2013-14 General Bulletin

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Glennan Building (7071)
http://engineering.case.edu/eecs/
Phone: 216.368.2800; Fax: 216.368.6888
Kenneth A. Loparo, PhD, Professor and Chair

Electrical Engineering and Computer Science (EECS) spans a spectrum of topics from (i) materials, devices, circuits, and processors through (ii) control, signal processing, and systems analysis to (iii) software, computation, computer systems, and networking.  The EECS Department at Case Western Reserve supports four synergistic degree programs: Electrical Engineering, Computer Science, Computer Engineering, and Systems & Control Engineering. Each degree program leads to the Bachelor of Science degree at the undergraduate level. The department also offers a Bachelor of Arts in Computer Science for those students who wish to combine a technical degree with a broad education in the liberal arts. At the graduate level, the department offers the Master of Science and Doctor of Philosophy degrees in Electrical Engineering, Computer Engineering, Systems & Control Engineering, and Computing & Information Sciences (i.e., computer science). We offer minors in Electrical Engineering, Computer Science (BS and BA), Computer Engineering, Systems & Control Engineering, and also in Computer Gaming, Artificial Intelligence (AI), and Electronics.  For supplemental information to this bulletin as well as the latest updates, please visit the EECS Department web site at http://eecs.case.edu.

EECS is at the heart of modern technology.  EECS disciplines are responsible for the devices and microprocessors powering our computers and embedded into everyday devices, from cell phones and tablets to automobiles and airplanes.  Healthcare is increasingly building on EECS technologies: micro/nano systems, electronics/instrumentation, implantable systems, wireless medical devices, surgical robots, imaging, medical informatics, bioinformatics, system biology, and data mining and visualization.  The future of energy will be profoundly impacted by EECS technologies, from smart appliances connected to the Internet, smart buildings that incorporate distributed sensing and control, to the envisioned smart grid that must be controlled, stabilized, and kept secure over an immense network.  EECS drives job creation and starting salaries in our fields are consistently ranked in the top of all college majors.  Our graduates work in cutting-edge companies--from giants to start-ups, in a variety of technology sectors, including computer and internet, healthcare and medical devices, manufacturing and automation, automotive and aerospace, defense, finance, energy, and consulting. 

Department Structure

EECS at Case Western Reserve is organized internally into two informal divisions: (i) Computer Science (CS); and (ii) Electrical, Computer, and Systems Engineering (ECSE). The chair of EECS is Professor Michael Branicky.

Educational Philosophy

The EECS department is dedicated to developing high-quality graduates who will take positions of leadership as their careers advance. We recognize that the increasing role of technology in virtually every facet of our society, life, and culture makes it vital that our students have access to progressive and cutting-edge higher education programs. The program values for all of the degree programs in the department are:

  • mastery of fundamentals
  • creativity
  • social awareness
  • leadership skills
  • professionalism

Stressing excellence in these core values helps to ensure that our graduates are valued and contributing members of our global society and that they will carry on the tradition of engineering leadership established by our alumni.

Our goal is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance their fields. To achieve this goal, the department offers a wide range of technical specialties consistent with the breadth of electrical engineering and computer science, including recent developments in the field. Because of the rapid pace of advancement in these fields, our degree programs emphasize a broad and foundational science and technology background that equips students for future developments. Our programs include a wide range of electives and our students are encouraged to develop individualized programs which can combine many aspects of electrical engineering and computer science.

Research

The research thrusts of the Electrical Engineering and Computer Science department include:

  1. Micro/Nano Systems
  2. Electronics and Instrumentation
  3. Robotics and Haptics
  4. Embedded Systems, including VLSI, FPGA
  5. Hardware Algorithms, Hardware Security, Testing/Verification
  6. Bioinformatics and Systems Biology
  7. Machine Learning and Data Mining
  8. Computer Networks and Distributed Systems
  9. Secure and Reliable Software
  10. Energy Systems, including Wind and Power Grid Management/Control
  11. Gaming, Simulation, Optimization
  12. Medical Informatics and Wireless Health

EECS participates in a number of groundbreaking collaborative research and educational programs, including the Microelectromechanical Systems Research Program, the Center for Computational Genomics, graduate program in Systems Biology and Bioinformatics, the Clinical & Translational Science Collaborative, the Great Lakes Energy Institute, and the VA Center for Advanced Platform Technology.

Electrical, Computer, and Systems Engineering Division

Kenneth A. Loparo, PhD
(Case Western Reserve University)
Nord Professor of Engineering and Chair of EECS
Stability and control of nonlinear and stochastic systems; fault detection, diagnosis, and prognosis; recent applications work in advanced control and failure detection of rotating machines, signal processing for the monitoring and diagnostics of physiological systems, and modeling, analysis, and control of power and energy systems

Swarup Bhunia, PhD
(Purdue University)
Associate Professor
Low power and robust nanoelectronics, adaptive nanocomputing, hardware security and protection, implantable electronics

Marc Buchner, PhD
(Michigan State University)
Associate Professor
Computer gaming and simulation, virtual reality, software-defined radio, wavelets, joint time-frequency analysis

M. Cenk Cavusoglu, PhD
(University of California, Berkeley)
Professor
Robotics, systems and control theory, and human-machine interfaces; with emphasis on medical robotics, haptics, virtual environments, surgical simulation, and bio-system modeling and simulation

Vira Chankong, PhD
(Case Western Reserve University)
Associate Professor
Large-scale optimization; logic-based optimization; multi-objective optimization; optimization applications in radiation therapy treatment planning, medical imaging, manufacturing and production systems, and engineering design problems

Philip Feng, PhD
(California Institute of Technology)
Assistant Professor
Nanoelectromechanical systems (NEMS), energy-efficient devices, advanced materials & devices engineering, bio/chemical sensors & biomedical microsystems, RF/microwave devices & circuits, low-noise measurement & precision instruments

Mario Garcia-Sanz, DrEng
(University of Navarra, Spain)
Milton and Tamar Maltz Professor in Energy Innovation
Robust and nonlinear control, quantitative feedback theory, multivariable control, dynamic systems, systems modeling and identification; energy innovation, wind energy, spacecraft, electrical, mechanical, environmental and industrial applications

Steven L. Garverick, PhD
(Massachusetts Institute of Technology)
Professor
Mixed-signal integrated circuit design, microelectromechanical system integration, sensor/actuator interfacing, data conversion, wireless communication, analog neural network circuits, medical instrumentation

Evren Gurkan-Cavusoglu, PhD
(Middle East Technical University)
Assistant Professor
Systems and control theory, systems biology, computational biology, biological system modeling, signal processing applied to biological systems, signal processing

Mingguo Hong, PhD
Associate Professor
Power systems, electricity markets, operation research, optimization, smart grid

Gregory S. Lee, PhD
(University of Washington)
Assistant Professor
Haptic devices, including low-power design and effects on perception; applications to robotic surgery and telesurgery; secure teleoperation

Wei Lin, PhD
(Washington University in St. Louis)
Professor
Nonlinear control, dynamic systems and homogeneous systems theory, H-infinity and robust control, adaptive control, system parameter estimation and fault detection, nonlinear control applications to under-actuated mechanical systems, biologically-inspired systems and systems biology

Behnam Malakooti, PhD, PE
(Purdue University)
Professor
Design and multi-objective optimization, manufacturing/production/operations systems, intelligent systems and networks, artificial neural networks, biological systems, intelligent decision making

Mehran Mehregany, PhD
(Massachusetts Institute of Technology)
Goodrich Professor of Engineering Innovation
Research and development at the intersections of micro/nano-electro-mechanical systems, semiconductor silicon carbide and integrated circuits

Francis "Frank" L. Merat, PhD, PE
(Case Western Reserve University)
Associate Professor
Computer and robot vision, digital image processing, sensors, titanium capacitors and power electronics; RF and wireless systems; optical sensors; engineering education

Pedram Mohseni, PhD
(University of Michigan)
Associate Professor
Biomedical microsystems, bioelectronics, wireless neural interfaces, CMOS interface circuits for MEMS, low-power wireless sensing/actuating microsystems

Wyatt S. Newman, PhD, PE
(Massachusetts Institute of Technology)
Professor
Mechatronics, high-speed robot design, force- and vision-based machine control, artificial reflexes for autonomous machines, rapid prototyping, agile manufacturing, mobile robotic platforms

C. A. Papachristou, PhD
(Johns Hopkins University)
Professor
VLSI design and CAD, computer architecture and parallel processing, design automation, embedded system design

Daniel Saab, PhD
(University of Illinois at Urbana-Champaign)
Associate Professor
Computer architecture, VLSI system design and test, CAD design automation

Sree N. Sreenath, PhD
(University of Maryland)
Professor
Systems biology complexity research (modeling, structural issues, and simulation); cell signaling, population behavior, and large-scale behavior; global issues and sustainable development

Xinmiao Zhang, PhD
(University of Minnesota)
Timothy E. and Allison L. Schroeder Associate Professor
VLSI architecture design for communications, digital signal processing, cryptosystems and medical instruments

Hongping Zhao, PhD
(Lehigh University)
Assistant Professor
Applied physics of semiconductor optoelectronics materials and devices, physics of semiconductor nanostructures, and semiconductors for light emitting diodes, lasers, and energy applications; emphasis on III-Nitride semiconductors

Christian A. Zorman, PhD
(Case Western Reserve University)
Associate Professor
Materials and processing techniques for MEMS and NEMS, wide bandgap semiconductors, development of materials and fabrication techniques for polymer-based MEMS and bioMEMS


Computer Science Division

Harold S. Connamacher, PhD
(University of Toronto)
Assistant Professor
Constraint satisfaction problems, graph theory, random structures, and algorithms

Chris Fietkiewicz, PhD
(Case Western Reserve University)
Assistant Professor
Applied and theoretical neuroscience, neuronal modeling, signal processing and signal analysis, electrophysiology, applications to epilepsy and respiratory control

Mehmet Koyuturk, PhD
(Purdue University)
T. & D. Schroeder Associate Professor of Computer Science and Engineering
Bioinformatics and computational biology, computational modeling and algorithm development for systems biology, integration, mining and analysis of biological data, algorithms for distributed systems

Michael Lewicki, PhD
(California Institute of Technology)
Associate Professor
Computational perception and scene analysis, visual representation and processing, auditory representation and analysis

Jing Li, PhD
(University of California, Riverside)
Associate Professor
Computational biology and bioinformatics, statistical genomics and functional genomics, systems biology, algorithms

Vincenzo Liberatore, PhD
(Rutgers University)
Associate Professor
Distributed systems, Internet computing, randomized algorithms

Gultekin Ozsoyoglu, PhD
(University of Alberta, Canada)
Professor
Graph databases and data mining problems in metabolic networks, metabolomics, and systems biology, bioinformatics, web data mining

Z. Meral Ozsoyoglu, PhD
(University of Alberta, Canada)
Andrew R. Jennings Professor of Computing
Database systems, database query languages and optimization, data models, index structures, bioinformatics, medical informatics

H. Andy Podgurski, PhD
(University of Massachusetts, Amherst)
Professor
Software engineering methodology and tools, especially use of data mining, machine learning, and program analysis techniques in software testing, fault detection and localization, reliable engineering and software security, electronic mediacal records, privacy

Michael Rabinovich, PhD
(University of Washington)
Professor
Computer networks, Internet performance evaluation, databases, utility computing

Soumya Ray, PhD
(University of Wisconsin, Madison)
Assistant Professor
Artificial intelligence, machine learning, reinforcement learning, automated planning, applications to interdisciplinary problems including medicine and bioinformatics

GQ (Guo-Qiang) Zhang, PhD
(Cambridge University, England)
Professor
Programming languages, theory of computation, logic and topology in computer science, knowledge representation, information technology, clinical and medical informatics, semantic web

Xiang Zhang, PhD
(University of North Carolina at Chapel Hill)
Assistant Professor
Computational genetics, bioinformatics, data mining, machine learning, databases


Research Faculty

Mehdi Bageri-Hamaneh, PhD
(Case Western Reserve University)
Research Assistant Professor
Simulation and modeling of biological systems, biomedical signal processing, electroencephalogram (EEG) source imaging

Michael Fu, PhD
(Case Western Reserve University)
Research Assistant Professor
Neuro-rehabilitation and motor-relearning, with emphasis on virtual environments, neuromuscular electrical stimulation, robotics, psychophysics, haptic interfaces, and brain-machine interfaces

Farhad Kaffashi, PhD
(Case Western Reserve University)
Research Assistant Professor
Signal processing of physiological time series data, systems and control

Joseph A. Potkay, PhD
(University of Michigan)
Research Assistant Professor
Medical microsystems, MEMS, microfluidics; microfabricated artificial organs, biocompatible sensor/actuator systems; energy harvesting and implantable power generators


Active Emeritus Faculty

George W. Ernst, PhD
(Carnegie Institute of Technology)
Emeritus Professor
Learning problem solving strategies, artificial intelligence, expert systems, program verification

Dov Hazony, PhD
(University of California, Los Angeles)
Emeritus Professor
Network synthesis, ultrasonics, communications

Wen H. Ko, PhD
(Case Institute of Technology)
Emeritus Professor
Solid state electronics, micro and nano sensors, biomedical instrumentation, implant telemetry

Mihajlo D. Mesarovic, PhD
(University of Belgrade)
Emeritus Professor
Complex systems theory, global issues and sustainable development, systems biology

Lee J. White, PhD
(University of Michigan)
Emeritus Professor
Software testing: regression testing, GUI testing, specification-based testing, testing of object-oriented software


Adjunct Faculty Appointments

Michael Adams, PhD
(Case Western Reserve University)
Adjunct Assistant Professor

Mark A. Allman, MSEE
(Ohio University)
Adjunct Instructor

Michael S. Branicky, ScD, PE
(Massachusetts Institute of Technology)
Adjunct Professor

Reza Jamesebi, PhD
(Case Western Reserve University)
Adjunct Assistant Professor

Suparerk Janjarasjitt, PhD
(Case Western Reserve University)
Adjunct Assistant Professor

Srinivas Raghavan, PhD
(Ohio State University)
Adjunct Professor

Gideon Samid, PhD
(Israel Institute of Technology)
Adjunct Assistant Professor

Shivakumar Sastry, PhD
(Case Western Reserve University)
Adjunct Associate Professor

William L. Schultz, PhD, PE
(Case Western Reserve University)
Adjunct Associate Professor

Marvin S. Schwartz, PhD
(Case Western Reserve University)
Adjunct Professor

Larry Sears
(Case Western Reserve University)
Adjunct Instructor

Amit Sinha, PhD
(Case Western Reserve University)
Adjunct Assistant Professor

Benjamin D. Smith, DMA
(University of Illinois at Urbana-Champaign)
Adjunct Instructor

Norman Tien, PhD
(University of California, San Diego)
Adjunct Professor

Peter J. Tsivitse, PhD
(Case Western Reserve University)
Adjunct Professor

Stephen D. Umans, PhD
(Massachusetts Institute of Technology)
Adjunct Professor

Olaf Wolkenhauer, PhD
(UMIST, Manchester)
Adjunct Professor

Qing-rong Jackie Wu, PhD
(Mayo Graduate School)
Adjunct Associate Professor


Secondary Faculty Appointments

Alexis R. Abramson, PhD
(University of California, Berkeley)
Associate Professor, Mechanical and Aerospace Engineering

Mark Griswold, PhD
(University of Würzburg, Germany)
Professor, Radiology

Thomas LaFramboise, PhD
(University of Illinois)
Associate Professor, Genetics

Roger D. Quinn, PhD
(Virginia Polytechnic Institute and State University)
Professor, Mechanical and Aerospace Engineering

Satya S. Sahoo, PhD
(Wright State University)
Assistant Professor, Center for Clinical Investigations

Nicole Sieberlich, PhD
(University of Wurzburg, Germany)
Assistant Professor, Biomedical Engineering

Matthew J. Sobel, PhD
(Stanford University)
Professor, Operations

Xiong (Bill) Yu, PhD, PE
(Purdue University)
Associate Professor, Civil Engineering

Electrical Engineering  |  Systems and Control Engineering  Computer Engineering  Computer Science  |  Suggested Programs of Study

Undergraduate Programs

The EECS department engineering offers accredited programs leading to BS degrees in:

  1. Electrical Engineering
  2. Systems and Control Engineering
  3. Computer Engineering
  4. Computer Science

These programs provide students with a strong background in the fundamentals of mathematics, science, and engineering. Students can use their technical and open electives to pursue concentrations in bioelectrical engineering, complex systems, automation and control, digital systems design, embedded systems, micro/nano systems, robotics and intelligent systems, signal processing and communications, and software engineering. In addition to an excellent technical education, all students in the department are exposed to societal issues, ethics, professionalism, and have the opportunity to develop leadership and creativity skills.

The Bachelor of Science degree programs in Computer Engineering, Electrical Engineering, and Systems and Control Engineering are accredited by the Engineering Accreditation Commission of ABET, www.abet.org.

The Bachelor of Science degree program in Computer Science is accredited by the Computing Accreditation Commission of ABET, www.abet.org.


Electrical Engineering

The Bachelor of Science program in electrical engineering provides our students with a broad foundation in electrical engineering through combined classroom and laboratory work, and prepares our students for entering the profession of electrical engineering, as well as for further study at the graduate level.

The educational mission of the electrical engineering program is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies that will advance the general field of electrical engineering.

Program Educational Objectives

  1. Graduates will be successful professionals obtaining positions appropriate to their background, interests, and education.
  2. Graduates will use continuous learning opportunities to improve and enhance their professional skills.
  3. Graduates will demonstrate leadership in their profession.

Student Outcomes

As preparation for achieving the above educational objectives, the BS degree program in Electrical Engineering is designed so that students attain:

  • an ability to apply knowledge of mathematics, science, and engineering
  • an ability to design and conduct experiments, as well as to analyze and interpret data
  • an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • an ability to function on multi-disciplinary teams
  • an ability to identify, formulate, and solve engineering problems
  • an understanding of professional and ethical responsibility
  • an ability to communicate effectively
  • the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
  • a recognition of the need for, and an ability to engage in life-long learning
  • a knowledge of contemporary issues
  • an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Core courses provide our students with a strong background in signals and systems, computers, electronics (both analog and digital), and semiconductor devices. Students are required to develop depth in at least one of the following technical areas: electromagnetics, signals and systems, solid state, computer hardware, computer software, control, and circuits. Each electrical engineering student must complete the following requirements.

Major in Electrical Engineering

Major Requirements

EECS 245Electronic Circuits4
EECS 246Signals and Systems4
EECS 281Logic Design and Computer Organization4
EECS 309Electromagnetic Fields I3
EECS 321Semiconductor Electronic Devices4
EECS 398Engineering Projects I4
EECS 399Engineering Projects II3
Applied statistics elective, chose one of the following:
Signal Processing
Communications and Signal Analysis
Digital Communications
Eighteen hours of approved technical electives including at least 9 hours of approved courses to constitute a depth of study

 

Breadth Requirement

ENGR 131Elementary Computer Programming3
ENGR 210Introduction to Circuits and Instrumentation4
EECS 281Logic Design and Computer Organization4
EECS 245Electronic Circuits4
EECS 246Signals and Systems4
EECS 309Electromagnetic Fields I3
STAT 332Statistics for Signal Processing3
EECS 321Semiconductor Electronic Devices4
EECS 398Engineering Projects I4
EECS 399Engineering Projects II3
Total Units36

 

Depth Requirement

Each student must show a depth of competence in one technical area by taking at least three courses from one of the following seven areas. This depth requirement may be met using a combination of the above core courses and a selection of open and technical electives.

Area I: Signals & Systems
EECS 246Signals and Systems4
EECS 313Signal Processing3
EECS 351Communications and Signal Analysis3
EECS 354Digital Communications3
EECS 381Hybrid Systems3

 

Area II: Computer Software
EECS 233Introduction to Data Structures4
EECS 337Compiler Design4
EECS 338Introduction to Operating Systems4
EECS 393Software Engineering3

 

Area III: Solid State
EECS 321Semiconductor Electronic Devices4
EMSE 314Electrical, Magnetic, and Optical Properties of Materials3
EECS 322Integrated Circuits and Electronic Devices3
EECS 415Integrated Circuit Technology I3

 

Area IV: Control
EECS 304Control Engineering I with Laboratory3
EECS 346Engineering Optimization3
EECS 381Hybrid Systems3
EECS 483Data Acquisition and Control3

 

Area V: Circuits
EECS 245Electronic Circuits4
EBME 310Principles of Biomedical Instrumentation3
EECS 344Electronic Analysis and Design3
EBME 418Electronics for Biomedical Engineering3
EECS 426MOS Integrated Circuit Design3

 

Area VI: Computer Hardware
EECS 281Logic Design and Computer Organization4
EECS 301Digital Logic Laboratory2
EECS 314Computer Architecture3
EECS 315Digital Systems Design4
EECS 316Computer Design3
EECS 318VLSI/CAD4

 

Statistics Requirement

STAT 332Statistics for Signal Processing *3
One of the following:3
Signal Processing
Communications and Signal Analysis
Digital Communications
Another class approved by advisor

*

STAT 333 Uncertainty in Engineering and Science may be substituted with approval of advisor


Design Requirement

EECS 398Engineering Projects I4
EECS 399Engineering Projects II3

In consultation with a faculty advisor, a student completes the program by selecting technical and open elective courses that provide in-depth training in one or more of a spectrum of specialties such as digital and microprocessor-based control, communications and electronics, solid state electronics, and integrated circuit design and fabrication. With the approval of the advisor a students may emphasize other specialties by selecting elective courses from other programs or departments.

Many courses have integral or associated laboratories in which students gain “hands-on” experience with electrical engineering principles and instrumentation. Students have ready access to the teaching laboratory facilities and are encouraged to use them during nonscheduled hours in addition to the regularly scheduled laboratory sessions. Opportunities also exist for undergraduate student participation in the wide spectrum of research projects being conducted in the department.

Cooperative Education Program in Electrical Engineering

There are many excellent Cooperative Education (CO-OP) opportunities for electrical engineering majors. A CO-OP student does two CO-OP assignments in industry or government. The length of each assignment is a semester plus a summer which is enough time for a student to complete a significant engineering project. The CO-OP program takes five years to complete because the student is typically gone from campus for two semesters.

BS/MS Program in Electrical Engineering

The department encourages highly motivated and qualified students to apply for admission to the five-year BS/MS Program in the junior year. This integrated program, which permits substitution of MS thesis work for the senior design project, provides a high level of fundamental training and in-depth advanced training in the student’s selected specialty. It also offers the opportunity to complete both the Bachelor of Science in Engineering and Master of Science degrees within five years.

Minor in Electrical Engineering

Students enrolled in degree programs in other engineering departments can have a minor specialization by completing the following courses:

EECS 245Electronic Circuits4
EECS 246Signals and Systems4
EECS 281Logic Design and Computer Organization4
EECS 309Electromagnetic Fields I3
Approved technical elective3
Total Units18

 

Minor in Electronics

The department also offers a minor in electronics for students in the College of Arts and Sciences. This program requires the completion of 31 credit hours, of which 10 credit hours may be used to satisfy portions of the students’ skills and distribution requirements. The following courses are required for the electronics minor:

MATH 125Math and Calculus Applications for Life, Managerial, and Social Sci I4
MATH 126Math and Calculus Applications for Life, Managerial, and Social Sci II4
PHYS 115Introductory Physics I4
PHYS 116Introductory Physics II4
ENGR 131Elementary Computer Programming3
ENGR 210Introduction to Circuits and Instrumentation4
EECS 246Signals and Systems4
EECS 281Logic Design and Computer Organization4
Total Units31

 


Systems and Control Engineering

The Bachelor of Science program in systems and control engineering provides our students with the basic concepts, analytical tools, and engineering methods which are needed in analyzing and designing complex technological and non-technological systems. Problems relating to modeling, decision-making, control, and optimization are studied. Some examples of systems problems which are studied include: modeling and analysis of complex energy, environmental, and biological systems; computer control of industrial plants; developing world models for studying environmental policies; and optimal planning and management in large-scale systems. In each case, the relationship and interaction among the various components of a given system must be modeled. This information is used to determine the best way of coordinating and regulating these individual contributions to achieve the overall goal of the system.

Major in Systems and Control Engineering

The mission of the Systems and Control Engineering program is to provide internationally recognized excellence for graduate and undergraduate education and research in systems analysis, design, and control. These theoretical and applied areas require cross-disciplinary tools and methods for their solution.

Program Educational Objectives

  1. Graduates will have applied systems methodology to multi-disciplinary projects that include technical, social, environmental, political, and/or economic factors.
  2. Graduates will use systems understanding, critical thinking and problem solving skills to analyze and design engineering systems or processes that respond to technical and societal needs as demonstrated by their measured professional accomplishments in industry, government and research.
  3. Graduates will facilitate multidisciplinary projects that bring together practitioners of various engineering fields in an effective, professional, and ethical manner as demonstrated by their teamwork, leadership, communication, and management skills.

Student Outcomes

As preparation for achieving the above educational objectives, the BS degree program in Systems and Control Engineering is designed so that students attain:

  • an ability to apply knowledge of mathematics, science, and engineering
  • an ability to design and conduct experiments, as well as to analyze and interpret data
  • an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • an ability to function on multi-disciplinary teams
  • an ability to identify, formulate, and solve engineering problems
  • an understanding of professional and ethical responsibility
  • an ability to communicate effectively
  • the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
  • a recognition of the need for, and an ability to engage in life-long learning
  • a knowledge of contemporary issues
  • an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

There are four elective sequences available within the BS program in systems and control engineering curriculum that represent the breadth of the discipline:

Area: 1 Dynamic Systems, Control and Signal Processing

MATH 201Introduction to Linear Algebra3
EECS 351Communications and Signal Analysis3
EECS 381Hybrid Systems3
EECS 401Digital Signal Processing3
EECS 408Introduction to Linear Systems3
EECS 416Convex Optimization for Engineering3
EECS 452Random Signals3
EECS 483Data Acquisition and Control3
EECS 489Robotics I3

 

Area 2: Systems Biology and Complex Systems Analysis

MATH 201Introduction to Linear Algebra3
EECS 381Hybrid Systems3
EECS 391Introduction to Artificial Intelligence3
EECS 396Independent Projects1 - 6
EECS 408Introduction to Linear Systems3
EECS 416Convex Optimization for Engineering3
BIOL 325Cell Biology3
BIOL 250Introduction to Cell and Molecular Biology Systems3

 

Area 3: Manufacturing, Robotics and Operational Systems

EECS 350/450Operations and Systems Design3
EECS 360/460Manufacturing and Automated Systems3
EECS 489Robotics I3
OPMT 450Project Management3
OPMT 420Six Sigma and Quality Management3
OPMT 476Strategic Sourcing3
OPMT 477Enterprise Resource Planning in the Supply Chain3

 

Area 4: Information Systems

EECS 233Introduction to Data Structures4
EECS 325Computer Networks I3
EECS 381Hybrid Systems3
EECS 391Introduction to Artificial Intelligence3
EECS 484Computational Intelligence I: Basic Principles3
EECS 491Artificial Intelligence3

 

 

Cooperative Education Program in Systems and Control Engineering

There are many excellent Cooperative Education (CO-OP) opportunities for systems and control engineering majors. A CO-OP student does two CO-OP assignments in industry or government. The length of each assignment is a semester plus a summer which is enough time for the student to complete a significant engineering project. The CO-OP program takes five years to complete because the student is typically gone from campus for two semesters.

BS/MS Program in Systems and Control Engineering

The department encourages highly motivated and qualified students to apply for admission to the five-year BS/MS Program in the junior year. This integrated program, which permits substitution of MS thesis work for the senior design project, provides a high level of fundamental training and in-depth advanced training in the student’s selected specialty. It also offers the opportunity to complete both the Bachelor of Science in Engineering and Master of Science degrees within five years.

Minor in Systems and Control Engineering

A total of five courses (15 credit hours) are required to obtain a minor in systems and control engineering. At least 9 credit hours must be selected from:

EECS 246Signals and Systems4
EECS 304Control Engineering I with Laboratory3
EECS 346Engineering Optimization3
EECS 352Engineering Economics and Decision Analysis3

The remaining credit hours can be chosen from EECS courses with the written approval of the faculty member (see the EECS web page for the current responsible faculty member) in charge of the minor program in the Systems and Control Program. A list of suggested EECS courses to complete the minor is:

EECS 324Simulation Techniques in Engineering3
EECS 313Signal Processing3
EECS 350Operations and Systems Design3
EECS 360Manufacturing and Automated Systems3

 


Computer Engineering

The Bachelor of Science program in Computer Engineering is designed to give a student a strong background in the fundamentals of computer engineering through combined classroom and laboratory work. A graduate of this program will be able to use these fundamentals to analyze and evaluate computer systems, both hardware and software. A computer engineering graduate would also be able to design and implement a computer system for general purpose or embedded computing incorporating state-of-the-art solutions to a variety of computing problems. This includes systems which have both hardware and software component, whose design requires a well-defined interface between the two, and the evaluation of the associated trade-offs.

The educational mission of the computer engineering program is to graduate students who have fundamental technical knowledge of their profession along with requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance the general field of computer engineering. Core courses provide our students with a strong background in digital systems design, computer organization, hardware architecture, and digital electronics.

Program Educational Objectives

  1. Graduates will be successful professionals obtaining positions appropriate to their background, interests, and education.
  2. Graduates will engage in life-long learning to improve and enhance their professional skills.
  3. Graduates will demonstrate leadership in their profession using their knowledge, communication skills, and engineering ability.

Student Outcomes

As preparation for achieving the above educational objectives, the BS degree program in Computer Engineering is designed so that students attain:

  • an ability to apply knowledge of mathematics, science, and engineering
  • an ability to design and conduct experiments, as well as to analyze and interpret data
  • an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • an ability to function on multi-disciplinary teams
  • an ability to identify, formulate, and solve engineering problems
  • an understanding of professional and ethical responsibility
  • an ability to communicate effectively
  • the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
  • a recognition of the need for, and an ability to engage in life-long learning
  • a knowledge of contemporary issues
  • an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Major in Computer Engineering

Major Requirements

EECS 132Introduction to Programming in Java3
ENGR 210Introduction to Circuits and Instrumentation4
EECS 233Introduction to Data Structures4
EECS 281Logic Design and Computer Organization4
EECS 301Digital Logic Laboratory2
EECS 302Discrete Mathematics3
EECS 314Computer Architecture3
EECS 315Digital Systems Design4
EECS 337Compiler Design4
One of the following:4
VLSI/CAD
Introduction to Operating Systems

 

Statistics Requirement

One Statistics elective may be chosen from:
STAT 312Basic Statistics for Engineering and Science3
STAT 313Statistics for Experimenters3
STAT 332Statistics for Signal Processing3
STAT 333Uncertainty in Engineering and Science3

 

Design Requirement

EECS 398Engineering Projects I4

In consultation with a faculty advisor, a student completes the program by selecting technical and open elective courses that provide in-depth training in principles and practice of computer engineering. With the approval of the advisor a student may emphasize a specialty of his/her choice by selecting elective courses from other programs or departments.

Many courses have integral or associated laboratories in which students gain “hands-on” experience with computer engineering principles and instrumentation. Students have ready access to the teaching laboratory facilities and are encouraged to use them during nonscheduled hours in addition to the regularly scheduled laboratory sessions. Opportunities also exist for undergraduate student participation in the wide spectrum of research projects being conducted in the department.

 

Cooperative Education Program in Computer Engineering

There are many excellent Cooperative Education (CO-OP) opportunities for computer engineering majors. A CO-OP student does two CO-OP assignments in industry or government. The length of each assignment is a semester plus a summer which is enough time for the student to complete a significant computing project. The CO-OP program takes five years to complete because the student is typically gone from campus for two semesters.

BS/MS Program in Computer Engineering

Highly motivated and qualified students are encouraged to apply to the BS/MS Program which will allow them to get both degrees in five years. The BS can be in Computer Engineering or a related discipline, such as mathematics or electrical engineering. Integrating graduate study in computer engineering with the undergraduate program allows a student to satisfy all requirements for both degrees in five years.

Minor in Computer Engineering

The department also offers a minor in computer engineering. The minor has a required two course sequence followed by a two course sequence in either hardware or software aspects of computer engineering. The following two courses are required for any minor in computer engineering:

EECS 281Logic Design and Computer Organization4
EECS 233Introduction to Data Structures4

Students should note that EECS 132 Introduction to Programming in Java is a prerequisite for EECS 233 Introduction to Data Structures.

The two-course hardware sequence is:

EECS 314Computer Architecture3
EECS 315Digital Systems Design4

The corresponding two-course software sequence is:

EECS 337Compiler Design4
EECS 338Introduction to Operating Systems4

In addition to these two standard sequences, a student may design his/her own depth area with the approval of the minor advisor. A student cannot have a major and a minor, or two minors, in both Computer Engineering and Computer Science because of the significant overlap between these subjects.


Computer Science

Bachelor of Science in Computer Science

The Bachelor of Science program in Computer Science is designed to give a student a strong background in the fundamentals of mathematics and computer science. A graduate of this program should be able to use these fundamentals to analyze and evaluate software systems and the underlying abstractions upon which they are based. A graduate should also be able to design and implement software systems which are state-of-the-art solutions to a variety of computing problems; this includes problems which are sufficiently complex to require the evaluation of design alternatives and engineering trade-offs. In addition to these program specific objectives, all students in the Case School of Engineering are exposed to societal issues, professionalism, and are provided opportunities to develop leadership skills.

Our mission is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance the field of computer science.

Program Educational Objectives

  1. To educate and train students in the fundamentals of computer science and mathematics, in order to analyze and solve computing problems, as demonstrated by their professional accomplishments in industry, government and graduate programs and measured within three to five years after graduation.
  2. To educate students with an understanding of real-world computing needs, as demonstrated by their ability to address technical issues involving computing problems encountered in industry, government and graduate programs and measured within three to five years after graduation.
  3. To train students to work effectively, professionally and ethically in computing-related professions, as demonstrated by their communications, teamwork and leadership skills in industry, government and graduate programs and measured within three to five years after graduation.

Student Outcomes

As preparation for achieving the above educational objectives, the BS degree program in Computer Science is designed so that students attain:

  • An ability to apply knowledge of computing and mathematics appropriate to the discipline
  • An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution
  • An ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs
  • An ability to function effectively on teams to accomplish a common goal
  • An understanding of professional, ethical, and social responsibilities
  • An ability to communicate effectively
  • An ability to analyze the impact of computing on individuals, organizations, and society, including ethical, legal, security, and global policy issues
  • Recognition of the need for and an ability to engage in continuing professional development
  • An ability to use current techniques, skills, and tools necessary for computing practice
  • An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices
  • An ability to apply design and development principles in the construction of software systems of varying complexity

Bachelor of Arts in Computer Science

The Bachelor of Arts program in Computer Science is a combination of a liberal arts program and a computing major. It is a professional program in the sense that graduates can be employed as computer professionals, but it is less technical than the Bachelor of Science program in Computer Science. This degree is particularly suitable for students with a wide range of interests. For example, students can major in another discipline in addition to computer science and routinely complete all of the requirements for the double major in a 4 year period. This is possible because over a third of the courses in the program are open electives. Furthermore, if a student is majoring in computer science and a second technical field such as mathematics or physics many of the technical electives will be accepted for both majors. Another example of the utility of this program is that it routinely allows students to major in computer science and take all of the pre-med courses in a four-year period.

Cooperative Education Program in Computer Science

There are many excellent Cooperative Education (CO-OP) opportunities for computer science majors. A CO-OP student does two CO-OP assignments in industry or government. The length of each assignment is a semester plus a summer which is enough time for the student to complete a significant computing project. The CO-OP program takes five years to complete because the student is typically gone from campus for two semesters.

BS/MS Program in Computer Science

Students with a grade point average of 3.2 or higher are encouraged to apply to the BS/MS Program which will allow them to get both degrees in five years. The BS can be in Computer Science or a related discipline, such as mathematics or electrical engineering. Integrating graduate study in computer science with the undergraduate program allows a student to satisfy all requirements for both degrees in five years.

Minor in Computer Science (BS or BSE)

For students pursuing a BS or BSE degree, the following three courses are required for a minor in computer science:

EECS 233Introduction to Data Structures4
EECS 338Introduction to Operating Systems4
EECS 340Algorithms and Data Structures3

A student must take an additional 4 credit hours of computing courses with the exclusion of EECS 132 Introduction to Programming in Java and ENGR 131 Elementary Computer Programming. EECS 302 Discrete Mathematics may be used in place of three of these credit hours since it is a prerequisite for EECS 340 Algorithms and Data Structures.  Students should note that EECS 132 Introduction to Programming in Java is a prerequisite for EECS 233 Introduction to Data Structures.

Minor in Computer Science (BA)

For students pursuing BA degrees, the following courses are required for a minor in computer science:

EECS 132Introduction to Programming in Java3
EECS 233Introduction to Data Structures4
MATH 125Math and Calculus Applications for Life, Managerial, and Social Sci I4

Two additional computing courses are also required for this minor.

Minor in Computer Gaming (CGM)

The minor is 16 hours as follows:

EECS 233Introduction to Data Structures4
EECS 324Simulation Techniques in Engineering3
EECS 366Computer Graphics3
EECS 390Advanced Game Development Project3
EECS 391Introduction to Artificial Intelligence3

The open elective in the spring of the first year is strongly recommended to be EECS 290 Introduction to Computer Game Design and Implementation.  In addition, it is recommended that one additional open elective be a “content creation” course taken from the following areas: Art, English, or Music.  Students should note that EECS 132 Introduction to Programming in Java is a prerequisite for EECS 233 Introduction to Data Structures.


Bachelor of Science in Engineering

Suggested Program of Study: Major in Electrical Engineering

First YearUnits
FallSpring
SAGES First Year Seminar4  
Principles of Chemistry for Engineers (CHEM 111)4  
Calculus for Science and Engineering I (MATH 121)4  
Elementary Computer Programming (ENGR 131)3  
Open elective 3  
PHED (2 half semester courses)0  
SAGES University Seminar  3
Chemistry of Materials (ENGR 145)  4
General Physics I - Mechanics (PHYS 121)b  4
Calculus for Science and Engineering II (MATH 122)  4
PHED (2 half semester courses)  0
Year Total: 18 15
 
Second YearUnits
FallSpring
General Physics II - Electricity and Magnetism (PHYS 122)b4  
Calculus for Science and Engineering III (MATH 223)3  
Introduction to Circuits and Instrumentation (ENGR 210)4  
Logic Design and Computer Organization (EECS 281)4  
SAGES University Seminar  3
Thermodynamics, Fluid Dynamics, Heat and Mass Transfer (ENGR 225)  4
Elementary Differential Equations (MATH 224)  3
Electronic Circuits (EECS 245)  4
Electromagnetic Fields I (EECS 309)  3
Year Total: 15 17
 
Third YearUnits
FallSpring
HM/SS electivea3  
Statistics for Signal Processing (STAT 332)c3  
Statics and Strength of Materials (ENGR 200)3  
Signals and Systems (EECS 246)4  
Approved technical electived3  
HM/SS electivea  3
Semiconductor Electronic Devices (EECS 321)  4
Applied Statistics Req.e  3
Approved technical electived  3
Approved technical electived  3
Year Total: 16 16
 
Fourth YearUnits
FallSpring
HM/SS electivea3  
Engineering Projects I (EECS 398)f4  
Open elective3  
Professional Communication for Engineers (ENGL 398)2  
Professional Communication for Engineers (ENGR 398)1  
Approved technical electived3  
HM/SS electivea  3
Engineering Projects II (EECS 399)  3
Approved technical electived  3
Approved technical electived  3
Open elective  3
Year Total: 16 15
 
Total Units in Sequence:  128

Hours Required for Graduation: 128

a

Humanities/Social Science course

b

Selected students may be invited to take PHYS 123 Physics and Frontiers I - Mechanics and PHYS 124 Physics and Frontiers II - Electricity and Magnetism in place of PHYS 121 General Physics I - Mechanics and PHYS 122 General Physics II - Electricity and Magnetism.

c

Students may replace STAT 332 Statistics for Signal Processing with STAT 333 Uncertainty in Engineering and Science if approved by their advisor.

d

Technical electives will be chosen to fulfill the depth requirement and otherwise increase the student’s understanding of electrical engineering. Courses used to satisfy the depth requirement must come from the department’s list of depth areas and related courses. Technical electives not used to satisfy the depth requirement are more generally defined as any course related to the principles and practice of electrical engineering. This includes all EECS courses at the 200 level and above, and can include courses from other programs. All non-EECS technical electives must be approved by the student’s advisor.

e

This applied statistics requirement must utilize statistics in electrical engineering applications and is typically selected from EECS 351 Communications and Signal Analysis or EECS 313 Signal Processing. Other courses are possible with approval of advisor.

f

CO-OP students may obtain design credit for one semester of Engineering Projects if their co-op assignment included significant design responsibility; however, the student is still responsible for such course obligations as reports, presentations, and ethics assignments. Design credit and fulfillment of remaining course responsibilities are arranged through the course instructor.

 

Bachelor of Science in Engineering

Suggested Program of Study: Major in Systems and Control Engineering

First YearUnits
FallSpring
SAGES First Year Seminar4  
Principles of Chemistry for Engineers (CHEM 111)4  
Calculus for Science and Engineering I (MATH 121)4  
Elementary Computer Programming (ENGR 131)3  
Open elective3  
PHED (2 half semester courses)0  
SAGES University Seminar  3
General Physics I - Mechanics (PHYS 121)b  4
Calculus for Science and Engineering II (MATH 122)  4
Chemistry of Materials (ENGR 145)  4
PHED (2 half semester courses)  0
Year Total: 18 15
 
Second YearUnits
FallSpring
General Physics II - Electricity and Magnetism (PHYS 122)b4  
Calculus for Science and Engineering III (MATH 223)3  
Introduction to Circuits and Instrumentation (ENGR 210)4  
Logic Design and Computer Organization (EECS 281)4  
SAGES University Seminar  3
Elementary Differential Equations (MATH 224)  3
STAT xxx Statistical Methods Coursec  3
Statics and Strength of Materials (ENGR 200)  3
Thermodynamics, Fluid Dynamics, Heat and Mass Transfer (ENGR 225)  4
Year Total: 15 16
 
Third YearUnits
FallSpring
HM/SS elective3  
Signals and Systems (EECS 246)4  
Simulation Techniques in Engineering (EECS 324)3  
Introduction to Global Issues (EECS 342)3  
Approved technical elective3  
HM/SS elective  3
Control Engineering I with Laboratory (EECS 304)  3
Control Engineering I Laboratory (EECS 305)  1
Engineering Optimization (EECS 346)  3
Approved technical elective e  3
Open elective  4
Year Total: 16 17
 
Fourth YearUnits
FallSpring
HM/SS elective3  
Professional Communication for Engineers (ENGL 398)2  
Professional Communication for Engineers (ENGR 398)1  
Engineering Economics and Decision Analysis (EECS 352)3  
Engineering Projects I (EECS 398)d4  
Approved technical electivef3  
HM/SS elective  3
Engineering Projects II (EECS 399)  3
Approved technical electivef  3
Approved technical electivef  3
Approved technical electivef  3
Year Total: 16 15
 
Total Units in Sequence:  128

Hours Required for Graduation: 128

b

Selected students may be invited to take  PHYS 123 Physics and Frontiers I - Mechanics and PHYS 124 Physics and Frontiers II - Electricity and Magnetism in place of PHYS 121 General Physics I - Mechanics and PHYS 122 General Physics II - Electricity and Magnetism.

c

Choose from STAT 312 Basic Statistics for Engineering and Science, STAT 332 Statistics for Signal Processing, or STAT 333 Uncertainty in Engineering and Science.

d

CO-OP students may obtain design credit for one semester of Engineering Projects if their co-op assignment included significant design responsibility; however, the student is still responsible for such course obligations as reports, presentations, and ethics assignments. Design credit and fulfillment of remaining course responsibilities are arranged through the course instructor.

e

Signal Processing or Communication Systems technical elective to be taken in any semester after EECS 246 Signals and Systems. This elective should be chosen from EECS 313 Signal Processing, EECS 351 Communications and Signal Analysis, or EECS 354 Digital Communications.

f

Technical electives from an approved list.

 

Bachelor of Science in Engineering

Suggested Program of Study: Major in Computer Engineering

First YearUnits
FallSpring
SAGES First Year Seminar4  
Principles of Chemistry for Engineers (CHEM 111)4  
Calculus for Science and Engineering I (MATH 121)4  
Introduction to Programming in Java (EECS 132)3  
Open elective3  
PHED (2 half semester courses)0  
SAGES University Seminar  3
General Physics I - Mechanics (PHYS 121)  4
Calculus for Science and Engineering II (MATH 122)  4
Chemistry of Materials (ENGR 145)  4
PHED (2 half semester courses)  0
Year Total: 18 15
 
Second YearUnits
FallSpring
SAGES University Seminar3  
General Physics II - Electricity and Magnetism (PHYS 122)4  
Calculus for Science and Engineering III (MATH 223)3  
Introduction to Circuits and Instrumentation (ENGR 210)4  
Introduction to Data Structures (EECS 233)4  
HM/SS elective  3
Elementary Differential Equations (MATH 224)  3
Statics and Strength of Materials (ENGR 200)  3
Logic Design and Computer Organization (EECS 281)  4
Technical electivea  3
Year Total: 18 16
 
Third YearUnits
FallSpring
HM/SS elective3  
Discrete Mathematics (EECS 302)3  
Thermodynamics, Fluid Dynamics, Heat and Mass Transfer (ENGR 225)4  
Compiler Design (EECS 337)4  
Technical electivea3  
Professional Communication for Engineers (ENGL 398)  2
Professional Communication for Engineers (ENGR 398)  1
Digital Logic Laboratory (EECS 301)  2
Computer Architecture (EECS 314)  3
Digital Systems Design (EECS 315)  4
Introduction to Operating Systems (EECS 338) (or Technical elective,3)b,a  4
Year Total: 17 16
 
Fourth YearUnits
FallSpring
HM/SS elective3  
Statistics electivec3  
Technical electivea3  
Technical elective (or EECS 318 VLSI/CAD) a,b3  
Open elective3  
HM/SS elective  3
Engineering Projects I (EECS 398)d  4
Technical electivea  3
Open elective  4
Year Total: 15 14
 
Total Units in Sequence:  129

Hours Required for Graduation: 129

a

Technical electives are more generally defined as any course related to the principles and practice of computer engineering. This includes all EECS courses at the 200 level and above, and can include courses from other programs. All non-EECS technical electives must be approved by the student’s advisor.

b

The student must take either EECS 318 VLSI/CAD (Fall Semester) or EECS 338 Introduction to Operating Systems (Spring Semester), and a three credit hour technical elective.

c

Chosen from: STAT 312 Basic Statistics for Engineering and Science, STAT 313 Statistics for Experimenters, STAT 332 Statistics for Signal Processing, STAT 333 Uncertainty in Engineering and Science

d

May be taken in the Fall semester if the student would like to take EECS 399 Engineering Projects II in the Spring semester.

 

 

Bachelor of Science

Suggested Program of Study: Major in Computer Science

First YearUnits
FallSpring
SAGES First Year Seminar4  
Principles of Chemistry for Engineers (CHEM 111)4  
Calculus for Science and Engineering I (MATH 121)4  
Introduction to Programming in Java (EECS 132)3  
PHED (2 half semester courses)0  
Open elective3  
SAGES University Seminar  3
General Physics I - Mechanics (PHYS 121)  4
Calculus for Science and Engineering II (MATH 122)  4
Chemistry of Materials (ENGR 145)  4
PHED (2 half semester courses)  0
Year Total: 18 15
 
Second YearUnits
FallSpring
SAGES University Seminar3  
General Physics II - Electricity and Magnetism (PHYS 122)4  
Calculus for Science and Engineering III (MATH 223)3  
Logic Design and Computer Organization (EECS 281)4  
Technical electivea,b3  
Elementary Differential Equations (MATH 224)  3
Discrete Mathematics (EECS 302)  3
Introduction to Data Structures (EECS 233)  4
HM/SS elective  3
Technical electivea  3
Year Total: 17 16
 
Third YearUnits
FallSpring
Compiler Design (EECS 337)4  
Algorithms and Data Structures (EECS 340)3  
HM/SS elective3  
Statistics electivec3  
Open elective3  
Professional Communication for Engineers (ENGL 398)  2
Professional Communication for Engineers (ENGR 398)  1
Computer Architecture (EECS 314)  3
Introduction to Operating Systems (EECS 338)  4
Introduction to Database Systems (EECS 341)  3
Theoretical Computer Science (EECS 343)  3
Year Total: 16 16
 
Fourth YearUnits
FallSpring
Software Engineering (EECS 393)3  
Computer Networks I (EECS 325)3  
Technical electivea3  
Technical electivea3  
Open elective3  
HM/SS elective  3
Introduction to Artificial Intelligence (EECS 391)  3
Senior Project in Computer Science (EECS 395)  4
Programming Language Concepts (EECS 345)  3
Open elective  3
Year Total: 15 16
 
Total Units in Sequence:  129

Hours Required for Graduation: 129

a

Chosen from the list of approved CS technical electives. All other technical electives must be approved by the student’s advisor.

b

ENGR 210 Introduction to Circuits and Instrumentation is recommended because it provides flexibility in choice of major and advanced EECS courses.

c

Chosen from: MATH 380 Introduction to Probability, STAT 312 Basic Statistics for Engineering and Science, STAT 313 Statistics for Experimenters, STAT 332 Statistics for Signal Processing, STAT 333 Uncertainty in Engineering and Science

 

Bachelor of Arts

Suggested Program of Study: Computer Science

First YearUnits
FallSpring
SAGES First Year Seminar4  
Math and Calculus Applications for Life, Managerial, and Social Sci I (MATH 125)4  
Introduction to Programming in Java (EECS 132)3  
HM/SS elective3  
Open elective3  
PHED (2 half semester courses)0  
SAGES University Seminar  3
Math and Calculus Applications for Life, Managerial, and Social Sci II (MATH 126)  4
HM/SS elective  3
Open elective  3
Open elective  3
PHED (2 half semester courses)  0
Year Total: 17 16
 
Second YearUnits
FallSpring
SAGES University Seminar3  
Logic Design and Computer Organization (EECS 281)4  
HM/SS elective3  
Open elective3  
Open elective3  
Discrete Mathematics (EECS 302)  3
Introduction to Data Structures (EECS 233)  4
HM/SS elective  3
Open elective  3
Open elective  3
Year Total: 16 16
 
Third YearUnits
FallSpring
Professional Communication for Engineers (ENGL 398)c2  
Professional Communication for Engineers (ENGR 398)c1  
Compiler Design (EECS 337)4  
Technical electivea3  
Open elective3  
Computer Architecture (EECS 314)  3
Introduction to Operating Systems (EECS 338)  4
Introduction to Database Systems (EECS 341)  3
Open elective  3
Year Total: 13 13
 
Fourth YearUnits
FallSpring
Algorithms and Data Structures (EECS 340)3  
Technical electivea3  
Open elective3  
Open elective3  
Open elective3  
Senior Project in Computer Science (EECS 395)b  4
Technical electivea  3
Open elective  3
Open elective  3
Open elective  3
Year Total: 15 16
 
Total Units in Sequence:  122

Hours Required for Graduation: 121

a

One technical elective must be a computer science course. The other two technical electives may be computer science, MATH or STAT courses.

b

SAGES capstone course

c

SAGES Departmental Seminar


Graduate Programs

The EECS department offers graduate study leading to the Master of Science and Doctor of Philosophy degrees in (a) Electrical Engineering; (b)  Computer Engineering; (c) Systems & Control Engineering; (d) Computing & Information Sciences (i.e., computer science). These graduate programs provide a balance of breadth and depth appropriate for each degree and support the department’s research thrust areas by emphasizing:

Electrical Engineering

Research in microelectromechanical systems (MEMS), micro/nano sensors, solid-state and photonic devices, wireless implantable biosensors, CMOS and mixed-signal integrated circuit design, robotics, surgical robotics and simulation, and haptics.

Systems and Control Engineering

Research in non-linear control, optimization, simulation, signal processing, systems biology, smart grid, and wind energy.

Computer Engineering

Research in VLSI design, programmable logic, computer architectures, embedded systems, design for testability, reconfigurable processors, and hardware security.

Computer Science

Research in bioinformatics, databases, software engineering, data mining, machine learning, pervasive networks, distributed systems, computational biology, and medical informatics. 

Incoming students are encouraged to apply for departmental teaching assistantships. In addition, training and research funds are used to provide assistantships that support the academic preparation and thesis research of graduate students. A limited number of fellowships providing partial support may also be available for students enrolled in the BS/MS program.

The department believes that the success of its graduates at all levels is due to emphasis on project and problem-oriented course material coupled with the broad-based curricular requirements.

MS students may select either Plan A which requires a research thesis or Plan B which does not require a thesis. Doctoral dissertations in all programs must be original contributions to the existing body of knowledge in engineering and science.

Academic requirements for graduate degrees in engineering are as specified by the Case School of Engineering in this bulletin. A more detailed set of rules and regulations for each degree program contained here is available from the department, and may also be found on the department Web page.

Graduate Certificate

A Graduate Certificate in Wireless Health is a new offering in the Department of Electrical Engineering and Computer Science.  For more details, please refer to the Wireless Health information on the Case School of Engineering website.

Wireless Health Graduate Study

This course-only (i.e., no thesis required) Master of Science (MS) degree in electrical or biomedical engineering offers a rich set of experiential courses, which together provide a solid grounding in fundamentals and a custom tailoring of breadth/depth of study in wireless health.  Students who complete this 9-course, 27-credit program will have the requisite knowledge to enter and advance the wireless health industry. The MS degree provides a comprehensive program that combines theory and practice to cover the entire wireless health value chain, positioning students with exceptional multidisciplinary training for entering and advancing the emerging field of wireless health.  A strong basis in fundamentals, across the spectrum of requisite disciplines, is built through 6 topic-specific required courses.  Each student further personalizes depth and breadth of study through selection of at least three courses from a menu of electives.  Topics of safety, security and privacy, as well as policy and regulatory issues, are integrated throughout the curriculum.

Facilities

Computer Facilities

The department computer facilities incorporate both Unix (primarily Solaris) and Microsoft Windows-based operating systems on high end computing workstations for education and research. A number of file, printing, database, and authentication servers support these workstations, as well as the administrative functions of the department. Labs are primarily located in the Olin and Glennan buildings, but include Nord Hall, and are networked via the Case network.

The Case network is a state-of-the-art, high-speed fiber optic campus-wide computer network that interconnects laboratories, faculty and student offices, classrooms, and student residence halls. It is one of the largest fiber-to-desktop networks anywhere in the world. Every desktop has a 1 Gbps (gigabit per second) connection to a fault-tolerant 10 Gbps backbone. To complement the wired network, over 1,200 wireless access points (WAPs) are also deployed allowing anyone with a laptop or wireless enabled PDA to access resources from practically anywhere on campus.

Off campus users, through the use of virtual private network (VPN) servers, can use their broadband connections to access many on campus resources, as well as software, as if they were physically connected to the Case network. The department and the university participate in the Internet2 and National Lambda Rail projects, which provides high-speed, inter-university network infrastructure allowing for enhanced collaboration between institutions. The Internet2 infrastructure allows students, faculty and staff alike the ability to enjoy extremely high performance connections to other Internet2 member institutions.

Aside from services provided through a commodity Internet connection, Case network users can take advantage of numerous online databases such as EUCLIDplus, the University Libraries’ circulation and public access catalog, as well as Lexus-Nexus™ and various CD-ROM based dictionaries, thesauri, encyclopedias, and research databases. Many regional and national institutional library catalogs are accessible over the network, as well.

EECS faculty are active users of the Microfabrication Laboratory and participants in the Advanced Platform Technology Center described under Interdisciplinary Research Centers.

Additional Department Facilities

Sally & Larry Sears Undergraduate Design Laboratory

This laboratory supports all departmental courses in circuits and includes a state-of-the-art lecture hall, a modernistic glass-walled lab, an electronics "store", and a student lounge and meeting area. Specialized lab space is available for senior projects and sponsored undergraduate programs. The lab is open to all undergraduates, and components are provided free of charge, so students can “play and tinker” with electronics and foster innovation and creativity. The laboratory provides access to PCs, oscilloscopes, signal generators, logic analyzers, and specialized equipment such as RF analyzers and generators. In addition, the lab includes full-time staff dedicated to the education, guidance and mentoring of undergraduates in the “art and practice” of hands-on engineering.

This is the central educational resource for students taking analog, digital, and mixed-signal courses in electronics, and has been supported by various corporations in addition to alumnus Larry Sears, a successful engineer and entrepreneur. Basic workstations consist of Windows-based computers equipped with LabView software, as well as Agilent 546xx oscilloscopes, 33120A Waveform Generators, 34401A Digital Multimeters, and E3631A power supplies.  Advanced workstations are similarly configured, but with a wider variety of high-performance test equipment.

Jennings Computer Center Lab

Supported by an endowment from the Jennings Foundation, this lab provides our students with the educational resources necessary for their classwork and exploration of the art of computing. This lab has both PCs and Sun Unix workstations, and includes two high-speed laser printers.

EECS Undergraduate Computer Lab

This laboratory (recently renovated with major funding provided by Rockwell Automation) on the 8th floor of the Olin building is accompanied by a suite of instructor/TA offices, and supports the freshman computing classes: ENGR 131 Elementary Computer Programming and EECS 132 Introduction to Programming in Java. Thirty student Macintosh workstations with underlying UNIX operating systems are available for hands-on instruction, and support the study of introductory programming at the university.

Nord Computer Laboratory

This is a general-purpose computer facility that is open 24 hours a day, to all students. The lab contains 50 PCs running Windows and four Apple Macintosh computers. Facilities for color printing, faxing, copying and scanning are provided. Special software includes PRO/Engineer, ChemCAD and Visual Studio. Blank CDs, floppy disks, transparencies and other supplies are available for purchase. Visit the website for more information.

Virtual Worlds (Gaming and Simulation) Laboratory

The Virtual Worlds Gaming and Simulation Lab forms the basis for experiential work in existing game related courses such as Artificial Intelligence, Graphics, and Simulation and for new gaming/simulation courses. Multi-disciplinary senior projects also use the lab facilities. In addition, a large number of significant cross-disciplinary immersive learning opportunities are available with the Cleveland Institute of Art, the CWRU Music department, and the CWRU School of Medicine.

The Virtual Worlds laboratory includes a PC room, a Console room, an Immersion room, an Audio room, a Medical Simulation room, and a Virtual Reality room containing:

  • 24 networked high-performance Alienware gaming quality PCs
  • Virtual reality components including three head mounted displays, three data gloves, a four sensor magnetic tracker, two inertial trackers, and three haptic interfaces
  • Game consoles, e.g. PS2, Xbox, Gamecube, Nintendo DS, PSP
  • Large screen 2-D and 3-D projection displays
  • Audio and music synthesis and production equipment

Database and Bioinformatics Research Laboratory

Primarily funded by equipment grants from the National Science Foundation and Microsoft Research, this laboratory provides PCs running Windows and Linux supporting research in database systems and bioinformatics.

Networks Laboratory

Supported through donations from both Cisco Systems and Microsoft Research, the networks lab has 15 stations complete with a PC, a Cisco switch and router, IP telephony equipment, as well as network patches back to a central rack where devices at one workstation may be routed to other equipment in the lab. A “library” of related equipment is also available.

Intelligent Networks & Systems Architecting (INSA) Research Laboratory

The Intelligent Networks & Systems Architecting (INSA) Research Laboratory is a state-of-the-art research facility dedicated to intelligent computer networks, systems engineering, design, and architecting. It includes optimization, simulation, artificial intelligent, visualization, and emulation. This lab has been partially supported by NASA’s Space Exploration programs for Human and Robotic Technology (H&RT). The INSA Lab is equipped with 10 high-performance workstations and 2 servers in a mixed Windows and Linux environment, with over 40 installed network interface cards providing connectivity to its wired and wireless research networks. It includes software packages such as GINO and LINDO, Arena simulation, ns2 and OPNET, as well as the STK satellite toolkit, artificial neural network, systems architecting and modeling, and statistical analysis and data management packages such as SPSS. The INSA Lab is also used for research in heterogeneous, sensor web, and mobile ad-hoc networks with space and battlefield applications.

VLSI Design Laboratory

This lab has been supported by the Semiconductor Research Corporation, NSF, NASA, Synopsys and Sun Microsystems. This laboratory has a number of advanced UNIX workstations that run commercial CAD software tools for VLSI design and is currently used to develop design and testing techniques for embedded system-on-chip.

Embedded Systems Laboratory

The Embedded Systems Laboratory is equipped with several Sun Blade Workstations running Solaris and Intel PCs running Linux. This lab has been recently equipped with advanced FPGA Virtex II prototype boards from Xilinx, including about 100 Xilinx Virtex II FPGAs and Xilinx CAD tools for development work. A grant-in-aid from Synopsys has provided the Synopsys commercial CAD tools for software development and simulation. This Lab is also equipped with NIOS FPGA boards from Altera, including software tools.

Mixed-Signal Integrated Circuit Laboratory

This research laboratory includes a cluster of Windows workstations and a UNIX server with integrated circuit design software (Cadence Custom IC Bundle), as well as a variety of equipment used in the characterization of mixed-signal (analog and digital) integrated circuits, which are typically fabricated using the MOSIS foundry service. Test equipment includes an IC probe station, surface-mount soldering equipment, logic and network/spectrum analyzers, an assortment of digital oscilloscopes with sample rates up to 1 GHz, and a variety of function generators, multi-meters, and power supplies.

Microelectromechanical Systems (MEMS) Research Laboratory

The MEMS Research Laboratory is equipped for microfabrication processes that do not require a clean room environment. These include chemical-mechanical polishing (two systems), bulk silicon etching, aqueous chemical release of free standing micromechanical components, and supercritical point drying. In addition to the fabrication capabilities, the lab is also well equipped for testing and evaluation of MEMS components as it houses wafer-scale probe stations, a vacuum probe station, a multipurpose vacuum chamber, and an interferometric load-deflection station. Two large (8 x 2 ft2) vibration isolated air tables are available for custom testing setups. The laboratory has a wide variety of electronic testing instruments, including a complete IV-CV testing setup.

BioMicroSystems Laboratory

This research laboratory focuses on developing wireless integrated circuits and microsystems for a variety of applications in biomedical and neural engineering. The laboratory contains several PC computers, software packages for design, simulation, and layout of high-performance, low-noise, analog/mixed-signal/RF circuits and systems, and testing/measurement equipment such as dc power supply, arbitrary function generator, multichannel mixed-signal oscilloscope, data acquisition hardware, spectrum analyzer, potentiostat, and current source meter.  Visit the website for more information.

Emerging Materials Development and Evaluation Laboratory

The EMDE Laboratory is equipped with tooling useful in characterizing materials for MEMS applications. The laboratory contains a PC-based apparatus for load-deflection and burst testing of micromachined membranes, a custom-built test chamber for evaluation and reliability testing of MEMS-based pressure transducers and other membrane-based devices, a probe station for electrical characterization of micro-devices, a fume hood configured for wet chemical etching of Si, polymers, and a wide variety of metals, tooling for electroplating, an optical reflectometer, and a supercritical-point dryer for release of surface micromachined devices. The lab also has a PC with layout and finite element modeling software for device design, fabrication process design and analysis of testing data.

Laboratory for Nanoscale Devices and Integrated Systems

This research lab explores new engineering and physics at the nanoscale, and by applying such knowledge, develops new devices and tools for emerging technological applications in the new frontiers of information, biomedical, and life sciences.  A primary current theme of the research is on developing nanoscale electromechanical systems (NEMS), based on exploration and understandings of mesoscopic devices fundamentals and new characteristics of various nanoscale structures and functional systems.  The lab has been developing NEMS with new functions and high performance, in combination with some of the latest advances in advanced materials, integrated circuits, and others, through crossdisciplinary explorations and collaboration.  The lab is dedicated to the development of various NEMS transducers, biosensors, high-frequency nanodevices, and high-precision instruments.  For more information, contact Dr. Philip Feng.

Some of the recent research highlights include: the first very-high-frequency silicon nanowire resonators and sensors, the first ultra-high-frequency self-sustaining oscillators (aka NEMS clocks), the first low-voltage (~1V), high-speed nanowire NEMS switches, and the first NEMS mass sensors for weighing single-biomolecules and for probing the noise arising from adsorbed atoms walking on the surface of a vibrating NEMS.

Control and Energy Systems Center (CESC)

The Control and Energy Systems Center (CESC) looks for new transformational research and engineering breakthroughs to build a better world, improving our industry, economy, energy, environment, water resources and society, all with sustainability and within an international collaboration framework.  With an interdisciplinary and concurrent engineering approach, the CESC focuses on bridging the gap between fundamental and applied research in advanced control and systems engineering, with special emphasis in energy innovation, wind energy, power systems, water treatment plants, sustainability, spacecraft, environmental and industrial applications.  Fundamental research foci are to gain knowledge and understanding on multi-input-multi-output physical worlds, nonlinear plants, distributed parameter systems, plants with non-minimum phase, time delay and/or uncertainty, etc., and to develop new methodologies to design quantitative robust controllers to improve the efficiency and reliability of such systems.  Applied research aims to develop advanced solutions with industrial partners, for practical control engineering problems in energy systems, multi-megawatt wind turbines, renewable energy plants, power system dynamics and control, grid integration, energy storage, power electronics, wastewater treatment plants, desalination systems, formation flying spacecraft, satellites with flexible appendages, heating systems, robotics, parallel kinematics, telescope control, etc.  The Center was established in 2009 with the support of the Milton and Tamar Maltz Family Foundation and the Cleveland Foundation.

Process Control Laboratory

This laboratory contains process control pilot plants and computerized hardware for data acquisition and process control that is used for demonstrations, teaching, and research. This laboratory also has access to steam and compressed air for use in the pilot processes that include systems for flow and temperature control, level and temperature control, pH control, and pressure control plants.

Dynamics and Control Laboratory

This laboratory contains data acquisition and control devices, PLCs, electromechanical systems, and mechanical, pneumatic, and electrical laboratory experiments for demonstrations, teaching, and research. Particular systems include: AC/DC servo systems, multi-degree-of-freedom robotic systems, rectilinear and torsional multi-degree-of-freedom vibration systems, inverted pendulum, magnetic levitation system, and a PLC-controlled low-voltage AC smart grid demonstration system that includes conventional and renewable (wind and solar) generation, battery and compressed air energy storage, residential, commercial and industry loads, a capacitor bank for real-time power factor correction, and advanced sensing and controls implemented through an interconnected system of intelligent software agents.

Courses

EECS 132. Introduction to Programming in Java. 3 Units.

Introduction to computer programming and problem solving with the Java language. Computers, operating systems, and Java applications; software development; conditional statements; loops; methods; arrays; classes and objects; object-oriented design; unit testing; strings and text I/O; inheritance and polymorphism; GUI components; application testing; abstract classes and interfaces; exception handling; files and streams; GUI event handling; generics; collections; threads; comparison of Java to C, C++, and C#.

EECS 233. Introduction to Data Structures. 4 Units.

The programming language Java; pointers, files, and recursion. Representation and manipulation of data: one way and circular linked lists, doubly linked lists; the available space list. Different representations of stacks and queues. Representation of binary trees, trees and graphs. Hashing; searching and sorting. Prereq: EECS 132.

EECS 245. Electronic Circuits. 4 Units.

Analysis of time-dependent electrical circuits. Dynamic waveforms and elements: inductors, capacitors, and transformers. First- and second-order circuits, passive and active. Analysis of sinusoidal steady state response using phasors. Laplace transforms and pole-zero diagrams. S-domain circuit analysis. Two-port networks, impulse response, and transfer functions. Introduction to nonlinear semiconductor devices: diodes, BJTs, and FETs. Gain-bandwidth product, slew-rate and other limitations of real devices. SPICE simulation and laboratory exercises reinforce course materials. Prereq: ENGR 210. Prereq or Coreq: MATH 224.

EECS 246. Signals and Systems. 4 Units.

Mathematical representation, characterization, and analysis of continuous-time signals and systems. Development of elementary mathematical models of continuous-time dynamic systems. Time domain and frequency domain analysis of linear time-invariant systems. Fourier series, Fourier transforms, and Laplace transforms. Sampling theorem. Filter design. Introduction to feedback control systems and feedback controller design. Prereq: ENGR 210. Prereq or Coreq: MATH 224.

EECS 251. Numerical Methods. 3 Units.

Introduction to basic concepts and algorithms used in the numerical solution of common problems including solving non-linear equations, solving systems of linear equations, interpolation, fitting curves to data, integration and solving ordinary differential equations. Computational error and the efficiency of various numerical methods are discussed in some detail. Most homework requires the implementation of numerical methods on a computer. Prereq: MATH 122 and either ENGR 131 or EECS 132.

EECS 281. Logic Design and Computer Organization. 4 Units.

Fundamentals of digital systems in terms of both computer organization and logic level design. Organization of digital computers; information representation; boolean algebra; analysis and synthesis of combinational and sequential circuits; datapaths and register transfers; instruction sets and assembly language; input/output and communication; memory. Prereq: ENGR 131 or EECS 132.

EECS 290. Introduction to Computer Game Design and Implementation. 3 Units.

This class begins with an examination of the history of video games and of game design. Games will be examined in a systems context to understand gaming and game design fundamentals. Various topics relating directly to the implementation of computer games will be introduced including graphics, animation, artificial intelligence, user interfaces, the simulation of motion, sound generation, and networking. Extensive study of past and current computer games will be used to illustrate course concepts. Individual and group projects will be used throughout the semester to motivate, illustrate and demonstrate the course concepts and ideas. Group game development and implementation projects will culminate in classroom presentation and evaluation. Prereq: EECS 132.

EECS 293. Software Craftsmanship. 4 Units.

A course to improve programming skills, software quality, and the software development process. Software design; Version control; Control issues and routines; Pseudo-code programming process and developer testing; Defensive programming; Classes; Debugging; Self-documenting code; Refactoring. Prereq: EECS 233.

EECS 296. Independent Projects. 1 - 3 Unit.

Independent projects in Computer Engineering, Computer Science, Electrical Engineering and Systems and Control Engineering. Recommended preparation: ENGR 131 or EECS 132. Prereq: Limited to freshmen and sophomore students.

EECS 297. Special Topics. 1 - 3 Unit.

Special topics in Computer Engineering, Computer Science, Electrical Engineering, and Systems and Control Engineering. Prereq: Limited to freshmen and sophomores.

EECS 301. Digital Logic Laboratory. 2 Units.

This course is an introductory experimental laboratory for digital networks. The course introduces students to the process of design, analysis, synthesis and implementation of digital networks. The course covers the design of combinational circuits, sequential networks, registers, counters, synchronous/asynchronous Finite State Machines, register based design, and arithmetic computational blocks. Prereq: EECS 281.

EECS 302. Discrete Mathematics. 3 Units.

A general introduction to basic mathematical terminology and the techniques of abstract mathematics in the context of discrete mathematics. Topics introduced are mathematical reasoning, Boolean connectives, deduction, mathematical induction, sets, functions and relations, algorithms, graphs, combinatorial reasoning. Offered as EECS 302 and MATH 304. Prereq: MATH 122 or MATH 124 or MATH 126.

EECS 304. Control Engineering I with Laboratory. 3 Units.

Analysis and design techniques for control applications. Linearization of nonlinear systems. Design specifications. Classical design methods: root locus, bode, nyquist. PID, lead, lag, lead-lag controller design. State space modeling, solution, controllability, observability and stability. Modeling and control demonstrations and experiments single-input/single-output and multivariable systems. Control system analysis/design/implementation software. Prereq: EECS 246 or equivalent.

EECS 305. Control Engineering I Laboratory. 1 Unit.

A laboratory course based on the material in EECS 304. Modeling, simulation, and analysis using MATLAB. Physical experiments involving control of mechanical systems, process control systems, and design of PID controllers. Coreq: EECS 304.

EECS 309. Electromagnetic Fields I. 3 Units.

Maxwell's integral and differential equations, boundary conditions, constitutive relations, energy conservation and Pointing vector, wave equation, plane waves, propagating waves and transmission lines, characteristic impedance, reflection coefficient and standing wave ratio, in-depth analysis of coaxial and strip lines, electro- and magneto-quasistatics, simple boundary value problems, correspondence between fields and circuit concepts, energy and forces. Prereq: PHYS 122. Prereq or Coreq: MATH 224.

EECS 312. Introduction to Electric Power Systems. 3 Units.

This course is intended to be an introduction to three-phase electric power systems. Modeling of system components including generators, transformers, loads, transmission lines. The per-unit system. One-line diagrams and equivalent circuits. Real and reactive power. Phasor diagrams. Voltage and frequency regulation. Load-flow analysis. Short-circuit calculations. Fault analysis using the techniques of symmetrical component analysis.

EECS 313. Signal Processing. 3 Units.

Fourier series and transforms. Analog and digital filters. Fast-Fourier transforms, sampling, and modulation for discrete time signals and systems. Consideration of stochastic signals and linear processing of stochastic signals using correlation functions and spectral analysis. Prereq: EECS 246.

EECS 314. Computer Architecture. 3 Units.

This course provides students the opportunity to study and evaluate a modern computer architecture design. The course covers topics in fundamentals of computer design, performance, cost, instruction set design, processor implementation, control unit, pipelining, communication and network, memory hierarchy, computer arithmetic, input-output, and an introduction to RISC and super-scalar processors. Prereq: EECS 281.

EECS 315. Digital Systems Design. 4 Units.

This course gives students the ability to design modern digital circuits. The course covers topics in logic level analysis and synthesis, digital electronics: transistors, CMOS logic gates, CMOS lay-out, design metrics space, power, delay. Programmable logic (partitioning, routing), state machine analysis and synthesis, register transfer level block design, datapath, controllers, ASM charts, microsequencers, emulation and rapid protyping, and switch/logic-level simulation. Prereq: EECS 281.

EECS 316. Computer Design. 3 Units.

Methodologies for systematic design of digital systems with emphasis on programmable logic implementations and prototyping. Laboratory which uses modern design techniques based on hardware description languages such as VHDL, CAD tools, and Field Programmable Gate Arrays (FPGAs). Prereq: EECS 281 and EECS 315.

EECS 318. VLSI/CAD. 4 Units.

With Very Large Scale Integration (VLSI) technology there is an increased need for Computer-Aided Design (CAD) techniques and tools to help in the design of large digital systems that deliver both performance and functionality. Such high performance tools are of great importance in the VLSI design process, both to perform functional, logical, and behavioral modeling and verification to aid the testing process. This course discusses the fundamentals in behavioral languages, both VHDL and Verilog, with hands-on experience. Prereq: EECS 281 and EECS 315.

EECS 319. Applied Probability and Stochastic Processes for Biology. 3 Units.

Applications of probability and stochastic processes to biological systems. Mathematical topics will include: introduction to discrete and continuous probability spaces (including numerical generation of pseudo random samples from specified probability distributions), Markov processes in discrete and continuous time with discrete and continuous sample spaces, point processes including homogeneous and inhomogeneous Poisson processes and Markov chains on graphs, and diffusion processes including Brownian motion and the Ornstein-Uhlenbeck process. Biological topics will be determined by the interests of the students and the instructor. Likely topics include: stochastic ion channels, molecular motors and stochastic ratchets, actin and tubulin polymerization, random walk models for neural spike trains, bacterial chemotaxis, signaling and genetic regulatory networks, and stochastic predator-prey dynamics. The emphasis will be on practical simulation and analysis of stochastic phenomena in biological systems. Numerical methods will be developed using both MATLAB and the R statistical package. Student projects will comprise a major part of the course. Offered as BIOL 319, EECS 319, MATH 319, BIOL 419, EBME 419, and PHOL 419. Prereq: MATH 224 or MATH 223 and BIOL 300 or BIOL 306 and MATH 201 or MATH 307 or consent of instructor.

EECS 321. Semiconductor Electronic Devices. 4 Units.

Energy bands and charge carriers in semiconductors and their experimental verifications. Excess carriers in semiconductors. Principles of operation of semiconductor devices that rely on the electrical properties of semiconductor surfaces and junctions. Development of equivalent circuit models and performance limitations of these devices. Devices covered include: junctions, bipolar transistors, Schottky junctions, MOS capacitors, junction gate and MOS field effect transistors, optical devices such as photodetectors, light-emitting diodes, solar cells, and lasers. Prereq: PHYS 122. Prereq or Coreq: MATH 224.

EECS 322. Integrated Circuits and Electronic Devices. 3 Units.

Technology of monolithic integrated circuits and devices, including crystal growth and doping, photolithography, vacuum technology, metalization, wet etching, thin film basics, oxidation, diffusion, ion implantation, epitaxy, chemical vapor deposition, plasma processing, and micromachining. Basics of semiconductor devices including junction diodes, bipolar junction transistors, and field effect transistors. Prereq: PHYS 122. Prereq or Coreq: MATH 224.

EECS 324. Simulation Techniques in Engineering. 3 Units.

Principles and techniques of continuous-time and discrete-event simulation which are powerful tools for analyzing a wide variety of complex engineering, systems biology and business problems. EXCEL, MATLAB/SIMULINK, and ARENA are used as the main computational and programming instruments to demonstrate the basic steps in dynamic systems modeling, discrete-event systems modeling as well as typical results of stochastic/Monte Carlo simulations, continuous/discrete-time simulations, and discrete-event simulations respectively. Design and evaluation of simulation experiments will also be covered. Recommended preparation: STAT 312, STAT 332, or STAT 333. Prereq: MATH 224.

EECS 325. Computer Networks I. 3 Units.

An introduction to computer networks and the Internet. Applications: http, ftp, e-mail, DNS, socket programming. Transport: UDP, TCP, reliable data transfer, and congestion control. Network layer: IP, routing, and NAT. Link layer: taxonomy, Ethernet, 802.11. Prereq: EECS 233 and Junior Standing or Instructor Consent.

EECS 326. Instrumentation Electronics. 3 Units.

A second course in instrumentation with emphasis on sensor interface electronics. General concepts in measurement systems, including accuracy, precision, sensitivity, linearity, and resolution. The physics and modeling of resistive, reactive, self-generating, and direct-digital sensors. Signal conditioning for same, including bridge circuits, coherent detectors, and a variety of amplifier topologies: differential, instrumentation, charge, and transimpedance. Noise and drift in amplifiers and resistors. Practical issues of interference, including grounding, shielding, supply/return, and isolation amplifiers. Prereq: ENGR 210 and (EECS 246, EBME 308 or EMAE 350).

EECS 337. Compiler Design. 4 Units.

Design and implementation of compilers and other language processors. Scanners and lexical analysis; regular expressions and finite automata; scanner generators; parsers and syntax analysis; context free grammars; parser generators; semantic analysis; intermediate code generation; runtime environments; code generation; machine independent optimizations; data flow and dependence analysis. There will be a significant programming project involving the use of compiler tools and software development tools and techniques. Prereq: EECS 233 and EECS 281.

EECS 338. Introduction to Operating Systems. 4 Units.

CPU scheduling, memory management, concurrent processes, semaphores, monitors, deadlocks, secondary storage management, file systems, protection, UNIX operating system, fork, exec, wait, UNIX System V IPCs, sockets, remote procedure calls, threads. Must be proficient in "C" programming language. Prereq: EECS 337.

EECS 339. Web Data Mining. 3 Units.

Web crawling technology, web search and information extraction, unsupervised and semi-supervised learning techniques and their application to web data extraction, social network analysis, various pagerank algorithms, link analysis, web resource discovery, web, resource description framework (RDF), XML, Web Ontology Language (OWL). Prereq: EECS 338, EECS 341, and (EECS 302 or MATH 304).

EECS 340. Algorithms and Data Structures. 3 Units.

Efficient sorting algorithms, external sorting methods, internal and external searching, efficient string processing algorithms, geometric and graph algorithms. Prereq: EECS 233 and (EECS 302 or MATH 304).

EECS 341. Introduction to Database Systems. 3 Units.

Relational model, ER model, relational algebra and calculus, SQL, OBE, security, views, files and physical database structures, query processing and query optimization, normalization theory, concurrency control, object relational systems, multimedia databases, Oracle SQL server, Microsoft SQL server. Prereq: EECS 233 and (EECS 302 or MATH 304).

EECS 342. Introduction to Global Issues. 3 Units.

This systems course is based on the paradigm of the world as a complex system. Global issues such as population, world trade and financial markets, resources (energy, water, land), global climate change, and others are considered with particular emphasis put on their mutual interdependence. A reasoning support computer system which contains extensive data and a family of models is used for future assessment. Students are engaged in individual, custom-tailored, projects of creating conditions for a desirable or sustainable future based on data and scientific knowledge available. Students at CWRU will interact with students from fifteen universities that have been strategically selected in order to give global coverage to UNESCO'S Global-problematique Education Network Initiative (GENIe) in joint, participatory scenario analysis via the internet.

EECS 343. Theoretical Computer Science. 3 Units.

Introduction to mathematical logic, different classes of automata and their correspondence to different classes of formal languages, recursive functions and computability, assertions and program verification, denotational semantics. MATH/EECS 343 and MATH 410 cannot both be taken for credit. Offered as EECS 343 and MATH 343. Prereq: EECS 302 or MATH 304.

EECS 344. Electronic Analysis and Design. 3 Units.

The design and analysis of real-world circuits. Topics include: junction diodes, non-ideal op-amp models, characteristics and models for large and small signal operation of bipolar junction transistors (BJTs) and field effect transistors (FETs), selection of operating point and biasing for BJT and FET amplifiers. Hybrid-pi model and other advanced circuit models, cascaded amplifiers, negative feedback, differential amplifiers, oscillators, tuned circuits, and phase-locked loops. Computers will be extensively used to model circuits. Selected experiments and/or laboratory projects. Prereq: EECS 245.

EECS 345. Programming Language Concepts. 3 Units.

This course studies important concepts underlying the design, definition, implementation and use of modern programming languages including syntax, semantics, names/scopes, types, expression, assignment, subprograms, data abstraction, and inheritance. Imperative, object-oriented, concurrent, functional, and logic programming paradigms are discussed. Illustrative examples are drawn from a variety of popular languages, such as C++, Java, Ada, Lisp, and Prolog. Prereq: EECS 233 and EECS 337.

EECS 346. Engineering Optimization. 3 Units.

Optimization techniques including linear programming and extensions; transportation and assignment problems; network flow optimization; quadratic, integer, and separable programming; geometric programming; and dynamic programming. Nonlinear optimization topics: optimality criteria, gradient and other practical unconstrained and constrained methods. Computer applications using engineering and business case studies. Recommended preparation: MATH 201.

EECS 350. Operations and Systems Design. 3 Units.

Introduction to design, modeling, and optimization of operations and scheduling systems with applications to computer science and engineering problems. Topics include, forecasting and time series, strategic, tactical, and operational planning, life cycle analysis, learning curves, resources allocation, materials requirement and capacity planning, sequencing, scheduling, inventory control, project management and planning. Tools for analysis include: multi-objective optimization, queuing models, simulation, and artificial intelligence.

EECS 351. Communications and Signal Analysis. 3 Units.

Fourier transform analysis and sampling of signals. AM, FM and SSB modulation and other modulation methods such as pulse code, delta, pulse position, PSK and FSK. Detection, multiplexing, performance evaluation in terms of signal-to-noise ratio and bandwidth requirements. Prereq: EECS 246 or requisites not met permission.

EECS 352. Engineering Economics and Decision Analysis. 3 Units.

Economic analysis of engineering projects, focusing on financial decisions concerning capital investments. Present worth, annual worth, internal rate of return, benefit/cost ratio. Replacement and abandonment policies, effects of taxes, and inflation. Decision making under risk and uncertainty. Decision trees. Value of information.

EECS 354. Digital Communications. 3 Units.

Fundamental bounds on transmission of information. Signal representation in vector space. Optimum reception. Probability and random processes with application to noise problems, speech encoding using linear prediction. Shaping of base-band signal spectra, correlative coding and equalization. Comparative analysis of digital modulation schemes. Concepts of information theory and coding. Applications to data communication. Prereq: EECS 246 or requisites not met permission.

EECS 359. Bioinformatics in Practice. 3 Units.

This course covers basic computational methods of organizing and analyzing biological data, targeting senior and junior level students from both mathematical/computational sciences and life sciences. The aim of the course is to provide the students with basic skills to be able to understand molecular biology data and associated abstractions (sequences, structure, gene expression, molecular network data), access to available resources (public databases, computational tools on the web). Implement basic computational methods for biological data analysis, and use understanding of these methods to solve other problems that arise in biological data analysis. Topics covered include DNA and protein sequence databases, pairwise sequence alignment and sequence search (dynamic programming, BLAST), multiple sequence alignment (HMMs, CLUSTAL-W), sequence clustering, motif finding, pattern matching, phylogenetic analysis (tree reconstruction, neighbor joining, maximum parsimony, maximum likelihood), gene finding, functional annotation, biological ontologies, analysis of gene expression data, and network biology (protein protein interactions, topology, modularity). Prereq: Junior or Senior Standing.

EECS 360. Manufacturing and Automated Systems. 3 Units.

Formulation, modeling, planning, and control of manufacturing and automated systems with applications to computer science and engineering problems. Topics include, design of products and processes, location/spatial problems, transportation and assignment, product and process layout, group technology and clustering, cellular and network flow layouts, computer control systems, reliability and maintenance, and statistical quality control. Tools and analysis include: multi-objective optimization, artificial intelligence, and heuristics for combinatorial problems. Offered as EECS 360 and EECS 460.

EECS 365. Complex Systems Biology. 3 Units.

Complex Systems Biology is an interdisciplinary course based on systems science, engineering, biology, and medicine. The objective is to provide students with an understanding of the current state of systems biology and major challenges ahead. The biological phenomena across the level of complexity will be considered from molecular to organisms and ecology to provide universality of the systems concepts for understanding the functions and behavior of biological systems. Case studies are used and a course project is required to be completed. Prereq: Junior Standing.

EECS 366. Computer Graphics. 3 Units.

Theory and practice of computer graphics: Basic elements of a computer graphics rendering pipeline. Fundamentals of input and display devices. Geometrical transformations and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Algorithms for clipping, hidden surface removal, and anti-aliasing. Rendering algorithms: introduction to local and global shading models, color, and lighting models for reflection, refraction, transparency. Real-time rendering methods and animation. Prereq: EECS 233.

EECS 371. Applied Circuit Design. 4 Units.

This course will consist of lectures and lab projects designed to provide students with an opportunity to consolidate their theoretical knowledge of electronics and to acquaint them with the art and practice of circuit and product design. The lectures will cover electrical and electronic circuits and many electronic and electrical devices and applications. Examples include mixed-signal circuits, power electronics, magnetic and piezo components, gas discharge devices, sensors, motors and generators, and power systems. In addition, there will be discussion of professional topics such as regulatory agencies, manufacturing, testing, reliability, and product cost. Weekly labs will be true "design" opportunities representing real-world applications. A specification or functional description will be provided, and the students will design the circuit, select all components, construct a breadboard, and test. The objective will be functional, pragmatic, cost-effective designs. Prereq: EECS 245.

EECS 374. Advanced Control and Energy Systems. 3 Units.

This course introduces applied quantitative robust and nonlinear control engineering techniques to regulate automatically renewable energy systems in general and wind turbines in particular. The course also studies the fundamentals for dynamic multidisciplinary modeling and analysis of large multi-megawatt wind turbines (mechanics, aerodynamics, electrical systems, control concepts, etc.). The course combines lecture sessions and lab hours. The 400-level includes an experimental lab competition, where the object is to design, implement, and experimentally validate a control strategy to regulate a real system in the laboratory (helicopter control competition or similar); it will also include additional project design reports. Offered as EECS 374 and EECS 474. Prereq: EECS 304.

EECS 376. Mobile Robotics. 4 Units.

Design of software systems for mobile robot control, including: motion control; sensory processing; localization and mapping; mobile-robot planning and navigation; and implementation of goal-directed behaviors. The course has a heavy lab component involving a sequence of design challenges and competitions performed in teams. Prereq: ENGR 131 or EECS 233.

EECS 381. Hybrid Systems. 3 Units.

Today, the most interesting computer code and microprocessor designs are "embedded" and hence interact with the physical world, producing a mixture of digital and analog domains. The class studies an array of tools for understanding and designing these "hybrid systems." Topics include: basics of language and finite state automata theory, discrete-event dynamic systems, Petri nets, timed and hybrid automata, and hybrid dynamical systems. Simulation, verification, and control concepts and languages for these models. Prereq: MATH 224 and (EECS 246 or EECS 302 or MATH 304).

EECS 390. Advanced Game Development Project. 3 Units.

This game development project course will bring together an interdisciplinary group of advanced undergraduate students in the fields of Electrical Engineering and Computer Science, Art, Music, and English to focus on the design and development of a complete, fully-functioning computer game (as an interdisciplinary team). The student teams are given complete liberty to design their own fully functional games from their original concept to a playable finished product, i.e., from the initial idea through to the wrapped box. The student teams will experience the entire game development cycle as they execute their projects. Responsibilities include creating a game idea, writing a story, developing the artwork, designing characters, implementing music and sound effects, programming and testing the game, and documenting the entire project. Recommended preparation: Junior or Senior standing and consent of instructor.

EECS 391. Introduction to Artificial Intelligence. 3 Units.

This course is an introduction to artificial intelligence. We will study the concepts that underlie intelligent systems. Topics covered include problem solving with search, constraint satisfaction, adversarial games, knowledge representation and reasoning using propositional and first order logic, reasoning under uncertainty, introduction to machine learning, automated planning, reinforcement learning and natural language processing. Recommended: basic knowledge of probability and statistics. Prereq: ENGR 131 or EECS 132.

EECS 393. Software Engineering. 3 Units.

Topics: Introduction to software engineering; software lifecycle models; development team organization and project management; requirements analysis and specification techniques; software design techniques; programming practices; software validation techniques; software maintenance practices; software engineering ethics. Undergraduates work in teams to complete a significant software development project. Graduate students are required to complete a research project. Recommended preparation for EECS 493: EECS 337. Offered as EECS 393 and EECS 493. Counts as SAGES Senior Capstone. Prereq: EECS 337.

EECS 394. Introduction to Information Theory. 3 Units.

This course is intended as an introduction to information and coding theory with emphasis on the mathematical aspects. It is suitable for advanced undergraduate and graduate students in mathematics, applied mathematics, statistics, physics, computer science and electrical engineering. Course content: Information measures-entropy, relative entropy, mutual information, and their properties. Typical sets and sequences, asymptotic equipartition property, data compression. Channel coding and capacity: channel coding theorem. Differential entropy, Gaussian channel, Shannon-Nyquist theorem. Information theory inequalities (400 level). Additional topics, which may include compressed sensing and elements of quantum information theory. Recommended Preparation: MATH 201 or MATH 307. Offered as MATH 394, EECS 394, MATH 494 and EECS 494. Prereq: MATH 223 and MATH 380 or requisites not met permission.

EECS 395. Senior Project in Computer Science. 4 Units.

Capstone course for computer science seniors. Material from previous and concurrent courses used to solve computer programming problems and to develop software systems. Professional engineering topics such as project management, engineering design, communications, and professional ethics. Requirements include periodic reporting of progress, plus a final oral presentation and written report. Scheduled formal project presentations during last week of classes. Counts as SAGES Senior Capstone. Prereq: Senior standing.

EECS 396. Independent Projects. 1 - 6 Unit.

Independent projects in Computer Engineering, Computer Science, Electrical Engineering, and Systems and Control Engineering. Limited to juniors and seniors. Prereq: Limited to juniors and seniors.

EECS 397. Special Topics. 1 - 6 Unit.

Special topics in Computer Engineering, Computer Science, Electrical Engineering, and Systems and Control Engineering. Prereq: Limited to juniors and seniors.

EECS 398. Engineering Projects I. 4 Units.

Capstone course for electrical, computer and systems and control engineering seniors. Material from previous and concurrent courses used to solve engineering design problems. Professional engineering topics such as project management, engineering design, communications, and professional ethics. Requirements include periodic reporting of progress, plus a final oral presentation and written report. Scheduled formal project presentations during last week of classes. Counts as SAGES Senior Capstone. Prereq: Senior Standing. Prereq or Coreq: ENGR 398 and ENGL 398.

EECS 399. Engineering Projects II. 3 Units.

Continuation of EECS 398. Material from previous and concurrent courses applied to engineering design and research. Requirements include periodic reporting of progress, plus a final oral presentation and written report. Prereq: Senior Standing.

EECS 400T. Graduate Teaching I. 0 Units.

This course will provide the Ph.D. candidate with experience in teaching undergraduate or graduate students. The experience is expected to involve direct student contact but will be based upon the specific departmental needs and teaching obligations. This teaching experience will be conducted under the supervision of the faculty member who is responsible for the course, but the academic advisor will assess the educational plan to ensure that it provides an educational experience for the student. Students in this course may be expected to perform one or more of the following teaching related activities: grading homeworks, quizzes, and exams, having office hours for students, tutoring students. Recommended preparation: Ph.D. student in EECS department.

EECS 401. Digital Signal Processing. 3 Units.

Characterization of discrete-time signals and systems. Fourier analysis: the Discrete-time Fourier Transform, the Discrete-time Fourier series, the Discrete Fourier Transform and the Fast Fourier Transform. Continuous-time signal sampling and signal reconstruction. Digital filter design: infinite impulse response filters, finite impulse response filters, filter realization and quantization effects. Random signals: discrete correlation sequences and power density spectra, response of linear systems. Recommended preparation: EECS 313.

EECS 405. Data Structures and File Management. 3 Units.

Fundamental concepts: sequential allocation, linked allocation, lists, trees, graphs, internal sorting, external sorting, sequential, binary, interpolation search, hashing file, indexed files, multiple level index structures, btrees, hashed files. Multiple attribute retrieval; inverted files, multi lists, multiple-key hashing, hd trees. Introduction to data bases. Data models. Recommended preparation: EECS 233 and MATH 304.

EECS 408. Introduction to Linear Systems. 3 Units.

Analysis and design of linear feedback systems using state-space techniques. Review of matrix theory, linearization, transition maps and variations of constants formula, structural properties of state-space models, controllability and observability, realization theory, pole assignment and stabilization, linear quadratic regulator problems, observers, and the separation theorem. Recommended preparation: EECS 304.

EECS 409. Discrete Event Systems. 3 Units.

A broad range of system behavior can be described using a discrete event framework. These systems are playing an increasingly important role in modeling, analyzing, and designing manufacturing systems. Simulation, automata, and queuing theory have been the primary tools for studying the behavior of these logically complex systems; however, new methods and techniques as well as new modeling frameworks have been developed to represent and to explore discrete event system behavior. The class will begin by studying simulation, the theory of languages, and finite state automata, and queuing theory approaches and then progress to examining selected additional frameworks for modeling and analyzing these systems including Petrinets, perturbation analysis, and Min-Max algebras.

EECS 412. Electromagnetic Fields III. 3 Units.

Maxwell's equations, macroscopic versus microscopic fields, field interaction with materials in terms of polarization vectors P and M. Laplace's and Poisson's equations and solutions, scalar and vector potentials. Wave propagation in various types of media such as anisotropic and gyrotropic media. Phase and group velocities, signal velocity and dispersion. Boundary value problems associated with wave-guide and cavities. Wave solutions in cylindrical and spherical coordinates. Radiation and antennas.

EECS 413. Nonlinear Systems I. 3 Units.

This course will provide an introduction to techniques used for the analysis of nonlinear dynamic systems. Topics will include existence and uniqueness of solutions, phase plane analysis of two dimensional systems including Poincare-Bendixson, describing functions for single-input single-output systems, averaging methods, bifurcation theory, stability, and an introduction to the study of complicated dynamics and chaos. Recommended preparation: Concurrent enrollment in EECS 408.

EECS 415. Integrated Circuit Technology I. 3 Units.

Review of semiconductor technology. Device fabrication processing, material evaluation, oxide passivation, pattern transfer technique, diffusion, ion implantation, metallization, probing, packaging, and testing. Design and fabrication of passive and active semi-conductor devices. Recommended preparation: EECS 322.

EECS 416. Convex Optimization for Engineering. 3 Units.

This course will focus on the development of a working knowledge and skills to recognize, formulate, and solve convex optimization problems that are so prevalent in engineering. Applications in control systems; parameter and state estimation; signal processing; communications and networks; circuit design; data modeling and analysis; data mining including clustering and classification; and combinatorial and global optimization will be highlighted. New reliable and efficient methods, particular those based on interior-point methods and other special methods to solve convex optimization problems will be emphasized. Implementation issues will also be underscored. Recommended preparation: MATH 201 or equivalent.

EECS 417. Introduction to Stochastic Control. 3 Units.

Analysis and design of controllers for discrete-time stochastic systems. Review of probability theory and stochastic properties, input-output analysis of linear stochastic systems, spectral factorization and Weiner filtering, minimum variance control, state-space models of stochastic systems, optimal control and dynamic programming, statistical estimation and filtering, the Kalman-Bucy theory, the linear quadratic Gaussian problem, and the separation theorem. Recommended preparation: EECS 408.

EECS 419. Computer System Architecture. 3 Units.

Interaction between computer systems hardware and software. Pipeline techniques - instruction pipelines - arithmetic pipelines. Instruction level parallelism. Cache mechanism. I/O structures. Examples taken from existing computer systems.

EECS 421. Optimization of Dynamic Systems. 3 Units.

Fundamentals of dynamic optimization with applications to control. Variational treatment of control problems and the Maximum Principle. Structures of optimal systems; regulators, terminal controllers, time-optimal controllers. Sufficient conditions for optimality. Singular controls. Computational aspects. Selected applications. Recommended preparation: EECS 408. Offered as EECS 421 and MATH 434.

EECS 422. Solid State Electronics II. 3 Units.

Advanced physics of semiconductor devices. Review of current transport and semiconductor electronics. Surface and interface properties. P-N junction. Bipolar junction transistors, field effect transistors, solar cells and photonic devices.

EECS 423. Distributed Systems. 3 Units.

Introduction to distributed systems; system models; network architecture and protocols; interprocess communication; client-server model; group communication; TCP sockets; remote procedure calls; distributed objects and remote invocation; distributed file systems; file service architecture; name services; directory and discovery services; distributed synchronization and coordination; transactions and concurrency control; security; cryptography; replication; distributed multimedia systems. Recommended preparation: EECS 338.

EECS 424. Introduction to Nanotechnology. 3 Units.

An exploration of emerging nanotechnology research. Lectures and class discussion on 1) nanostructures: superlattices, nanowires, nanotubes, quantum dots, nanoparticles, nanocomposites, proteins, bacteria, DNA; 2) nanoscale physical phenomena: mechanical, electrical, chemical, thermal, biological, optical, magnetic; 3) nanofabrication: bottom up and top down methods; 4) characterization: microscopy, property measurement techniques; 5) devices/applications: electronics, sensors, actuators, biomedical, energy conversion. Topics will cover interdisciplinary aspects of the field. Offered as EECS 424 and EMAE 424.

EECS 425. Computer Networks I. 3 Units.

An introduction to computer networks and the Internet. Applications: http, ftp, e-mail, DNS, socket programming. Transport: UDP, TCP, reliable data transfer, and congestion control. Network layer: IP, routing and NAT. Link layer: taxonomy, Ethernet, 802.11. Recommended preparation: EECS 338 or consent of instructor.

EECS 426. MOS Integrated Circuit Design. 3 Units.

Design of digital and analog MOS integrated circuits. IC fabrication and device models. Logic, memory, and clock generation. Amplifiers, comparators, references, and switched-capacitor circuits. Characterization of circuit performance with/without parasitics using hand analysis and SPICE circuit simulation. Recommended preparation: EECS 344 and EECS 321.

EECS 427. Optoelectronic and Photonic Devices. 3 Units.

In this course, we will study the optical transitions, absorptions, and gains in semiconductors. We will discuss the optical processes in semiconductor bulk as well as low dimensional structures such as quantum well and quantum dot. The fundamentals, technologies and applications of important optoelectronic devices (e.g., light-emitting diodes, semiconductor lasers, solar cells and photo-detectors) will be introduced. We will learn the current state-of-the-art of these devices. Recommended Preparation: EECS 321.

EECS 428. Computer Communications Networks II. 3 Units.

Introduction to topics and methodology in computer networks and middleware research. Traffic characterization, stochastic models, and self-similarity. Congestion control (Tahoe, Reno, Sack). Active Queue Management (RED, FQ) and explicit QoS. The Web: overview and components, HTTP, its interaction with TCP, caching. Overlay networks and CDN. Expected work includes a course-long project on network simulation, a final project, a paper presentation, midterm, and final test. Recommended preparation: EECS 425 or permission of instructor.

EECS 433. Database Systems. 3 Units.

Basic issues in file processing and database management systems. Physical data organization. Relational databases. Database design. Relational Query Languages, SQL. Query languages. Query optimization. Database integrity and security. Object-oriented databases. Object-oriented Query Languages, OQL. Recommended preparation: EECS 341 and MATH 304.

EECS 434. Microfabricated Silicon Electromechanical Systems. 3 Units.

Topics related to current research in microelectromechanical systems based upon silicon integrated circuit fabrication technology: fabrication, physics, devices, design, modeling, testing, and packaging. Bulk micromachining, surface micromachining, silicon to glass and silicon-silicon bonding. Principles of operation for microactuators and microcomponents. Testing and packaging issues. Recommended preparation: EECS 322 or EECS 415.

EECS 435. Data Mining. 3 Units.

Data Mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Topics to be covered includes: Data Warehouse and OLAP technology for data mining, Data Preprocessing, Data Mining Primitives, Languages, and System Architectures, Mining Association Rules from Large Databases, Classification and Prediction, Cluster Analysis, Mining Complex Types of Data, and Applications and Trends in Data Mining. Recommended preparation: EECS 341 or equivalent.

EECS 437. Advanced Topics in Data Mining and Bioinformatics. 3 Units.

This course will cover a large number of active data mining and bioinformatics research areas, which include but not limited to: text mining, sequence analysis, network/graph mining, microarray analysis, and mining mobile objects. Students are expected to understand various methods and approaches employed in these research areas and have critical thinking on the advantages and disadvantages of these approaches. In addition, students need to complete a course-long project which exhibits the independent research capability in these data mining and bioinformatics areas. Recommended preparation: EECS 340, EECS 435.

EECS 439. Web Data Mining. 3 Units.

Web crawling technology, web search and information extraction, unsupervised and semi-supervised learning techniques and their application to web data extraction, social network analysis, various pagerank algorithms, link analysis, web resource discovery, web, resource description framework (RDF), XML, Web Ontology Language (OWL). Recommended preparation: EECS 338, EECS 341.

EECS 440. Machine Learning. 3 Units.

Machine learning is a subfield of Artificial Intelligence that is concerned with the design and analysis of algorithms that "learn" and improve with experience, While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms developed in this area have become very useful in analyzing and predicting the behavior of complex systems. Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems. This course is focused on algorithms for machine learning: their design, analysis and implementation. We will study different learning settings, including supervised, semi-supervised and unsupervised learning. We will study different ways of representing the learning problem, using propositional, multiple-instance and relational representations. We will study the different algorithms that have been developed for these settings, such as decision trees, neural networks, support vector machines, k-means, harmonic functions and Bayesian methods. We will learn about the theoretical tradeoffs in the design of these algorithms, and how to evaluate their behavior in practice. At the end of the course, you should be able to: --Recognize situations where machine learning algorithms are applicable; --Understand, represent and formulate the learning problem; --Apply the appropriate algorithm(s), or if necessary, design your own, with an understanding of the tradeoffs involved; --Correctly evaluate the behavior of the algorithm when solving the problem. Prereq: EECS 391 or EECS 491 or consent of instructor.

EECS 441. Internet Applications. 3 Units.

This course exposes students to research in building and scaling internet applications. Covered topics include Web services, scalable content delivery, applications of peer-to-peer networks, and performance analysis and measurements of internet application platforms. The course is based on a collection of research papers and protocol specifications. Students are required to read the materials, present a paper in class, prepare short summaries of discussed papers, and do a course project (team projects are encouraged). Prereq: EECS 325 or EECS 425.

EECS 444. Computer Security. 3 Units.

General types of security attacks; approaches to prevention; secret key and public key cryptography; message authentication and hash functions; digital signatures and authentication protocols; information gathering; password cracking; spoofing; session hijacking; denial of service attacks; buffer overruns; viruses, worms, etc., principles of secure software design, threat modeling; access control; least privilege; storing secrets; socket security; RPC security; security testing; secure software installation; operating system security; database security; web security; email security; firewalls; intrusions. Recommended preparation: EECS 337.

EECS 450. Operations and Systems Design. 3 Units.

Introduction to design, modeling, and optimization of operations and scheduling systems with applications to computer science and engineering problems. Topics include, forecasting and times series, strategic, tactical, and operational planning, life cycle analysis, learning curves, resources allocation, materials requirement and capacity planning, sequencing, scheduling, inventory control, project management and planning. Tools for analysis include: multi-objective optimization, queuing models, simulation, and artificial intelligence.

EECS 451. Introduction to Digital Communications. 3 Units.

Analysis and design of modern digital communications systems: introduction to digital communication systems, review of basic analog and digital signal processing for both deterministic and stochastic signals, signal space representation, basis functions, projections and matched filters, pulse shaping, pulse amplitude modulation, quadrature amplitude modulation, deterministic performance and performance in noise, carrier frequency and phase tracking, symbol timing synchronization, source coding and channel coding. Extensive computer-based design exercises using Matlab and Simulink to design and test digital modems and communication systems. Prereq: STAT 332 or equivalent.

EECS 452. Random Signals. 3 Units.

Fundamental concepts in probability. Probability distribution and density functions. Random variables, functions of random variables, mean, variance, higher moments, Gaussian random variables, random processes, stationary random processes, and ergodicity. Correlation functions and power spectral density. Orthogonal series representation of colored noise. Representation of bandpass noise and application to communication systems. Application to signals and noise in linear systems. Introduction to estimation, sampling, and prediction. Discussion of Poisson, Gaussian, and Markov processes.

EECS 454. Analysis of Algorithms. 3 Units.

This course presents and analyzes a number of efficient algorithms. Problems are selected from such problem domains as sorting, searching, set manipulation, graph algorithms, matrix operations, polynomial manipulation, and fast Fourier transforms. Through specific examples and general techniques, the course covers the design of efficient algorithms as well as the analysis of the efficiency of particular algorithms. Certain important problems for which no efficient algorithms are known (NP-complete problems) are discussed in order to illustrate the intrinsic difficulty which can sometimes preclude efficient algorithmic solutions. Recommended preparation for EECS 454: MATH 304 and (EECS 340 or EECS 405). Offered as EECS 454 and OPRE 454.

EECS 458. Introduction to Bioinformatics. 3 Units.

Fundamental algorithmic methods in computational molecular biology and bioinformatics discussed. Sequence analysis, pairwise and multiple alignment, probabilistic models, phylogenetic analysis, folding and structure prediction emphasized. Recommended preparation: EECS 340, EECS 233.

EECS 459. Bioinformatics for Systems Biology. 3 Units.

Description of omic data (biological sequences, gene expression, protein-protein interactions, protein-DNA interactions, protein expression, metabolomics, biological ontologies), regulatory network inference, topology of regulatory networks, computational inference of protein-protein interactions, protein interaction databases, topology of protein interaction networks, module and protein complex discovery, network alignment and mining, computational models for network evolution, network-based functional inference, metabolic pathway databases, topology of metabolic pathways, flux models for analysis of metabolic networks, network integration, inference of domain-domain interactions, signaling pathway inference from protein interaction networks, network models and algorithms for disease gene identification, identification of dysregulated subnetworks network-based disease classification. Offered as EECS 459 and SYBB 459. Prereq: EECS 359 or EECS 458 or BIOL 250.

EECS 460. Manufacturing and Automated Systems. 3 Units.

Formulation, modeling, planning, and control of manufacturing and automated systems with applications to computer science and engineering problems. Topics include, design of products and processes, location/spatial problems, transportation and assignment, product and process layout, group technology and clustering, cellular and network flow layouts, computer control systems, reliability and maintenance, and statistical quality control. Tools and analysis include: multi-objective optimization, artificial intelligence, and heuristics for combinatorial problems. Offered as EECS 360 and EECS 460.

EECS 466. Computer Graphics. 3 Units.

Theory and practice of computer graphics: object and environment representation including coordinate transformations image extraction including perspective, hidden surface, and shading algorithms; and interaction. Covers a wide range of graphic display devices and systems with emphasis in interactive shaded graphics. Laboratory. Recommended preparation: EECS 233.

EECS 467-1. Commercialization and Intellectual Property Management. 3 Units.

This interdisciplinary course covers a variety of topics, including principles of intellectual property and intellectual property management, business strategies and modeling relevant to the creation of start-up companies and exploitation of IP rights as they relate to biomedical-related inventions. The goal of this two-semester course is to address issues relating to the commercialization of biomedical-related inventions by exposing law students, MBA students, and Ph.D. candidates (in genetics and proteomics) to the challenges and opportunities encountered when attempting to develop biomedical intellectual property from the point of early discovery to the clinic and market. Specifically, this course seeks to provide students with the ability to value a given technological advance or invention holistically, focusing on issues that extend beyond scientific efficacy and include patient and practitioner value propositions, legal and intellectual property protection, business modeling, potential market impacts, market competition, and ethical, social, and healthcare practitioner acceptance. The course will meet over two consecutive semesters--fall and spring--and is six credit hours (three credits each semester). During these two semesters, law students, MBA students, and Ph.D. candidates in genomics and proteomics will work in teams of five (two laws students, two MBA students and one Ph.D. candidate), focusing on issues of commercialization and IP management of biomedical-related inventions. The instructors will be drawn from the law school, business school, and technology-transfer office. Please visit the following website for more information: fusioninnovate.com Offered as LAWS 367, MGMT 467, GENE 367 and GENE 467.

EECS 467-2. Commercialization and Intellectual Property Management. 3 Units.

This interdisciplinary course covers a variety of topics, including principles of intellectual property and intellectual property management, business strategies and modeling relevant to the creation of start-up companies and exploitation of IP rights as they relate to biomedical-related inventions. The goal of this two-semester course is to address issues relating to the commercialization of biomedical-related inventions by exposing law students, MBA students, and Ph.D. candidates(in genetics and proteomics) to the challenges and opportunities encountered when attempting to develop biomedical intellectual property from the point of early discovery to the clinic and market. Specifically, this course seeks to provide students with the ability to value a given technological advance or invention holistically, focusing on issues that extend beyond scientific efficacy and include patient and practitioner value propositions, legal and intellectual property protection, business modeling, potential market impacts, market competition, and ethical, social, and healthcare practitioner acceptance. The course will meet over two consecutive semesters--fall and spring--and is six credit hours (three credits each semester). During these two semesters, law students, MBA students, and Ph.D. candidates in genomics and proteomics will work in teams of five (two law students, two MBA students, and one Ph.D. candidate), focusing on issues of commercialization and IP management of biomedical-related inventions. The instructors will be drawn from the law school, business school, medical school, and technology-transfer office. Please visit the following website for more information: fusioninnovate.com Offered as MGMT 467, LAWS 367, GENE 367 and GENE 467.

EECS 474. Advanced Control and Energy Systems. 3 Units.

This course introduces applied quantitative robust and nonlinear control engineering techniques to regulate automatically renewable energy systems in general and wind turbines in particular. The course also studies the fundamentals for dynamic multidisciplinary modeling and analysis of large multi-megawatt wind turbines (mechanics, aerodynamics, electrical systems, control concepts, etc.). The course combines lecture sessions and lab hours. The 400-level includes an experimental lab competition, where the object is to design, implement, and experimentally validate a control strategy to regulate a real system in the laboratory (helicopter control competition or similar); it will also include additional project design reports. Offered as EECS 374 and EECS 474. Prereq: EECS 304.

EECS 476. Mobile Robotics. 3 Units.

Design of software systems for mobile robot control, including: motion control; sensory processing; localization and mapping; mobile-robot planning and navigation; and implementation of goal-directed behaviors. The course has a heavy lab component involving a sequence of design challenges and competitions performed in teams.

EECS 478. Computational Neuroscience. 3 Units.

Computer simulations and mathematical analysis of neurons and neural circuits, and the computational properties of nervous systems. Students are taught a range of models for neurons and neural circuits, and are asked to implement and explore the computational and dynamic properties of these models. The course introduces students to dynamical systems theory for the analysis of neurons and neural learning, models of brain systems, and their relationship to artificial and neural networks. Term project required. Students enrolled in MATH 478 will make arrangements with the instructor to attend additional lectures and complete additional assignments addressing mathematical topics related to the course. Recommended preparation: MATH 223 and MATH 224 or BIOL 300 and BIOL 306. Offered as BIOL 378, COGS 378, MATH 378, BIOL 478, EBME 478, EECS 478, MATH 478 and NEUR 478.

EECS 480A. Introduction to Wireless Health. 3 Units.

Study of convergence of wireless communications, microsystems, information technology, persuasive psychology, and health care. Discussion of health care delivery system, medical decision-making, persuasive psychology, and wireless health value chain and business models. Understanding of health information technology, processing of monitoring data, wireless communication, biomedical sensing techniques, and health monitoring technical approaches and solutions. Offered as EECS 480A and EBME 480A.

EECS 480B. The Human Body. 3 Units.

Study of structural organization of the body. Introduction to anatomy, physiology, and pathology, covering the various systems of the body. Comparison of elegant and efficient operation of the body and the related consequences of when things go wrong, presented in the context of each system of the body. Introduction to medical diagnosis and terminology in the course of covering the foregoing. Offered as EECS 480B and EBME 480B.

EECS 480C. Biomedical Sensing Instrumentation. 3 Units.

Study of principles, applications, and design of biomedical instruments with special emphasis on transducers. Understanding of basic sensors, amplifiers, and signal processing. Discussion of the origin of biopotential, and biopotential electrodes and amplifiers (including biotelemetry). Understanding of chemical sensors and clinical laboratory instrumentation, including microfluidics. Offered as EECS 480C and EBME 480C. Prereq: EECS/EBME 480A, EECS/EBME 480B

EECS 480D. The Health Care Delivery Ecosystem. 3 Units.

Health care delivery across the continuum of care in the United States, including health policy and reform, financing of care, comparative health systems, population health, public health, access to care, care models, cost and value, comparative effectiveness, governance, management, accountability, workforce, and the future. Discussions of opportunities and challenges for wireless health, integrated into the foregoing topics. Perspective on health care delivery in other countries. Offered as EECS 480D and EBME 480D.

EECS 480E. Wireless Communications and Networking. 3 Units.

Essentials of wireless communications and networking, including teletraffic engineering, radio propagation, digital and cellular communications, wireless wide-area network architecture, speech and channel coding, modulation schemes, antennas, security, networking and transport layers, and 4G systems. Hands-on learning of the anatomy of a cell phone, and a paired wireless health device and its gateway. Offered as EECS 480E and EBME 480E.

EECS 480F. Physicians, Hospitals and Clinics. 3 Units.

Rotation through one or more health care provider facilities for a first-hand understanding of care delivery practice, coordination, and management issues. First-hand exposure to clinical personnel, patients, medical devices and instruments, and organizational workflow. Familiarity with provider protocols, physician referral practices, electronic records, clinical decision support systems, acute and chronic care, and inpatient and ambulatory care. Offered as EECS 480F and EBME 480F.

EECS 480M. Introduction to Medical Informatics. 3 Units.

Current state and emerging trends in Medical Informatics (MI) and associated health information systems. Definition, principles, applications (including electronic records (EMR, HER, PHR), clinical decision and knowledge support, telehealth, MI-based reengineering, system adoption, data, data management, system interoperability, privacy and protected health information, information security, regulatory issues, impact of wireless technology on emerging health information system and processes. Offered as EECS 480M and EBME 480M.

EECS 480P. Advanced Biomedical Instrumentation. 3 Units.

Analysis and design of biosensors in the context of biomedical measurements. Base sensors using electrochemical, optical, piezoelectric, and other principles. Binding equilibria, enzyme kinetics, and mass transport modalities. Adding the "bio" element to base sensors and mathematical aspects of data evaluation. Applications to clinical problems and biomedical research. Offered as EECS 480P and EBME 480P.

EECS 480R. User Experience Engineering. 3 Units.

Social, cognitive, behavioral, and contextual elements in the design of healthcare technology and systems. User-centered design paradigm from a broad perspective, exploring dimensions of product user experience and learning to assess and modify the design of healthcare technology. Practical utilization of user centered design method and assessment techniques for approaching a design problem. Offered as EECS 480R and EBME 480R.

EECS 480S. Wireless Health Product Development. 3 Units.

Integrating application requirements, market data, concept formulation, design innovation, and manufacturing resources for creating differentiated wireless health products that delight the user. Learning user-centric product development best practices, safety, security and privacy considerations, and risk management planning. Understanding the regulatory process. Identifying and managing product development tradeoffs. Offered as EECS 480S and EBME 480S.

EECS 483. Data Acquisition and Control. 3 Units.

Data acquisition (theory and practice), digital control of sampled data systems, stability tests, system simulation digital filter structure, finite word length effects, limit cycles, state-variable feedback and state estimation. Laboratory includes control algorithm programming done in assembly language.

EECS 484. Computational Intelligence I: Basic Principles. 3 Units.

This course is concerned with learning the fundamentals of a number of computational methodologies which are used in adaptive parallel distributed information processing. Such methodologies include neural net computing, evolutionary programming, genetic algorithms, fuzzy set theory, and "artificial life." These computational paradigms complement and supplement the traditional practices of pattern recognition and artificial intelligence. Functionalities covered include self-organization, learning a model or supervised learning, optimization, and memorization.

EECS 485. VLSI Systems. 3 Units.

Basic MOSFET models, inverters, steering logic, the silicon gate, nMOS process, design rules, basic design structures (e.g., NAND and NOR gates, PLA, ROM, RAM), design methodology and tools (spice, N.mpc, Caesar, mkpla), VLSI technology and system architecture. Requires project and student presentation, laboratory.

EECS 486. Research in VLSI Design Automation. 3 Units.

Research topics related to VLSI design automation such as hardware description languages, computer-aided design tools, algorithms and methodologies for VLSI design for a wide range of levels of design abstraction, design validation and test. Requires term project and class presentation.

EECS 488. Embedded Systems Design. 3 Units.

Objective: to introduce and expose the student to methodologies for systematic design of embedded system. The topics include, but are not limited to, system specification, architecture modeling, component partitioning, estimation metrics, hardware software codesign, diagnostics.

EECS 489. Robotics I. 3 Units.

Orientation and configuration coordinate transformations, forward and inverse kinematics and Newton-Euler and Lagrange-Euler dynamic analysis. Planning of manipulator trajectories. Force, position, and hybrid control of robot manipulators. Analytical techniques applied to select industrial robots. Recommended preparation: EMAE 181. Offered as EECS 489 and EMAE 489.

EECS 490. Digital Image Processing. 3 Units.

Digital images are introduced as two-dimensional sampled arrays of data. The course begins with one-to-one operations such as image addition and subtraction and image descriptors such as the histogram. Basic filters such as the gradient and Laplacian in the spatial domain are used to enhance images. The 2-D Fourier transform is introduced and frequency domain operations such as high and low-pass filtering are developed. It is shown how filtering techniques can be used to remove noise and other image degradation. The different methods of representing color images are described and fundamental concepts of color image transformations and color image processing are developed. One or more advanced topics such as wavelets, image compression, and pattern recognition will be covered as time permits. Programming assignments using software such as MATLAB will illustrate the application and implementation of digital image processing.

EECS 491. Artificial Intelligence. 3 Units.

This course covers advanced topics in Artificial Intelligence. Topics include representing knowledge using directed and undirected probabilistic graphical models, associated exact and approximate inference algorithms, statistical relational learning, advanced topics in reinforcement learning and automated planning. Prereq: EECS 391 or consent.

EECS 492. VLSI Digital Signal Processing Systems. 3 Units.

Digital signal processing (DSP) can be found in numerous applications, such as wireless communications, audio/video compression, cable modems, multimedia, global positioning systems and biomedical signal processing. This course fills the gap between DSP algorithms and their efficient VLSI implementations. The design of a digital system is restricted by the requirements of applications, such as speed, area and power consumption. This course introduces methodologies and tools which can be used to design VLSI architectures with different speed-area tradeoffs for DSP algorithms. In addition, the design of efficient VLSI architectures for commonly used DSP blocks is presented in this class. Recommended preparation: EECS 485.

EECS 493. Software Engineering. 3 Units.

Topics: Introduction to software engineering; software lifecycle models; development team organization and project management; requirements analysis and specification techniques; software design techniques; programming practices; software validation techniques; software maintenance practices; software engineering ethics. Undergraduates work in teams to complete a significant software development project. Graduate students are required to complete a research project. Recommended preparation for EECS 493: EECS 337. Offered as EECS 393 and EECS 493. Counts as SAGES Senior Capstone.

EECS 494. Introduction to Information Theory. 3 Units.

This course is intended as an introduction to information and coding theory with emphasis on the mathematical aspects. It is suitable for advanced undergraduate and graduate students in mathematics, applied mathematics, statistics, physics, computer science and electrical engineering. Course content: Information measures-entropy, relative entropy, mutual information, and their properties. Typical sets and sequences, asymptotic equipartition property, data compression. Channel coding and capacity: channel coding theorem. Differential entropy, Gaussian channel, Shannon-Nyquist theorem. Information theory inequalities (400 level). Additional topics, which may include compressed sensing and elements of quantum information theory. Recommended Preparation: MATH 201 or MATH 307. Offered as MATH 394, EECS 394, MATH 494 and EECS 494.

EECS 495. Nanometer VLSI Design. 3 Units.

Semiconductor industry has evolved rapidly over the past four decades to meet the increasing demand on computing power by continuous miniaturization of devices. Now we are in the nanometer technology regime with the device dimensions scaled below 100nm. VLSI design using nanometer technologies involves some major challenges. This course will explain all the major challenges associated with nanoscale VLSI design such as dynamic and leakage power, parameter variations, reliability and robustness. The course will present modeling and analysis techniques for timing, power and noise in nanometer era. Finally, the course will cover the circuit/architecture level design solutions for low power, high-performance, testable and robust VLSI system. The techniques will be applicable to design of microprocessor, digital signal processor (DSP) as well as application specific integrated circuits (ASIC). The course includes a project which requires the student to work on a nanometer design issue. Recommended preparation: EECS 426 or EECS 485.

EECS 500. EECS Colloquium. 0 Units.

Seminars on current topics in Electrical Engineering and Computer Science.

EECS 500T. Graduate Teaching II. 0 Units.

This course will provide the Ph.D. candidate with experience in teaching undergraduate or graduate students. The experience is expected to involve direct student contact but will be based upon the specific departmental needs and teaching obligations. This teaching experience will be conducted under the supervision of the faculty member who is responsible for the course, but the academic advisor will assess the educational plan to ensure that it provides an educational experience for the student. Students in this course may be expected to perform one or more of the following teaching related activities: grading homeworks, quizzes, and exams, having office hours for students, running recitation sessions, providing laboratory assistance. Recommended preparation: Ph.D. student in EECS department.

EECS 516. Large Scale Optimization. 3 Units.

Concepts and techniques for dealing with large optimization problems encountered in designing large engineering structure, control of interconnected systems, pattern recognition, and planning and operations of complex systems; partitioning, relaxation, restriction, decomposition, approximation, and other problem simplification devices; specific algorithms; potential use of parallel and symbolic computation; student seminars and projects. Recommended preparation: EECS 416.

EECS 518. Nonlinear Systems: Analysis and Control. 3 Units.

Mathematical preliminaries: differential equations and dynamical systems, differential geometry and manifolds. Dynamical systems and feedback systems, existence and uniqueness of solutions. Complicated dynamics and chaotic systems. Stability of nonlinear systems: input-output methods and Lyapunov stability. Control of nonlinear systems: gain scheduling, nonlinear regulator theory and feedback linearization. Recommended preparation: EECS 408.

EECS 520. Robust Control. 3 Units.

One of the most important problems in modern control theory is that of controlling the output of a system so as to achieve asymptotic tracking of prescribed signals and/or asymptotic rejection of undesired disturbances. The problem can be solved by the so-called regulator theory and H-infinity control theory. This course presents a self-contained introduction to these two important design methods. The intention of this course is to present ideas and methods on such a level that the beginning graduate student will be able to follow current research. Both linear and nonlinear results will be covered. Recommended preparation: EECS 408.

EECS 523. Advanced Neural Microsystems. 3 Units.

This course will cover the latest advances in neuroengineering with specific attention to integrated microsystems targeting wired/wireless multichannel interfacing with the nervous system at the cellular level in biological hosts. The aim is to provide students familiar with microfabrication and integrated circuit design with an application-driven, system-level overview of sensors and microelectronics in microsystems format for neural engineering. Recommended preparation: EECS 426.

EECS 526. Integrated Mixed-Signal Systems. 3 Units.

Mixed-signal (analog/digital) integrated circuit design. D-to-A and A-to-D conversion, applications in mixed-signal VLSI, low-noise and low-power techniques, and communication sub-circuits. System simulation at the transistor and behavioral levels using SPICE. Class will design a mixed-signal CMOS IC for fabrication by MOSIS. Recommended preparation: EECS 426.

EECS 527. Advanced Sensors: Theory and Techniques. 3 Units.

Sensor technology with a primary focus on semiconductor-based devices. Physical principles of energy conversion devices (sensors) with a review of relevant fundamentals: elasticity theory, fluid mechanics, silicon fabrication and micromachining technology, semiconductor device physics. Classification and terminology of sensors, defining and measuring sensor characteristics and performance, effect of the environment on sensors, predicting and controlling sensor error. Mechanical, acoustic, magnetic, thermal, radiation, chemical and biological sensors will be examined. Sensor packaging and sensor interface circuitry.

EECS 531. Computer Vision. 3 Units.

The goal of computer vision is to create visual systems that recognize objects and recover structures in complex 3D scenes. This course emphasizes both the science behind our understanding of the fundamental problems in vision and the engineering that develops mathematical models and inference algorithms to solve these problems. Specific topics include feature detection, matching, and classification; visual representations and dimensionality reduction; motion detection and optical flow; image segmentation; depth perception, multi-view geometry, and 3D reconstruction; shape and surface perception; visual scene analysis and object recognition.

EECS 589. Robotics II. 3 Units.

Survey of research issues in robotics. Force control, visual servoing, robot autonomy, on-line planning, high-speed control, man/machine interfaces, robot learning, sensory processing for real-time control. Primarily a project-based lab course in which students design real-time software executing on multi-processors to control an industrial robot. Recommended preparation: EECS 489.

EECS 600. Special Topics. 1 - 18 Unit.

Offered as EECS 600 and SYBB 600.

EECS 600T. Graduate Teaching III. 0 Units.

This course will provide Ph.D. candidate with experience in teaching undergraduate or graduate students. The experience is expected to involve direct student contact but will be based upon the specific departmental needs and teaching obligations. This teaching experience will be conducted under the supervision of the faculty member who is responsible for the course, but the academic advisor will assess the educational plan to ensure that it provides an educational experience for the student. Students in this course may be expected to perform one or more of the following teaching related activities running recitation sessions, providing laboratory assistance, developing teaching or lecture materials presenting lectures. Recommended preparation: Ph.D. student in EECS department.

EECS 601. Independent Study. 1 - 18 Unit.


EECS 602. Advanced Projects Laboratory. 1 - 18 Unit.


EECS 620. Special Topics. 1 - 18 Unit.


EECS 621. Special Projects. 1 - 18 Unit.


EECS 649. Project M.S.. 1 - 9 Unit.


EECS 651. Thesis M.S.. 1 - 18 Unit.


EECS 701. Dissertation Ph.D.. 1 - 18 Unit.

Prereq: Predoctoral research consent or advanced to Ph.D. candidacy milestone.