413 Olin Building (7071)
Phone 216-368-4033; Fax 216-368-6888
B. Ross Barmish, Department Chair
e-mail chair@eecs.cwru.edu
http://www.eecs.cwru.edu

The Department of Electrical Engineering and Computer Science spans the technologies at the forefront of our economy and our society. Professionals in these fields are responsible for developing microprocessors and personal computers, and the operating systems, computer software, and Internet applications which run on them. Almost every modern device contains an integral computer chip. New developments in such areas as medical electronics, automotive safety and control, automated manufacturing, and entertainment electronics continue to provide opportunities for our graduates.

The Department of Electrical Engineering and Computer Science (EECS) is structured into four programs: electrical engineering, computer engineering, systems and control engineering, and computer science. Each area offers a degree program which leads to the Bachelor of Science degree. All engineering programs in the department are accredited by the Accreditation Board for Engineering and Technology (ABET). 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 and information sciences.

The Electrical Engineering component of the department taught its first electrical engineering class in 1886 making it one of the oldest in the nation. The department has always been innovative and first in many things. The Systems & Control Engineering program was the first of its kind to be accredited by ABET and grew out of the Systems Research Center, originally founded in 1959. The computer engineering program was the nation’s first ABET accredited computer engineering program.

The EECS department is dedicated to producing high-quality graduates who will take positions of leadership. We recognize that the increasing role of technology in virtually every facet of our culture– communications, transportation, health care, the environment, and even our system of wealth distribution – makes it vital that engineering-oriented students have access to progressive and cutting-edge programs stressing excellence in:

Emphasizing these core values will help ensure that tomorrow’s 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 & computer science, including recent developments in the field. Because of the rapid pace of change in these fields our degree programs emphasize a broad technical background that equips students for future developments. As a result, 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. The department prepares students for careers in engineering with degrees in electrical engineering, computer engineering, computer science or systems & control engineering.

The department programs emphasize a mastery of fundamentals which will enable students to deal with new technological developments and interact with professionals in other fields. This is achieved by ensuring that our graduates have:

Technological development continues to result in new technologies and/or new problems. We ensure that our graduates are creative and able to apply their engineering knowledge to new problems by

We live in a complex technological society which requires that our graduates have a broad education necessary to understand the consequences of engineering solutions in the broader context of their impact upon people and the environment. We ensure that our graduates are socially aware by

We develop our students as professionals by developing their communications and leadership skills and additionally by

B. Ross Barmish, Ph.D. (Cornell University)
Department Chair, Nord Professor
Control systems, robustness, probabilistic methods, Monte Carlo simulation

Randall D. Beer, Ph.D. (Case Western Reserve University)
Professor
Computational neuroscience, autonomous robotics

Michael S. Branicky, Sc.D. (Massachusetts Institute of Technology)
Associate Professor
Intelligent systems and control, hybrid systems, learning, real-time and distributed control over networks, applications to robotics and flexible manufacturing

Marc Buchner, Ph.D. (Michigan State University)
Associate Professor
Computer simulation of complex systems, control of industrial systems, analysis of discrete event and combined systems

Vira Chankong, Ph.D. (Case Western Reserve University)
Associate Professor
Large-scale and multi-objective optimization and its application to engineering problems, manufacturing and production systems, improvement of magnetic resonance imaging, decision theory, and risk analysis

Funda Ergun, Ph.D. (Cornell University)
Schroeder Assistant Professor
Program testing routing and quality of service in high speed networks, packet classification, randomized algorithms, learning theory

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

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

Dov Hazony, Ph.D. (University of California, Los Angeles)
Professor
Network syntheses, ultrasonics, communications

Vincenzo Liberatore, Ph.D. (Rutgers)
Assistant Professor
Distributed Systems, internet computing, randomized algorithms

Wei Lin, Ph.D. (Washington University)
Associate Professor
Nonlinear dynamic systems and geometric control theory, H-infinity and mixed H-2/H-infinity and robust control, adaptive control, system parameter estimation, adaptive and nonlinear control for robotics manipulators

Kenneth Loparo, Ph.D. (Case Western Reserve University)
Professor
Stability and control of nonlinear and stochastic systems, analysis and control of discrete event systems, intelligent control systems and failure detection. Recent applications work focuses on the control and failure detection of rotating machines.

Behnam Malakooti, Ph.D. (Purdue University)
Professor
Industrial engineering, computer-aided manufacturing, man-machine systems, AI, Multiple criteria decision making and optimization

Mehran Mehregany, Ph.D. (Massachusetts Institute of Technology)
Silicon and silicon carbide microelectromechanical systems (MEMS), micromachining and microfabrication and related integrated circuits, materials, and modeling issues

Frank Merat, Ph.D. (Case Western Reserve University), PE (Ohio)
Associate Professor and Associate Chair for Undergraduate Studies
Wireless networks, RF communications, optical MEMS devices, computer vision and image processing, neural networks

Mihajlo D. Mesarovic, Ph.D. (Serbian Academy of Science)
Cady Staley (Hanna) Professor
Complex systems theory, global issues and sustainable development

Wyatt Newman, Ph.D. (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

Gultekin Ozsoyoglu, Ph.D. (University of Alberta, Canada)
Professor
Databases, multimedia computing, digital libraries

Z. Meral Ozsoyoglu, Ph.D. (University of Alberta, Canada)
Professor
Database theory, logic databases, database query and optimization

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

Stephen M. Phillips, Ph.D. (Stanford University), PE (Ohio)
Associate Professor
Applications of control and signal processing to robotics and automation

Andy Podgurski, Ph.D. (University of Massachusetts at Amherst)
Associate Professor
Software engineering methodology and tools, software architecture and design, distributed systems, software testing and reliability estimation

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

S. Cenk Sahinalp, Ph.D. (University of Maryland)
Assistant Professor
Design, analysis and experimental evaluation of algorithms for pattern matching and indexing; data compression, communication networks and computational molecular biology

N. Sreenath, Ph.D. (University of Maryland)
Associate Professor
Large-scale systems, policy analysis, sustainable development, integrated assessment, global and environmental issues (water resources and global climate change), control theory applications and medical informatics

Massood Tabib-Azar, Ph.D. (Rensselaer Polytechnic Institute)
Professor
Semiconductor material and device characterizations, optical signal processing, novel high-frequency and high-power devices and circuits, spectroscopy and low temperature measurement, novel super-resolution near-field imaging probes, quantum computing

Lee J. White, Ph.D. (University of Michigan)
Professor and Associate Chair for Graduate Studies
Software testing, current projects include regression testing, study of domain testing, specification-based testing and testing of object-oriented software

Darrin Young, Ph.D. (University of California, Berkeley)
Assistant Professor
Micromachined sensors, high-Q passive components and integrated low power analog circuits for wireless communications

GQ (Guo-Qiang) Zhang, Ph.D. (Cambridge University, England)
Associate Professor
Programming languages, theory of computation, logic and topology in computer science

Coleman B. Brosilow, Ph.D. (Brooklyn Polytechnic Institute)
Professor, Chemical Engineering

Robert V. Edwards, Ph.D. (Johns Hopkins University)
Professor, Chemical Engineering

Joseph Koonce, Ph.D. (University of Wisconsin, Madison)
Professor, Biology Department

Joan Carletta, Ph.D. (Case Western Reserve University)
Adjunct Assistant Professor

Howard Chizeck, Sc.D. (Massachusetts Institute of Technology)
Adjunct Professor

Benjamin F. Hobbs, Ph.D. (Cornell University)
Adjunct Professor

Pat Howard, Ph.D. (Case Western Reserve University)
Adjunct Assistant Professor

Peter Kinman, Ph.D. (University of Southern California)
Adjunct Assistant Professor

Geoffrey Lockwood, Ph.D. (University of Toronto, Canada)
Adjunct Assistant Professor (Cleveland Clinic)

Marvin Schwartz, Ph.D. (Case Western Reserve University)
Adjunct Assistant Professor

Peter Tsivitse
Adjunct Professor

Clayton Van Dorn, Ph.D. (Syracruse University)
Adjunct Assistant Professor

Chris Zorman, Ph.D. (Case Western Reserve University)
Adjunct Assistant Professor

Paul C. Claspy, Ph.D. (Case Institute of Technology)
Emeritus Associate Professor
Communications, and imaging, lasers and electro-optics

Robert E. Collin, Ph.D. (Imperial College, University of London, England)
Emeritus Professor
Electromagnetic theory, antennas, propagation, microwave components and systems

Sheldon Gruber, Sc.D. (Massachusetts Institute of Technology)
Emeritus Professor
Signal processing, machine vision and industrial inspection

Wen H. Ko, Ph.D. (Case Institute of Technology)
Emeritus Professor
Solid state sensors and devices, biomedical implants, telemetry

Irv Lefkowitz, Ph.D. (Case Institute of Technology)
Emeritus Professor
Automation and computer control of industrial processes

Osman K. Mawardi, Ph.D. (Harvard University)
Emeritus Professor
Plasma Physics, energy conversation and storage, applied superconductivity

Harry W. Mergler, Ph.D. (Case Institute of Technology)
Leonard Case Emeritus Professor
Digital systems, systems engineering, logical design computer control, metrology

Yoh-Han Pao, Ph.D. (Pennsylvania State University)
George S. Dively Emeritus Professor
Pattern recognition, signal and image processing, computational intelligence, intelligent systems

Frederick J. Way III
Emeritus Professor

EECS programs at Case Western Reserve encompass a wide spectrum of activities. Some of the major activities include biorobotics and computational intelligence, automation and robotics, solid-state devices and MEMS, communications, nanoelectronics and nanometrology techniques, global and large-system modeling, software engineering,and databases and bioinformatics. Much of this research is multi-disciplinary in nature involving faculty members from Materials Science and Engineering, Biology, Psychology, Civil Engineering, and Mechanical and Aerospace Engineering.

The faculty of the department actively pursue research in the areas described below. Students pursue their thesis research under the supervision of a faculty member who is a recognized authority in his field. Support for thesis research comes from a related research project or program under the direction of the faculty. For further information on research opportunities, the department chair should be contacted.

Algorithms - Professors Ergun, Liberatore and Sahinalp
Basic theoretical and applied work in randomized algorithms, program testing and correcting, learning theory, learning theory, multivariate optimization, data structures, string and sequence algorithms, combinatorial and statistical pattern matching and indexing, embedding of metric spaces, data compression and complexity of communication, algorithmic analysis of massive data sets, sketching and streaming models, parallel computation and circuit layouts, experimental algorithmics and performance evaluations.

Automation, Sensing, Actuation and Machine Intelligence - Professors Barmish, Branicky, Liberatore, Loparo, Malakooti, Merat, Newman, Pao and Phillips
Research activities include neural network applications; pattern recognition; artificial intelligence; hybrid systems, process automation; intelligent machine tool control; in-process gauging and control; adaptive learning methods applicable to robotics; system identification and adaptive control; intelligent control; the application of artificial intelligence to robotics systems and manufacturing; compliant control of robotics systems; non-contact inspection of production quality; machine vision for robotics applications; agile manufacturing systems; machine vision and image processing; rapid prototyping of computer-generated 3-D objects in engineering materials; computational intelligence, principles and applications; distributed computational intelligence in network client/server mode; computational intelligence and associative memories; robustness considerations and related statistical techniques.

Circuits and Computer-Aided Design - Professors Garverick, Young and Merat
Research activities include SiC circuits, and mixed-signal CMOS integrated circuit design for applications in MEMS, biomedical instrumentation, and robotics, MEMS RF high-Q tuning components for mobile communication circuits, MEMS sensors for biomedical and inertial sensing applications, microfabrication and integrated circuits process development.

Computer Networks - Professors Ergun, Liberatore, Malakooti and Sahinalp
Research activities include data dissemination, background distributed computing, distributed middleware and services, overlay networks, quality of service, routing, random graphs for network modeling, and packet filtering and classification; development of intelligent networks using intelligent mobile agents.

Computational Genomics - Professors Sahinalp, M. Ozsoyoglu, Pao and Buchner
Current research activities include: (1) computational studies of large scale genome duplication and other genome-wide rearrangements; (2) phylogenetics of the human genome, (3) algorithmic tools for pattern/motif search and discovery.

Computational Neuroscience and Autonomous Robotics - Professor Beer
Using computer simulation and theoretical analyses of models of complete neural/body/environment systems, this research pursues two objectives. First, it seeks to better understand the neural mechanisms of behavior in animals. Second, it seeks to apply biological control principles to the design of autonomous robots with the flexibility ad robustness of animals. The tools employed in this work include continuous-time recurrent neural networks, evolutionary algorithms, and dynamical systems theory. This research is highly interdisciplinary, and includes collaborators from the Department of Biology and the Department of Mechanical and Aerospace Engineering.

Control Applications - Professors Barmish, Branicky, Buchner, Loparo, Liberatore, Lin and Phillips
Topics include: (1) The development of anti-lock braking systems using fuzzy logic control methods; (2) Development of methods of automotive control and computer assisted tools for engineering analysis and design (e.g., development of computer based tools for system level failure mode effect analysis); (3) Developing technology for advanced power train, energy management, sensing and control strategies for electric vehicles; (4) the use of methods of control engineering to solve problems involving industrial and manufacturing processes; (5) developing advanced analysis and design tools for robotic assembly, agile manufacturing, (6) control over networks (QoS provisioning and multi-agent software).

Control, Filtering and Robustness - Professors Barmish, Lin and Loparo
Topics include: (1) nonlinear control theory work addressing questions regarding the behavior, stability and control of dynamic systems that are inherently nonlinear in the relationships between their inputs, outputs, and internal states; (2) stochastic control theory work involving the study of the behavior, stability and control of dynamic systems that possess an element of randomness in their operation over time; (3) stochastic filtering theory work, investigating the extraction of information about internal variables of a system on the basis of (possibly noise corrupted) measurements of system outputs; and (4) Robust control and analysis with emphasis on new Monte Carlo techniques and models for addressing system uncertainty.

Database Systems - Professors M. Ozsoyoglu and G. Ozsoyoglu
This research area focuses on performance issues in relational databases, database query processing and distributed database query processing, file allocation in distributed databases, database design, object-oriented databases, statistical database security problems, and relational interfaces for non-relational databases.

Design Methodologies and Design Automation - Professors Saab and Papachristou
This research area is concerned with the development of behavioral and structural level design methodologies and tools for the creation of VLSI-based systems and for multiple-processor architectures. Central to this work is the continued development of a third-generation design automation system for VLSI.

Electromagnetics, High Frequency Communications and Devices - Professors Hazony, Ko, Merat, Tabib-Azar and Young
Research activities include electromagnetic propagation and scattering, high frequency acoustic circuits, generation and detection of extremely sharp pulses, in situ monitoring in aggressive environments, biotelemetry, wireless communications for in situ arrays of biosensors.

Expert Systems - Professors Ernst, Malakooti, Merat, Paoand and Zhang
The research on expert systems is primarily concerned with using artificial intelligence techniques to represent and reason about knowledge. Current research includes (1) common-sense reasoning; (2) development of multiple criteria based expert systems for solving design and facility layout problems; and (3) applied research in a number of different challenging applications, such as fault diagnosis in discrete event systems. Most of these applications are based on knowledge which has been extracted from experts in the application domains.

Fault Detection and Diagnosis - Professors Loparo and Lin
Research combining advanced theoretical topics with solutions to industrial problems of high relevance and economic importance. Topics include: (1) the detection specific identification of failure events in systems and, when possible, the detection of incipient failures, through the use of nonlinear filtering of measured system inputs and outputs; and (2) the use of nonlinear dynamics and chaos theory for failure detection, the use of chaos concepts and other advanced model-based methods for vibration signature analysis.

Global Systems Analysis and Sustainable Development - Professor Mesarovic and Sreenath
This research addresses one of the most challenging tasks of systems science and systems engineering, i.e., to understand the world as a system and develop methods to assess the evolution of the system. In order to advance understanding of the global system, two principle obstacles are being addressed: complexity by using a multi-level, hierarchical architecture and uncertainty by interactive human/computer reasoning support process. The focus of the research is on interaction between global issues which represents a distinguishing characteristic of the global future (referred to as the global problematique). A range of issues are considered-from demographic transition and aging to carrying capacity, prospects for global climate change, impact of financial markets on development, etc. Collaborative research with a global network of universities is underway through the UNESCO Global-problematique Education Network Initiative (Genie). The Network is made up of fifteen universities from countries around the world strategically selected in order to provide a global coverage. Joint research with member institutions is conducted via the Internet. The research ranges from modeling and methods of complex systems analysis under true uncertainty analysis of specific issues such as global coordination of greenhouse gas emission reduction policies, water resources and health carrying capacity of Africa, etc.

Identification and Adaptive Control - Professors Lin and Buchner
Research directed towards specific application problems and the development of new theory. Topics include: (1) adaptive control of nonlinear systems, adaptive control of multi-input, multi-output systems having unknown and time varying input-output delays; (2) predictive adaptive control of non-minimum phase systems and the development computationally efficient methods of predictive control; (3) development and application of methods for real-time identification of parameters for linear systems having unknown input-output delays, and for nonlinear systems.

Industrial, Production, Operational, Management Systems - Professors Malakooti and Chankong
Optimization, multiple criteria decision making, and artificial intelligence techniques are used to improve quality, productivity, and cost efficiency of real-world problems including development of computer aided and integrated manufacturing/production planning and control; facility layout design, assembly line balancing; pattern recognition and clustering applied to group technology family formation; scheduling, machine set-up, tool life, and machinability. Research activities include applying optimization, decision making, multiple objectives (criteria), AI, artificial neural networks, pattern recognition/clustering to facility layout design, group technology, and assembly systems as well as developing multiple objective optimization and analysis for machine set-up, supervision, tool life, machinability, and sensing devices.

Intelligent Systems, Neural Nets And Fuzzy Logic - Professors Loparo, Pao, Branicky, Merat, Malakooti and Tabib-Azar
The use of methods of "machine intelligence" to accomplish control of systems. Particular topics of interest include: (1) the use of feed forward artificial neural nets to detect tool wear in parts machining processes, and to model load demand of electric power systems; (2) the use of fuzzy logic methods to attain anti-lock braking for automobiles, to control manufacturing processes and chemical processes, to detect events of gait in neuro-prosthetic systems that provide walking for paraplegics using electrical stimulation; (3) the analysis of combined discrete and continuous state ‘hybrid’ dynamic systems; and (4) novel computation techniques such as quantum computing and associated networks.

Logic, programming, and verification - Professor Zhang
Research activities include the semantics of programming languages and logic and models for reasoning about software, hardware, and security-critical systems.

Mathematical Modeling and Systems Analysis of Global Change Phenomena - Professors Mesarovic and Sreenath
The use of mathematical modeling of global economic and physical phenomena, in conjunction with computer simulation, to develop alternative scenarios of the future. This work involves a determination of what changes are possible within an environmental system, on the basis of the structure of mathematical models that represent its behavior (or hypotheses about its behavior).

Optimization and Decision Theory and Methods - Professors Malakooti and Chankong
Basic theoretical work and specific applications. Topics include: (1) Multi-objective optimization theory; (2) Algorithms for machine part formation problems; (3) Clustering algorithms for data compression; (4) Algorithms and tools for VLSI design; (5) Algorithms and methods for facility location and layout in manufacturing systems; (6) the use of systems analysis and decision theory methods to solve problems of the electric utility industry, such as quantification of the implications of transmission constraints for generation costs and resource planning.; (7) methods for the design of magnetic resonance imaging (MRI) pulse sequences, for clinical MR images. to allow for the removal of motion artifacts (e.g., in images of the liver) and enhancements of images specific tissue types; and (8) the application of systems analysis and decision theory methods to problems of information flow and control in health care.

Semiconductor Materials and Devices - Professors Tabib-Azar, Mehregany, Young, Garverick and Ko
Research activities include design, modeling, fabrication, testing, and application of a wide range of micro-to-nano systems, with particular emphasis on supporting materials technology, including silicon carbide. Example devices include micromachined components, sensors; actuators; opto-mechanical devices including scanners and switches; electronic devices; microwave probes; electromagnetic devices and filters, and wireless communication components and subsystems. Example applications are in fields such as transportation, telecommunications, space, biomedical, and industrial control.

Signal processing - Professors Buchner, Loparo, Merat and Pao
Research activities include neural network signal and information processing, image processing, time-frequency signal analysis, processing of genomic information, detection of tornados in radar images using wavelets, two-dimensional periodicity transforms.

Software Architecture and Design - Professors Podgurski , White and Zhang
The objective of this research is to develop, specify, and analyze prototypical or reference architectures for important families of software applications, such as those used in Internet commerce, manufacturing, biomedical control, and avionics, and to derive general principles and methodologies such as formal verification for the design of complex software systems.

Software Testing and Reliability - Professors White and Podgurski
This research focuses on improving the quality of software. One approach to testing software is to identify and correct defects applied to object-oriented software and specifically to GUI systems. Also there is a research project on data coverage testing, where the emphasis is to predict when testing can be stopped, as further testing can be shown to be only marginal in effectiveness.

Space Communications and Networks - Professors Ergun, Kinman, Ko, Malakooti, Merat, G. Ozsoyoglu, M. Ozsoyoglu, Papachrostou, Phillips, Sahinalp and Young
This research is primarily concerned with developing communications and networking solutions for near- and deep-space applications. Current research includes: (1) MEMS tunable antennas for power efficient wireless communications; (2) miniature silicon optical reflectors which can be electrostatically deformed and steered; (3) tiled arrays of processors which can be reconfigured for a variety of communications and signal processing applications; (4) semantic-based database inquiry and data warehousing for space assets; (5) protocols and control architectures for remote teleoperation of robots; (6) protocols and systems for the intelligent routing of data in space networks; (7) wireless networks of biosensors for monitoring of astronauts; and (8) efficient utilization of radio spectrum for space communications, performance modeling of radio communications using advanced coding schemes, Doppler and range measurements to space vehicles.

Computer Facilities
The department computer facilities incorporate both UNIX (primarily Solaris) and Microsoft Windows-based operating systems on high end computing workstations for its educational and research labs. 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, Glennan and Smith buildings and are connected to each other via CWRUnet.

CWRUnet 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 at the University. CWRUnet is one of the largest fiber-to-desktop networks anywhere in the world. Every desktop has or will have a 1 Gbps (gigabit per second) connection to the rest of the campus network backbone, which runs on fault-tolerant 10 Gbps and faster fiber-optic links. In an effort to expand network availability to complement the wired network already in place, more than 1,000 wireless access points (WAPs) are being deployed, allowing students with laptops and wireless enabled PDAs to access CWRUnet resources from practically anywhere on campus.

Off campus users, through the use of CWRUnet’s high capacity virtual private network (VPN) servers, can use their home dial-up or broadband connections to access many on campus resources as well as software as if they were physically connected to CWRUnet.

The department and the University also participate in the Internet2 project, which provides a 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 standard services provided through a commodity Internet connection, CWRUnet users can take advantage of numerous on-line 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.

Smith Computer Lab
General purpose computer facilities for undergraduate instruction is provided by the Smith Laboratory which contains about 70 PCs, a number of Macintosh power PCs and ten SUN Sparc-5 UNIX workstations.

Jennings Computer Center Labs
Supported by an endowment from the Jennings Foundation, these labs provide our students with the education resources necessary both for their classes and to explore their interest in the art of computing.

Database and Multimedia Laboratory
Primarily funded by NSF equipment grants, this laboratory provides specialized equipment for research into multimedia and database systems.

VLSI Design Laboratory
Supported by the Silicon Research Corporation and industry, this laboratory has a number of UNIX workstations which run CAD software for VLSI design. This laboratory is currently used to develop testing techniques for digital design.

Autonomous Robotics Laboratory
Primarily funded by ONR and other federal sources, this laboratory has a number of computer workstations and robots which are used to conduct research into robotics, autonomous agents and biological simulation.

Electronic Circuits Lab
This laboratory has been primarily supported by the Hewlett-Packard Company and is the basic resource for students taking analog, digital and mixed-signal electronics classes. All instrumentation in the lab is computer-interfaced and students can even conduct experiments from their dorm rooms.

Analog Workstations
266 MHz NT workstations are equipped with LabView software. The workstations have HP-IB instrument interfaces connected to Hewlett-Packard 546xx oscilloscopes, 33120A Waveform Generators, 34401A Digital Multimeters, and E3631A power supplies.

Digital Workstations
450 MHz NT workstations and Sun Workstations support Xylinx FPGA hardware/software.

Additional instrumentation includes a Hewlett-Packard 4155B semiconductor parameter, Hewlett-Packard 54616TC mixed-signal test stations, Hewlett Packard logic analyzers, and Hewlett-Packard high-frequency oscilloscopes.

Lester J. Kern Computational Laboratory
This laboratory is used by students enrolled in "Electromechanical Energy Conversion," as well as for research in robotics and mechatronics. Laboratory facilities include: four lab stations for demonstrating machine characteristics and basic steady-state and dynamic system performance, four Sun SPARC UNIX workstations, and real-time data acquisition systems for interaction with lab experiments and control of machines.

Microcomputer Laboratory
This laboratory contains approximately 25 Microcomputers (these are mostly high end Pentiums and a few Macintosh Power PC’s), along with a complement of laser printers, network connections (university fiber optic network and LAN), and scientific software (MATLAB, VISSIM, Mathematica, GINO, LINDO, etc.).

Process Control Laboratory
This laboratory contains process control pilot plants, computerized hardware for process control and demonstration/research facilities. This wet lab has access to steam and compressed air for use in the pilot plants.

Timken Foundation Dynamics and Control Laboratory
Contains mechanical, pneumatic and electrical laboratory experiments for teaching and research purposes. This includes PLCs, motors and robotics systems.

Global Systems Laboratory
This laboratory consists of various PC and Sun Sparc workstations containing databases from the UN, World Watch Institute, World Resources Institute, U.S. Government, etc., and policy and scenario analysis software.

Rockwell Automation Machinery Diagnostics and Control Laboratory
This laboratory is focused upon machinery diagnostics and failure prediction. Several test stands will provide instrumentation for machinery lifetime prediction and sensor development. Additional instrumentation will provide for remote operation of the test stands.

Micro-electronic Device Modeling and Characterization Lab
Affiliated with our MicroFabrication Laboratory MFL, this laboratory is equipped with dc measurement capabilities for evaluating semiconductor device performance. Device modeling is done on Sun SPARC and HP workstations.

Hans Jaffe Ultrasonics Laboratory
This laboratory is dedicated to the study and fabrication of specialized ultrasonic transducers. Facilities include pulsar receivers, specialized scopes, precision signal generators, and piezoelectric devices.

Center for Automation and Intelligent Systems Research
Supported in part by CAMP, Inc. through the State of Ohio’s Thomas Edison research center program, this educational and research center contains multiple laboratories including:

These laboratories are equipped with a diverse range of modern scientific and CAD workstations, computer controlled robots, materials handling devices, image processing and computer vision systems. These laboratories support research activities in robotics, agile manufacturing, multimedia internet applications to manufacturing, rotating machinery diagnostics, optical sensing and process control.

MicroFabrication Laboratory
This laboratory has been funded by many agencies including the State of Ohio and DARPA. The MicroFabrication Laboratory (MFL) is a state-of-the-art clean room facility for the fabrication of microelectromechanical systems (MEMS) and microelectronic devices. The Class 100 facility supports the University’s strong interdisciplinary MEMS research program by providing on-campus fabrication capabilities for a broad range of research projects by investigators from a number of departments within the university; it is also accessible by external organizations for prototype fabrication and R&D. The MFL offers a broad spectrum of micromachining processes, including bulk and surface micromachining, wafer bonding, and micro-molding. These capabilities are augmented by a 2-micron CMOS process for the fabrication of integrated microsensors/microactuators.

The Center for Computational Genomics
Established by a $2.2 million grant from the Charles B. Wang Foundation, Inc. this interdisciplinary center (EECS, Genetics, and Biostatistics & Epidemiology) employs computer science to analyze the function of genes and proteins in health and disease. The Center’s lab provides high-power computing resources (2GHz Dells with 1 GB DRAM) for computational genomics research.

PLC Control and Automation Laboratory
This laboratory uses Allen-Bradley PLC’s for data acquisition and real-time control of complex processes. Currently the PLCs control a multi-train HO model system and a five-floor, two-car elevator system.

ENGR 131 Freshman Computing Lab
This lab is used to support the freshman ENGR 131 Elementary Computer Programming class. The laboratory provides personal computers and Lego Mindstorm robot kits which freshman use to learn about how computers can be used to control mechanisms, as well as to study C/C++ programming.

The undergraduate program in electrical engineering, which leads to the Bachelor of Science in Engineering degree, provides a broad foundation in electrical engineering through combined classroom and laboratory work and prepares the student for entering the profession of electrical engineering as well as for further study at the graduate level.

Core courses provide the student with a strong background in mathematics, physical sciences and the fundamentals of engineering. Each electrical engineering student must take the following core courses:

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. Note that this depth requirement may be met using a combination of the above core courses and a selection of open and technical electives.

Area I: Electromagnetics
EECS 309 Electromagnetic Fields I
EECS 310 Electromechanical Energy Conversion
EECS 311 Electromagnetic Fields II

Area II: Signals & Systems
EECS 246 Signals and Systems
EECS 313 Signal Processing
EECS 347 Network Synthesis
EECS 351 Communications and Signal Analysis
EECS 354 Digital Communications
EECS 396 Hybrid Systems

Area III: Computer Software
EECS 233 Data Structures
EECS 337 Systems Programming
EECS 338 Operating Systems

Area IV: Solid State
EECS 321 Semiconductor Electronic Devices
EMSE 314 Electrical, Optical and Magnetic Properties of Matter
EECS 322 Integrated Circuits and Electronic Devices

Area V: Control
EECS 304 Control Engineering I
EECS 310 Electromechanical Energy Conversion
EECS 383 Microprocessor Applications to Control
EECS 346 Engineering Optimization
EECS 396 Hybrid Systems

Area VI: Circuits
EECS 245 Electronic Circuits
EBME 310 Biomedical Instrumentation
EECS 344 Electronic Circuit Design
EECS 382 Microprocessor Based Design
EBME 418 Biomedical Electronics
EECS 426 MOS Integrated Circuit Design

Area VII: Computer Hardware
EECS 281 Computer Organization
EECS 382 Microprocessor Based Design
EECS 301 Computer Design Lab
EECS 314 Computer Architecture
EECS 315 Digital Systems Design

In consultation with a faculty advisor, the student completes the program by selecting technical and open elective courses that provide in-depth training in one or more of a variety 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 their advisors 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 equipment. Students have ready access to the laboratory facilities and are encouraged to work in the various laboratories during nonscheduled hours in addition to the regularly scheduled laboratory sessions. Opportunities also exist for undergraduate student participation in many of the wide variety of research projects being conducted within the program.

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

Minor in Electronics
The department also offers a minor in electronics for students in the College of Arts and Science. This program requires the completion of 29 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:

Cooperative Education Program
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.

B.S./M.S. Program
The department encourages students with at least a 3.5 grade point average to apply for admission to the five-year bachelors/master’s program in the junior year. This integrated program, which permits substitution of M.S. 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.

The Bachelor of Science program in Computer Engineering is designed to give a student a strong background in the fundamentals of mathematics, physics, and computer engineering and science. A graduate of this program should be able to use these fundamentals to analyze and evaluate computer systems, both hardware and software. A graduate should also be able to design and implement computer systems, both hardware and software, which are state of the art solutions to a variety of computing problems. This includes systems which have both a hardware and a software component, whose design requires a well defined interface between the two, and the evaluation of the associated engineering trade-offs. In addition to these program specific objectives, all students in the EECS department are exposed to societal issues, professionalism, and have the opportunity to develop leadership skills.

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:

In addition to these two standard sequences, the student may design his/her own 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.

Cooperative Education Program
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.

B.S./M.S. Program
Students with a grade point average of 3.2 or higher are encouraged to apply to the B.S./M.S. Program which will allow them to get both degrees in five years. The B.S. 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.

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-off’s. In addition to these program specific objectives, all students in the EECS department are exposed to societal issues, professionalism, and have the opportunity to develop leadership skills.

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 much less technical than the Bachelor of Science program in Computer Science. It is particularly suitable for students with a wide variety 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 4 year period.

Minor In Computer Science (B.S. or B.S.E.)
For students pursuing a B.S. or B.S.E. degree, the following three courses are required for a minor in computer science:

A student must take an additional four credit hours of computing courses with the exclusion of ENGR 131. MATH 304 (Discrete Mathematics) may be used in place of three of these credit hours because it is a prerequisite for EECS 340.

Minor In Computer Science (B.A.)
For students pursuing B.A. degrees, the following courses are required for a minor in computer science:

Two additional computing courses are also required for this minor.

Cooperative Education Program
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.

B.S./M.S. Program
Students with a grade point average of 3.2 or higher are encouraged to apply to the B.S./M.S. Program which will allow them to get both degrees in five years. The B. S. 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.

The systems and control engineering B.S. program provides the student with the basic concepts, analytical tools, and engineering methods which are useful 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: computer control of industrial plants, development of 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 their individual contributions to achieve the overall goal of the system. What may be best for an individual component of the system may not be the best for the system as a whole.

There are three elective sequences available within our B.S. degree curriculum:

Control Systems
The Control Systems sequence is directed toward developing skills in dynamic system modeling, analysis, automation, remote control, real-time data acquisition and feedback control.

Systems Analysis
The Systems Analysis sequence focuses on modeling, optimization, decision making and planning methods.

Industrial and Manufacturing Systems
The Industrial and Manufacturing Systems sequence provides education in the application of systems analysis, decision making and automation methods to industrial production and manufacturing problems.

All three sequences use concepts of modeling, data analysis, computer simulation, and optimization. Computers play a central role in the systems and control curriculum, not only for engineering and mathematical computation, but also for computer simulation, automatic control, real-time data acquisition and signal processing.

Minor Program 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 nine credit hours must be selected from:

The remaining credit hours can be chosen from EECS courses with the written approval of the 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:

Cooperative Education Program
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.

B.S./M.S. Program
The department encourages students with at least a 3.2 grade point average to apply for admission to the five-year bachelors/master’s program in the junior year. This integrated program, which permits substitution of M.S. 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.

Control Engineering and Signal Processing
EECS 306 Control Engineering II

EECS 396 Hybrid Systems

EECS 401 Digital Signal Processing

EECS 404 Digital Control

EECS 409 Discrete Event Systems

EECS 417 Introduction to Stochastic Control

Control Systems Analysis and Engineering
EECS 414 Complex Systems Modeling and Analysis

EECS 416 Engineering Optimization

EECS 429 Risk and Decision Analysis

OPRE 432 Simulation

OPRE 426 Stochastic Processes in Operations Research

Manufacturing, Industrial, and Operational Systems
EECS 350 Production and Operational Systems

EECS 360 Manufacturing and Integrated Systems

OPMT 351 Logistical Systems

OPMT 353 Quality Control and Management

EECS 450 Production and Operational Systems

EECS 460 Manufacturing and Integrated Systems

OPRE 424 Scheduling

The programs in computer engineering and computing and information sciences are similar in that they each require a strong background in both computer hardware and software, as well as a substantial amount of "hands-on," experience. The programs differ in that engineering is based mainly in physical sciences, while computer science is more strongly based in mathematical sciences as applied to more abstract notions such as properties of programming languages, analysis of algorithms, complexity considerations, and proof of correctness. The department believes that the success of its graduates at all levels is largely due to the emphasis on project and problem-oriented course material coupled with the broad-based curricular requirements. Doctoral dissertations must be original contributions to the existing body of knowledge in computer engineering and science.

The electrical engineering program offers graduate study leading to the Master of Science and Doctor of Philosophy degrees. The programs are comprehensive and basic, emphasizing four major areas in which the faculty are actively engaged in research: (1) automation, sensing, intelligence and actuation; (2) solid state electronics; (3) electromagnetic, high frequency communications and devices; and (4) circuits, signal processing, and computer-aided design. Academic requirements for graduate degrees in engineering are as specified for The Case School of Engineering in this bulletin, however, some exceptions are noted below. All current rules and regulations for this department are detailed in a graduate student handbook, available from the department office, which supersedes any rules contained here. A number of teaching and research assistantships are available, on a competitive basis, for the full support of qualified students. In addition, a limited number of tuition assistantships are also available for partial support of graduate students.

Graduate programs in systems and control engineering include the following areas of concentration: control theory (adaptive control, stochastic filtering and control, nonlinear control), optimization and decision theory (multi-objective and large scale system theory), control of industrial and manufacturing systems (facilities layout, flexible manufacturing), biomedical control system design and analysis (control of neural prostheses, automatic control of therapeutic drug delivery), energy systems (power distribution and production planning, load forecasting), and global and environmental system analysis and control.(resource constraints: water, energy etc., carrying capacity and global climate change).

Research funds are used to provide assistantships that support the thesis research of graduate students. Current research funding is provided by Elsag-Bailey, Rockwell Automation, the Ford Motor Company, the Cleveland Advanced Manufacturing Program (CAMP), the Electric Power Research Institute (EPRI), the National Institutes of Health (NIH), National Institute of Nursing Research(NINR),the National Science Foundation (NSF), the U.S. Department of Veterans Affairs-Rehabilitation Research and Development Program (VA-RR&D), the Office of Naval Research (ONR), the U.S. Agency for International Development (US-AID) and United National Education, Scientific Cultural Organization (UNESCO).

EECS 212. Signals, Systems, and Control (3)
Characterization of continuous-time signals and systems. Laplace transforms, constant coefficient differential equations. Modeling of dynamical systems. Introduction to control system analysis and design. Prereq: MATH 224.

EECS 214. Signals, Systems, and Control Laboratory (1)
A laboratory course based on the material in EECS 212. Analysis and simulation using MATLAB/Simulink. Laboratory experiments involving signal processing and control. Coreq: EECS 212.

EECS 216. Fundamental System Concepts (3)
Develops framework for addressing problems in science and engineering that require an integrated, interdisciplinary approach, including the effective management of complexity and uncertainty. Introduces fundamental system concepts in an integrated framework. Properties and behavior of phenomena regardless of the physical implementation through a focus on the structure and logic of information flow. Systematic problem solving methodology using systems concepts. Prereq: MATH 224.

EECS 233. Introduction to Data Structures (4)
The programming language C++; pointers, files, variant records, 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. Laboratory. Prereq: ENGR 131.

EECS 245. Electronic Circuits (4)
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. Coreq: MATH 224.

EECS 246. Signals and Systems (4)
The sinusoidal steady state and phasor analysis. Bode plots and their relationship to the frequency domain representation of signals. Gain-bandwidth product, slew-rate and other limitations of real devices. Filter design. Frequency domain considerations including Fourier series and Fourier transforms. Sampling theorem. The Discrete Fourier Transform. The z-transform and digital signal processing. Accompanying laboratory exercises which reinforce classroom lectures. Prereq: ENGR 210 and MATH 224.

EECS 251. Numerical Methods (3)
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: ENGR 131 and MATH 122.

EECS 281. Logic Design and Computer Organization (4)
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.

EECS 285. Engineering in Community Service I (3)
Project-oriented course; students work on "real" engineering projects of benefit to the community and in partnership with community "customers." Project teams consists of a mix of sophomores, juniors, and seniors. Students perform engineering design tasks as appropriate to their technical background. Emphasis on teamwork, communication skills, customer awareness, and professional responsibility. Prereq: Sophomore standing in EECS.

EECS 290. Special Topics (1-18)
Limited to sophomores and juniors. Prereq: Consent of instructor.

EECS 301. Digital Logic Laboratory (2)
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 Machine, register based design, and arithmetic computational block. Prereq: EECS 281.

EECS 304. Control Engineering I with Laboratory (3)
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 212.

EECS 305. Control Engineering I Laboratory (1)
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. Prereq: EECS 212 or equivalent. Coreq: EECS 304.

EECS 306. Control Engineering II with Laboratory (3)
Advanced techniques for control of dynamic systems. State-space modeling, analysis, and controller synthesis; introduction to nonlinear control systems: phase plane methods, bang-bang control, time-optimal control; describing functions analysis and design techniques; discrete time systems and controllers. Advanced control design methods implementation. Prereq: EECS 304.

EECS 309. Electromagnetic Fields I (3)
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: MATH 223 and PHYS 122. Coreq: MATH 224.

EECS 310. Electromechanical Energy Conversion (4)
Electromechanical dynamics, modeling and control. Forces in quasistatic magnetic systems. Energy conversion properties of rotating machines. Analysis and control of DC servomotors, AC servomotors, reluctance machines, inductance machines, and magnetic bearing. Analysis of electromagnetic sensors. Electronic communication, torque linearization through computer controls and flux-vector control. Electromechanical properties are measured in the lab and high-performance controls are constructed and tested. Prereq: EECS 309.

EECS 311. Electromagnetic Fields II (3)
Boundary value problems, guided electromagnetic waves, rectangular and circular waveguides, strip lines, losses in waveguiding structures, scattering, wave optics and wave propagation in anisotropic media, ferrites and plasmas, resonant systems, cavities, microwave networks, multiport networks, scattering matrix formulation, radiation and antennas, radiation from dipoles, apertures and simple arrays. Prereq: EECS 309.

EECS 313. Signal Processing (3)
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)
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)
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 317. Computer Design Laboratory (2)
Sequence of laboratory projects provide practical experience in computer-aided design techniques for computer and digital system design. Hardware system modeled and simulated at register transfer and switching transistor level.

EECS 318. Computer-Aided Design (4)
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 with state-of-the-art computer-aided design tools. Prereq: EECS 281 and EECS 321.

EECS 321. Semiconductor Electronic Devices (4)
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. Laboratory experiments to characterize some of the above devices. Prereq: EECS 309.

EECS 322. Integrated Circuits and Electronic Devices (3)
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: EECS 321.

EECS 324. Simulation Techniques in Engineering (3)
Discrete event systems and simulation concepts. Discrete event simulation with batch and interactive languages. Coreq: ENGL 398.

EECS 329. Design of Object-Oriented Systems (3)
This course provides an opportunity to gain an understanding of the concepts and technology of object-oriented systems and learn system design techniques that take full advantage of this technology. Students also develop competence in programming with the object-oriented features of C++. Prereq: EECS 233.

EECS 337. Systems Programming (4)
Lexical analyzers; symbol tables and their searching; assemblers, one-pass and two-pass, conditional assembly, and macros; linkers and loaders; interpreters, pcodes, threaded codes; introduction to compilation, grammar, parsing, and code generation; preprocessors; text editors, line-oriented and screen-oriented; bootstrap loaders, ROM monitors, interrupts, and device drivers. Laboratory. Prereq: EECS 233 and EECS 281.

EECS 338. Introduction to Operating Systems (4)
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 340. Algorithms and Data Structures (3)
Efficient sorting algorithms, external sorting methods, internal and external searching, efficient string processing algorithms, geometric and graph algorithms. Prereq: EECS 233 and MATH 304.

EECS 341. Introduction to Database Systems (3)
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.

EECS 342. Introduction to Global Issues (3)
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 Case Western Reserve 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)
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. Prereq: MATH 304. Cross-listed as MATH 343.

EECS 344. Electronic Analysis and Design (3)
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)
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, EECS 337.

EECS 346. Engineering Optimization (3)
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. Prereq: MATH 201.

EECS 347. Network Synthesis (3)
Design techniques for the construction of filters, delayors, predictors, analog computer networks, and necessary and sufficient requirements for the realization of practical networks. Prereq: EECS 246 or equivalent.

EECS 348. Communication Electronic Cir (4)

EECS 350. Industrial and Production Systems Engineering (3)
Time and motion study, human factors and safety engineering, man-machine systems, quality control and reliability, project management, scheduling, sequencing, inspection and maintenance of industrial processes.

EECS 351. Communications and Signal Analysis (3)
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 equivalent.

EECS 352. Engineering Economics and Decision Analysis (3)
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)
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 351 recommended.

EECS 355. RF Communications (3)
Coverage of modern communications circuits and systems with a particular emphasis upon mobile communications. Cellular communications, modulation methods, user access schemes. Individual system components: tuned small signal amplifiers and power amplifiers, mixers, detectors, and frequency synthesizers. Low-power design considerations. Prereq: EECS 351.

EECS 356. Microwave Engineering (3)
Transmission lines and circuit analysis, waveguides, modes of propagation, impedance matching techniques, scattering matrix, waveguide components, striplines, resonators, microwave theory, filters, microwave solid state devices. Prereq: EECS 311.

EECS 358. Domain Theoretic Methods for Artificial Intelligence (3)
Resolution for propositional logic and completeness via Zorn’s Lemma, Domain theory and topology through three-value logic. Default reasoning and extensions. Clausal logic for Scott domains and Smyth power domains. Power defaults theory and the semantics of nonmonotonic reasoning and disjunctive logic programming. Prereq: EECS 343, EECS 391, MATH 307, or PHIL 306. Cross-listed as MATH 350.

EECS 360. Manufacturing, Operations, and Automated Systems (3)
Introduction to design, modeling, analysis, and optimization of production, automation computer-integrated, and manufacturing systems. Topics include, design of products and processes, statistical quality control: confirming design, design of location/spatial problems, transportation and assignment problems, product-oriented layout (including assembly line balancing), process oriented layout (including quadratic assignment problem and steepest descent exchange heuristics), group technology and clustering, cellular and network flow layouts, machining supervisions optimization and numerical control. Tools for analysis for each of the above problems include: optimization, multiple criteria decision-making (MCDM), and heuristics for combinatorial problems. Applications to computer science and engineering problems are also covered. Prereq: Junior or senior level standing in engineering or consent of instructor.

EECS 375. Autonomous Robotics (3)
Introduction to the design, construction and control of autonomous mobile robots. The first half of the course consists of focused exercises on mechanical construction with LEGO, characteristics of sensors, motors and batteries, and control strategies for autonomous robots. In the second half of the course, students design, build and program their own complete robots that participate in a public competition. All work is performed in groups. Biologically-inspired approaches to the design and control of autonomous robots are emphasized throughout. Prereq: Consent of instructor. Cross-listed as BIOL 375.

EECS 381. Hybrid Systems (3)
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 either EECS 246 or MATH 304.

EECS 382. Microprocessor-Based Design (3)
Microprocessor architectures, memory design, timing, polled and interrupt driven I/O, microprocessor support devices, microcontrollers, integrated hardware/software design considerations. Prereq: ENGR 210 and EECS 281.

EECS 383. Microprocessor Applications to Controls (3)
Digital control and its implementation using microprocessors. Z-transforms. Time response characteristics, steady-state error, mapping from the s-plane to the z-plane. Digital controller design-stability testing methods, gain and phase margins, PID controllers, digital filter structures. Prereq: EECS 246 or equivalent.

EECS 385. Engineering in Community Service II (3)
Project-oriented course; students work on "real" engineering projects of benefit to the community and in partnership with community "customers." Project teams consists of a mix of sophomores, juniors, and seniors. Students perform engineering design, project specification, and technical research as appropriate to their technical background. Emphasis on project planning and organization, teamwork, project management, communication skills, customer awareness, and professional responsibility. Prereq: Junior or Senior standing in EECS.

EECS 391. Introduction to Artificial Intelligence (3)
Overview of artificial intelligence, knowledge representation, search, game-playing, logic rule-based systems, AI programming languages, learning, neural networks, evolutionary algorithms, natural language understanding, planning, robotics. Prereq: ENGR 131.

EECS 396L. Special Topics (1-6)
(Credit as arranged.) Limited to juniors and seniors.

EECS 397L. Special Topics in Electrical Engineering (1-6)
(Credit as arranged.) Limited to juniors and seniors. Prereq: Consent of instructor.

EECS 398M. Software Engineering (3)
Issues in the development of complex software systems. Software lifecycle models. Software engineering methodology, requirements, analysis and specification design implementation, validation, and maintenance. Team development of a significant applications program. Prereq: EECS 337.

EECS 398N. Engineering Projects I (3)
Project experience in the application of course material to practical systems engineering problems. Identification of project, literature review, and proposal preparation for EECS 399.

EECS 399L. Senior Project in Electrical Engineering II (4)
Prereq: EECS 398L (or concur).

EECS 399M. Computer Engineering Design Project (3)
Capstone course for computer engineering seniors. Material from previous and concurrent courses used to solve hardware and/or software design problems. Formal presentations of the projects scheduled during last week of classes.

EECS 399N. Engineering Projects II (3)
Elective projects with emphasis on engineering design. Capstone engineering project. Prereq: EECS 398N.

EECS 400T. Graduate Teaching I (0)
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. Prereq: Ph.D. student in EECS department.

EECS 401. Digital Signal Processing (3)
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. Prereq: EECS 313.

EECS 404. Digital Control Systems (3)
Analysis and design techniques for computer based control systems. Sampling, hybrid continuous-time/discrete-time system modeling; sampled data and state space representations, controllability, observability and stability, transformation of analog controllers, design of deadbeat and state feedback controllers; pole placement controllers based on input/output models, introduction to model identification, optimal control and adaptive control. Prereq: EECS 304.

EECS 405. Data Structures and File Management (3)
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. Prereq: EECS 233 and MATH 304.

EECS 408. Introduction to Linear Systems (3)
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. Prereq: EECS 304.

EECS 409. Discrete Event Systems (3)
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 410. Ultrasonic Engineering (3)
Acoustical waves in fluids and solids, surface acoustic waves, transmission phenomena, radiators, transducers, filters, flow measurements, pulse echo techniques, flaw detection, sonar, imaging, holography.

EECS 411. Introduction to Logic Programming (3)
Basic constructs of logic programs, terms, facts, rules, queries. Logic programs for manipulating recursive data structures. Unification and the logic programming computation model. How Prolog realized the abstract computational mode. Arithmetic, structure inspection, metalogical and extralogical techniques in Prolog. Advanced programming techniques: nondeterminism, difference structures, DCGS, meta-interpreters. Applications. Prereq: EECS 233.

EECS 412. Electromagnetic Fields III (3)
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)
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. Coreq: EECS 408.

EECS 414. Complex Systems Modeling and Analysis (3)
The concept of a complex system as a relationship of identifiable subsystems. Modeling of large-scale systems by aggregation, perturbation, via system identification and by the use of fuzzy logic. The structural properties of large-scale systems. A hierarchical, multi-level approach to large-scale systems analysis and synthesis. Coordination by the interaction balance and by interaction prediction principles. Decentralized decision making and control of large-scale systems. Near optimum system design. Structure and stability of fuzzy control systems.

EECS 415. Integrated Circuit Technology I (3)
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. Prereq: EECS 322.

EECS 416. Optimization Theory and Techniques (3)
Underlying theory of linear, nonlinear, multilevel, and multiobjective optimization. Techniques include linear programming and extensions, quadratic programming, dynamic programming, decomposition coordination schemes for multilevel optimization. Methods for generating Pareto optimal solutions in multiobjective optimization. Applications to engineering problems. Prereq: MATH 201 or equivalent.

EECS 417. Introduction to Stochastic Control (3)
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. Prereq: EECS 408.

EECS 418. System Identification and Adaptive Control (3)
Parameter identification methods for linear discrete time systems: maximum likelihood and least squares estimation techniques. Adaptive control for linear discrete time systems including self-tuning regulators and model reference adaptive control. Consideration of both theoretical and practical issues relating to the use of identification and adaptive control.

EECS 419. Computer System Architecture (3)
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. Prereq: EECS 338.

EECS 420. Solid State Electronics I (3)
Quantum mechanics and solid state physics. Crystal structures, electrons in periodic structures, band structures, transport phenomenon, nonequilibrium process, lattice dynamics, scattering mechanisms, surface and interface physics; physics of semiconductor electronic devices. Prereq: EECS 321.

EECS 421. Optimization of Dynamic Systems (3)
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. Prereq: EECS 408. Cross-listed as MATH 434.

EECS 422. Solid State Electronics II (3)
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)
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. Prereq: EECS 338.

EECS 425. Computer Communications Networks (3)
Covers computer network architecture. Topics include: network applications; types of networks; network architecture; OSI, TCP/IP and ATM reference models; transmission media; the telephone system; ISDN and ATM error detection and correction; data link protocols; channel allocation; LAN protocols; bridges; routing; congestion control; internetworking; transport services and protocols; TCP/IP and ATM protocols; socket programming; security; Domain Name System; Simple Network Management Protocol; e-mail, WWW; Java; Corba; distributed multimedia. Prereq: EECS 338.

EECS 426. MOS Integrated Circuit Design (3)
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. Prereq: EECS 344 and EECS 321.

EECS 427. MEMS for Sensing and Communication (3)
This course covers basic MEMS fabrication technologies and device operating principles of MEMS resonators and inertial sensors such as accelerometers and gyroscopes. Critical issues regarding sensing resolution and low noise interface electronics design will be discussed. MEMS applications such as low noise oscillators, filters, switches, etc. for wireless communications will also be covered.

EECS 428. Web Computing (3)
The goal of this course is to acquire expertise in state-of-the-art Web technology, including performance evaluation, servers, caching, security, and search engines. Expected work includes bi-weekly homework assignments (includes small projects), final class project suggested by students, midterm, and final. Coreq: EECS 425 or permission of instructor.

EECS 429. Risk and Reliability Methods for Engineers (3)
Probabilistic models and methods for risk, reliability, and quality engineering; Markov decision processes; stochastic dynamic programming; stochastic programming and other methods for risk analysis; failure models; qualitative fault analysis; reliability analysis of systems; life data analysis and accelerated life testing; design of experiments for quality engineering; statistical quality control; and acceptance sampling for quality control.

EECS 430. Object-Oriented Software Development (3)
Covers advanced methodology for the design of large software systems. Topics include: object-oriented analysis and design; encapsulation; inheritance; subtype and parametric polymorphism; object-oriented programming languages; design patterns; application frameworks; software architecture; user-interfaces; concurrent and distributed objects. Prereq: EECS 337 or consent of instructor.

EECS 431. Software Engineering (3)
Design of software systems working from specifications; top-down decomposition using stepwise refinement; object-oriented methods; prototyping. Software metrics and testing; software quality and reliability; maintenance; human factors. Homework involves working in teams on large software projects. Prereq: EECS 337.

EECS 432. Compiler Construction (3)
Top-down and bottom-up recognizers for context-free grammars; LR(k) parsers, error recovery, semantic analysis, storage allocation for block structured languages, optimization, code generation. Homework involves writing a compiler for a block structured language. Prereq: EECS 337.

EECS 433. Database Systems (3)
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. Prereq: EECS 341 and MATH 304.

EECS 434. Microfabricated Silicon Electromechanical Systems (3)
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. Prereq: EECS 322 or EECS 415.

EECS 435. Data Mining (3)
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. Prereq: EECS 341 or equivalent.

EECS 436. Advances in Databases (3)
Advanced topics in databases will be covered in this course. Query optimization in object-oriented databases, temporal databases, issues in multimedia databases, databases and Web, graphical query interfaces. Basic knowledge in databases is required. Prereq: EECS 433.

EECS 437. Optical Communication (3)
In this course, suitable for graduate students or advanced undergraduates interested in photonics, a broad range of topics will be covered in the field of optical communication, with an aim to provide a sophisticated perspective of current technology and trends in optical communication components, systems, and networks. Prereq: EECS 309.

EECS 438. Biomedical Microdevices (3)
Topics related to current research in Microelectromechanical systems (MEMS) technology for biomedical applications. Review of fabrication technologies for semiconductor and plastic materials, microscale transport behavior, biocompatibility and materials issues, microfluidic devices for biochemical analysis, miniaturized sensors and actuators for implantable medical instrumentation, and microstructures for tissue engineering.

EECS 440. Automata and Formal Languages (3)
(See MATH 410.) Cross-listed as MATH 410.

EECS 445. Formal Verification (3)
Introduction and survey of principles and methodologies in formal specification and verification of systems (hardware, software, hybrid). Prereq: EECS 345 or graduate standing.

EECS 450. Production and Operations Systems (3)
Fundamental theories and techniques, decision making, and artificial intelligence for solving production/manufacturing problems. Formulation, modeling, planning, and control of production problems at three levels: strategic, tactical, and operational (long term, medium, and short term). Specific problems include aggregate planning, project planning, scheduling, line balancing, sequencing, and machine set-up. Special emphasis will be given on decomposition and control of computer integrated systems, on-line and off-line supervisory planning, and man/machine systems.

EECS 452. Random Signals (3)
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)
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. Prereq: MATH 304 and (EECS 340 or EECS 405). Cross-listed as OPRE 454.

EECS 455. Wireless Communications (3)
Cellular telephone systems, wireless networks, receiver architectures, noise characterization, error-correction coding, digital modulation, multiple-access technologies, multipath fading. Prereq: STAT 332 and EECS 351 or consent of instructor.

EECS 456. Microwave Engineering (3)
Transmission line theory, propagation in waveguides, coaxial lines, striplines. Circuit theory of microwave systems, multi-port circuits, equivalent circuits. Foster’s Reactance Theorem. Scattering matrix. Smith Charts, impedance matching and transformation using stub tuners and transformers. Electromagnetic resonators. Prereq: EECS 412.

EECS 458. Introduction to Bioinformatics (3)
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. Prereq: EECS 340, EECS 233.

EECS 459X. Domain Theoretic Methods for Artificial Intelligence (3)
(See EECS 358.) Cross-listed as MATH 450.

EECS 460. Manufacturing, Design, and Automated Systems (3)
The course is designed primarily for graduate engineering students who wish to know about the fundamentals and modeling of production/automation/manufacturing systems. The course provides a survey of various topics in production automation and computer-aided and integrated manufacturing with emphasis on decision making, optimization, and modeling. Topics include computerized process planning, on-line and off-line supervisory computer control, computerized discrete production systems, numerical control, monitoring and planning, flexible manufacturing systems, group technology, materials handling systems, man/machine systems and requirements, design and analysis of assembly systems, and computerized facility layout design problems. The course presents a step-by-step and cohesive account of concepts, theories, and procedures for solving modern manufacturing and production problems with emphasis on computer applications. Prereq: Consent of instructor.

EECS 462. Research Topics in Lasers and Optics (3)
Topics related to current research, e.g., laser theory, coherent optics, optical information processing.

EECS 463. Techniques of Model-based Control (3)
Strategies of process control centered around the use of process models in the control system. Topics include single loop, feed forward, cascade and multivariable internal model control. Tuning controllers to accommodate process uncertainty. Treatment of control effect and output constraints in model predictive control and modular-multivariable control. Prereq: EECS 304. Cross-listed as ECHE 463.

EECS 466. Computer Graphics (3)
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. Prereq: EECS 233.

EECS 473. Multimedia and Web Computing (3)
Multimedia is an important application area that will be at the center for next-generation computer systems and software design. It is a fast-changing technology, and, already, in the industry, there is a significant demand for computer scientists/engineers with multimedia system design knowledge. The objective of EECS 473 is to present design issues for multimedia systems from specification to software implementation and testing. This will include multimedia basics, data capture/models/compression, synchronization models, multimedia servers, OS support for multimedia, multimedia communication systems, and multimedia user interfaces. There will be a project about designing and implementing a multimedia system. Students are expected to know Unix systems programming (System V IPCs, fork, exec, etc.), RPC, thread and socket programming. Prereq: ENGR 131, EECS 233, and EECS 338.

EECS 475. Autonomous Robotics (3)
Introduction to the design, construction and control of autonomous mobile robots. The first half of the course consists of focused exercises on mechanical construction with LEGO, characteristics of sensors, motors and batteries, and control strategies for autonomous robots. In the second half of the course, students design, build and program their own complete robots that participate in a public competition. All work is performed in groups. Biologically-inspired approaches to the design and control of autonomous robots are emphasized throughout. Prereq: Consent of instructor. Cross-listed as BIOL 475.

EECS 477. The Dynamics of Adaptive Behavior (3)
Introduction to embodied, situated, and dynamical approaches to the design and analysis of autonomous agents and animals. Topics include recurrent neural networks, coupled neural/body/environment systems, and evolution and analysis of neural circuits. Behavior studied include examples from motor control, perception, learning, and cognition. Prereq: ENGR 131 and MATH 224. Cross-listed as BIOL 477.

EECS 478. Computational Neuroscience (3)
Computer simulation 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 circuits, as well as to cable theory, passive and active compartmental modeling, numerical integration methods, models of plasticity and learning, models of brain systems, and their relationship to artificial neural networks. Term project required. Two lectures per week. Cross-listed as BIOL 478, EBME 478, and NEUR 478.

EECS 479. Seminar in Computational Neuroscience (3)
Readings and discussion in the recent literature on computational neuroscience, adaptive behavior, and other current topics. Cross-listed as BIOL 479.

EECS 483. Data Acquisition and Control (3)
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)
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)
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)
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 487. Computational Intelligence II (3)
This course is concerned with the combined use of the methods of computational intelligence in the performance of complex real-world tasks. Tasks considered include learning models of ‘opaque’ systems, design and operation of fuzzy control systems, neural-net computing control of systems, optimal control, adaptive learning of time-variant time series, data compression, classification, self-organization of objects into categories, inductive reasoning, decision-making interpretation of signal and images. Prereq: EECS 484.

EECS 488. Embedded Systems Design (3)
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)
(See EMAE 489.) Prereq: EMAE 181. Cross-listed as EMAE 489.

EECS 490. Computer Processing of Images (3)
Introduction of computer vision methodologies. Includes the images systems: optics and detectors and geometric relationships between scene and image, 3-D scene scanning and imaging techniques including stereovision and laser rangefinders. Digital signal processing in 2-D and optical preprocessing of images. Real-time digital signal transmission of dynamic images and HDTV. Hardware issues in processing of vision information. Prereq: EECS 246 or equivalent or consent of instructor.

EECS 491. Intelligent Systems I (3)
Artificial intelligence and programming techniques used in design and implementation of intelligent systems. Problem solving and game playing by computer, different representation of problems and games, and their associated solution methods. Knowledge representation: logic, semantic networks frames. Programming in LISP and Prolog.

EECS 500. EECS Colloquium (0)
Seminars on current topics in Electrical Engineering and Computer Science.

EECS 500T. Graduate Teaching II (0)
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. Prereq: Ph.D. student in EECS department.

EECS 515. Decision Theory with Applications (3)
Fundamentals of decision theory and analysis of decision processes in systems. Elementary decision analysis. Single and multiattribute utility theory under both certainty and uncertainty. Bayesian decision analysis. Sequential decision processes including dynamic programming and Markov processes. Analysis of multi-person decision processes and game theory as related to management decisions. Applications to large-scale systems and to decision support systems.

EECS 516. Large Scale Optimization (3)
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. Prereq: EECS 416.

EECS 518. Nonlinear Systems: Analysis and Control (3)
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. Prereq: EECS 408 and EECS 421.

EECS 519. Differential Geometric Nonlinear Control (3)
This advanced course focuses on the analysis and design of nonlinear control systems, with special emphasis on the differential geometric approach. Differential geometry has proved to be an extremely powerful tool for the analysis and design of nonlinear systems, similar to the roles of the Laplace transformation and linear algebra in linear systems. The objective of the course is to present the major methods and results of nonlinear systems and provide a mathematical foundation, which will enable students to follow the recent developments in the constantly expanding literature. This course will also benefit those students from Electrical, Mechanical, Chemical and Biomedical Engineering, who are doing research in the fields that involve nonlinear control problems. Prereq: EECS 408 or equivalent.

EECS 523. Multiobjective and Hierarchical Systems (3)
This course covers basic concepts of hierarchical, multi-level systems, Lagrangian decompositions, and coordination principles. Fundamentals and recent advances in theory, methodology and applications of multiple criteria decision making (MCDM) with single and multiple decision makers are included as are: interactive MCDM methods; multiple objectives for discrete and continuous models; multi-objective programming methods, hierarchical overlapping coordination with single and multiple objectives; multi-objective, multi-stage impact analysis; and applications to large-scale systems and to decision support systems. Cross-listed as OPRE 523.

EECS 526. Integrated Mixed-Signal Systems (3)
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. Prereq: EECS 426.

EECS 527. Advanced Sensors: Theory and Techniques (3)
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 senso