim drafting. Both patent prosecution and litigation topics are covered. This course is open to all undergrad and grad students -- technical background not required.
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Winter 2018: Internet Foundations

Course No.: EECS 498-002
Credit Hours: 2 credits
Instructor: Mohammed Islam
Prerequisites: MUST BE TAKEN PASS/FAIL

Course Description:
This course introduces students to the fundamentals of the internet. You use the internet every day, and in this course we permit you to look under the hood to see the basics of how the internet works. The course is specifically intended for students who do not specialize in computers or computer science. We start by reviewing the differences between various applications, such as world wide web, skype, and Bit-Torrent. The 4-layer internet model will be explained, which includes the application, transport, network and link layers. Application layer examples include WWW, HTTP, email, DNS and P2P Applications. The two most commonly used Transport Layer protocols are TCP and UDP. The Internet uses IP as the Network Layer, and routers perform the IP layer functions. The Link Layers used most commonly include Ethernet (wired) and IEEE 802.11 or WiFi (wireless). Other topics covered briefly include Wireless and Mobile Networks, Software Defined Networks, Data Center Networks and Network Security. By taking this course you will have a better appreciation of how computer networks work and how your computer communicates over the internet.
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Winter 2018: Power System Markets and Optimization

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course covers the fundamentals of electric power system markets and the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. The course will highlight recent advances including convex relaxations of the optimal power flow problem, and formulations/solutions to stochastic dispatch problems. Problems will be placed in the context of actual electricity markets, and new issues, such as incorporation of renewable resources and demand response into markets, will be covered. All students will conduct an individual research project.
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Winter 2018: Network Information Theory

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: EECS 501 or equivalent

Course Description:
With the emergence of numerous applications, such as 5G and IoT, involving different types of communication networks, such as packet-switched networks, wireless sensor networks and mobile cellular wireless networks, there has been a significant interest in obtaining a deeper understanding of transmission, storage and processing of information in these networks.

Network information theory deals with information in communication networks, i.e., obtaining optimal performance limits as well as ecient information processing strategies to achieve these limits in such networks. A communication network is modeled as a system involving many transmitters and receivers working with many information sources and channels. There have been several exciting new developments in the recent past in this area.
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Fall 2017: Self-Driving Cars: Perception & Control

Course No.: EECS 498-009
Credit Hours: 4 credits
Instructor: Matthew Johnson-Roberson
Prerequisites: Programming skills in Python & MATLAB, Some C++

Course Description:
This course will teach the theoretical underpinnings of self-driving car algorithms and the practical application of the material in hands-on labs. Highlights will include field trips to M-City, a 32-acre autonomous vehicle site on the U's North Campus, demos and rides in full size autonomous vehicles, and small group work with a competition where students test their own self-driving car algorithms.
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Fall 2017: EECS 598-004 Laser Plasma Diagnostics

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Louise Willingale
Prerequisites: EECS 537 or permission of instructor

Course Description:
High power laser pulses are used to both create and diagnose high-energy density systems. In this course, we will discuss the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses. We will explore the diagnostics used to characterize high-energy density plasmas through optical and other radiation measurements as well as backlighting techniques. Other important aspects of performing experiments, such as target positioning techniques, will be touched on. In addition to the material discussed in lectures, students will consider real experimental data and recent research publications to learn analysis techniques, gain appreciation for physical limitations (such as instrument resolution and background signals), and comparison with theoretical models. This course is suitable for graduate students studying plasma physics, optics and laser science and other related areas.
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Fall 2017: VLSI for Signal Processing and Communication Systems

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Hun-Seok Kim
Prerequisites: See instructor

Course Description:
This course will survey methodologies to design energy efficient and/or high-performance VLSI systems for the state-of-the-art image/audio processing, machine learning, and wireless communication systems. The primary focus of the course is on designing hardware efficient algorithms and energy-aware VLSI IC architectures to deliver the performance and efficiency requiredby various signal processing applications. The course will be a mix of lectures and student-led presentations/projects. The content will be suitable for senior undergraduates or graduate students interested in hardware-efficient signal processing algorithms andtheir VLSI implementations.
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Fall 2017: Quantum Nanotechnology

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Duncan Steele
Prerequisites: MATH 215/216, PHYSICS 240, co-req of EECS 230

Course Description:
This course aims to introduce students to basic concepts in quantum physics that are relevant to novel device concepts.
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Fall 2017: Power System Dynamics and Control

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course will introduce angle and voltage stability concepts and consider control strategies for improving dynamic performance. It will provide an overview of nonlinear dynamical systems, including geometrical properties of solutions, Lyapunov methods for approximating the region of attraction, and bifurcation analysis.
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Fall 2017: Beyond CMOS: Emerging Nanotechnologies

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or graduate standing

Course Description:
This course will survey the devices, circuit architectures, and integration challenges facing the semiconductor industry in the "More than Moore" era, using a mix of lectures, discussions, and student-led projects. The content will be suitable for junior/senior undergraduates or graduate students interested in IC design/VLSI or solid state materials and device/nanotechnology.
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Fall 2017: Introduction to Algorithmic Robotics

Course No.: EECS 498-006
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: EECS 280 (EECS 281 and MATH 214 are recommended)

Course Description:
An introduction to the algorithms that form the foundation of robot planning, state estimation, and control. Topics include optimization, motion planning, forward and inverse kinematics, position control, representations of uncertainty, Kalman filters, particle filters, and principle component analysis. Assignments focus on programming a robot to perform tasks in simulation.
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Fall 2017: Introduction to Distributed Systems

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Harsha Madhlastha
Prerequisites: EECS 482

Course Description:
In this class, you will learn the core principles and techniques that apply to enable low latency and high throughput, maximize reliability, and preserve consistency semantics.
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Winter 2017: Theory and Practice of Data Compression

Course No.: EECS 553
Credit Hours: 3 credits
Instructor: David Neuhoff
Prerequisites: EECS 501, Probability and Random Processes

Course Description:
Data compression (also called source coding) is the process of creating binary representations of data from sources such as speech, images, audio, video or text. This course gives a broad introduction to the theory and practice of lossy compression, where perfect reproductions are not possible or require too many bits (for example for speech, images, audio, video), and some introduction to lossless compression, where perfect reproductions are required (for example for text or other discrete data). Particular attention is paid to compressing images, speech and video.

The lossy compression methods include a number of quantization techniques: scalar, vector, predictive (e.g. DPCM), transform (e.g., JPEG, MPEG, H.26X), subband (e.g., MP3, wavelet, JPEG2000), predictive and adaptive quantizers (e.g., CELP as used in cell phones to compress speech). The theory is mainly high-resolution quantization theory.

The lossless compression methods include Huffman, conditional, run-length, Lempel-Ziv, and arithmetic codes. The theory is entropy theory.

Students gain experience in data compression via a term project.

The course is oriented toward first and second year graduate students. No previous introduction to data compression is presumed.
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Winter 2017: Channel Coding Theory and Applications

Course No.: EECS 650
Credit Hours: 3 credits
Instructor: Hessam Mahdavifar
Prerequisites: (Advisory) EECS 501 and MATH 419

Course Description:
Coding theory is the science of the systematic study of structured sets called codes, providing reliable communications and data storage in noisy environments. Today, error-correcting codes are among the fundamental parts of any communication system and data storage system.

The classical approach to construct such structured sets has been to consider certain algebraic objects such as vector spaces and finite fields. In the first part of this course, we cover some required background to study linear binary block codes and algebraic codes over finite fields. In particular, some of the well-known classical codes such as Reed-Solomon codes and BCH codes are studied.

Another approach to construct structured sets or codes has been to exploit properties of certain graphical models and trellises. This approach was essentially born by the invention of convolutional codes in 50s but was mostly discarded till 90s due to the lack of computational power. The invention of turbo codes and the re-discovery of low-density parity-check codes in 90s led to the birth of a new subfield of coding theory called modern coding theory. In the second part of the course, we study some essential aspects of modern coding theory.

A classical goal of information theory set by Shannon 70 years ago has been to construct explicit codes with practical encoder and decoder that achieve the fundamental limit of channel capacity. This goal was finally accomplished by the invention of polar codes in 2009. This has revolutionized the field of coding and information theory as many open problems have been solved using polar codes and the new notion of channel polarization. Besides being asymptotically optimal, polar codes have also been shown to perform very well at short block length which has led to their adoption in 5G wireless communication systems. In the third part of the course, we study polar codes and channel polarization together with practical aspects of their implementation.
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Winter 2017: Advanced Topics and the Design of Power Electronics

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Al Avestruz
Prerequisites: EECS 418 and EECS 460 or equivalents

Course Description:
This class will address some advanced topics and techniques in power electronics and the craft of design through case studies.
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Winter 2017: EECS 598 - Optics and Quantum Spectroscopy of Semiconductors

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Mack Kira
Prerequisites: PHYSICS 240 and (EECS 334 or 434 or 320 or 540)

Course Description:
This lecture will provide a pragmatic and brief introduction to solidstate theory, manybody formalism, and semiconductor quantum optics to explore pragmatic possibilities for nanotechology.
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Related Topics:  Course Announcements  

Winter 2017: Waves & Imaging in Random Media

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: John Schotland
Prerequisites: Basic partial differential equations; some knowledge of probability theory

Course Description:
This is a special topics course. The focus is on the theory of wave propagation in inhomogeneous media in various asymptotic regimes including: (i) geometrical optics of high-frequency waves (ii) homogenization of low-frequency waves in periodic and random media (iii) radiative transport and diusion theory for high-frequency waves in random media. Applications to inverse problems in imaging will be considered. The necessary tools from asymptotic analysis, scattering theory and probability will be developed as needed. The course is meant to be accessible to graduate students in mathematics, physics and engineering.
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Related Topics:  Course Announcements  

Winter 2017: Organic Electronics: Fundamentals

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Steve Forrest
Prerequisites: Senior level quantum physics, electricity and magnetism

Course Description:
In this course, we will trace the history, science and modern applications of organic electronic technology.
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Related Topics:  Course Announcements  

Winter 2017: Formal Verification of Hardware and Software Systems

Course No.: EECS 598
Credit Hours:
Instructor: Karem Sakallah
Prerequisites:

Course Description:
This course explores the latest advances in automated proof methods for checking whether or not certain properties hold under all possible executions of a complex hardware or software system.
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Winter 2017: Multidisciplinary Capstone Design Project - Supplemental Information

Course No.: EECS498-006 and EECS 498-007
Credit Hours: 3 credits
Instructor: Anthony Grbic or Greg Wakefield
Prerequisites: See PDF

Course Description:
See attached PDF
[More Info]

Winter 2017: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites:

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects. This pilot douse is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for ECE masters students interested in meaningful project experiences.

For WN17, we will have several projects with a biomedical focus as well as energy, sports, spaceflight, and other areas needing EECS students (e.g. sensor/electronics, embedded systems, controls, and wireless). Please contact Prof. Gilchrist with questions.
[More Info]

Winter 2017: Internet Foundations

Course No.: EECS 398
Credit Hours: 1 credit
Instructor: Mohammed Islam
Prerequisites: ENGR 101 or EECS 183

Course Description:
This course introduces students to the fundamentals of the internet. You use the internet every day, and in this course we permit you to look under the hood of the internet. We start by reviewing the differences between various applications, such as world wide web, skype, and Bit-Torrent. The 4-layer internet model will be explained, which includes the application, transport, network and link layers. Internet protocol and TCP/IP communication will be reviewed, along with a detailed discussion of how packet switching and routers work. The link and physical layer description will include explanations of how WiFi and Ethernet networks work
[More Info]
Related Topics:  Course Announcements  

Winter 2017: Motion Planning

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: A linear algebra class and significant programming experience

Course Description:
This course will cover the major topics of motion planning including (but not limited to) planning for manipulation with robot arms and hands, mobile robot path planning with non-holonomic constraints, multi-robot path planning, high-dimensional sampling-based planning, and planning on constraint manifolds. Students will implement motion planning algorithms in open-source frameworks, read recent literature in the field, and complete a project that draws on the course material.
[More Info]
Related Topics:  Course Announcements  

Winter 2017: Social Computing Systems

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: EECS 493 or permission of instructor

Course Description:
This course will be based on reading from the social computing research literature. Practical projects will give students experience using and creating online social computing platforms. A significant team-based final project component will let students gain experience designing and building the types of systems we will study. Students will select a topic, and then propose, design, and build a real system.
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Winter 2017: Hands On Robotics

Course No.: EECS 498
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: MATH 216 or permission of instructor

Course Description:
Take Hands On Robitics, a design course where you learn robotics by building robots using the CKBot modular robot system! Covering concepts in robotics from kinematics, control, to programming.
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Related Topics:  Course Announcements  

Winter 2017: Quantum Information, Probability and Computing

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: Permission of instructor

Course Description:
Extended introduction and overview of the field of quantum information, quantum probability and quantum computing
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Related Topics:  Course Announcements  

Winter 2017: Grid Integration of Renewable Energy Sources

Course No.: EECS 498/598
Credit Hours: 4 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314

Course Description:
This course will consider large-scale integration of renewable generation in electricity grids.
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Related Topics:  Course Announcements  

Winter 2017: Intro Distributed Systems

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Harsha Madhyastha
Prerequisites: EECS 482

Course Description:
In this class, you will learn the core principles and techniques that apply across various scenarios to maximize performance, reliability, efficiency, etc.
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Winter 2017: Information Science

Course No.: EECS 398-001
Credit Hours: 4 credits
Instructor: Clayton Scott
Prerequisites: MATH 116 and ENGR 101 or equivalent

Course Description:
This course develops the theory of information, and applies that theory to understand several modern technologies for information processing and analysis.
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Fall 2016: Introduction to Autonomous Robotics

Course No.: EECS 398-004
Credit Hours: 3 credits
Instructor: Chad Jenkins
Prerequisites: Permission of instructor

Course Description:
This course covers the essentials of robot modeling and autonomy.
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Fall 2016: Fundamentals of the Internet

Course No.: EECS 398-001
Credit Hours: 2 credits
Instructor: Mohammed Islam
Prerequisites: None

Course Description:
This course introduces students to the fundamentals of the internet. You use the internet every day, and in this house we permit you to "look under the hood" of the internet. By taking this course you will have a better appreciation of how computer networks work and how your computer communicates over the internet.
[More Info]

Fall 2016: Electric Distribution Systems

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463

Course Description:
This course covers the fundamentals of electric power distribution systems and electric loads. Topics to be covered include introduction to distribution grids, power flow in distribution grids, distribution transformers, fundamentals of electric loads, and electric load modeling.
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Fall 2016: Hybrid Systems: Specification, Verification and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or (EECS 560 + permission of instructor)

Course Description:
This course will provide a working knowledge of several analysis and design techniques to guarantee safety, reliability and performance of hybrid systems.
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Fall 2016: Mining Large-Scale Graph Data

Course No.: EECS 598-004
Credit Hours: 4 credits
Instructor: Danai Koutra
Prerequisites: Basic knowledge of linear algebra, programming and machine learning

Course Description:
This course will cover recent methods and algorithms for analyzing large-scale graphs, as well as applications in various domains (e.g., neuroscience, web science, social science, computer networks). The focus will be on scalable and practical methods, and students will have the chance to analyze large-scale datasets.
[More Info]

Fall 2016: Power Semiconductor Devices

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or equivalent or graduate standing

Course Description:
Power devices are at the heart of all modern electronics, from the grid and renewable energy sources to fuel-efficient vehicles and mobile devices. This course will cover semiconductor switches and rectifiers for discrete and integrated power electronics.
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Fall 2016: Advanced Topics in Electric Drives

Course No.: EECS 598-007
Credit Hours: 4 credits
Instructor: Heath Hofmann
Prerequisites: EECS 560 (co-requisite)

Course Description:
This course will cover advanced topics in electric drives, such as:

* Nonlinear modeling of electric machines, and subsequent controller design

*Discrete-time control implementations of field-oriented control techniques

*Real-time parameter estimation for online condition monitoring of electric machines

Students will gain hands-on experience with these techniques in the Power and Energy Instructional Laboratory. The course will have a final project where students will design and implement their own control algorithm.
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Fall 2016: Quantum Nanotechnology

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Duncan Steel
Prerequisites: MATH 215 and 216, PHYSICS 240, co-req EECS 230 or permission

Course Description:
The development and application of nano-technology is impacting nearly all the fields of engineering, from those who are developing it to those who use it. Future engineers working to design new devices will need a skill set that is considerably broadened to include the behavior of materials and devices when they become sufficiently small. Devices like transistors and quantum well lasers have already forced engineers to understand the impact of Fermi-Dirac statistics and energy quantization on devices. However, the emergent field of nano-technology is revealing that the concepts we have from our current scale devices is no longer adequate to predict correct device experience. Moreover, in this new regime, new physical properties are emerging that may revolutionize how we think of information and its storage, transmission and processing. This course aims to introduce students to basic concepts in quantum physics that our relevant to novel device concepts. The course will explore the new properties of nano-vibrators, quantum LC circuits, the role of loss, the impact of the quantum vacuum on nano-switches, coherent superposition, quantum entanglement and light, one photon at a time.
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Fall 2016: An Introduction to Networks

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 203 and EECS 301 (or equivalent) recommended

Course Description:
This course serves as an introduction to the broad class of networks: how these networks are connected, how they form, how processes and transactions take place on them, and how they are being transformed and interconnected in the modern world.
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Fall 2016: Learn to be a Software Consultant

Course No.: EECS 498-005
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
As part of UMichs Innovate Blue, the School of Information offers a Design Clinic (see description below) where budding entrepreneurs come with software projects and receive UI/UX consulting from SI students. However, the entrepreneurs oftentimes have questions about software design and development (questions about prototyping tools, underlying architecture, etc.).

In this 498, then, CSE students would serve two 2.5 hour/week shifts in the Design Clinic providing software design/development consulting to entrepreneurs and they would create template apps as demos; in addition, CSE students will participate in learning sessions with the other consultants. CSE students will develop consulting skills as they provide real consulting to users of the Design Clinic.

Requirements: Senior Status in the CSE major. For permission to register, please contact Elliot Soloway: soloway@umich.edu
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Winter 2016: Multidisciplinary Capstone Design Project - Supplemental Information

Course No.: EECS 498-006 and EECS 498-007
Credit Hours: 3 credits
Instructor: Tony Grbic or Greg Wakefield
Prerequisites: See PDF

Course Description:
See attached PDF
[More Info]

Winter 2016: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites: EECS student

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects. This pilot douse is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for ECE masters students interested in meaningful project experiences.

For WN16, we will have several projects with a biomedical focus as well as energy, sports, spaceflight, and other areas needing EECS students (e.g. sensor/electronics, embedded systems, controls, and wireless). Please contact Prof. Gilchrist with questions.
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Winter 2016: Information Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 281

Course Description:
This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, querying, and classifying data. Basic retrieval models, algorithms, and IR system implementations will be covered.
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Winter 2016: Introduction to Autonomous Robotics

Course No.: EECS 398-002
Credit Hours: 3 credits
Instructor: Chad Jenkins
Prerequisites: Linear algebra (MATH 214, 217, 417, 419) and data structures (EECS 281 or equivalent)

Course Description:
This course will cover the essentials of robot modeling and autonomy. See flyer website for more details.
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Winter 2016: Social Computing Systems

Course No.: EECS 498-008
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: EECS 493 or permission of instructor

Course Description:
Computation rarely exists in isolation. From social media, to collaboration and coordination tools, to crowdsourcing and collective intelligence, technology has risen from use as an individual tool for focused domains to play a role in or even mediate a majority of social interactions today. Social Computing is the study of this interplay between social processes and the computation that supports and augments them. This course will cover topics including social media, systems for supporting collective action, data mining and analysis, crowdsourcing, human computation, and peer production.
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Winter 2016: Computing for Computer Scientists

Course No.: EECS 398-003
Credit Hours: 1 credit
Instructor: Pat Pannuto
Prerequisites: None

Course Description:
Learn the tools that every computer scientist should know: Shells, Scripting, Makefiles, Version Control, Compilers, Text Editors, Debugging. This class is a 1 credit seminar meeting weekly on Fridays from 1:30-2:30, designed for early-career EECS students.
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Winter 2016: Power System Markets & Optimization

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463

Course Description:
This course covers the fundamentals of electric power system markets, and the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. The course will highlight recent advances including convex relaxations of the optimal power flow problem, and formulations/solutions to stochastic dispatch problems. Problems will be placed in the context of actual electricity markets, and new issues, such as incorporation of renewable resources and demand response into markets, will be covered. All students will conduct an individual research project.
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Winter 2016: Intelligent Interactive Systems (IIS)

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or permission of instructor

Course Description:
Today's world is becoming increasingly automated. This includes not only explicit interactions with automated systems, but also implicit sensing that accompanies many popular technologies. Explicit interactions include speech-based question answering with Siri and Google Voice. But what can we learn implicitly? How can we take advantage of the wealth of pervasive and ubiquitous computing platforms? How can we leverage distributed sensor environments? These are the questions that increasingly underlie Intelligent Interactive Systems (IIS). The focus of this class will be on providing methods that can be used to answer these questions and a semester-long project that ties these questions together through a new interactive technology.
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Winter 2016: Learn To Be A Software Consultant By Doing Consulting!

Course No.: EECS 498-009
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
As part of UMichs Innovate Blue, the School of Information offers a Design Clinic (see description below) where budding entrepreneurs come with software projects and receive UI/UX consulting from SI students. However, the entrepreneurs oftentimes have questions about software design and development (questions about prototyping tools, underlying architecture, etc.).

In this 498, then, CSE students would serve two 2.5 hour/week shifts in the Design Clinic providing software design/development consulting to entrepreneurs and they would create template apps as demos; in addition, CSE students will participate in learning sessions with the other consultants. CSE students will develop consulting skills as they provide real consulting to users of the Design Clinic.
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Winter 2016: Advanced Topics and Design of Power Electronics

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Al-Thaddeus Avestruz
Prerequisites: EECS 418 and EECS 460 or equivalents

Course Description:
This class will address some advanced topics and techniques in power electronics and the craft of design through case studies. Topics may include switched capacitor circuits, resonant power conversion, magnetics, wireless power transfer, and instrumentation, among other. Advanced methods in the analysis, manufacturing, and control of power electronics will also be discussed.
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Winter 2016: Topics in Optoexcitonic Engineering

Course No.: EECS 598-009
Credit Hours: 3 credits
Instructor: Parag Deotare
Prerequisites: Introductory electromagnetics and solid state physics or permission of instructor

Course Description:
This seminar course will review recent research and developments on topics in Nanophotonics and Excitonic Engineering with potential applications in data communication and processing. Topics covered will be related to engineering interaction of light with nanoscale systems, optical interactions between nanosystems and resonance phenomenon. Students read research papers followed by a brief lecture introducing the important related concepts before the papers are open for discussion in the class. Students/teams will also spend last 4 weeks investigating a current research problem posed by a faculty member. This will entail reading and spending time in the faculty lab and will be followed by a presentation to the full class.
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Winter 2016: Hands On Robotics

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: Engineering and science seniors and grad students

Course Description:
Hands On Robotics is a robotics course based on building robots using the CKBot modular robot system. The course will cover basic concepts in robotics: kinematics, control, programming and design.

Open to EECS seniors and up; all other engineering and science seniors and graduate students with permission of instructor.
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Winter 2016: Carbon Nanoelectronics and Nanophotonics

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Zhaohui Zhong
Prerequisites: EECS 520 or permission of instructor

Course Description:
Carbon based nanomaterials, in particular carbon nanotube and graphene, have generated great excitements over the past decade due to their unique electrical, optical and mechanical properties. This special topic course introduces theories and experimental works on carbon nanotube and graphene based electronic and photonics devices. The course will also have two student labs of testing graphene and nano electronics.
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Winter 2016: Organic Electronics: From Fundamentals to Applications

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Steve Forrest
Prerequisites: Senior level quantum physics, solid state physics, electricity and magnetism

Course Description:
In this course, we will trace the history, science and modern applications of organic electronic technology. The first half of the course is dedicated to understanding the fundamentals of organic semiconductor materials. This includes consideration of crystal structure, bonding forces, and structure-property relationships of both small molecule and polymer semiconductors. We then provide a comprehensive description of the physics leading to their unique optical and electrical properties. What are the characteristics that make organic semiconductors (sometimes known as excitonic materials) different from conventional semiconductors such as Si and GaAs? The second half of the course concentrates on applications that exploit the unique characteristics of organics. We focus particularly on light emission in OLEDs, and how electron spin plays a significant role in organics, particularly in contrast to inorganic semiconductors. Then we address light detection in photodetectors and solar cells. Will the potentially low cost of these devices ultimately lead to their widespread use? Finally, we will examine advances in thin film transistors, lasers, and even molecular electronic devices, and their prospect for use in new, and even traditional optoelectronic applications.
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Winter 2016: Random Matrix Theory

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 551 or linear algebra, basic probability

Course Description:
This course covers the theory and algorithms emerging from the study of random matrices as it is currently applied in signal processing, machine learning, statistics and science. Topics include random sample covariance matrices, random graphs, spectral limit theorems such as Wigner's semi-circle and Marcenko-Pastur laws, free probability, randomized numerical linear algebra, matrix statistics, passage to the continuum limit, moment methods, matrix completion and compressed sensing.
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Winter 2016: Information Science

Course No.: EECS 398-001
Credit Hours: 4 credits
Instructor: Clayton Scott
Prerequisites: MATH 116 and (ENGR 101 or equivalent)

Course Description:
This course will examine the basic mathematical theory of information, and apply that theory to understand several modern technologies for information processing and analysis.

Projected Syllabus: 4-5 weeks: Essentials of Shannons information theory, including entropy, data compression, transmission over noisy channels, and error correcting codes 2-3 weeks: Encryption, from historical ciphers to modern crypto systems 3-4 weeks: Extracting information from data: information retrieval and machine learning 3-4 weeks: Frequency concepts: Fourier analysis, AM and FM radio, sampling and reconstruction, spectrum spreading, and digital signal processing
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Winter 2016: Plasma Chemistry

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Mark Kushner
Prerequisites: Familiarity with fundamentals of plasmas and electron collisions in partially ionized gases

Course Description:
Low temperature plasmas are used for materials and microelectronics proc-essing, plasma aided combustion, lighting, lasers and medicine. This course will address the plasma initiated chemistry and plasma surface interactions of these systems. Electron impact, ion-molecule and excited state reactions, radiation transport; and the reaction of these species with inorganic, organic and liquid surfaces will be discussed.
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Fall 2015: Randomness and Computation

Course No.: EECS 598-04
Credit Hours: 4
Instructor: Grant Schoenebeck
Prerequisites: EECS 376

Course Description:
Randomness and the tools of probability theory have proven central in many areas of modern science, including, perhaps surprisingly, the design and analysis of algorithms. This course will be organized around the main tools and techniques (linearity of expectation, the second moment method, Chernoff bounds, martingales, Lovasz-Local Lemma, Monte Carlo Markov Chain, etc) used in probabilistic analysis of algorithms. Along the way, students will be exposed to a large variety of classic theoretical computer science works resulting from the applications of these same tools to both randomized algorithms and the analysis of random combinatorial objects (e.g. graphs, Boolean formulae) and deterministic algorithms applied to random inputs drawn from some distribution.

Advanced applications covered may include the Talagrands inequality; social networks; streaming algorithms; distributed algorithms; quantum computation; approximation algorithms; semidefinite programs; cryptographic protocols, and more. Specific advanced topics included will depend on the interests of the students.

If you are interested in theoretical computer science (TCS) or tools of probabilistic analysis, it should be a fun course. It will assume basic theory understanding (at the level of 376) and basic probability theory, and the methodology will be that of formal mathematical proofs. The course will be targeted as an introductory course for CSE graduate students studying theory (very broadly speaking)though others should benefit as well, including advanced undergraduates and graduate students from other areas. This course will count for a theory breadth requirement CSE masters and PhD students and for a depth requirement for PhD students. See course website for more information.
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Fall 2015: Graph Mining and Exploration at Scale: Methods and Applications

Course No.: EECS 598-012
Credit Hours: 4 Credits
Instructor: Danai Koutra
Prerequisites: Prerequisites for Lec 012: Basic knowledge of Linear Algebra, Probability Theory/Statistics, and Programming (e.g., Python, JAVA, Matlab, R) or Permission of Instructor

Course Description:
Graphs naturally represent information ranging from links between webpages to friendships in social networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we extract useful knowledge, understand the underlying processes, and make interesting discoveries?

This course will cover recent models and algorithms for exploring and making sense of large graphs, as well as applications in various domains (e.g., web, social science, computer networks, neuroscience). The focus will be on scalable, practical, but also principled methods, and students will have the chance to analyze large-scale datasets. The topics that we will cover include ranking, label propagation, clustering and community detection, summarization, similarity, and anomaly detection.

Fall 2015: Human Computation and Crowdsourcing Systems

Course No.: 598-011
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: Programming fluency; senior undergraduate or graduate standing in either EECS, or Permission of Instructor

Course Description:
Using human intelligence to solve computational tasks -- also called human computation -- has enabled the creation of software systems that go well beyond the current boundaries of artificial intelligence (AI). Making open recruitment calls to large, often heterogeneous, groups of people (crowdsourcing) has allowed human computation to be scaled to provide on-demand services and even real-time responses. This course will cover the core work in human computation and crowdsourcing, with a focus on techniques for creating interactive intelligent systems that are powered by a combination of human and machine intelligence. We will also touch on the theory underlying many of the current approaches (e.g., game theory, voting theory, and machine learning), and potential ethical concerns raised by these systems (e.g., ensuring fair wages, and end-user privacy)."

Fall 2015: Data Science for Medicine

Course No.: EECS 498-005
Credit Hours: 3 credits
Instructor: Zeeshan Syed
Prerequisites: EECS281 or equivalent

Course Description:
With increasing amounts of medical data becoming available there is an opportunity to significantly reduce the burden imposed by major diseases in a data-driven manner. This course provides students with a hands-on introduction to computational advances offering significant improvements in our ability to understand, diagnose, and treat major healthcare conditions. During the semester we will explore several foundational topics in data science for medicine, including data representation, data manipulation, data analysis, and data visualization with a review of organ system physiology and common medical data elements. Students will be introduced to these topics during lectures, with the class focusing on breadth instead of a focus on any single topic in depth to provide an opportunity to sample and apply data science techniques. The course also focuses on providing students with a significant opportunity to investigate the application of these ideas to real-world clinical challenges. Students will be expected to supplement theory in data science for medicine with a semester long project on actual medical data. Students will be encouraged to think creatively about traditionally hard problems and required to perform group research exposing them to designing practical data science systems for medical care. Students will also be exposed to research and potential entrepreneurship opportunities beyond the class.

Fall 2015: Plasmonics

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Somine Eunice Lee
Prerequisites: None

Course Description:
Plasmonics is the study of optical phenomena related to the electromagnetic response of conductors. The furled of plasmonics has recently been accelerated by the rapid advancements in nano fabrication. The interaction of light with nanoscale objects renders unique optical, electronic, magnetic and thermal properties useful to a wide range of areas, including electrical engineering, biomedical engineering, chemical engineering, mechanical engineering, material science, chemistry and physics.
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Fall 2015: Power System Dynamics and Control

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course will introduce angle and voltage stability concepts and consider control strategies for improving dynamic performance. It will provide and overview of nonlinear dynamical systems, including geometrical properties of solutions, Lyapunov methods for approximating the region of attraction, and bifurcation analysis.
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Fall 2015: Foundations of Computer Vision

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: Graduate standing or permission of instructor

Course Description:
Computer Vision seeks to extract useful information from images, video and other visual content. This course will introduce the breadth of modern computer vision through a few foundational problems that span various topic areas. Examples of possible foundational problems include image formation and projective geometry, robust model fitting, perceptual priors, matching and similarity, invariance, motion and multi view geometry. The foundational problems will be tied to specific applications such as feature extraction, segmentation, structure from motion, and action recognition.
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Fall 2015: Hybrid Systems: Specification, Verification and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or EECS 560 + permission of instructor

Course Description:
Hybrid systems, dynamical systems where continuous dynamics and discrete events interact, are ubiquitous and can be found in many different contexts. Examples are as diverse as manufacturing processes, biological systems, energy systems, medical devices, robotics systems, automobiles and aircrafts. Advances in computing and communications technologies have enabled engineering such systems with a high degree of complexity. Most of these systems are safety-critical, hence their correctness must be verified before they can be deployed. This course will provide a working knowledge of several analysis and design techniques to guarantee safety, reliability and performance of such systems.
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Fall 2015: An Introduction to Social, Economic and Technological Networks

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EE203 and/or EECS 301 are recommended

Course Description:
Networks are everywhere. We encounter a variety of networks of different sizes and forms on a daily basis: societal networks such as the network of retweets of a certain has tag on Twitter or the friends network on Facebook; technological networks such as the Internet with the telecommunication network of computers, the links between webpages, the groupings of users generated by recommendation systems for predictions or the network of users on BitTorrent downloading a specific file; and economic networks such as trade networks or supply-chain networks. Some of these networks emerge naturally such as many societal networks, while others are planned such as the public transportation or road network. We depend on the efficient functioning of these networks to transact many of our activities.

This course serves as an introduction to the broad class of networks described above: how these networks are connected, how they form, how processes and transactions take place on them, and how they are being transferred and interconnected in the modern world. Students will learn how to develop and apply mathematical models and tools from graph theory, linear algebra, probability and game theory in order to analyze network processes such as how opinions and fads are spread on networks, how sponsored advertisements are developed, how web content is displayed, how recommendation systems work, etc.
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Fall 2015: EECS 498: Control of Manufacturing Systems

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Semyon Meerkov
Prerequisites: Elementary probability theory

Course Description:
Manufacturing is a major source of national wealth. Losing manufacturing, a country is losing its wealth. Until recently, methods of design and control of manufacturing systems has been based on "weak" engineering - experience, common sense, and, in some cases, simulations. Efficient manufacturing requires more: rigorous analytical methods. Such methods have emerged during the last 25 years. The results obtained, with emphasis on control and management, will be discussed in the course.

The course is directed towards undergraduate students from all CoE departments interested in careers involving design/manufacturing of products, e.g. automobiles, aircraft, semiconductors, computer/communication devices, etc. The skill acquired should make the students knowledgable in various facets of manufacturing and marketable as engineering managers of manufacturing operations.
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Winter 2015: Waves & Imaging in Random Media

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: John Schotland
Prerequisites: Basic partial differential equations; some knowledge of probability theory

Course Description:
This is a special topics course. The focus is on the theory of wave propagation in in homogenous media in various asymptotic regimes including: (i) geometrical optics of high-frequency waves (ii) homogenization of low-frequency waves in periodic and random media (iii) radiative transport and diffusion theory for high-frequency waves in low-frequency random media. Applications to inverse problems in imaging will be considered.
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Winter 2015: Collabrified Mobile Apps for K-12

Course No.: EECS 498-008
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
In this course, students will create apps to support learners in K-12. The apps will employ the Collabrify SDK that students at UMich developed -- a software development kit that enables a developer to take an app that is meant as a solo-user app and turn that app into one that supports two or more simultaneous users!!
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Winter 2015: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-005
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites: Permission of instructor

Course Description:
This pilot course is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for EE masters students interested in meaningful project experiences.
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Winter 2015: Advanced Topics in Analog ICs

Course No.: EECS 598-005
Credit Hours: 4 credits
Instructor: David Wentzloff and Michael Flynn
Prerequisites: EECS 413 and co-requisite EECS 522

Course Description:
This course will cover design and analysis of advanced analog and mixed-signal integrated circuits, beyond what is covered in EECS 511 and EECS 522.
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Winter 2015: Network Information Theory

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: EECS 501 or equivalent

Course Description:
This course aims to develop a set of mathematical tools to study communication problems that arise in networks. A strong emphasis will be put on obtaining an intuitive framework to think about these problems. This course is aimed at graduate students working in the areas of electrical engineering, computer science, statistics, and mathematics.
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Winter 2015: Performance Systems: Mobile Phones as Musical Instruments

Course No.: EECS 498-003 and EECS 598-003
Credit Hours: 3 or 4 credits
Instructor: Georg Essl
Prerequisites: EECS 493 or graduate standing or permission of instructor

Course Description:
In this course, you will design your own mobile phone musical instruments, write your own pieces for this new genre, and develop mobile music performance practice in a unique blend of music performance and engineering.
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Winter 2015: Embedded Systems: An Application-Centered Approach

Course No.: EECS 598-002
Credit Hours: 4 credits
Instructor: Robert Dick
Prerequisites: EECS 311 or 312 or 373 or 482 or equivalent or permission of instructor

Course Description:
Embedded systems are computers within other devices such as wearable devices, automobiles, sensor networks, and medical devices. The focus of this course is to give students an understanding of the process of going from an idea to a product or research finding in the field of embedded systems.
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Winter 2015: Probabilistic Graphical Models for Vision and Beyond

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: EECS 501 or graduate-level proficiency with probability and statistics

Course Description:
This course will cover probabilistic graphical models in detail starting from the basics and pushing through contemporary results. There will be an emphasis on driving problem formulations from computer vision but our coverage will be broad; connections to other application areas will be discussed when plausible.
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Winter 2015: Grid Integration of Alternative Energy Sources

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
This course will present a variety of alternative energy sources, along with energy processing technologies that are required for power system connection. Topics will be covered at a level suited to establish a broad understanding of the various technologies, and of the associate system implications.
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Winter 2015: Control of Discrete Event Systems

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Stephane Lafortune
Prerequisites: EECS 566 or EECS 598-005 in Fall 2013 or permission of instructor

Course Description:
This course will cover advanced topics on control of discrete event systems, with focus on the following topics: distributed and decentralized control architectures; synthesis methodologies for controllers under safety and liveness properties; comparison of synthesis techniques for specifications described by automata and by temporal logics; joint control and diagnosis problems for fault-tolerant control; discussion of relevant case studies.
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Winter 2015: Information Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 281

Course Description:
This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, querying, and classifying data.
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Fall 2014: Foundations of Computer Vision

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: Permission of instructor

Course Description:
Computer Vision seeks to extract useful information from images. This course begins the fundamentals of image formation and then organizes the remaining material according to the class of information to be extracted. The course has been designed to present an introduction to computer vision targeted to graduate students. The course will balance theory and application both in lectures and assignments.
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Fall 2014: Practical Machine Learning

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Jenna Wiens
Prerequisites: EECS 445 or EECS 545 or permission of instructor

Course Description:
In this seminar class we will cover the basics of practical machine learning and data mining while focusing on real-world applications. We will read and critique recent applied ML work in the fields of sports analytics, data-driven medicine, finance, and personalized education. At the same time, we will review a complementary set of papers to help guide our discussion in terms of the pragmatic aspects of ML e.g., feature engineering, cross-validation, and performance measures. The overall goal of the class is for students to gain a deeper understanding of the practical challenges and pitfalls associated with applying machine learning tools and techniques in a real-world setting.

Fall 2014: Probabilistic Analysis of Large Scale Systems

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 501 or permission of instructor

Course Description:
This course will focus on emerging topics in epidemics and diffusions, queueing systems, analysis of randomized algorithms, Bayesian information cascades, network analysis and random graphs.
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Fall 2014: Laser Plasma Diagnostics

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Louise Willingale
Prerequisites: EECS 537 or permission of instructor

Course Description:
This course will cover the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses.
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Fall 2014: Hands-On Robotics

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: MATH 216 or permission of instructor

Course Description:
This course will cover basic concepts in robotics, such as kinematics, control, programming and design.
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Fall 2014: EECS 598-001 Analysis of Electric Power Distribution Systems and Loads

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463 or equivalent

Course Description:
This course covers the fundamentals of electric power distribution systems and electric loads. We will start with an introduction to distribution grids, including their components, typical topologies, and operational strategies. Other topics include power flow in distribution grids and transformers as well as electric loads, including electric load modeling, analysis, and control methodologies.
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Fall 2014: EECS 598-002 Power Semiconductor Devices

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or permission of instructor

Course Description:
In this course, you will explore semiconductor devices for both discrete and integrated power electronics. Power switches and rectifiers including the power MOSFET, IGBT, HEMT, thyristors, Schottky and pin diodes, as well as emerging power devices will be covered.
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Fall 2014: Applied matrix algorithms for signal processing, data analysis and machine learning

Course No.: 453
Credit Hours: 4
Instructor: Raj Nadakuditi
Prerequisites: EECS 301 or MATH 425 or STATS 215 or STATS 412 or STATS 426 or IOE 265 or equivalent

Course Description:
Theory and application of matrix algorithms to signal processing, data analysis and machine learning. Theoretical topics include subspaces, eigenvalue and singular value decomposition, projection theorem, constrained, regularized and unconstrained least squares techniques and iterative algorithms. Applications such as image deblurring, ranking of webpages, image segmentation and compression, social networks, circuit analysis, recommender systems and handwritten digit recognition. Greater emphasis on applications than in EECS 551.
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Winter 2014: EECS 498-003 Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-003
Credit Hours: 3-4 credits
Instructor: Brian Gilchrist
Prerequisites: Permission of instructor, senior or grad standing recommended

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects.

This pilot course is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for EE masters students interested in meaningful project experiences.

For WN14, many of the projects (though not all) will have a biomedical theme that will require EECS students especially with interests in sensors, embedded systems, and wireless.

Please contact Professor Gilchrist with any questions.
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Winter 2014: EECS 598-007 Infrastructure for Vehicle Electrification

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or 314

Course Description:
This course covers the fundamentals of the physical and cyber infrastructures that will underpin large-scale integration of plug-in electric vehicles.
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Winter 2014: EECS 498-002 Intelligent Interactive Systems

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or graduate standing

Course Description:
The focus of the course is developing effective speech-based user modeling for interactive systems. We will focus on a series of assistive domains that demonstrate the societal benefit of work in this field, including applications in: depression, autism, and aphasia. Topics will include basic speech modeling, feature handling techniques, data classification, visualization, and interactive system design.
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Winter 2014: EECS 598-006 Plasmonics

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Somin Lee
Prerequisites: Permission of instructor

Course Description:
This course will review Maxwells equations for the electric and magnetic fields in conductors at low frequencies. Students will be introduced to nanofabrication, including top-down and bottom-up fabrication techniques. Students will also be introduced to characterization techniques of nanoscale objects, including electron microscopy, atomic force microscopy and near field microscopy. Finally, optical, electronic, magnetic, thermal and biomedical applications of plasmonics will be discussed.
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Winter 2014: EECS 598-005 Statistical Learning Theory

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Clayton Scott
Prerequisites: EECS 501 or equivalent

Course Description:
In this course we will prove performance guarantees that quantify the ability of a machine learning algorithm to generalize from training data to unseen test data. Potential topics to be covered include concentration of measure, uniform deviation bounds, empirical and structural risk minimization, Rademacher complexity, Vapnik-Chervonenkis theory, consistency and rates of convergence, margin-based bounds, stability bounds, and application of these theories to learning algorithms such as decision trees, boosting, support vector machines, and kernel density estimators.
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Winter 2014: Random matrix theory, algorithms and signal processing applications

Course No.: 598
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 551 or Linear Algebra equivalent, Basic Probability

Course Description:
This course covers the theory and algorithms emerging from the study of random matrices as it is currently applied in signal processing, machine learning, statistics and science.

Topics include random sample covariance matrices, random graphs, spectral limit theorems such as Wigner's semi-circle and Marcenko-Pastur laws, free probability, randomized numerical linear algebra, matrix statistics, passage to the continuum limit, moment methods, matrix completion and compressed sensing.

There will be a special focus on presenting the theory in a manner that facilitates the development of new applications and allowing students that already have a topic in mind to apply these ideas to their topic.

Emerging applications in signal processing, network analysis, wireless communications and statistical physics will be discussed.

The course requirement will be a term project. Students will form teams of two or work individually. Each team will select a project topic, will study a set of papers related to the topic, will write a critique of the papers, and will give an oral presentation at the end of the semester.

No textbook is required for this course. Throughout the lectures papers will be distributed to the class, and references to the relevant literature will be given.

For more information about this course please contact the instructor.
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Winter 2014: EECS 498-001 Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 216, EECS 401 and EECS 451

Course Description:
This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, querying, and classifying data. Basic retrieval models, algorithms, and IR system implementations will be covered. While the course will primarily focus on IR techniques for textual data, it will also address IR for other media, including images/videos, music/audit files, and geospatial information.

The course will also address topics in Web search, including Web crawling, link analysis, search engine development, social media, and crowd sourcing. Throughout the course, there will be two or three invited lectures from people working at major companies in the field (e.g., Google, Yahoo, Microsoft, Facebook, Twitter).
[More Info]

Fall 2013: EECS 598-005 Hybrid Systems Control

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or EECS 560 and instructor permission

Course Description:
Hybrid systems, dynamical systems where continuous dynamics and discrete events interact, are ubiquitous and can be found in many different contexts. Examples are as diverse as manufacturing processes, biological systems, energy systems, medical devices, robotics systems, automobiles and aircrafts. Advances in computing and communications technologies have enabled engineering such systems with a high degree of complexity. Most of these systems are safety-critical, hence their correctness must be verified before they can be deployed. This course will provide a working knowledge of several analysis and design techniques to guarantee safety, reliability and performance of such systems.
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Fall 2013: ENGR 390/599 Imagine Innovate Act

Course No.: ENGR 390
Credit Hours: 3 credits
Instructor: Jasprit Singh
Prerequisites:

Course Description:
This course is open to students interested in technology and/or culture and/or wellness. To learn more about the course, contact Prof. Jasprit Singh (singh@umich.edu) or Dr. John Hinckley (ncko@umich.edu). Flyer attached!
[More Info]

Fall 2013: EECS 598 Ultra-Low-Power CMOS Circuit Design

Course No.: EECS 598
Credit Hours: Up to 4 credits
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or EECS 427 or permission of instructor

Course Description:
Following the trajectory of the Moores Law, the integration density of VLSI chips has grown exponentially from two thousand transistors per chip in the early Seventies (i4004) to over one billion transistors (Itanium) in 2009. During this time, CMOS VLSI design has witnessed multiple generations of evolution as the CMOS circuit design focus gradually shifted from Silicon real estate (in the late 70s) to timing closure (in the late 80s), to power aware (in the late 90s), and then to process variations(reliability) at sub-100 nm transistor dimensions. This course envisages studying energy-aware CMOS circuit design techniques that are currently being used in building low-power (at nominal supply voltage) and ultra-low-power (in subthreshold region) VLSI systems. Students interested in taking this course must have basic background in CMOS design (equivalent to EECS 312) and are expected to know circuitequations for minimization of power consumption as well as energy-delay optimization. The course will mainly focus on various aspects of sub-threshold CMOS circuit design as outlined below.
[More Info]

Fall 2013: EECS 598 VLSI Digital Signal Processing Systems

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Zhengya Zhang
Prerequisites: EECS 427 or permission of instructor

Course Description:
Digital signal processing (DSP) systems have been enabled by the advances in very-large scale-integrated (VLSI) technologies. New DSP applications constantly impose new challenges on VLSI implementations. These implementations must satisfy real-time constraints imposed by the applications and must fit increasingly stringent area and power envelope. This course will survey methodologies needed to design efficient and high-performance custom or semi-custom VLSI systems for DSP applications. The primary focus of the course is on design of architectures, algorithms, and circuits, which can be operated with small area and low power consumption to deliver a high speed and functional performance.
[More Info]

Fall 2013: EECS 598 Electricity Networks and Markets

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course covers the principles and practices that underpin reliable and economical operation of power systems. Power system networks and modeling will be discussed, and an overview of closed-loop controls and basic stability concepts will be provided. System control centres will be considered, primarily in terms of supervisory control and data acquisition (SCADA) and energy management system (EMS) requirements. Power system state estimation will be presented, along with techniques for on-line evaluation of system reliability. The course will investigate optimal generation scheduling and dispatch, including unit commitment, economic dispatch, optimal power flow, and automatic generation control (AGC). Electricity market structures and mechanisms will be presented, with consideration given to the roles of day-ahead and real-time markets, energy and capacity markets, bilateral trading, and markets for ancillary services. The issues that arise from trading over transmission networks will be considered. A comparison of various markets, including MISO, PJM, AEMO, and the failed Californian market will be undertaken. Issues arising from the variability and uncontrollability of renewable generation will be explored.
[More Info]

Winter 2013: EECS 598-007: Advanced Topics in Computer Vision

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Silvio Savarese
Prerequisites: EECS 442 or EECS 545 or equivalent

Course Description:
The course surveys recent developments in high level computer vision such as object recognition and categorization, action and event recognition, object tracking and human motion analysis, spatial and temporal reasoning for scene reconstruction and understanding, organization and indexing of visual data from large databases, mobile computer vision. The course also explores advanced classification and inference algorithms for high level visual tasks.
[More Info]

Winter 2013: EECS 598-005 Waves and Imaging in Random Media

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: John Schotland
Prerequisites: Basic partial differential equations and some knowledge of probability theory

Course Description:
(Course is cross-listed with MATH 651)

This is a special topics course. The focus is on the theory of wave propagation in inhomogeneous media in various asymptotic regimes including: (i) geometrical optics of high frequency waves (ii) homogenization of low-frequency waves in periodic and random media (iii) radiative transport and diusion theory for high-frequency waves in low-frequency random media. Applications to inverse problems in imaging will be considered. The necessary tools from asymptotic analysis, scattering theory and probability will be developed as needed. The course is meant to be accessible to graduate students in mathematics, physics and engineering.
[More Info]

Winter 2013: EECS 598-003 Carbon Nanoelelectronics and Nanophotonics

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Zhaohui Zhong
Prerequisites: EECS 420 or permission of instructor

Course Description:
Carbon based nanomaterials, in particular carbon nanotube and graphene, have generated great excitements over the past decade due to their unique electrical, optical, and mechanical properties. This special topic course introduces theories and experimental works on carbon nanotube and graphene based electronic and photonic devices. The course will also have two student labs of testing graphene nanoelectronics.
[More Info]

Winter 2013: EECS 598-002: Terahertz Technology & Applications

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Mona Jarrahi
Prerequisites: EECS 320 and (330 or 334)

Course Description:
This course will provide graduate students with an overview on the unique specifications of terahertz waves and potential applications as well as the state of the current terahertz systems and the major technological challenges in the field. The topics covered in this course are THz Detectors (single-photon detectors, microbolometers, Golay cells, Pyroelectric detectors, diode detectors, and focal-plane arrays), THz Sources (vacuum-electronics-based, semiconductor-based, photoconduction-based and nonlinearity-based), THz electronic components (waveguides, Metamaterials, filters and modulators), sensing with THz radiation (THz spectroscopy, imaging and tomography), and THz applications (biology, medicine, space sciences, pharmaceutical industry, security and communications).
[More Info]

Winter 2013: EECS 498-004 Grid Integration of Alternative Energy Sources

Course No.: EECS 498-004
Credit Hours: 4 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
The course will present a variety of alternative energy sources, along with energy processing technologies that are required for power system connection. System integration issues will be addressed, with consideration given to impacts on current power system design philosophies and operating principles. Topics will be covered at a level suited to establishing a broad understanding of the various technologies, and of the associated system implications.
[More Info]

Winter 2013: EECS 598-008 Medical Device Security

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Kevin Fu
Prerequisites: Graduate standing or permission of instructor

Course Description:
This course teaches students the engineering concepts and skills for creating more trustworthy software-based medical devices ranging from pacemakers to radiation planning software to mobile medical apps. Topics span computer engineering, human factors, and regulatory policy. Students will master technical skills in reverse engineering, static analysis, fuzz testing, hazard analysis, validation, requirements engineering, radio-frequency communication, physiological sensing, and fundamental concepts from system engineering that lead to safer and more effective medical devices that are increasingly interconnected and wirelessly controlled.

Students will apply the newly learned concepts and skills by analyzing the security of a real-world medical device in a hands-on term project. Interdisciplinary teams will consist of students from complementary backgrounds to mimic the composition of teams at medical device manufacturers and regulatory bodies. Occasional guest speakers from medical device manufacturers, hospitals, and government will complement the classroom activities with critical lessons from the front lines.
[Full Story]

Winter 2013: Advanced Signal Processing & Applications

Course No.: 498
Credit Hours: 3
Instructor: Prof. Raj Rao Nadakuditi
Prerequisites: 401, 451

Course Description:
Course No.: 498
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 451, EECS 401 or permission of instructor

Course Description:
This is an course on advanced topics in signal processing designed to follow up on principles learned in EECS 451 and EECS 401. The central theme of the course is the application of tools from linear algebra to signal processing. Theoretical topics include solving least-squares problems, eigenvalues and eigenvalues, the singular value decomposition, Markov chains, power method. Synergistic applications covered include image compression, handwriting recognition, Googles PageRank algorithm, eigen-faces, community detection in networks, and deconvolution. Students are expected to be familiar with material covered in EECS 451 and EECS 401 and should have basic MATLAB programming skills (such as writing loops, plotting functions, etc.)
[More Info]

Winter 2013: EECS 598-010 Plasma Chemistry and Plasma Surface Interactions

Course No.: EECS 598-010
Credit Hours: 3 credits
Instructor: Mark Kushner
Prerequisites:

Course Description:
Low temperature plasmas are used for materials and microelectronics processing, plasma aided combustion, lighting, lasers and medicine. This course will address the plasma initiated chemistry and plasma surface interactions of these systems. Electron impact, ion-molecule and excited state reactions, radiation transport; and the reaction of these species with inorganic, organic and liquid surfaces will be discussed.
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Winter 2013: EECS 598-009 Resonant Power Converters

Course No.: EECS 598-009
Credit Hours: 3 credits
Instructor: Juan Manuel Rivas Davila
Prerequisites: EECS 418 or power electronic design or permission of instructor

Course Description:
In this course, we will study the design of Resonant power converters converters which are capable of operating at higher frequencies than their "hard-switch" counterparts. Resonant converter are found in high performance applications where high control bandwidth and high power density are required. We will also explore practical design issues and trade o in selecting converter topologies in high performance application. We will discuss the design and modeling of high frequency magnetic elements, gate drives and resonant snubbers.
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Winter 2013: EECS 498-002 Human-Centered Computing

Course No.: EECS 498 - 002
Credit Hours: 3 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or graduate standing

Course Description:
In this course we will cover the techniques that underlie the state of-the-art systems in the human-centered computing field. Students will develop a critical understanding of HCC systems ranging from data collection to human state recognition to feedback. The course evaluation will include homework, a midterm exam, and a final project.
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Winter 2013: EECS 598-001 Solar Cell Device Physics

Course No.: EECS 589-001
Credit Hours: 3 credits
Instructor: Jamie Phillips
Prerequisites: EECS 421 or graduate standing. Previous knowledge of semiconductor physics is essential for the course.

Course Description:
This course will focus on the physical operation of diode solar cell devices, and detailed analysis of factors that determine the ultimate power conversion efficiency. Topics of study will include internal quantum efficiency of solar cell materials, diode device structures, light management, and current and future solar cell technologies.
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Winter 2013: EECS 600 Function Space Methods in System Theory

PLEASE NOTE: THIS COURSE IS NOT OFFERED EVERY SEMESTER!

Topics covered will include vector spaces, normed spaces, and Hilbert spaces; the Projection Theorem and complete orthogonal systems; linear operators, bounded operators, adjoint operators; The Hahn-Banach Theorem; the Riez Representation Theorem; Duality. Most of the class will be to learn about results on abstract and possibly infinite-dimensional spaces, but with time we will also explore finite dimensional results specifically for optimization; e.g. Farkas lemma and its role in the proof of KKT Theorem.
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Fall 2012: EECS 498 Introduction to Machine Learning

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Satinder Singh
Prerequisites: EECS 281 or (EECS 203 and substantial programming experience)

Course Description:
Making sense of data, whether it comes from social settings like Twitter or from scientific experiments in a research laboratory or from NASA observatories, is a problem of great interest to society. Machine learning approaches help us classify, cluster, display, predict, and decide how to act based on data. In this course, we will learn about and program machine learning algorithms and evaluate them on various kinds of data (from twitter feeds, flickr feeds, video, blogs, as well as scientific data).

Fall 2012: EECS 598 Power System Dynamics and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or instructor permission

Course Description:
Syllabus:

1. Overview of angle and voltage stability concepts, nonlinear dynamical systems, geometrical properties of solutions, Lyapunov methods, bifurcation analysis.

2. Modeling, differential algebraic systems, hybrid dynamical systems.

3. Small disturbance (linear) stability analysis.

4. Large disturbance (nonlinear) analysis, numerical integration, trajectory sensitivities, shooting methods, parameter estimation, grazing, limit cycles.

5. Future grid responsiveness, non-disruptive load control, model predictive control.
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Fall 2012: EECS 598 Mazumder

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or instructor permission

Course Description:
This course envisages studying energy-aware CMOScircuit design techniques that are currently being used in building low-power (at nominal supply voltage)and ultra-low-power (in subthreshold region) VLSI systems. Students interested in taking this coursemust have basic background in CMOS design (equivalent to EECS 312) and are expected to know circuitequations for minimization of power consumption as well as energy-delay optimization. The course willmainly focus on various aspects of subthreshold CMOS circuit design as outlined below.
[More Info]

Fall 2012: EECS 598 FMMs and Integral Equation Solvers

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Eric Michielssen
Prerequisites: EECS 530 or instructor permission

Course Description:
This course focuses on integral equation methods for solving the classical partial differentialequations of mathematical physics along with fast algorithms that effect their iterative solution. Our focuswill be fast (multipole) algorithms for low-rank-under-compression kernels (e.g. Poisson), and Greenfunctions for the Helmholtz and time-dependent wave equations (including their Maxwell equationextensions). Additional topics include FFT and Butterfly-accelerated solvers, and recent developments indirect solvers. At the end of the course, students should be able to comfortably read current fast algorithmliterature and use fast multipole constructs in their own research.
[More Info]

Fall 2012: Ultra-Low-Power CMOS Circuit Design

Course No.: 598
Credit Hours: 3
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or EECS 427 or Instructors consent.

Course Description:
Meeting Pattern: Mon and Wed
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Fall 2012: EECS 498 - Hands-On Robotics

Course No.: EECS 498
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: EECS senior standing; other majors and grad students with instructor approval

Course Description:
Engineering seniors and grad students are invited to sign up for Hands On Robotics a robotics course based on building robots using the CKBot modular robot system.

The course will cover basic concepts in Robotics: kinematics, control, programming and design. Grade is 80% team project reports; 20% quizzes. Class meets twice a week for two hours in the lab, with additional lab access provided for working on projects.
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Fall 2012: EECS 566 Discrete Event Systems

Course No.: EECS 566
Credit Hours: 3 credits
Instructor: Stephane Lafortune
Prerequisites: Graduate standing in EE:Systems, CSE, ME, AERO, or IOE, or senior standing with instructor permission

Course Description:
This course is intended for engineering and computer science graduate students who want to learn about dynamic systems with discrete state spaces and event-driven transitions. Discrete Event Systems, as they are called, arise in the modeling of technological systems such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. In embedded and networked systems, discrete event dynamics are coupled with continuous dynamics, giving rise to what are called Hybrid Systems or Cyber-Physical Systems. This course will introduce students to the modeling, analysis, and control of Discrete Event Systems. The primary emphasis will be on the logical, or untimed, behavior and associated verification and supervisory control problems. Timed Automata and Hybrid Automata will also be introduced. Examples from the above areas will be used throughout the course to illustrate the main concepts.
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Winter 2012: Ubiquitous Parallelism

Course No.: EECS 598
Credit Hours: 3
Instructor: Satish Narayanasamy
Prerequisites: EECS 470 or 482 or grad standing

Course Description:
Processors with over hundred cores have already become a reality. However, technologies that can allow mainstream programmers to take advantage of this massive parallelism remains to be a grand challenge in computer science. This course will cover recent advances that seek to address this challenge. We will discuss holistic solutions that cut across the computing stack from languages to processor design. Specific topics include high-productivity languages, transactional memory, deterministic parallel computing, GPGPU, MapReduce, multi-core OS, active testing, speculative parallelism, etc.

The course includes a term project. We may be able to get you access to latest parallel programming tools and systems for your project such as compute resources in a cloud, many-core systems, Thread checker, record-n-replay tool, debugging tools such as CHESS, etc.
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Winter 2012: Autonomous Robotics Laboratory

Course No.: EECS 498
Credit Hours: 4
Instructor: Ed Olson
Prerequisites: EECS 281 or instructor permission

Course Description:
This course will provide students with essential theoreticalbackground and hands-on experience in central topics inrobotics. These include: kinematics, inverse kinematics,sensors and sensor processing, and motion planning. Teamsof students will explore these subjects through a series ofchallenge-themed laboratory exercises. Successful studentswill develop a pragmatic understanding of both theoreticalprinciples and real-world issues, enabling them to designand program robotic systems incorporating sensing,planning, and acting.

We explore these topics from a computer scienceperspective, but we will also cover critical robotics topicsthat are often omitted from computer science curricula.These may include, for example, electrical circuits, controlsystems, Kalman filters, mechanics, and dynamics.Specialized computer science topics such as embeddedsystems programming, real time operating systems, artificialintelligence, etc., may also make appearances. Nobackground is assumed in these areas.

The course is intended for upper-level computer scienceundergraduates, though any one with the appropriatebackground is welcome.
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Winter 2012: Advanced Signal Processing & Applications

Course No.: 498
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 451, EECS 401 or permission of instructor

Course Description:
This is an course on advanced topics in signal processing designed to follow up on principles learned in EECS 451 and EECS 401. The central theme of the course is the application of tools from linear algebra to signal processing. Theoretical topics include solving least-squares problems, eigenvalues and eigenvalues, the singular value decomposition, Markov chains, power method. Synergistic applications covered include image compression, handwriting recognition, Googles PageRank algorithm, eigen-faces, community detection in networks, and deconvolution. Students are expected to be familiar with material covered in EECS 451 and EECS 401 and should have basic MATLAB programming skills (such as writing loops, plotting functions, etc.)
[More Info]

Winter 2012: EECS 598 Machine Learning Applications in Human-Centered Computing in

Course No.: EECS 598
Credit Hours: 3
Instructor: Emily Mower
Prerequisites: Students should have familiarity with probability theory and machine learning tools

Course Description:
Human-centered computing (HCC) is the science of decoding human behavior. HCC seeks to provide a computational account of aspects of human behavior ranging from interaction patterns to individual emotion expression using techniques drawn from both signal processing and machine learning. However, the complexity of this new domain necessitates alterations to the techniques common within the machine learning field and a fundamental understanding of the domains under analysis.

In this seminar course we will cover the development of and state-of-the-art systems in the human-centered computing field. Students will develop a critical understanding of HCC systems ranging from human state recognition and classification systems to human-in-the-loop systems to quantitative human behavior analysis systems. The course evaluation will include student presentations of published HCC work and a final HCC-based project.

Winter 2012: EECS 598 Electricity Networks and Markets

Course No.: EECS 598
Credit Hours: 3
Instructor: Ian Hiskens
Prerequisites: EECS 463 or instructor permission

Course Description:
This course covers the principles and practices that underpin reliable and economical operation of power systems.
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Winter 2012: EECS 598: Electromechanics

Course No.: EECS 598
Credit Hours: 3
Instructor: Heath Hofmann
Prerequisites: EECS 230 or equivalent or graduate standing

Course Description:
In this course we will discuss the analysis and design of electromechanical devices, with an emphasis on power and energy applications. Devices based upon mechanical forces generated by both electromagnetic fields and materials with electromechanical material properties will be considered.
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Winter 2012: EECS 598 Carbon Nanoelectronics and Nanophotonics

Course No.: EECS 598-003
Credit Hours: 3
Instructor: Zhaohui Zhong
Prerequisites: EECS 420 or instructor permission

Course Description:
Carbon based nanomaterials, in particular carbon nanotube and graphene, have generated great excitements over the past decade due to their unique electrical, optical, and mechanical properties. This special topic course introduces theories and experimental works on carbon nanotube and graphene based electronic and photonic devices. The course will also have two student labs of experimental testing of graphene nanoelectronics.
[More Info]

Winter 2012: EECS 598-001 Nano-Optics

Course No.: 598-001
Credit Hours: 3
Instructor: John Schotland
Prerequisites: Graduate standing

Course Description:
Classical and quantum optics of the near-eld. Review of Maxwell's equations. Evanescent waves and radiation theory in the near eld. Lorentz model and optics of metals. Green's functions and plane-wave decompositions. Diffraction from small holes and arrays of small holes. Scattering from point scatterers and spheres. Method of coupled dipoles. Surface plasmons and plasmon polaritons. Near-eld microscopy. Coherence theory in the near-eld. Review of eld quantization. Spontaneous emission and Wigner-Weisskopf theory. Purcell effect. Fluorescence near surfaces. FRET. Casimir effect. The course is meant to be accessible to engineering and physics graduate students.
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Winter 2012: EECS 598-002 Power Electronic Design

Course No.: EECS 598-002
Credit Hours: 3
Instructor: Juan Manuel Rivas Davila
Prerequisites: EECS 418 or instructor permission

Course Description:
In this course, we will study the practical issues related to the practical design of power electronic converters. We will also explore the tradeoffs involved in selecting among the different circuits used to convert ac to dc, dc to ac and back to dc over a wide range of power levels suitable for different applications. In Power Electronic Design, as a multidisciplinary field, we will discuss circuits, control, magnetic design, thermal management and semiconductors and put this knowledge in a very practical context.
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Fall 2011: EECS 650 - Channel Coding Theory and Applications

Course No.: 650
Credit Hours:
Instructor: Prof. Achilleas Anastasopoulos
Prerequisites: EECS 501

Course Description:
Classical coding theory (which was founded almost 50 years ago) studies codes from theiralgebraic viewpoint. It served communication theorists and practitioners well, but essentiallyfailed to reach the goal set by information theory, i.e., to provide codes that come close tochannel capacity. However, 15 years ago, channel coding theory was revolutionized by theinvention of turbo codes and the re-invention of low-density parity-check codes. This revolution led to the birth of the new subfield of modern coding theory.

In the first part of the course, we will review some basic results from information andcoding theory (e.g., error exponents) in order to see what is the best one should expect froma good code. In the second part, we will study families of good codes, collectively referred toas turbo-like codes. Their asymptotic and finite-length performance, and their encoding anddecoding complexity will be studied. This investigation will conclude by looking at familiesof codes that provably approach the capacity of the binary erasure channel and we will askthe question of whether everything that Shannon predicted has been achieved.

The third part of the course deals with the multi-antenna wireless fading channel, whichpromises bandwidth efficiencies on the order of tens of bits per second per Hertz. Its capacitywill be investigated and families of space-time codes will be introduced and analyzed.

In the last part of this course a few topics in modern coding theory that have the potentialto drive the state of the art in the next twenty years will be presented. Such topics includechannel coding with transmitter side information, coding in the presence of feedback, connectionsbetween communications and control, coding for multi-user channels, recent capacityachieving codes such as polar codes, etc. [Full Story]

Fall 2011: Computation for Predictive and Personalized Medicine

Course No.: EECS598
Credit Hours: 3
Instructor: Zeeshan Syed
Prerequisites: EECS281/STATS412 (or equivalent with permission of instructor)

Course Description:
This course provides a multi-disciplinary, hands-on introduction to designing computational systems to address the needs of modern medicine, through real-world projects and clinical/industrial partners. The goal of this course is to develop the knowledge and skills needed to create computational systems for predictive and personalized medicine that can have translational impact in different application domains (e.g., cardiology, psychiatry, critical care). The class consists of lectures and discussions, with students focusing on a semester long project to develop a solution to an important clinical challenge in close collaboration with local partners who are experts in relevant fields. Topics include the main concepts of decision analysis, predictive modeling, data management, biostatistics, and disease pathophysiology. Emphasis will be placed on the advantages and disadvantages of using these methods in real-world systems.

Fall 2011: EECS 418 Power Electronics

Course No.: 418
Credit Hours: 4
Instructor: H. Hofmann
Prerequisites: EECS 215 and EECS 216, and preceded or accompanied by EECS 320, or graduate standing

Course Description:
Lectures: Monday & Wednesday 3-4:30 Lab: Thursday or Friday 3-6



Meeting the future's energy and environmental challenges will require the efficient conversion of energy. For example, renewable forms of energy must be integrated with the nation's 60Hz AC electricity grid. Furthermore, hybrid electric vehicles require efficient energy conversion in order to improve their fuel economy over conventional vehicles. Power electronic circuits are a key component of these systems. Power electronic circuits are circuits that efficiently convert one form of electrical energy (e.g., AC, DC) into another.

This course will discuss the circuit topologies used to efficiently convert AC electrical power to DC, DC power from one voltage 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. A key aspect of power electronic circuits is the control algorithm used to achieve the desired behavior (e.g., output voltage regulation), and so control theory as it applies to these circuits will also be discussed.
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Fall 2011: VLSI Digital Signal Process Systems

Course No.: EECS 598-002
Credit Hours: 3
Instructor: Zhengya Zhang
Prerequisites: EECS 427 or permission of instructor

Course Description:
Digital signal processing (DSP) systems have been enabled by the advances in very-large scale-integrated (VLSI) technologies. New DSP applications constantly impose new challenges on VLSI implementations. These implementations must satisfy real-time constraints imposed by the applications and must fit increasingly stringent area and power envelope. This course will survey methodologies needed to design efficient and high-performance custom or semi-custom VLSI systems for DSP applications. The primary focus of the course is on design of architectures, algorithms, and circuits, which can be operated with small area and low power consumption to deliver a high speed and functional performance.
[More Info]

Fall 2011: Radio Frequency MEMS

Course No.: EECS 598-001
Credit Hours: 3
Instructor: Mina Rais-Zadeh
Prerequisites: EECS 414 (introduction to MEMS) or permission of the instructor

Course Description:
* Introduction to RF and RF MEMS* Overview of MEMS processing, both silicon and non-silicon based* Design and high-frequency modeling of MEMS structures using HFSS* Electrostatic, piezoelectric and magnetic actuation for RF MEMS*Reconfigurable architectures- Technology trends* Semester-long team project on a topic related to course material

Micro-electromechanical devices and systems (MEMS) can greatly enhance the performance of RF integrated circuit as they can operate with much lower power in a smaller size compared to their integrated counterparts. This course covers the operation principle, design, fabrication, and technology trend of high-frequency micromechanical devices with focus on those most used for communication application. Devices and systems covered in this course include resonators, switches, filters, phase-shifters, tunable passives, and reconfigurable modules. The need for high-Q devices will be explained in detail and the physical phenomena that limit the performance and scaling of RF MEMS will be discussed. In addition, students will learn about accurate modeling of MEMS in electrical domain, transduction mechanism commonly used in MEMS, and design techniques used to achieve high performance (high power handling, high linearity, low-loss, etc).
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Fall 2011: Embedded System Design and Synthesis

Course No.: 598-3
Credit Hours: 4
Instructor: Robert P. Dick
Prerequisites: (EECS 370 or EECS 373) and (EECS 280) or equivalent or permission of instructor.

Description: Embedded systems are computers within other devices such as automobiles and medical devices. This course will survey the field of embedded system analysis, design, and synthesis and introduce open research topics in the automatic design of reliable, high-performance,low power consumption, inexpensive embedded systems, e.g., smartphones, distributed sensing systems, and multimedia devices. Commonly, half of those attending are graduate students and half are undergraduate students, who typically do very well if ambitious.

Required Text: None

Reference Texts:

  • Wayne Wolf, "Computers as Components: Principles of Embedded Computing System Design", Morgan Kaufman, 2001.
  • Robert Dick, Multiobjective Synthesis of Low-Power Real-Time Distributed Embedded Systems, Dept. of Electrical Engineering,Ph.D. dissertation, Princeton University, 2002.

Readings: Numerous research papers and book chapters will be assigned.Students will write brief summaries of the assigned articles.

Course Goals: Prepare students for research in embedded system synthesis and design. Introduce real-time systems and embedded operating systems basics. Complete original projects that may serve as foundations for further research.

Prerequisites by Topic:
  • Computer programming
  • Algorithm analysis and design
  • Fundamentals of logic design and computer organization


Lecture topics:
  • Introduction to embedded systems
  • Embedded system applications
  • Overview of heterogeneous multiprocessor system-on-chip design problem
  • Models and languages
  • Formal methods for designing reliable embedded systems
  • Heterogeneous multiprocessor synthesis
  • Reliability optimization
  • Real-time systems
  • Scheduling
  • Compilation techniques for embedded systems
  • Embedded operating systems
  • Low-power and power-aware design
  • Novel fabrication techniques for compact and low-power embedded systems
  • Emerging applications (e.g., sensing and actuation intensive applications and user-aware computing)
  • Hardware and software data compression for use in embedded systems
  • Review and student presentations on short projects


Projects: Students will complete one small project and one main project. The instructor will propose a number of possible small project topics. Students may select from among these or propose their own ideas. Small project reports and presentations will be required.The main course project is often an extended version of the small project, but this is not required.

Examples of previous projects include commercially used operating system modules for increasing available memory in smartphone-class embedded systems, a distributed air quality sensing and reporting system, and security-enhancing techniques to implicitly determine whether an embedded system is being used by its owner or a thief,hardware to improve operating lifespans of distributed sensing systems, and scheduling and synthesis algorithms to improve FPGA performance.

Exams: There will be a final exam covering the assigned reading.

Grading:
  • Projects 50%
  • Presentations 25%
  • Literature summaries 10%
  • Exams 15%
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Fall 2011: Auditory Displays and Interfaces

Course No.: EECS 498-005
Credit Hours: 4
Instructor: Wakefield
Prerequisites: EECS 281 or GUI programming proficiency in Matlab

Course Description:
From the honking of a car horn to the immersion of 3-D audio, sound is used to display information to users as well as to support a user's exploration of alternative (virtual) realities. Auditory Displays and Interfaces introduces key concepts in human-computer interfaces, acoustics, signal processing, auditory perception, and psychometric theory which are important in the design and performance characterization of sonic user interfaces (SUIs). A particular SUI development platform (written in Matlab) is presented and used throughout the course to support each of the concepts taught through programming exercises. Student performance is assessed on the basis of homework/small programming projects, exams, and an individual project.

Winter 2011: EECS 598-003: Advanced Topics in Analog ICs

Course No.: 598
Credit Hours: 4
Instructor: David Wentzloff, Michael Flynn
Prerequisites: EECS 413 and Co-requisite EECS 522

Course Description:
Meets TTH 1:30-3:30


This course will cover design and analysis of advanced analog and mixed-signal integrated circuits, beyond what is covered in EECS 511 and 522. The first half of this course will be lecture based, with lectures covering topics in analog and RF integrated circuits including the design of complete RF systems, analog and all-digital phase-locked loops, serial links, clock and data recovery circuits, and Gm-C filters. Students will complete problem sets and small projects relevant to the lecture material. During the second half of the course students will survey relevant papers from leading IC conferences and journals and will present a summary of an assigned paper to the class.

Winter 2011: EECS 419 Electric Machinery and Drives

Course No.: 419
Credit Hours: 4
Instructor: Heath Hofmann
Prerequisites: EECS 216 or graduate standing

Course Description:
Generation of forces and torques in electromechanical devices. Power electronic drives, motion control. DC machines. AC machines, surface mount permanent magnet machines, induction machines. Applications examined include electric propulsion drives for electric/hybrid vehicles, generators for wind turbines, and high-speed motor/alternators for flywheel energy storage systems. Laboratory experience with electric drives.
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Winter 2011: EECS 590: Advanced Programming Languages

Course: EECS 590
Credit Hours: 4
Instructor: Chandrasekhar Boyapati
Prerequisites:

Course Description:
This is a 4-credit course that covers basic and advanced topics in programming languages, and shows how good programming languages can significantly improve the reliability and security of software systems. This course has three objectives: 1) To understand fundamental concepts in programming languages, 2) To study some recent topics and trends in PL research, and 3) To gain experience planning and carrying out a semester long PL research project. This course counts as a software kernel course and towards software area qualification for CSE graduate students. This course also counts as an upper level CS technical elective for CS-ENGR and CS LSA undergraduate students. Please see the course web page for further information.
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Winter 2011: EECS 598-001 Terahertz Technology and applications

Course No.: EECS 598-001
Credit Hours: 3
Instructor: Mona Jarrahi
Prerequisites: EECS 320 and EECS (330 or 334)

Meets MW 10:30-12 in EECS 1008
Course Description:
This course will provide graduate students with an overview on the unique specifications of terahertz waves and potential applications as well as the state of the current terahertz systems and the major technological challenges in the field. The topics covered in this course are THz Sources (vacuum-electronics-based, semiconductor-based, photoconduction-based and nonlinearity-based), THz Detectors (single-photon detectors, microbolometers, Golay cells, Pyroelectric detectors and focal-plane arrays), THz electronic components (waveguides, Metamaterials, filters and modulators), sensing with THz radiation (THz spectroscopy, imaging and tomography), and THz applications (biology, medicine, space sciences, pharmaceutical industry, security and communications).

Winter 2011: EECS 598-006 Electromagnetic Metamaterials

Course No.: EECS 598-006
Credit Hours: 3
Instructor: Anthony Grbic
Prerequisites: EECS 330

Course Description:
Meets TTH 10:30-12 in DOW 1018



This course will cover engineered structures possessing tailored electromagnetic properties, known today simply as metamaterials.


The course material will include classical microwave structures like periodically loaded transmission lines and waveguides, corrugated surfaces, wire arrays, and more recent examples such as high impedance surfaces, electromagnetic bandgap structures, negative refractive index and artificial magnetic media. Optical structures such as photonic bandgap materials and metal-dielectric plasmonic structures will also be touched upon. The course will allow graduate students to develop an intuitive feel for the electromagnetic response of various structures through exact and approximate methods. Effective medium theories will be developed for those structures operating in the long wavelength regime, and distributed circuit concepts utilized to gain understanding.
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Winter 2011: EECS 498-006/SI 517-001 Cloud Computing in the Commute

Course No.: EECS 498-006/SI 517-001
Credit Hours: 3
Instructor: Brian Noble, Jason Flinn
Prerequisites: EECS 281 or EECS 282 or Graduate Standing, or Permission of the Instructors.

Course Description:
Meets TTH 3-4:30 in GGBL 2233.
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Winter 2011: EECS 498-004 Grid Integration of Alternative Energy Sources

Term: WN11
Course No.: EECS 498-004
Credit Hours: 4
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
W 8:30-10:30 and F 8:30-10:30
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Winter 2011: EECS 498-001 Solid State Power Devices

Term: WN11
Course No.: EECS 498-001
Credit Hours: 4
Instructor: Gholamhassan Lahiji
Prerequisites: EECS 320 or graduate standing

Course Description:
LEC MW 12:00-1:30 in 3433EECS DIS F 1:30-2:30 in 3433 EECS

Power semiconductor devices are at the heart of modern power electronic systems, and are expected to play an increasingly large role in reducing energy losses and in adapting power systems to new energy sources, especially renewable sources of energy. The goal of this course is to prepare students to analyze and design semiconductor devices and smart power integrated circuits for low and high power applications with different materials and technologies. The course covers the physics and fabrication of various power devices, whose capabilities span a broad range of voltages, currents and switching speeds. Of course, special attention is paid to Insulated Gate FET devices, which in addition to being commonly used in conventional digital and analog circuits, are also the devices of choice for power electronics. We start the course with a broad review of ideal power devices, semiconductor material properties, breakdown voltages and a detailed analysis of PN junctions along with the analysis and fabrication of power diodes, Schottky and pin diodes. The physics and fabrication process of basic MOS structures will be covered in detail along with the fabrication technology of different power MOS transistors. Bipolar Junction transistors and their combination with MOS technology, which has resulted in Insulated Gate Bipolar Transistors, will also be covered in this course. JFET based power devices are another group of devices that is under development and research, and will also be covered. Then the physics and fabrication of four and higher layer devices will be discussed, including SCR, GTO, and MCT devices. Temperature effects and packaging are other important issues in power devices that will also be discussed in this course, along with the fabrication of devices with large band gap materials like SiC and GaN.

Prerequisite: EECS320, or graduate standing.
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Winter 2011: EECS 498/598-005 Performance Systems: Mobile Phones as Musical Instruments

Term: Winter 2011
Course No.: EECS 498/598, PAT 461/511/561
Credit Hours: 3
Instructor: Georg Essl
Prerequisites: EECS 280 (required), HCI, Graphics, Multimedia, PAT courses (optional)

Course Description:
Meets TTH 2:30-4 in COOL G906
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Winter 2011: EECS 598-004 Introduction to Quantum Information and Computing

Term: Winter 2011
Course No.: EECS 598-004
Credit Hours: 3
Instructor: Kim Winick
Prerequisites: Graduate standing in engineering, the physical sciences, computer science or mathematics and a basic knowledge of linear algebra

Course Description:
Time and Place: Monday and Wednesdays 3 pm 4:30 pm, 3433 EECS Bldg.
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Winter 2011: Introduction to Machine Learning

Term: Winter 2011
Course No.: 498-002
Credit Hours: 3
Instructor: Satinder Singh
Prerequisites: EECS 281 (or EECS 203, substantial programming experience, & permission of instructor)

Note: this course satisfies an Upper-Level Comp. Sci. Elective

Course Description:
Making sense of data, whether it comes from commercial settings like Twitter or from scientific experiments in a research laboratory, is a problem of great interest to society. Machine learning approaches help us classify, cluster, display, predict, and decide how to act based on data.

In this course, we will learn about and program machine learning algorithms and evaluate them on data from twitter feeds, flickr feeds, video, blogs, as well as scientific data. Some familiarity with C++ or Java will be helpful for we will use Weka (machine learning) libraries as well as Processing libraries in our assignments.

Winter 2011: EECS 598-002 Solar Cell Device Physics

Term: Winter 2011
Course No.: EECS 598-002
Credit Hours: 3
Instructor: Jamie Phillips (jphilli@umich.edu)
Prerequisites: EECS 421 or graduate standing. Previous knowledge of semiconductor physics is essential for the course.

Course Description:
CLASS MEETINGS: Tuesdays and Thursdays, 1:30-3:00pm, 3433 EECS
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Fall 2010: Ubiquitous Parallelism

Term: Fall 2010
Course No.: 598
Credit Hours: 3
Instructor: Satish Narayanasamy
Prerequisites: Graduate standing or 482 or Instructor's permission

Course Description:
Processors with over hundred cores have already become a reality. However, technologies that can allow mainstream programmers to take advantage of this massive parallelism remains to be a grand challenge in computer science. This course will cover recent advances that seek to address this challenge. We will discuss holistic solutions that cut across the computing stack from languages to processor design. Specific topics include high-productivity languages, transactional memory, deterministic parallel computing, GPGPU, MapReduce, multi-core OS, active testing, speculative parallelism, etc.

This course will include a term project. You will get access to latest parallel programming tools and systems for your project such as Intel's 48-core Single Cloud Computer, Thread checker, pinPlay record-n-replay, and CHESS for active testing.

Reading list, syllabus and more information could be found at the course website:

http://eecs.umich.edu/~nsatish/courses/598-f10/
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Fall 2010: Unsupervised Feature Learning

Term: Fall 2010
Course No.: EECS 598
Credit Hours: 4
Instructor: Honglak Lee
Prerequisites: EECS 492 (Introduction to AI) or 545 (Machine Learning) or permission of instructor.

Course Description:
In recent years, there has been much interest in algorithms thatlearn feature hierarchies from unlabeled data. In particular, deeplearning methods such as deep belief networks, sparse coding-based methods, convolutional networks, and deep Boltzmann machines, have shown promise and have already been successfully applied to a variety of tasks in computer vision, audio processing, natural language processing, information retrieval, and robotics.

In this seminar course, we will focus on reviewing principles andrecent progress in unsupervised learning and deep learning algorithms, with a goal of developing useful features for machine learning applications. Topics include sparse coding, autoencoders, restricted Boltzmann machines, and deep belief networks. The course will require an open-ended research project.

Fall 2010: Infrastructure for Vehicle Electrification

Term: Fall 2010
Course No.: EECS 598
Credit Hours:
Instructor: Prof. Ian Hiskens
Prerequisites: EECS 215 or 314 (or permission of instructor)

Course Description:
This course covers the fundamentals of the physical and cyber infrastructures that will underpin large-scale integration of plug-in electric vehicles. PEV charger technology will be examined, with a view to establishing grid-side characteristics. V2G converter requirements will be considered. The physical power system infrastructure will be presented, beginning with an overview of power system structure and operations, through distribution system design, to consumer installations. Quality-of-supply issues and protection requirements will be addressed. The information infrastructure and regulatory framework required to support various business models for flexible PEV charging and V2G applications will be presented. Control strategies that are appropriate for large-scale PEV integration will be considered. Upon completion of the course, students should have a comprehensive knowledge of the structure, capabilities and limitations of the physical and cyber infrastructures required to support PEVs.
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Adaptive Art AESTHETIC POSSIBILITIES OF ALGORITHMS, COMPUTATION, AND MACHINE LEARNING

Term: Fall 2010
Course No.: 498
Credit Hours: 3
Instructor: Satinder Bavej (EECS) and Osman Khan (A & D)
Prerequisites:

Course Description:
This course will meet with ARTDES 300-007.
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Intro to Computer Security

Term: Fall 2010
Course No.: EECS 398-001
Credit Hours: 4
Instructor: J. Alex Halderman
Prerequisites: EECS 281; EECS 370 recommended

Course Description:
Course will meet TTH 10:30-12 in EECS 3427 and Fri 2:30-3:30 in EECS 1003
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Power Electronics

Term: Fall 2010
Course No.: EECS 498-009
Credit Hours: 4
Instructor: Heath Hofmann
Prerequisites: (EECS 215 and EECS 216) or Graduate Standing

Course Description:
Course will meet MW 3-4:30 in EECS 3427
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Autonomous Robotics Laboratory

Term: Fall 2010
Course No.: EECS 498-006
Credit Hours: 4
Instructor: Ed Olson
Prerequisites: EECS 281 (or permission of instructor)

Course Description:
Want to learn how to program smart robots? Take EECS 498-006: Autonomous Robotics Laboratory!

**498 is approved as an upper level elective for CS and CE students, and as a flexible technical elective for EE students.**

The prerequisite is EECS 281, though permission will generally be granted to students with 280 and either 373 or 461. Register early; the class is likely to fill up quickly.

A highlights video from last term is available here:

http://april.eecs.umich.edu/courses/eecs498_f09/videos/

You can also view last term's website:

http://april.eecs.umich.edu/courses/eecs498_f09/wiki

Course Objective

This course will provide students with essential theoretical background and hands-on experience in central topics in robotics. These include: kinematics, inverse kinematics, sensors and sensor processing, and motion planning. Teams of students will explore these subjects through a series of challenge-themed laboratory exercises. Successful students will develop a pragmatic understanding of both theoretical principles and real-world issues, enabling them to design and program robotic systems incorporating sensing, planning, and acting.

We explore these topics from a computer science perspective, but we will also cover critical robotics topics that are often omitted from computer science curricula. These may include, for example, electrical circuits, control systems, Kalman filters, mechanics, and dynamics. Specialized computer science topics such as embedded systems programming, real time operating systems, artificial intelligence, etc., may also make appearances. No background is assumed in these areas.

The course is intended for upper-level computer science undergraduates, though any one with the appropriate background is welcome. If you have any questions, please feel free to ask Prof. Olson, but please check the wiki first!




Related Topics:  Lab-Artificial Intelligence  

Performance Systems - Building a Mobile Phone Ens.

Term: Fall 2010
Course No.: 498-008
Credit Hours: 3
Instructor: Georg Essl
Prerequisites: EECS 280

Course Description:
FA 2010Electrical Engineering and Computer ScienceEECS 498 - Special TopicsSection 008Performance Systems - Building a Mobile Phone Ensemble Credits:3

Undergrad and GradMeet Together ClassesEECS 598 - Special Topics, Section 004PAT 461 - Performance Systems, Section 001PAT 511 - Engin App Media Tech, Section 001PAT 561 - Perf Systems, Section 001 Primary Instructor:Essl,Georg

This course introduces the students to the process of setting up, coding for and performing in a mobile phone ensemble. The students will learn how to program mobile phones to be able to interactively play synthesis algorithms on them, as well as engage with the sensory capabilities of the device to harness their interactive capabilities. We will also engage with the technical limitation of the devices and explore technological ways to overcome these short-coming. The goal of the class is to teach the process of building a new performance system from scratch by moving from setting up infrastructure to ultimately playing live, ensemble-based music with mobile phones and the class closes with performances created by the students for the ensemble they created.

Target audience: Upper-class undergraduate and/or entering graduate students in PAT, EECS, and related fields

Data Structures Behind Internet Applications

Term: Fall 2010
Course No.: 498-001
Credit Hours: 3
Instructor: Seth Pettie
Prerequisites: EECS 203 and (EECS 281 *OR* Informatics Majors w/EECS 282

Course Description:
Description: This course covers the theory behind the major internet applications.Students will learn, among other topics, the text indexing datastructures that make search engines possible,the data structures behind peer-to-peer file sharing networks,data structures for finding shortest path queries in road networks,algorithms for ranking webpages,and explanations of the "small world" phenomenon in social networks.

Random matrix theory, algorithms and signal processing applications

Term: Fall 2010
Course No.: 598
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: Basic linear algebra and probability

Course Description:
This course covers and the theory and algorithms emerging from the study of random matrices as it is currently applied in signal processing, statistics and science. Topics include random sample covariance matrices, spectral limit theorems such as Wigner's semi-circle and Marcenko-Pastur laws, free probability, randomized numerical linear algebra, matrix statistics, passage to the continuum limit, moment methods, and compressed sensing. There will be a special focus on presenting the theory in a manner that facilitates the development of new applications and allows students that already have a topic in mind to to apply these ideas. Emerging applications in signal processing, wireless communications and statistical physics will be discussed.

EECS 598: Wireless Sensor Networks

Term: Winter 2010
Course No.: EECS 598
Credit Hours: 3
Instructor: Prabal Dutta
Prerequisites:

Course Description:
Room: 1018 Dow
Time: TuTh: 10:30 AM - 12:00 PM
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ADVANCED LASERS AND PHOTONICS

Term: Winter 2010
Course No.: 438
Credit Hours: 4
Instructor: A. Galvanauskas
Prerequisites:

Course Description:
Lectures: TTH 1230-130PM in 1303 EECS
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Introduction to Machine Learning

Term: Winter 2010
Course No.: EECS 498 - 004
Credit Hours: 3
Instructor: Satinder Singh Baveja
Prerequisites: EECS 281 or Consent of Instructor

Course Description:
The course is a programming-focused introduction to Machine Learning. Increasingly, extracting value from data is an important contributor to the global economy across a range of industries. The field of Machine Learning provides the theoretical underpinnings for data-analysis as well as more broadly for modern artificial intelligence approaches to building artificial agents that interact with data.

In this course, students will learn about all three subareas of Machine Learning: 1) Supervised learning (approaches to regression and classification), 2) Unsupervised learning (approaches to density estimation, and clustering/dimensionality reduction), and 3) Reinforcement Learning (approaches to sequential decision-making). The course will emphasize understanding the foundational algorithms and tricks of the trade through implementation and basic-theoretical analysis. Real data sets will be used whenever feasible to encourage understanding of practical issues.

Students will be expected to program in Matlab as well as in one of C/C++/Java (students will have a choice).

Related Topics:  Lab-Artificial Intelligence  

Electric Machinery and Drives

Term: Winter 2010
Course No.: EECS 498-007
Credit Hours: 4
Instructor: Heath Hofmann
Prerequisites: EECS 216 or equivalent
Lectures: TTH 12-1:30, W 12-1 in EECS 3433
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Bio-molecular Feedback Systems

Term: Winter 2010
Course No.: EECS 498-002
Credit Hours: 3
Instructor: Domitilla Del Vecchio
Prerequisites: MATH 116

Course Description:
Lectures: MW 1-2:30 in EWRE 104
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Embedded System Design and Synthesis

Description

Embedded systems are computers within other devices such as automobiles andmedical devices. This course will survey the field of real-time embeddedsystem design and synthesis and introduce open research topics in the automaticdesign of reliable, high-performance, low power consumption, inexpensiveembedded systems.

Required text: None

Reference texts

  • Wayne Wolf, "Computers as Components: Principles of EmbeddedComputing System Design", Morgan Kaufman, 2001.
  • Robert Dick, Multiobjective Synthesis of Low-Power Real-TimeDistributed Embedded Systems, Dept. of ElectricalEngineering, Ph.D. dissertation, Princeton University, 2002.
  • Numerous research papers and book chapters will beassigned. Students will write brief summaries of theassigned articles.

Course goals

Prepare students for research in embeddedsystem synthesis and design. Introduce real-time systemsand embedded operating systems basics. Complete originalprojects that may serve as foundations for further research.

Prerequisites

  • Knowledge of computer organization, e.g., EECS 470 (Computer Architecture) or EECS 370 (Introduction to Computer Organization) or EECS 373 (Design ofMicroprocessor Based Systems), similar course from another university, orsimilar experience and
  • Knowledge of computer programming and algorithm design, e.g., EECS 281(Data Structures and Algorithms), similar course from another university, orsimilar experience.

Please email the instructor if you are missing aprerequisite but believe your background might be sufficient.

Prerequisites by topics

  • Computer programming,
  • Algorithm analysis and design, and
  • Fundamentals of logic design and computer organization

Lecture topics

  1. Introduction to embedded systems
  2. Overview of heterogeneous multiprocessor system-on-chip design problem
  3. Models and languages
  4. Formal methods for designing reliable embedded systems
  5. Heterogeneous multiprocessor synthesis
  6. Reliability optimization
  7. Real-time systems
  8. Scheduling
  9. Compilation techniques for embedded systems
  10. Embedded operating systems
  11. Low-power and power-aware design
  12. Novel fabrication techniques for compact and low-power embedded systems
  13. Emerging applications (e.g., sensing and actuation intensive applications and user-aware computing)
  14. Hardware and software data compression for use in embedded systems
  15. Review and student presentations on short projects

Projects

Students will complete one small project and one main project. Theinstructor will propose a number of possible small project topics. Studentsmay select from among these or propose their own ideas. Small project reportsand presentations will be required. The main course project is often anextended version of the small project, but this is not required.

Exams: There will be a final exam covering the assignedreading.

Grading

  • Projects: 50%
  • Presentations: 25%
  • Literature summaries: 10%
  • Exams: 15%
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Model-based image reconstruction methods

Term: W10
Course No.: 755
Credit Hours: 2-3
Instructor: Jeff Fessler
Prerequisites: At least one of EECS 516, 556, or 564

Course Description:
The goal of this course is to bridge the gap between500-level EECS courses such as EECS 516,556,564 andthe modern image formation literature.

Topics include: image restoration; tomographic image reconstruction;reconstruction from Fourier samples and MR image reconstruction;regularization; special optimization algorithms; analysis of spatialresolution / noise / detectability for nonlinear algorithms;compressed sensing and reconstruction from under-sampled data.

Applications of this material include all imaging modalities thatinvolve inverse problems, including X-ray CT, MRI, RADAR, SONAR,PET, SPECT, optical imaging (e.g., microscopy and astronomy),acoustic tomography, super-resolution imaging from video sequences, etc.

See web site for more information.
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Advanced Topics in AI: Robot Learning

Term: W10
Course No.: 692
Credit Hours: 4
Instructor: Kuipers
Prerequisites: 492, 545, or permission of instructor

Course Description:
This is a research seminar on how robots can learn autonomouslythe structure of the sensorimotor system; control laws for effectiveaction; foundational concepts such as Space, Objects, and Actions;and causal and taxonomic theories that help them make sense of the world around them. We will draw on current research in AI, machine learning, robotics, and developmental psychology. The course will require student presentations and a substantial term project.
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Related Topics:  Lab-Artificial Intelligence  

EECS 498-001 INTRODUCTION TO DISCRETE EVENT SYSTEMS

Term: Winter, 2010
Course No.: 498-001
Credit Hours: 3
Instructor: Stephane Lafortune
Prerequisites: Senior or Graduate standing in EE, CS, CE, ME, or AERO

Course Description:
This course is intended for engineering and computer science seniors and graduate students who want to learn about dynamic systems with discrete state spaces and event-driven transitions. Discrete Event Systems, as they are called, arise in the modeling of technological systems such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. In embedded and networked systems, discrete event dynamics are coupled with continuous dynamics, giving rise to what are called Hybrid Systems. This course will introduce students to the modeling, analysis, and control of discrete event systems. The modeling of hybrid systems will also be introduced. Examples from the above areas will be used throughout the course to illustrate the main concepts.

This course is offered in place of EECS 661, the graduate-level course on Discrete Event Systems that is normally offered in the fall of even years. EECS 661 will not be offered in Fall 2010; the next expected offering of EECS 661 is Fall 2012. Graduate students who intended to take 661 in Fall 2010 should take 498-W10 instead; it will count for graduate credit. 498-W10 is also open to undergraduate seniors. The class material will be adjusted accordingly.

There are no specific course prerequisites other than senior or graduate standing.

For planning purposes, please contact the instructor if you plan to enroll.

Textbook:"Introduction to Discrete Event Systems - Second Edition" by C. Cassandras and S. Lafortune, Springer, 2007

Grading: Homework assignments, two exams, and a short project.

Syllabus: Most of Chapters 2, 4, and 5; part of Chapter 3 of textbook.

Areas of Interest:

  • Finite-state automata models of discrete event systems: notions of deadlock and livelock, product and parallel composition, observer and diagnoser automata.
  • Petri net models of discrete event systems: reachability analysis with coverability tree, structural analysis with invariants.
  • Supervisory control of discrete event systems modeled by automata: controllability and observability, nonblocking control.
  • Control of Petri nets by place invariants.
  • Timed automata models of discrete event systems: parallel composition, reachability analysis by untiming.
  • Hybrid automata models of hybrid systems: basic notions.

The software tool DESUMA will be used in the course.

EECS634: NONLINEAR OPTICS

Term: Fall, 2009
Course No.: EECS634 (Phys611 APP PHYS611)
Credit Hours:
Instructor: Professor Herbert G. Winful
Prerequisites: A graduate course in optics or electromagnetics

Course Description:

  • The variety of nonlinear optical phenomena
  • The time-domain nonlinear response function; anharmonic oscillator model
  • Volterra series expansion for the nonlinear polarization
  • The nonlinear susceptibility; frequency-domain nonlinear polarization
  • Second-order nonlinear effects: second harmonic generation, sum frequency generation, difference frequency generation, optical rectification
  • Phase matching, quasi-phase matching, periodically poled nonlinear materials
  • Parametric amplification and oscillation
  • Cascaded second-order nonlinearities
  • Third order nonlinear effects: third harmonic generation, four-wave mixing, intensity-dependent refractive index, self-phase modulation, self-focusing, optical bistability, optical phase conjugation, pulse compression, polarization instabilities
  • Temporal solitons, spatial solitons
  • Nonlinear periodic structures, gap solitons
  • Stimulated Raman scattering, stimulated Brillouin scattering
  • Nonlinearities in fiber-optic communications
  • Photorefractive nonlinear optics

Grading: Homework 30% Midterm Exam 30% Final Exam 40%

Biomedical Machine Learning

Term: Fall 09
Course No.: 598-002
Credit Hours: 3
Instructor: Zeeshan Syed
Prerequisites: Stat 412 or IOE 265; MATH 216; Experience with MATLAB; or graduate standing

Course Description:
Explores modern machine learning in the context of real-world medical applications. Introduces students to different learning and feature extraction techniques for physiological data, and develops intuition on how these methods can be used to solve hard clinical problems in disease diagnosis, prevention and management. Topics covered include time-frequency analysis, non-linear dynamics, supervised and unsupervised learning, and symbolic analysis; with clinical applications from cardiology, neuroscience, obstetrics, oncology, surgery and intensive care monitoring. Focus on extensive hands-on experience with actual clinical data. Students expected to complete a final project using the methods learned in the course.

Target audience: Graduate students or advanced engineering undergraduates interested in healthcare applications. No prior experience in either machine learning or medicine is required, but basic knowledge of probability and statistics is assumed.

Lectures: TTH 12-1:30

Related Topics:  Lab-Artificial Intelligence  

Performance Systems Building a Mobile Phone Ensemble

Term: Fall 2009
Course No.: 498-007/598-001
Credit Hours: 3
Instructor: Georg Essl
Prerequisites: EECS 280 (required), HCI, Graphics, Multimedia, PAT courses (optional)

Course Description:
This course introduces the students to the process of setting up, coding for and performing in a mobile phone ensemble. The students will learn how to program mobile phones to be able to interactively play synthesis algorithms on them, as well as engage with the sensory capabilities of the device to harness their interactive capabilities. We will also engage with the technical limitation of the devices and explore technological ways to overcome these short-coming. The goal of the class is to teach the process of building a new performance system from scratch by moving from setting up infrastructure to ultimately playing live, ensemble-based music with mobile phones and the class closes with performances created by the students for the ensemble they created.

Target audience: Upper-class undergraduate and/or entering graduate students in PAT, EECS, and related fields
Lectures: TTH 4-5:30 PM

Power System Analysis and Design

Term: Fall 2009
Course No.: EECS498-004
Credit Hours: 4
Instructor: Ian Hiskens
Prerequisites: EECS215 or EECS314

Course Description:
The course will establish the basic principles of power system operation and control, under normal conditions and when faults occur. It will develop the models and tools necessary for analysing system behavior, and provide opportunities for using those tools in design processes. Optimal generation dispatch will be developed, and electricity market implementation issues addressed. The impact of renewable generation on power system operation will be considered.
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Autonomous Robotics Laboratory

Term: Fall 2009
Course No.: EECS498
Credit Hours: 4
Instructor: Edwin Olson
Prerequisites: EECS280

Course Description:
This course will provide students with essential theoretical background and hands-on experience in central topics in robotics. These include: kinematics, inverse kinematics, sensors and sensor processing, and motion planning. Teams of students will explore these subjects through a series of challenge-themed laboratory exercises. Successful students will develop a pragmatic understanding of both theoretical principles and real-world issues, enabling them to design and program robotic systems incorporating sensing, planning, and acting.

We explore these topics from a computer science perspective, but we will also cover critical robotics topics that are often omitted from computer science curricula. These may include, for example, electrical circuits, control systems, Kalman filters, mechanics, and dynamics. Specialized computer science topics such as embedded systems programming, real time operating systems, artificial intelligence, etc., may also make appearances. No background is assumed in these areas.

The course is intended for upper-level computer science undergraduates, though any one with the appropriate background is welcome.
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Related Topics:  Lab-Artificial Intelligence  

EECS 498-001: Constructing Applications for Smartphones

Term: Fall 2009
Course No.: EECS 498-001
Credit Hours: 3
Instructor: Elliot Soloway
Prerequisites: Senior Status in CS or permission of Instructor

Course Description:
This seminar will be a project-oriented, software construction-focused course. We will design and build applications for a broad range of smartphones (e.g., Apple's iPhone, Google's Android, RIM's Blackberry, and Microsoft's Windows Mobile). Students will form project teams for the design and development effort. Each team will choose which platform they will address.

To better inform the design and development efforts, we will review key resources such as websites, blogs, articles, and books. In particular, to help contextualize the construction effort, we will use "The Art of the Start" by Guy Kawasaki as the core textbook in the course.

The intent of the course is to produce commercially viable applications for smartphones.

Admission to course: Senior status in CS required. However, should students from other areas (e.g., business, LS&A) wish to participate, they will need the permission of the instructor.

EECS 498-005: Introduction to Quantum and Statistical Mechanics for Engineers

Term: FA09
Course No.: 498 Lec 005
Credit Hours: 4
Instructor: Fred Terry
Prerequisites: EECS (320 or 330 or 334), or graduate standing, or permission of the instructor

Course Description:
Modern electronic and optoelectronic devices are built using nanometer-scale structures. Both the properties of the materials and the physics of the devices themselves are governed by quantum mechanics. Understanding the behavior of these devices at temperature above 0K requires a baseline understanding of statistical mechanics (thermodynamics). This course will cover concepts in elementary quantum mechanics and statistical physics, introduces applied quantum physics, and emphasizes an experimental basis for quantum mechanics. Concepts covered will include: Schrodinger's equation applied to the free particle, tunneling, the harmonic oscillator, and hydrogen atom, variational methods, Fermi-Dirac, Bose-Einstein, and Boltzmann distribution functions, and simple models for metals, semiconductors, and devices. The class is intended for senior and first year graduate students in electrical engineering or closely related fields who have not had a substantial prior introduction to these topics (such as Physics 453 and 406).

EECS 590: Advanced Programming Languages

Term: Winter 2009
Course No.: EECS 590
Credit Hours: 4
Instructor: Chandrasekhar Boyapati
Prerequisites:

Course Description:
This is a 4-credit course that covers basic and advanced topics in programming languages, and shows how good programming languages can significantly improve the reliability and security of software systems. This course has three objectives: 1) To understand fundamental concepts in programming languages, 2) To study some recent topics and trends in PL research, and 3) To gain experience planning and carrying out a semester long PL research project. This course counts as a software kernel course and towards software area qualification for CSE graduate students. This course also counts as an upper-level CS technical elective for CS-ENGR and CS-LSA undergraduate students. Please see the course web page for further information.
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EECS485: WEB DATABASES & INFORMATION SYSTEMS

Term: Winter 2009
Course No.: EECS 485
Credit Hours: 4
Instructor: Dr. Scott D. Wood,
Prerequisites: EECS 484 recommended

Course Description:
This capstone course is a contemporary exploration of modern web-based information systems. It will integrate concepts from multiple computer science topics used in the design, development, systems. While broad in scope, it will also cover several key concepts in depth, including:

  • web databases and applications
  • information architecture and search
  • system security
  • site analysis and design
  • web usability and testing
  • n-tiered architectures
  • web services
Students will learn how to incorporate these concepts into an engineering process that includes design, analysis, development and testing, using technologies such as HTTP, XML, SQL, Ruby, Rails, JavaScript, AJAX, CSS, RSS, SOAP, WSDL, and UDDI. Students will form project teams to implement assignments on Linux-based Apache web servers using open-source components. At the end of this course, students will understand the science behind web-based information systems and the engineering principles for integrating key web technologies. [Full Story]

EECS 598: Special Topics in Computer Vision

Term: Winter 09
Course No.: EECS 598-003
Credit Hours: 3
Instructor: Prof. Silvio Savarese
Prerequisites: Knowledge of linear algebra and probability are necessary for understanding the material covered in this class. Some knowledge of computer vision is desirable but not required. MATLAB or equivalent programming experience is expected. Please note that EECS 598 can count toward the signal processing major or minor in the same way that 556 ordinarily would.

Course Description:
Course DescriptionThe course surveys recent developments in high level computer vision such asobject recognition and categorization, action and event recognition, objecttracking and human motion analysis, spatial and temporal reasoning for scenereconstruction and understanding, organization and indexing of visual data fromlarge databases. The course also explores recent machine learning techniquessuch as graphical models and inference algorithms for high level visual tasks.

Requirements:

  • Present 1-2 set of papers
  • Read papers and participate at class discussion during paperpresentations
  • Course project: replicate existing methods or implement new researchideas.

    Grading policy:
  • Class participation & discussion: 20%
  • Paper presentation (quality, clarity, depth, etc.): 30%
  • Course project (quality of the project presentation, work, writing, etc): 50%



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  • EECS 598: Current Topics in Optoelectronics

    Term: Winter 2009
    Course No.: EECS 598
    Credit Hours: 2
    Instructor: Professor Pallab Bhattacharya
    Prerequisites:

    Course Description:

    Graduate students and seniors specializing in the solid state/optoelectronics/optics areas are encouraged to enroll in this course. There will be lectures/seminars by the instructor and faculty members in SSEL conducting research in relevant areas. 3-4 lectures/seminars will be given by experts from outside the university. Some topics to be covered are:

    • Surface-emitting laser
    • Quantum dot lasers and amplifiers
    • Surface plasmon enhanced nanolasers
    • Spin polarized lasers
    • Quantum dot detectors
    • Organic light emitting diodes
    • Semiconductor laser applications and reliability Issues
    • Resonant cavity detectors

    The lectures/seminars will cover device physics and technology, device characteristics and applications.

    The first meeting will be on Tuesday, January 13, 2009.

    Information Retrieval

    Term: Winter 2009
    Course No.: 498
    Credit Hours: 3
    Instructor: Dragomir Radev
    Prerequisites:

    Course Description:
    This course is about understanding, evaluating, and building searchengines. The topics include models of IR, vector-space similarity,tokenization, stemming, indexing and retrieval, word distributions,automated indexing, query expansion, text classification andclustering, lexical semantics, latent semantic indexing, web crawling,random graph models, harmonic functions, centrality, pagerank andhits, models of the web, webometrics, document modeling, textsummarization, question answering, etc.

    Required text:

    Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze,Introduction to Information Retrieval, Cambridge University Press. 2008. ISBN: 0521865719.http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html

    EECS 598-2: Algorithms for Robotics

    Term: Winter 2009
    Course No.: EECS 598-2
    Credit Hours: 3
    Instructor: Edwin Olson
    Prerequisites: Programming experience (Java/C++)

    Course Description:
    This course will present and critically examine contemporary algorithms for robot perception (using a variety of modalities), state estimation, mapping, and path planning. Significant programming exercises and a substantial project will give students the opportunity to try algorithms themselves and to propose improvements. The goal of this course is to prepare students for research in robot algorithms.
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    Grid Integration of Alternative Energy Sources

    Term: Winter 2009
    Course No.: EECS 498
    Credit Hours: 3
    Instructor: Ian Hiskens
    Prerequisites: EECS 215 or 314 or permission of instructor

    Course Description:
    The course will present a variety of alternative energy sources, along with energy processing technologies required for power system connection. System integration issues will be addressed, with consideration given to impacts on current design philosophies and operating procedures. Topics will be covered at a level suited to establishing a broad understanding of the various technologies, and of the associated system implications. The course will develop tools necessary for analysis and design of alternative energy systems.
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    Computer Vision EECS 442

    Term: Fall 2008
    Course No.: 442
    Credit Hours: 4
    Instructor: Prof. Savarese
    Prerequisites: Linear algebra; some knowledge of probability & statistics; MATLAB

    Course Description:
    The course is an introduction to 2D and 3D computer vision. Topics include:cameras models, the geometry of multiple views; shape reconstructionmethods from visual cues: stereo, shading, shadows, contours; low-levelimage processing methodologies such as edge detection, feature detection;mid-level vision techniques (segmentation and clustering); Basic high-levelvision problems: face detection, object and scene recognition, objectcategorization, and human tracking.
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    EECS 406: High-Tech Entrepreneurship

    Term: Fall, 2008
    Course No.: 406
    Credit Hours: 4
    Instructor: Professor Mohammed N. Islam
    Prerequisites: Senior or Graduate Standing (Juniors or Sophomores will also be

    Course Description:
    The technology sector represents a significant portion of the economy of every industrialized nation. In the U.S., more than one third of the gross national product and about half of private-sector spending on capital goods are related to technology. Therefore, particularly in the U.S. economic growth depends on the health and contributions of technology businesses.

    This course is about Technology Entrepreneurship, which is a style of business leadership that involves identifying high-potential, technology-intensive commercial opportunities, gathering resources such as talent and capital, and managing rapid growth and significant risks using principled decision-making skills. Technology ventures exploit break-through advancements in science and engineering to develop better products and services for customers. The leaders of technology ventures demonstrate focus, passion, and an unrelenting will to succeed.
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    EECS 598: Human-Inspired Computing

    Term: Winter 2008
    Course No.: EECS 598
    Credit Hours:
    Instructor: Todd Austin
    Prerequisites:

    Course Description:
    EECS 598: Human-Inspired Computing
    Instructor: Prof. Todd Austin, austin@umich.edu, 4637 CSE
    Lectures: Tues-Thur 12:00-1:30pm, 1003 EECS

    This course covers recent research topics in computer engineering related to human-inspired computing applications. Specifically, we will be examining sensing and control applications on and within the human body, such as health sensing and assisted-living applications. In support of these applications we will study a variety of supporting technologies, including sensor processors, bio-implant technologies, bio-chemical sensing applications, neural-signal processing, and radio-frequency identification. The research studied in the course will have strong foundations in embedded computing, computer architecture, networking, signal processing, low-power electronics, and distributed computing.

    The goal of the class is to give students the background knowledge necessary to go forward and apply their core research technologies into the emerging domain of human-inspired computing. The primary evaluation criteria are the quality of student's written paper critiques and in-class presentations of assigned research papers,and a semester-long team research project. The project teams will be composed (as far as possible) of a mix of students with EECS background and students with backgrounds in one or more of the application domains. The prerequisite of the class is graduate standing, although undergraduate students in EECS with coursework in programming (EECS 280), networking (EECS 489) and hardware (EECS 370 and 373) will also be able to take this course.

    Related Topics:  Lab-Artificial Intelligence  

    EECS 584: Advanced Database Systems

    Term: Fall 2007
    Course No.: EECS 584
    Credit Hours: 4
    Instructor: Jignesh Patel
    Prerequisites: EECS 484 or permission of instructor

    Course Description:
    EECS 584 will cover a number of advanced topics in development of database management systems (DBMS) and the application of DBMSs in modern applications.

    Topics to be discussed include advanced concurrency control and recovery techniques, query processing and optimization strategies for relational database systems, advanced access methods, parallel and distributed database systems, extensible database systems, data analysis on large databases.

    The course material will be drawn from a number of papers in the database literature. We will cover 2-3 papers per week, and all students attending the class are expected to read the papers before coming to the lecture. Before each class you will be required to hand in a brief summary (~350 words) of the paper that will be discussed in the class. The summary should not a facsimile of the abstract of the paper, but should be your assessment of the key contributions and limitations of the paper. For the summaries, do not provide a section by section play of what is covered in the paper. Simply summarize the key points that stood out when you read the paper. Long summaries will simply be returned without being graded. The reviews will be graded on a scale of 0-4, with 4 being the highest grade. 5% of the course grade is allocated for paper summaries.
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    EECS 498: High-Tech Entrepreneurship

    Term: Fall, 2007
    Course No.: 498
    Credit Hours: 4
    Instructor: Mohammed N. Islam
    Prerequisites: Senior or Graduate Standing (Juniors or Sophomores will also be

    Course Description:
    The technology sector represents a significant portion of the economy of every industrialized nation. In the U.S., more than one third of the gross national product and about half of private-sector spending on capital goods are related to technology. Therefore, particularly in the U.S. economic growth depends on the health and contributions of technology businesses.

    This course is about Technology Entrepreneurship, which is a style of business leadership that involves identifying high-potential, technology-intensive commercial opportunities, gathering resources such as talent and capital, and managing rapid growth and significant risks using principled decision-making skills. Technology ventures exploit break-through advancements in science and engineering to develop better products and services for customers. The leaders of technology ventures demonstrate focus, passion, and an unrelenting will to succeed.

    [More Info]

    EECS 598-2, Photonic Crystals

    Term: Fall07
    Course No.: EECS 598-002
    Credit Hours: 3
    Instructor: Almantas Galvanauskas
    [More Info]

    INTRODUCTION TO DISCRETE EVENT AND HYBRID SYSTEMS

    Term: Fall 2007
    Course No.: 498-002
    Credit Hours: 3
    Instructor: Stephane Lafortune
    Prerequisites: Junior standing

    Course Description:
    This course is intended for undergraduate students who want to learn about dynamic systems with discrete state spaces and event-driven transitions. Discrete Event Systems, as they are called, arise in the modeling of technological systems such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. In embedded and networked systems, discrete event dynamics are coupled with continuous dynamics, giving rise to what are called Hybrid Systems.
    This course will introduce students to the modeling and analysis of discrete event and hybrid systems. Examples from the above areas will be used throughout the course to illustrate the main concepts.
    There are no specific course prerequisites; however, the course is aimed at juniors and seniors in EE, CE, CS, or ME. Some basic knowledge of probability (from e.g., Math 425 or EECS 401 or IOE 265 or Stat 412) is recommended for the last part of the course.

    Syllabus:
    Finite-state automata models of discrete event systems: notions of deadlock and livelock, product and parallel composition, observers, diagnosers.
    Petri net models of discrete event systems: reachability analysis with coverability tree, structural analysis with invariants.
    Timed automata models of discrete event systems: parallel composition, reachability analysis by untiming.
    Hybrid automata models of hybrid systems: basic notions.
    Stochastic models of discrete event systems: stochastic automata, Markov chains, introduction of queueing models.
    Introduction to discrete event simulation.

    Textbook:"Introduction to Discrete Event Systems - Second Edition"by C. Cassandras and S. Lafortune, Springer, 2007

    Grading:Homework assignments, two exams, and a project.

    Several software tools will be used in the course: UMDES, DESUMA, Matlab with Stateflow and SimEvents.
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    Nanoelectronics

    Term: Winter 2007
    Course No.: EECS598-004
    Credit Hours: 3
    Instructor: Wei Lu
    Prerequisites: EECS421 or permission by instructor

    Course Description:
    EECS 598: NanoelectronicsWei LuThis is a graduate level course aimed to provide students a comprehensive understanding on nanoelectronics, and covers both novel MOSFET device structures and emerging research device structures based on the bottom-up paradigm. We plan to fill the gap between the fast pacing research in nanotechnology and the current graduate curricula which focus on conventional CMOS devices. We will begin by first performing an in-depth analysis of the device principles and factors that affect the performance of MOSFET, followed by discussions on the challenges and technological innovations (boosters) that are currently being developed to sustain the historical trend of transistor scaling. Following that, we will carry out a critical survey of emerging research devices that may drive technology beyond CMOS. Topics include transistor device principles and scaling rules, high-k dielectrics, mobility enhancement factors, SOI devices, ballistic and single-electron devices, nanowires and nanotubes, and molecule and spin based devices.Prerequisite: EECS 421 or permission by instructor.Meeting time: MW, 9:00-10:30amLocation: EECS 3427Office: 2417-A EECSEmail: wluee@eecs.umich.eduTextbooks:Fundamentals of Modern VLSI Devices by Yuan Taur and Tak H. NingCambridge University Press, 1st edition (October 13, 1998) ISBN: 0521559596Nanoelectronics and Information Technology by Rainer WaserJohn Wiley & Sons 2 edition (April 22, 2005) ISBN: 3527405429
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    Seminar on Mobile Computing Projects

    Term: winter, 2006
    Course No.: 498
    Credit Hours: 4
    Instructor: Soloway
    Prerequisites: permission of the instructor

    Course Description:
    It is inevitable that all computing will be mobile. Jeff Hawkins, Inventor of the Palm Pilot, 1991The Wii's controller is the new mouse. Somebody said that. In this seminar we will explore the emerging space of mobile computing efforts, from cellphones to portable gaming consoles, from PDA's to smart appliances. We will analyze existing systems in order to develop a framework for the design of new systems. In 498, then, we will form teams of three, to design and prototype mobile computing systems. Projects will be developed by the student teams or projects will be provided by the instructor. Following the discipline developed in 481 and 497, teams will develop SRS and SDS documents. Class presentations will also be made.

    Seminar on semantics of computer and natural languages

    Term: W 07
    Course No.: EECS 598 sec 2
    Credit Hours: 3
    Instructor: Bill Rounds
    Prerequisites: EECS 376 or mathematical maturity

    Course Description:
    This is a one-time seminar course focusing on some of the problems I have been interested in over time. We look at mathematical models of language, natural and computer, and think about complexity and conciseness. We also look at logics, broadly construed, and some issues in concurrency and control. Primary meeting of the course is W 1:30 - 3:30 PM in 3941 CSE. First meeting Monday Jan 8 in 1032 FXB.
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    Solid State Lighting and Solar Cells

    Term: Winter 2007
    Course No.: EECS 598-005
    Credit Hours: 3
    Instructor: P. C. Ku
    Prerequisites: EECS 429 or graduate standing

    Course Description:
    Do you know that we can reduce 25% of the electricity consumption and 10% of the total energy need by replacing the old-fashioned light bulbs with highly efficient solid-state devices? Do you know solar cells with efficiency as high as 87% can be achieved if we properly engineer the compound semiconductor materials? Come and join us in discovering new applications of compound semiconductor materials in the saving and generation of energy. In this course, we will discuss the science and technology behind these increasingly important research fields. We will give an in-depth overview of the physics, materials engineering, device structures, fabrication, and circuit integration. We will put special emphasis on the design and optimization of the technology. We will focus primarily on solid-state lighting and solar cells technologies using compound semiconductor materials such as GaN, InGaP, GaAs and etc. We will mention very little on the organic materials but students who are interested in organic devices will probably still find part of this course interesting. Students who have taken EECS 529 are welcome to enroll in this class too as the overlap will be minimal. This will be the first dedicated entry-level graduate course focusing on optoelectronic technologies in energy applications. Motivated undergraduate students are highly encouraged to join us too. This course will be targeted for senior undergraduate students and graduate students. We will review relevant basics at the beginning of the class but prior background in the level of EECS 429 or equivalent is highly recommended.Please feel free to contact me if you have any question or comment.For EECS/SSEL graduate students: This course can be claimed to fulfill the Solid State Kernel requirement under both Solid State Technology/Circuits and Solid State Devices categories.Textbooks:Primary - 1.E. F. Schubert, Light-Emitting Diodes, 2nd edition, Cambridge (2006)2.M. A. Green, Third Generation Photovoltaics: Advanced Solar Conversions, Springer (2006)Reference - 1.S. Nakamura et al., The Blue Laser Diode, 2nd edition, Springer (2000)

    EECS 498/598 ORGANIC AND MOLECULAR ELECTRONICS

    Term: WINTER 2007
    Course No.: EECS 498-003, 598-007
    Credit Hours: 3
    Instructor: Jerzy Kanicki
    Prerequisites: Senior undergrad or Rackham graduate standing

    Course Description:
    ORGANIC AND MOLECULAR ELECTRONICS

    Course Information
    Instructor:Prof. Jerzy Kanicki
    Office:2307 EECS Building
    tel. 734 - 936 - 0964
    e-mail: kanicki@eecs.umich.edu

    Office Hours:Any time per appointment

    Class Meetings:Monday & Wednesday 3:00 4:30 p.m. 3433 EECS building

    Course Texts:Will be provided

    Course Format:Two 90-min lectures per week

    Course Grading:Two individual assignments: 30%
    Term group paper: 30%
    Final group presentation: 30%
    Class attendance and participation: 10%

    Final Examination: Term group paper and final presentation.

    Homework's:Two device analysis assignments

    Goals:This course is intended for senior undergraduate and graduate students interested in the basic interdisciplinary science of the organic semiconductors and their application to different device structures. The fundamental science and technology to be addressed in this class are inherently interdisciplinary; they fall at the intersection of three disciplines: chemistry, physics and engineering. The fundamental optical, opto-electrical and electronic, and carrier transport properties of the organic macromolecules used in practical organic and molecular devices will be discussed. Comprehensive analysis of the operating principles of thin-film organic semiconductor devices such as light-emitting and photovoltaic devices, thin-film transistors, optical, chemical and biological sensors, and molecular rectifiers will be addressed in details during this class. Finally storage and electrical stability of the devices and their possible applications to future printed plastic electronics will be discussed.

    Introduction to Synthetic Biology

    Term: W 2007
    Course No.: ChE496/BME499
    Credit Hours:
    Instructor: Various-Including Prof. Domitilla del Vecchio
    Prerequisites: Permission of Instructor

    Course Description:
    ChE496/BME499 Introduction to Synthetic Biology Winter 2007. Course Organizer Dr. Peter Woolf, pwoolf@umich.edu Monday & Wednesday 12:30-2:302315 GG Brown, North Campus

    Can we actually design and engineer biological machines? The emerging field of synthetic biology suggests that this process is far easier than some may expect. In fall 2006, a team with nearly a dozen Michigan undergraduates successfully competed in an international competition to create synthetic biological systems, so you can too. Read more about the competition.

    This course is primarily directed toward undergraduates interested in engineering, biology, physical sciences, art, and business. Graduate students in these areas are also welcome to participate. The course content will cover the design, fabrication, informatics, and modeling of synthetic genetic systems. Due to the interdisciplinary nature of the topic, students will work in groups to help train their peers in complementary skill sets.

    The course is a mixture of lectures, hands on wet lab experience, and computing lab experience. The goal is to provide students with a deep understanding of the techniques and literature surrounding synthetic biology.

    The course will have a final group project in which a team of students propose a novel synthetic genetic system following the template of the intercollegiate genetically engineered machines(iGEM) competition (http://parts2.mit.edu/)

    Advanced Programming Languages

    Term: Winter 2007
    Course No.: EECS 590
    Credit Hours: 4
    Instructor: Chandrasekhar Boyapati
    Prerequisites:

    Course Description:
    This is a 4-credit course that covers basic and advanced topics in programming languages, and shows how good programming languages can significantly improve the reliability of software systems. This course has three objectives: 1) To understand fundamental concepts in programming languages, 2) To study some recent topics andtrends in PL research, and 3) To gain experience planning and carrying out a semester long individual PL research project. This course counts as a software kernel course and towards software area qualification for CSE graduate students. This course also counts as an upper-level CS technical elective for CS-ENGR and CS-LSA undergraduate students. Please see the course web page for further information.
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    Nanocircuits and Nanoarchitectures

    Term: FALL 2006
    Course No.: 498/598
    Credit Hours: 3
    Instructor: Prof. Pinaki Mazumder
    Prerequisites: Instructor's Consent

    Course Description:
    Pervasive applications of microelectronics in all walks of our life that ushered in the 21st Century era of triumvirate information, bio and nano technologies have fueled the growth of multi-billion-transistor Silicon integrated circuits. Advances in integrated circuit technologies have been steadily accomplished during the past four decades by continually shrinking the feature sizes of MOS transistors and the accompanying metal interconnects with a view to achieving higher density, faster clock rate, and lower power consumption. As the feature sizes of MOS devices are further scaled down deep into sub-50 nanometer dimensions, the Silicon industry is swiftly approaching the realms of nanoelectronics where device fabrication will mandate the creation of precision structures by manipulation of a string of atoms. It is expected that the nano-scale Silicon technologies in conjunction with other emerging quantum and nanotechnologies will bring about a profound and radical changes in our technology-centric society in the same ways as the revolutionary monolithic fabrication technologies have brought about the electronic revolution in the last century.
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    EECS 509, BIOMEMS

    Term: Fall 2006
    Course No.: EECS 509
    Credit Hours: 3
    Instructor: Michel Maharbiz
    Day & Time: MW 10:30AM -12PM

    Course Description:
    This course will cover the latest advances in bioMEMS, with specific attention to microsystems targeting developmental biology and cell culture. We will use an organism's development --from genome to multicellular tissue-- as a framework for teaching bioMEMS devices: from microPCR chips to microfluidic mixers to tissue scaffolds. The aim is to provide students familiar with microfabrication and microsystems with a context from which to view and evaluate bioMEMS devices and innovations. We will cover implantable and diagnostic microsystems in the latter part of the course. The course will consist of lectures followed by in-class paper review and discussion led by students: the bulk of the technology will be presented through published literature. Critical evaluation of publications will be demanded. A principal component of the grade will be a written NSF or NIH exploratory proposal, to be due at the conclusion of the course.

    EECS 490: Programming Languages

    Term: Fall 2006
    Course No.: EECS 490
    Credit Hours: 4
    Instructor: Chandrasekhar Boyapati
    Prerequisites: EECS 281

    Course Description:
    This is a 4-credit course that introduces fundamental concepts in programming languages. The course covers different programming languages including functional, imperative, object-oriented, and logic programming languages; different programming language constructs for naming, control flow, memory management, concurrency, and modularity; as well as methodologies, techniques, and tools for writing correct and maintainable programs.

    EECS 490 counts as an upper-level CS technical elective for CS-ENGR and CS-LSA undergraduate students. It also counts as a 400-level elective for CSE graduate students.

    Please see the course web page for further information.
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    EECS 598-001, Electromagnetic Metamaterials

    Term: Fall 2006
    Course No. EECS 598 -001
    Instructor: Tony Grbic
    Topic: Electromagnetic Metamaterials
    Prerequisites: EECS 330 or equivalent
    Time: TTH 10:30-12
    Room: EECS 3427

    Course Description:
    This course will cover engineered structures possessing tailored electromagnetic properties, or properties that are difficult to achieve using conventional materials. The course material will include classical microwave structures like periodically loaded transmission lines and waveguides, corrugated surfaces, wire arrays, and more recent examples such as high impedance surfaces, electromagnetic bandgap structures, negative refractive index and artificial magnetic media. Photonic structures such as photonic bandgap materials and metal-dielectric plasmonic structures will also be touched upon. The course will allow graduate students to develop an intuitive feel for the electromagnetic response of various structures through exact and approximate methods. Effective medium theories will be developed for those structures operating in the long wavelength regime, and distributed circuit concepts utilized to gain understanding.

    EECS 598-002: Nanophotonics and Nanofabrication

    Term: Winter 2006
    Instructor: P.C. Ku, 2417G EECS, (734) 764-7134, peicheng@umich.edu
    Lectures: TTh: 1:30 pm to 3:30 pm
    Room: 3433 EECS
    Office hours: TTh 3:45 pm - 5 pm or by appointment.
    Grading (4 credit hours): Homework (5-6 times; 50%) and term paper (50%).
    Prerequisites: EECS 334 or EECS 423 or EECS 434 or Graduate Standing

    Sure you know iPod gets nano! But do you know that photonics gets nano-er? We will review key topics in nano-photonics as well as the enabling fabrication technologies including both top-down and bottom-up approaches. It is an all-encompassing field that has applications in a diverse range of fields from information technology, IC manufacturing, material science, to life science and health care. Whether you are a serious researcher or simply would like to know why the sky is blue or the sunset is red, we welcome you!

    Miniaturization (aka Moore’s Laws) is one of the key driving forces contributing to the growth and prosperity of the "silicon era" in the past few decades. Shrinking the device feature size not only allows us to pack more transistors in one chip but also lowers the power consumption and improves the overall performance. With the device feature reaching the "nano" scale, device interface, surface, size and defects all become important and new physics can be observed. Some of these pose great challenges for us in keeping up with the Moore’s Laws but some can become advantages for novel devices that were not achievable in the "pre-nano" era. One example is the laser diode which has been widely used in our daily life in CD/DVD players, internets, supermarket barcode scanners and etc. A laser diode converts the electrical current into a coherent light ray by recombining the electrons and holes in a nano-scale active region in which the energy states are quantized. Without the "nano" active region, a laser diode would not be efficient enough to be practical.

    In this course, we will review key topics in nano-photonics and the related nano-fabrication technologies. Nano-photonics is the topic that studies either the photonics in nanoscale structures or the use of photonics in them. It has impacts on a range of fields from information technology, IC manufacturing, material science to health care. We plan to discuss the following topics.

    A. Overview
    1. Is nano a hype or the future?
    B. Nano-photonics
    1. Basics (basic properties of light and concepts of photons)
    2. Scaling in optics
    3. Light-matter interaction
    4. Dielectric optics
    i. Dispersion engineering (guiding, slow light, fast light, …)
    ii. Photonic crystals
    5. Metal optics (plasmonics)
    C. Nano-fabrication
    1. Overview. How do we put the right material at the right place with the right size and shape?
    2. Top-down approach
    i. Patterning (litho/etch)
    ii. Thin-film (deposition, damascene)
    3. Bottom-up approach
    i. self-assembly and chemical synthesis
    D. Student term paper presentation

    Logic and Formal verification

    Term: W 06
    Course No.: EECS 480
    Credit Hours: 4
    Instructor: Bill Rounds
    Prerequisites: EECS 376 or mathematical maturity

    Course Description:
    EECS 480 is a new course which explores the uses of logic in verifying hardware and software systems. It requires EECS 376, or appropriate mathematical maturity. The course is appropriate for both undergraduates and graduate students, particularly those in software who have an interest in programming languages. The course focuses on those aspects of logic which have been particularly useful in verifying systems. We look in particular those pieces of logic which have been developed into verifiers, or which have been made into automated theorem proving systems. We look at propositional logic and predicate logic, and then branch into newer kinds of logic such as temporal logic, showing how you can implement these. For example, the temporal logic CTL was developed into the SMV model-checking software and used to verify circuits. We also look at Hoare logic, and we consider efficient data structures like binary decision diagrams.
    [Full Story]

    EECS 590: Advanced Programming Languages

    Term: Winter 2006
    Course No.: EECS 590
    Credit Hours: 4
    Instructor: Chandrasekhar Boyapati

    Course Description:
    This is a 4-credit course that covers basic and advanced topics in programming languages, and shows how good programming languages can significantly improve the reliability of software systems. This course has three objectives: 1) To understand fundamental concepts in programming languages, 2) To study some recent topics and trends in PL research, and 3) To gain experience planning and carrying out a semester long individual PL research project.

    This course counts as a software kernel course and towards software area qualification for CSE graduate students. This course also counts as an upper-level CS technical elective for CS-ENGR and CS-LSA undergraduate students.

    Please see the course web page for further information.
    [Full Story]

    Advanced Natural Language Processing and Information Retrieval

    Term: Winter 2006
    Course No.: 767
    Credit Hours: 3
    Instructor: D. Radev
    Prerequisites: EECS 595 or EECS 597 or SI 650 or permission of instructor

    Course Description:
    The course will focus on reading recent research papers on topics in NLP and IR such as statistical machine translation, expectation maximization, text classification, sentiment and polarity analysis, information extraction using conditional random fields, document models for IR, semi-supervised learning, latent semantic analysis, spectral methods, noisy channel models, graph-based ranking methods, label propagation algorithms, etc. The course is appropriate for students who have already taken either of the following classes: "Natural Language Processing", "Information Retrieval", "Language and Information", and/or "Machine Learning". I can also grant exemptions to other motivated students who can convince me that they can follow the course material.
    [Full Story]

    EECS 598(002)-Introduction to Nanoelectronics

    Term: Fall 2005
    Course #: EECS 598-002
    Instructor: Wei Lu, (wluee@eecs.umich.edu)
    Title : Introduction to Nanoelectronics
    Prerequisites : EECS 320 (or equivalent) and Graduate Standing
    Credit hours: 4
    Days & Times: TTH 10:30-12
    Course Details: Introduction to Nanoelectronics This course will be carried out in a series of lectures covering recent advances in nanoscale science and technology, with emphasis on nanoelectronics. In the first half of the course, we will have an overview of the novel properties and device structures when classical transport is replaced by quantum transport as the device size is reduced down to nanometer scale, as well as new fabrication and characterization techniques developed for these nanoscale devices. In the second half, we will study in detail several systems which have emerged as the leading candidates to drive the state of the art in future electronics. Such systems include single electron devices, carbon nanotubes, semiconductor nanowires and molecular electronics. We will also briefly explore approaches alternative to electronics, such as spintronics and quantum computing.

    prerequisite: Basic familiarity with quantum mechanics and solid state physics at the level of undergraduate courses.

    Channel Coding Theory

    Term: Fall 2005
    Course No.: EECS 650
    Credit Hours: 3
    Instructor: Achilleas Anastasopoulos
    Prerequisites: EECS 501 (EECS 554 recommended)

    Course Description:
    Following the successful introduction of the "modern coding theory" course in Fall 04 (as a seminar course EECS 598-4), and also the current state of the art in the field, the "channel coding theory" course (EECS 650) this Fall semester is completely redesigned. It will be a mixture of "classic" and "modern" coding theory.

    Thus, the interested student will see the classic-algebraic viewpoint (including the study of linear BCH and RS codes), as well as the modern viewpoint (includes the study of the family of turbo-like and space-time codes).

    Below is a more detailed description of the course material.



    This is an advanced course on channel coding techniques.

    In the first part of the course, we will study some basic results from information and coding theory in order to see what is the best one should expect from a good code..

    The study of channel codes is initiated with a review of linear codes, with emphasis on their algebraic structure (this complements the viewpoint taken in EECS 554, where the focus was on practical implementation of encoders and decoders). Next, the theory of finite fields is presented in some detail, as this is the basic tool for the study of non-binary linear codes. These results will be utilized for the construction of cyclic codes, and their significant subclass, namely BCH codes and Reed-Solomon (RS)codes. This will conclude the study of ``classical coding theory''.

    Classical coding theory (which was founded almost 50 years ago) studies codes from their algebraic viewpoint. It served communication theorists and practitioners well, but essentially failed to reach the goal set by information theory, i.e., to provide codes that come close to channel capacity. However, 10 years ago, channel coding theory was revolutionized by the invention of ``turbo codes'' and the re-invention of ``low-density parity-check codes''. This revolution led to the birth of the new subfield of ``modern coding theory''.

    In the second part of the course, we will study families of good codes, collectively referred to as turbo-like codes. Their asymptotic performance, and their encoding and decoding complexity will be studied. This investigation will conclude by asking the question of whether everything that Shannon predicted 50 years ago has been achieved.

    The last part of the course deals with the multi-antenna wireless fading channel, which promises bandwidth efficiencies on the order of tens of bits per second per Hertz. Its capacity will be investigated and families of ``space-time codes'' will be introduced and analyzed.

    EECS 490: Programming Languages

    Term: Fall 2005
    Course No.: EECS 490
    Credit Hours: 4
    Instructor: Chandrasekhar Boyapati
    Prerequisites: EECS 281

    Course Description:
    This is a new 4-credit course that introduces fundamental concepts in programming languages. The course covers different programming languages including functional, imperative, object-oriented, and logic programming languages; different programming language constructs for naming, control flow, memory management, concurrency, and modularity; as well as methodologies, techniques, and tools for writing correct and maintainable programs.

    EECS 490 counts as an upper-level CS technical elective for CS-ENGR and CS-LSA undergraduate students. It also counts as a 400-level elective for CSE graduate students.

    Please see the course web page for further information.
    [Full Story]

    EECS 598-3: Advanced Programming Languages

    This is a 4-credit course that covers basic and advanced topics in programming languages, and shows how good programming languages can significantly improve the reliability of software systems. This course has three objectives: 1) To understand fundamental concepts in programming languages, 2) To study some recent topics and trends in PL research, and 3) To gain experience planning and carrying out a semester long individual PL research project.

    EECS 598-3 has been approved for 500-level graduate credit. This course will count towards software area qualification and as an MS and PhD kernel course.

    Please see the course web page for further information.
    [Full Story]

    EECS 511 New course in Analog-Digital Interfaces

    This new 4 credit course covers most of the well known analog-to-digital and digital-to-analog conversion schemes. The theory of analog-digital conversion, as well as metrics and testing of analog-digital interfaces are also discussed Both Nyquist rate and oversampling converters are covered. Nyquist rate schemes include flash, folding, multi-step and pipeline. The main focus is on CMOS circuits but some bipolar schemes are also discussed. The emphasis is on designing circuits that can be built on state-of-the-art commercial integrated circuit processes. Related topics in mixed signal design are also covered. The course begins with a short review of mixed-signal design. Common building blocks, such as comparators and opamps are examined in detail. However, students are expected to have a good knowledge of analog design fundamentals (i.e. feedback, small signal analysis, stability etc.) and should also be familiar with spice or spectre, before taking this course. EECS 413 (or an equivalent) is a prerequisite. Design work is a significant part of this course. Students design and model complete converters. Design is done with the aid of Matlab, Composer and Spectre. Follow the link below for information on the projects from an earlier special topics offering of this material. The course includes homeworks, design assignments, a mid-term and a design project. [Full Story]

    W05,EECS 598-1: PRODUCTION SYSTEMS ENGINEERING

    WINTER 2005

    EECS 598-1

    PRODUCTION SYSTEMS ENGINEERING

    Instructor S.M. Meerkov

    This course is devoted to rigorous engineering methods for analysis, design, and continuous improvement of production systems in large volume manufacturing. The main topics include:

    1. Quantitative methods for analysis of production systems
    2. Analytical methods for design of lean in-process buffering
    3. Analytical methods for design of lean finished goods buffering
    4. Measurement-based methods for identification, monitoring, and elimination of production systems bottlenecks
    5. System-theoretic properties of production lines in large volume manufacturing

    The techniques included in this course have been developed at the University of Michigan during the last 15 years. They have been applied in numerous projects at GM, Ford and DaimlerChrysler.

    The only prerequisite for the course is elementary probability theory. However, all probability notions, necessary for the course, will be reviewed.

    The course includes weekly homework assignments, one midterm, and a project to be presented as a final exam.

    The course is 3 credit hours. It is open to all graduate students in CoE and UMBS. Advanced undergraduates are also welcome.

    To accommodate industrial audience pursuing continued education, the course will meet once a week, from 6 to 9 pm. The day of the week is yet to be determined (depending on students’ convenience and scheduling constraints).

    EECS 580: Advanced Computer Graphics

    • Instructor: Igor Guskov
    • Times: Mondays and Wednesdays 12:30pm-2:30pm
    This is a graduate-level course in Computer Graphics, with emphasis on geometric modeling and real-time rendering techniques. Intended audience for this course includes CSE graduate students interested in graphics, as well as undergraduate students who took EECS 487 but would like to learn more. Basic knowledge of OpenGL and working knowledge of C/C++ is assumed.
    The course is on the software qual exam list in CSE PhD program.
    Prerequisites: EECS 487 (or equivalent) or graduate standing [Full Story]

    EECS398-1: Data Structures, Algorithms, and Advanced Applications

    Prereq: EECS 203, 280, and strong programming background. Can be used in lieu of EECS 281 to satisfy the CS (both CoE and LSA) degree requirement and cannot be taken for credit along with EECS 281. (4 credits)

    An intensive coverage of EECS 281 material, supplemented with more advanced data structures and algorithms used in practical applications such as text compression, textual search, predictive input, one-way hash functions, etc. Potential advanced data structures covered include skip list, splay tree, calendar queue, bloom filter, quad-tree, and others. Emphasis placed on practical applications of these algorithms and data structures. Several fairly specific and well-defined programming problems assigned. Students are assumed to have strong programming background.

    The workload for this course is intended to be about the same as for EECS 281. For more information on workload and grading policy please visit the course web site listed above. [Full Story]

    W05,EECS 598-2: Circuits and Architectures for Wireless Applications

    Term: Winter 05
    Course#: 598-2
    Instructor: Ranjit Gharpurey
    Credit hours: 4
    Schedule: MW 10:30AM-12:30PM
    Prerequisites: Required EECS 413 or equivalent.

    Course Description:
    This course will cover design and implementation issues in physical layer architectures for wireless applications at the RF/analog and mixed-signal levels. The course will begin with an introduction to impairments in physical layers such as noise and distortion.  An overview of specialized circuits employed in wireless systems and their relevant metrics will be provided. The discussion will include a description of transceivers from RF to baseband and PLL architectures. The emphasis will be on integrated solutions in CMOS/Bipolar/BiCMOS technologies. This will cover popular implementations such as direct-conversion, low-IF, heterodyne, polar etc. Choice of architectures, technology trade-offs and partitioning issues between analog and digital sections will be considered.  The discussion will use case studies from several narrow-band (such as cellular and WLAN)  and also emerging broadband wireless systems.

    EECS 498-1: Computer Security and Trustworthy Computing

    Prerequisite: EECS 482. 4 credits. Semester: Winter 2005 Instructor: Professor Atul Prakash Security components and threats. Integrity: one-way hash functions, digital signatures. Confidentiality: stream and block ciphers, efficiency considerations in use of cryptography. Authentication methods and their security analysis: passwords, biometrics, smartcards, network authentication protocols such as Kerberos and SSL. Authorization: access control models, discretionary-access control and mandatory-access control policies. Software security analysis: attack trees. Software attacks and writing secure code: exploits of buffer overflows, race conditions, misplaced trust. Security testing: fault-injection techniques, fuzzers, code analysis tools. OS kernel attacks and defenses. Auditing and intrusion detection. Perimeter security: firewalls. Overall system planning for security. Group programming projects. [Full Story]

    EECS 598: Societal Impact of Microsystems

    Fall 2004: Societal Impact of Microsystems Offered as EECS 598, Section 6 (K. D. Wise) This course offers a look at the challenges our global society will face during the next fifty years and how microsystems can help meet them. Classes will consist of lectures by invited speakers followed by discussion. The course will examine issues such as the population explosion and its expected impacts on energy consumption, pollution, and global warming. Developments in transportation, health care, space exploration, information technology, and homeland security will also be considered along with engineering ethics. This is a course every graduate should take because creative engineering is the only way we will meet these challenges, and they must be met. 2 credits, Graded, Th. 3:30 - 5:30pm, Chrysler Room 151 Prerequisite: Graduate Standing in Engineering.

    EECS 584: Advanced Database Systems

    EECS 584 will cover a number of advanced topics in development of database management systems (DBMS) and the application of DBMSs in modern applications. Topics to be discussed include advanced concurrency control and recovery techniques, query processing and optimization strategies for relational database systems, advanced access methods, database resource management, parallel and distributed database systems, extensible database systems, data analysis on large databases, and application of DBMS techniques in XML-based applications, mobile applications and bioinformatics. For more details see http://www.eecs.umich.edu/courses/eecs584/ [Full Story]

    EECS 598: BioMEMS

    EECS 598: BioMEMS Professor Michel M. Maharbiz Tues/Thurs 10:30am – 12:00pm Offered Fall 2004 for the first time, this graduate seminar will cover the latest advances in bioMEMS, with specific attention to microsystems targeting developmental biology and cell culture. We will use an organism’s development --from genome to multicellular tissue-- as a framework for teaching bioMEMS devices: from microPCR chips to microfluidic mixers to tissue scaffolds. The aim is to provide students familiar with microfabrication and microsystems with a context from which to view and evaluate bioMEMS devices and innovations. We will cover implantable and diagnostic microsystems in the latter part of the course. The course will consist of lectures followed by in-class paper review and discussion led by students: the bulk of the technology will be presented through published literature. Critical evaluation of publications will be demanded. A principal component of the grade will be a written NSF or NIH exploratory proposal, to be due at the conclusion of the course.

    EECS 475 : Intro to Cryptography (Fall 2004)

    Cryptography plays a fundamental role in building secure computing and communication systems. With its fascinating history through centuries and intriguing connections to deep mathematical ("how quickly can we factor an integer?") and philosophical ("what is randomness?") questions, Cryptography is an important and beautiful subject. With increasing concerns over privacy, security, and authenticity of data and communications in our wired (and wireless) society, cryptographic applications are bound to pervade our lives. Cryptography is, and will continue to be, a vast and exciting area of research in Computer Science and Mathematics. This course is an introduction to the art and science of cryptography. At the end of the course, students should be well-prepared to apply the core scientific principles of cryptography to build secure software and communication systems as well as to pursue more advanced courses and state of the art research in cryptography.

    This course will study fundamental concepts, algorithms, encryption schemes, and protocols in cryptography. Main topics include: symmetric (private key) encryption, public key encryption, hash functions, digital signatures, and key distribution. The course emphasizes a rigorous mathematical study of the various cryptographic schemes and their security in terms of algorithmic complexity. A nontrivial part of the course will be devoted to algorithmic and mathematical background from number theory and algebra needed to gain a solid understanding of cryptography. Popular cryptographic schemes such as AES and RSA will be highlighted and their security will be rigorously investigated. Detailed syllabus is available from the course web site (link below).

    This is a 4-credit course approved as an upper-level CS technical elective for undergraduate students in CS-ENGR and CS-LSA. This course is also approved as a cognate course for Math Majors. Advanced undergraduate and beginning graduate students in Computer Science and Engineering and Mathematics are invited to take this course. Graduate students in EECS can also take it as a 400-level elective course. Grading will be based on homework assignments, a mid-term, and a final project/term paper.
    [Full Story]

    EECS 478: Logic Circuit Synthesis and Optimization

    Advanced design of logic circuits. Technology constraints. Theoretical foundations. Computer-aided design algorithms. Two-level and multilevel optimization of combinational circuits. Optimization of finite-state machines. High-level synthesis techniques: modeling, scheduling, and binding. Verification and testing. [Full Story]

    EECS 684: Current Topics in Database Systems

    Database systems have come along a long way since their inception in the 1970s. Database Management Systems (DBMSs) have been widely successful and are the heart of most information management system. However, there are a number of significant challenges that future DBMSs must meet if they are to continue playing the center role in information processing and management. We are on the verge of a new revolution in ubiquitous computing in which zillions of devices, ranging from small personal digital assistants (PDAs) to “invisible” embedded sensor devices, will demand answers to queries under a wide range of system conditions. These devices will rely on a distributed backend infrastructure to deliver the query results. The data sets in the back-end systems are growing at astonishing rates, demanding scalable distributed data management techniques. Furthermore, the data sets are increasingly complex, and are not limited to simple alphanumeric data types (which traditional relational DBMS manage very effectively). Database query processing and database storage techniques that exist today fall far short of meeting the demands of these future systems. What then are the techniques that will deliver this new world to us? This is the question that we will explore in this course. The course will focus primarily on query processing and query evaluation techniques that are likely to be applicable in mobile, distributed, and sensor database environments of the future. Since most of the questions in this area are unanswered, this course will be very exploratory. [Full Story]

    EECS 498-3: Computational Logic

    How can you be sure that a hardware or software system is really correct? This course is a chance to put logic and automata theory into action! Until recently, testing has been the only way to ``validate'' designs, especially of hardware components.The alternative approach of actually proving correctness has not been practical. But a new development in this area has changed all that: easy-to-use model checking systems. In this course we will study formal verification methods with an emphasis on model checking. You will learn to use the SMV model checking system developed at Carnegie Mellon and use it to verify a significant hardware or software system. This is your chance to learn about an emerging technology! There will be three hours of lecture and an one hour of discussion each week. Both graduate and undergraduate students welcome; prerequisites are EECS 203 and 376. Prof. Bill Rounds [Full Story]

    EECS 598-005: Analog-Digital Interfaces

    This 4 credit course covers most of the well known analog to digital and digital to analog conversion schemes. The theory of analog-digital conversion, as well as metrics and test are also discussed. Both Nyquist rate and oversampling converters are covered. Nyquist rate schemes include flash, folding, multi-step and pipeline. The main focus is on CMOS circuits but some bipolar schemes are also discussed. The emphasis is on designing circuits that can be built on state-of-the-art commercial processes. The course begins with a short review of mixed-signal design. Common building blocks, such as comparators and opamps will be examined in detail. However, students are expected to have a good knowledge of analog design fundamentals (i.e. feedback, small signal analysis, stability etc.) and should also be familiar with spice or spectre, before taking this course. EECS 413 (or an equivalent) is a prerequisite. Design work is a significant part of this course. Students design and model complete converters. Design is done with the aid of Matlab, Composer and Spectre. Follow the link below for information on the projects from an earlier special topics offering of this material. [Full Story]

    EECS 598-2: Computer and Network Security

    A special topics 3-credit graduate course on computer and network security is being offered in Winter 2004. See the course web page for further information. [Full Story]

    EECS598.006, Winter 2004: Theory of quantum computation and information

    Instructor

    Yaoyun Shi, shiyy@eecs, 764-3308, EECS 2233

    Meeting schedule

    MW3:00-4:30, 153 EWRE

    As a result of remarkable theoretical advances in recent years, quantum information science has drawn enthusiastic participations from scientists in many fields. It has been demonstrated that quantum information behaves fundamentally different from classical information, and, it appears that computers based on exact quantum mechanical principles can be dramatically more powerful than those currently deployed.

    This course is an introduction to the theory of quantum computation and information. Topics include foundations of quantum mechanics, quantum algorithms and complexity, quantum information theory, quantum entanglement, quantum error-correcting, and quantum cryptography. It is intended for all interested and mathematically mature audiences with a strong background in linear algebra. Prior knowledge in theoretical computer science, classical information theory, or quantum mechanics is useful, but not necessary.

    Difference with the Fall 2002 course

    For this course we aim at a more diverse group of audiences (mathematicians, physists, computer scientists, electrical engineerers, etc.), and will discuss a wider range of topics, though at a lower level of depth. The focus will be on the very most important results and techniques.

    Prerequisites

    Graduate standing or permission by the instructor. A solid background in linear algebra is necessary.

    Credits: 3 Units

    This course counts for the CSE 500 level course requirement.

    Coursework

    I will lecture for all the meeting time except for two or three lectures when the student will present their project. There will be no exams. Besides attending the lectures and reading books/lecture notes to keep up with the class, the students are required to do the following.
    • Homework (60%): about 6 in total.
    • Scribing (20%): taking notes and typeset it in latex.
    • Term Project (20%): working with a group of 3 or 4, students are asked to read a set of papers in their chosen direction, write a report, and present it in class.

    Reference books

    No textbook is completely satisfying for this course. Among the following three books, more materials will be taken from 3, which is available online.
    1. Isaac L. Chuang and M. A. Nielsen. Quantum Computation and Information, Cambridge University Press, December 2000.
    2. A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi. Classical and Quantum Computation, American Mathematical Society, July 2002.
    3. John Preskill. Quantum Information and Computation, Lecture notes available here.
    [Full Story]

    EECS 215 Spring 2003

    Course materials for EECS 215 lectures and homework assignments in the Spring term 2003 are available at the following web link. [Full Story]

    EECS 661 - Discrete Event Systems

    Offering: This course is offered EVERY OTHER YEAR in the fall semester.

    Instructor: Stephane Lafortune
    Room 4234A EECS, 763-0591
    stephane@eecs.umich.edu, www.eecs.umich.edu/~stephane

    Time: M-W-F: 10:30 - 11:30 am;

    Location: Rm. 3433 EECS

    Prerequisite: Graduate standing

    Textbook: ``Introduction to Discrete Event Systems'' by C. Cassandras and S. Lafortune, Kluwer (1999).
    (See www.eecs.umich.edu/~stephane/Book for further information about this book.)

    Grading: Homework assignments, two mid-term exams, and a project.

    Description:

    This course is intended for engineering and computer science graduate students (Master's or Ph.D. level) who want to learn about modeling, analysis, and control of ``discrete event dynamical systems'' (DES). DES arise in the modeling of technological systems such as automated manufacturing systems, communication networks, distributed software systems, process control systems, and traffic control systems. The ``activity'' in these systems is governed by operational rules designed by humans; their dynamics are therefore characterized by asynchronous occurrences of discrete events.

    The class will consider two modeling formalisms for DES: automata (or state machines) and Petri nets. We will consider both untimed and timed versions of these models. We will first study techniques to analyze the system behavior (e.g., reachability, blocking properties, diagnosability). Then we will consider feedback control of the system in order to achieve desired properties such as avoidance of illegal states and illegal sequences of events, absence of deadlock and livelock, etc. We will consider control problems under full and partial event observation and under partial event controllability.

    The software package UMDES-LIB will be used throughout the course for model analysis and controller synthesis. (See http://www.eecs.umich.edu/umdes/projects/lib/umdeslib.html for further information about UMDES-LIB.)

    Syllabus: We will cover the first five chapters of the textbook:

    Information: For more information, please contact the instructor.

    EECS 598-02 Randomized Computation

    Winter 2002

    Instructor: Satyanarayana V. Lokam (satyalv@eecs.umich.edu)

    Time and Place: Tue and Thu 11:30 AM -- 1:30 PM, 3433 EECS.

    Credit Hours: 4 CSE students can take this course for 500+ level credit.

    Prerequisites: A basic course in algorithms or computational complexity and familiarity with elementary algebra and probability theory.

    Description: For several important problems, use of randomness yields the most efficient algorithms known, or the simplest, or both. In fact, randomness is essential for certain tasks in cryptography and distributed computation. As a result, research in randomized algorithms has seen a phenomenal growth in recent years. In the first half of this course, we will study a collection of techniques for effectively using randomization and for analyzing randomized algorithms. We will pick examples from a variety of settings and problem areas.

    Given that randomization helps, where do computers get their randomness from? In practice, we often use Pseudo-Random Generators (PRG's). But, when is the output of a PRG ``random enough" to replace a source of pure random bits? How do we construct such good PRG's? Even assuming computers do have access to ``pure randomness," how dramatic a speedup can we achieve using randomness? In the second half of the course, we will study recent results that address such issues. Important results in this area have rigorously established the intimate relations among the notions of randomness, computation,and information.

    Attempts to understand some basic scientific questions, use of simple, but elegant, mathematics, and building rigorous foundations for important practical applications are some of the interesting highlights of this course.

    Main Topics:

    • Background from Probability Theory
    • Randomized Algorithms
    • Derandomization
    • Pseudo-Random Generators
    Grading: Homework and term papers/final projects.

    Homework: 50%
    Term Paper/Final Project: 50%

    For more up-to-date information, please visit: http://www.eecs.umich.edu/~satyalv/rand/

    EECS 598-07 Nonlinear Fiber Optical Devices

    EECS 598 SECTION 7

    Description: Study of nonlinear optical devices that operate with multiple nonlinear processes to perform operations including switching, amplification, pulse generation, optical limiting, optical clock recovery and frequency switching. There will be a focus on the nonlinear processes in high energy and high power, ultrafast fiber lasers. There will be a look at the properties of photonic crystals including their nonlinear optical properties. The course requirement will be the design of your own device or the analysis of an existing device.

    Prerequisites: EECS 538 or EECS 634 or permission of instructor

    Credits: 1 credit

    Instructor: EECS Adjunct Dr. Donald J. Harter  dharter@eecs.umich.edu

    EECS 598-5: Mathematical Modeling and Simulation Techniques for Networking

    Instructor: Mingyan Liu

    Communication networks have become increasingly complex systems. With the rapid expansion of the Internet, it is important that we are equipped with proper tools to analyze and gain insight on the performance, dynamics, technical and social implications of various mechanisms used in the Internet. In this course we will study two classes of such tools: mathematical modeling and simulation. Both of them are widely used in networking. On the one hand mathematical modeling, via abstraction, can be tractable, fast and intuitive, as well as facilitate optimization and sensitivity analysis. On the other hand simulation can be much more detailed and can handle large-scale systems.

    In this course we will review mathematical modeling techniques based on a range of principles and examine their effectiveness, particularly, the relationship between the simplicity of a model and its usefulness. The study will be highly application-oriented, in that there is always a very clear realistic subject to be modeled, be it a protocol, a channel or a policy. Under the second theme of the course we will review the techniques of computer simulation, which is widely used to study complex systems, and also often used to validate mathematical models. We will NOT show how to use a particular simulation tool (e.g.,NS-2, OPNET), but will show the basic statistical and discrete event concepts underlying most simulation tools. We will also discuss how to use simulation in a scientific way.

    This course will consist of both lectures (i.e., presentation by the instructor) and discussions (i.e., discussion on assigned papers in class). The subjects covered in this course are as follows (the sequence is subject to change):
    - Modeling of multiple access channels (e.g., channel errors, IEEE802.11)
    - Optimal routing and blocking probabilities
    - Performance modeling of TCP
    - Congestion control, rate control and utility maximizing
    - Simulation and the Monte Carlomethod
    - Internet traffic and self-similarity

    For each of these subjects there will be a list of selected papers as reading assignment. The final grade will be based on
    - the summary review on paper reading assignment;
    - participation in class discussions;
    - a term project/paper and presentation

    For more information, please visit http://www.eecs.umich.edu/~mingyan/598W02/ .