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.
COMPUTER SCIENCE
The Bachelor of Science program in Computer Science is designed to give a student a strong background in the fundamentals of mathematics and computer science. A graduate of this program should be able to use these fundamentals to analyze and evaluate software systems and the underlying abstractions upon which they are based. A graduate should also be able to design and implement software systems which are state of the art solutions to a variety of computing problems; this includes problems which are sufficiently complex to require the evaluation of design alternatives and engineering tradeoff’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 premed 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:
• EECS 233 Introduction to Data Structures
• EECS 338 Introduction to Operating Systems
• EECS 340 Algorithms and Data Structures
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:
• ENGR 131 Elementary Computer Programming
• EECS 233 Introduction to Data Structures
• MATH 125 Mathematics I
Two additional computing courses are also required for this minor.
Cooperative Education Program
There are many excellent Cooperative Education (COOP) opportunities for
computer science majors. A COOP student does two COOP 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 COOP 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.
SYSTEMS AND CONTROL ENGINEERING
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 nontechnological systems. Problems relating to modeling, decisionmaking, 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 largescale 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, realtime 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, realtime 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:
• EECS 212 Signals, Systems and Control (3)
• EECS 214 Signals, Systems and Control Lab (1)
• EECS 304 Control Engineering I with Laboratory (3)
• EECS 346 Engineering Optimization (3)
• EECS 352 Engineering Economics and Decision Analysis (3)
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:
• EECS 110 Problem Solving & Systems Engineering
• EECS 324 Simulation Methods in Engineering
• EECS 313 Signal Processing
• EECS 306 Control Engineering II
• EECS 350 Production and Operational Systems
• EECS 360 Manufacturing and Integrated Systems
Cooperative Education Program
There are many excellent Cooperative Education (COOP) opportunities for
systems and control engineering majors. A COOP student does two COOP 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 COOP 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 fiveyear 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 indepth 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
Graduate Programs
COMPUTER ENGINEERING AND SCIENCE GRADUATE STUDIES
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 "handson," 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 problemoriented course material coupled with the broadbased curricular requirements. Doctoral dissertations must be original contributions to the existing body of knowledge in computer engineering and science.
ELECTRICAL ENGINEERING AND APPLIED PHYSICS GRADUATE STUDIES
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 computeraided 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.
SYSTEMS ENGINEERING GRADUATE STUDIES
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 (multiobjective 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 ElsagBailey, 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 AffairsRehabilitation Research and Development Program (VARR&D), the Office of Naval Research (ONR), the U.S. Agency for International Development (USAID) and United National Education, Scientific Cultural Organization (UNESCO).
ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (EECS)
Undergraduate Courses
EECS 212. Signals, Systems, and Control (3)
Characterization of continuoustime 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: oneway 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 timedependent electrical circuits. Dynamic waveforms and elements:
inductors, capacitors, and transformers. First and secondorder circuits, passive
and active. Analysis of sinusoidal steady state response using phasors. Laplace
transforms and polezero diagrams. Sdomain circuit analysis. Twoport networks,
impulse response, and transfer functions. Introduction to nonlinear semiconductor
devices: diodes, BJTs, and FETs. Gainbandwidth product, slewrate 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. Gainbandwidth product, slewrate
and other limitations of real devices. Filter design. Frequency domain considerations
including Fourier series and Fourier transforms. Sampling theorem. The Discrete
Fourier Transform. The ztransform 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 nonlinear 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)
Projectoriented 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 (118)
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, leadlag controller design. State space modeling,
solution, controllability, observability and stability. Modeling and control
demonstrations and experiments singleinput/singleoutput 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. Statespace modeling,
analysis, and controller synthesis; introduction to nonlinear control systems:
phase plane methods, bangbang control, timeoptimal 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, indepth analysis of coaxial
and strip lines, electro and magnetoquasistatics, 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 fluxvector
control. Electromechanical properties are measured in the lab and highperformance
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. FastFourier
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, inputoutput, and an introduction to RISC and superscalar 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 layout, 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/logiclevel simulation. Prereq: EECS
281.
EECS 317. Computer Design Laboratory (2)
Sequence of laboratory projects provide practical experience in computeraided
design techniques for computer and digital system design. Hardware system modeled
and simulated at register transfer and switching transistor level.
EECS 318. ComputerAided Design (4)
With Very Large Scale Integration (VLSI) technology there is an increased
need for ComputerAided 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 handson experience with stateoftheart computeraided
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, lightemitting 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 ObjectOriented Systems (3)
This course provides an opportunity to gain an understanding of the concepts
and technology of objectoriented systems and learn system design techniques
that take full advantage of this technology. Students also develop competence
in programming with the objectoriented features of C++. Prereq: EECS 233.
EECS 337. Systems Programming (4)
Lexical analyzers; symbol tables and their searching; assemblers, onepass
and twopass, conditional assembly, and macros; linkers and loaders; interpreters,
pcodes, threaded codes; introduction to compilation, grammar, parsing, and code
generation; preprocessors; text editors, lineoriented and screenoriented;
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, customtailored,
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 Globalproblematique
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. Crosslisted as MATH 343.
EECS 344. Electronic Analysis and Design (3)
The design and analysis of realworld circuits. Topics include: junction
diodes, nonideal opamp 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.
Hybridpi model and other advanced circuit models, cascaded amplifiers, negative
feedback, differential amplifiers, oscillators, tuned circuits, and phaselocked
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, objectoriented, 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, manmachine
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 signaltonoise
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 baseband
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. Lowpower
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 threevalue 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. Crosslisted as MATH 350.
EECS 360. Manufacturing, Operations, and Automated Systems (3)
Introduction to design, modeling, analysis, and optimization of production,
automation computerintegrated, and manufacturing systems. Topics include, design
of products and processes, statistical quality control: confirming design, design
of location/spatial problems, transportation and assignment problems, productoriented
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 decisionmaking (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. Biologicallyinspired
approaches to the design and control of autonomous robots are emphasized throughout.
Prereq: Consent of instructor. Crosslisted 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, discreteevent 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. MicroprocessorBased 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. Ztransforms.
Time response characteristics, steadystate error, mapping from the splane
to the zplane. Digital controller designstability testing methods, gain and
phase margins, PID controllers, digital filter structures. Prereq: EECS 246
or equivalent.
EECS 385. Engineering in Community Service II (3)
Projectoriented 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, gameplaying,
logic rulebased systems, AI programming languages, learning, neural networks,
evolutionary algorithms, natural language understanding, planning, robotics.
Prereq: ENGR 131.
EECS 394X. Senior Project I (3)
EECS 396L. Special Topics (16)
(Credit as arranged.) Limited to juniors and seniors.
EECS 396M. Special Topics: Computer Science (19)
EECS 396N. Special Topics (118)
EECS 397L. Special Topics in Electrical Engineering (16)
(Credit as arranged.) Limited to juniors and seniors. Prereq: Consent of
instructor.
EECS 398L. Senior Project in Electrical Engineering I (4)
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.
Graduate Courses
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 discretetime signals and systems. Fourier analysis:
the Discretetime Fourier Transform, the Discretetime Fourier series, the Discrete
Fourier Transform and the Fast Fourier Transform. Continuoustime 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 continuoustime/discretetime 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, multiplekey
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 statespace techniques.
Review of matrix theory, linearization, transition maps and variations of constants
formula, structural properties of statespace 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 MinMax 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, metainterpreters. 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 waveguide 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 PoincareBendixson,
describing functions for singleinput singleoutput 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 largescale systems by aggregation, perturbation, via system identification
and by the use of fuzzy logic. The structural properties of largescale systems.
A hierarchical, multilevel approach to largescale systems analysis and synthesis.
Coordination by the interaction balance and by interaction prediction principles.
Decentralized decision making and control of largescale 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 semiconductor 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 discretetime stochastic systems.
Review of probability theory and stochastic properties, inputoutput analysis
of linear stochastic systems, spectral factorization and Weiner filtering, minimum
variance control, statespace models of stochastic systems, optimal control
and dynamic programming, statistical estimation and filtering, the KalmanBucy
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 selftuning 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, timeoptimal controllers. Sufficient
conditions for optimality. Singular controls. Computational aspects. Selected
applications. Prereq: EECS 408. Crosslisted 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. PN 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; clientserver 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; email, 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 switchedcapacitor 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 stateoftheart Web
technology, including performance evaluation, servers, caching, security, and
search engines. Expected work includes biweekly 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. ObjectOriented Software Development (3)
Covers advanced methodology for the design of large software systems. Topics
include: objectoriented analysis and design; encapsulation; inheritance; subtype
and parametric polymorphism; objectoriented programming languages; design patterns;
application frameworks; software architecture; userinterfaces; concurrent and
distributed objects. Prereq: EECS 337 or consent of instructor.
EECS 431. Software Engineering (3)
Design of software systems working from specifications; topdown decomposition
using stepwise refinement; objectoriented 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)
Topdown and bottomup recognizers for contextfree 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. Objectoriented
databases. Objectoriented 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 siliconsilicon 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 objectoriented 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.) Crosslisted 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
setup. Special emphasis will be given on decomposition and control of computer
integrated systems, online and offline 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 (NPcomplete 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). Crosslisted as OPRE
454.
EECS 455. Wireless Communications (3)
Cellular telephone systems, wireless networks, receiver architectures, noise
characterization, errorcorrection coding, digital modulation, multipleaccess
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, multiport 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.) Crosslisted 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 computeraided and integrated manufacturing with emphasis on decision making,
optimization, and modeling. Topics include computerized process planning, online
and offline 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 stepbystep 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 Modelbased 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 modularmultivariable control. Prereq: EECS 304. Crosslisted 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
nextgeneration computer systems and software design. It is a fastchanging
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. Biologicallyinspired
approaches to the design and control of autonomous robots are emphasized throughout.
Prereq: Consent of instructor. Crosslisted 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. Crosslisted 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. Crosslisted 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. Crosslisted 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, statevariable 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 selforganization,
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, computeraided 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 realworld tasks. Tasks considered
include learning models of ‘opaque’ systems, design and operation
of fuzzy control systems, neuralnet computing control of systems, optimal control,
adaptive learning of timevariant time series, data compression, classification,
selforganization of objects into categories, inductive reasoning, decisionmaking
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. Crosslisted 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, 3D
scene scanning and imaging techniques including stereovision and laser rangefinders.
Digital signal processing in 2D and optical preprocessing of images. Realtime
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 multiperson
decision processes and game theory as related to management decisions. Applications
to largescale 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: inputoutput 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, multilevel 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;
multiobjective programming methods, hierarchical overlapping coordination with
single and multiple objectives; multiobjective, multistage impact analysis;
and applications to largescale systems and to decision support systems. Crosslisted
as OPRE 523.
EECS 526. Integrated MixedSignal Systems (3)
Mixedsignal (analog/digital) integrated circuit design. DtoA and AtoD
conversion, applications in mixedsignal VLSI, lownoise and lowpower techniques,
and communication subcircuits. System simulation at the transistor and behavioral
levels using SPICE. Class will design a mixedsignal CMOS IC for fabrication
by MOSIS. Prereq: EECS 426.
EECS 527. Advanced Sensors: Theory and Techniques (3)
Sensor technology with a primary focus on semiconductorbased devices. Physical
principles of energy conversion devices (sensors) with a review of relevant
fundamentals: elasticity theory, fluid mechanics, silicon fabrication and micromachining
technology, semiconductor device physics. Classification and terminology of
sensors, defining and measuring sensor characteristics and performance, effect
of the environment on sensors, predicting and controlling sensor error. Mechanical,
acoustic, magnetic, thermal, radiation, chemical and biological sensors will
be examined. Sensor packaging and sensor interface circuitry. Prereq: EECS 322
or EECS 415 and EECS 434.
EECS 531. Computer Vision (3)
Geometric optics, ray matrics, calibration of monocular and stereo imaging
systems. Adaptive camera thresholding and image segmentation, morphological
and convolutional image processing. Selected topics including edge estimation
and industrial inspection, optimal filtering, model matching, CADbased vision
and range image processing. Neuralnet image processing. Modelbased computer
vision for scene interpretation and autonomous systems. Prereq: EECS 490 or
equivalent.
EECS 550. Neuromechanics Seminar (0)
(See EBME 550.) Crosslisted as EBME 550.
EECS 583. Implementation of Nonlinear Control (3)
Nonlinear control with emphasis on applications. Basic theory including
describing functions, equivalent gains, and Lyapunov stability. Emphasis on
digital implementation of nonlinear controllers for high performance applications
such as servomechanisms, manipulators, and aerospace systems. Comparison of
nonlinear and linear designs. Laboratory experiments and CAD tools for controller
performance verification.
EECS 589. Robotics II (3)
Survey of research issues in robotics. Force control, visual servoing, robot
autonomy, online planning, highspeed control, man/machine interfaces, robot
learning, sensory processing for realtime control. Primarily a projectbased
lab course in which students design realtime software executing on multiprocessors
to control an industrial robot. Prereq: EECS 489.
EECS 591. Intelligent Systems II (3)
EECS 600. Special Topics (118)
EECS 600T. Graduate Teaching III (0)
This course will provide Ph.D. candidate with experience in teaching undergraduate
or graduate students. The experience is expected to involve direct student contact
but will be based upon the specific departmental needs and teaching obligations.
This teaching experience will be conducted under the supervision of the faculty
member who is responsible for the course, but the academic advisor will assess
the educational plan to ensure that it provides an educational experience for
the student. Students in this course may be expected to perform one or more
of the following teaching related activities running recitation sessions, providing
laboratory assistance, developing teaching or lecture materials presenting lectures.
Prereq: Ph.D. student in EECS department.
EECS 601. Independent Study (118)
EECS 602. Advanced Projects Laboratory (118)
EECS 620. Special Topics (118)
EECS 621. Special Projects (118)
EECS 649. Project M.S. (19)
EECS 651. Thesis M.S. (118)
EECS 701. Dissertation Ph.D. (118)
EECS 702. Appointed Dissertation Fellow (9)
BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN ELECTRICAL ENGINEERING
Freshman Year 
ClassLabCredit Hours 
Fall
HM/SS elective 
(303) 
CHEM 111 Chemistry I 
(404) 
MATH 121 Calculus I 
(404) 
ENGR 131 Elementary Computer Programming 
(303) 
ENGL 150 Expository Writing 
(303) 
PHED 101 Physical Education 
(030) 
Total 
(17317) 
Spring
Open elective a 
(303) 
ENGR 145 Chemistry of Materials 
(404) 
PHYS 121 Physics I: Mechanics b 
(404) 
MATH 122 Calculus II 
(404) 
PHED 102 Physical Education 
(030) 
Total 
(15315) 
Sophomore Year
Fall
PHYS 122 Physics II Electricity & Magnetism 
(404) 
MATH 223 Calculus III 
(303) 
ENGR 210 Circuits and Instrumentation 
(324) 
EECS 281 Computer Organization, Logic Design 
(324) 
Total 
(13415) 
Spring
HM/SS Sequence I 
(303) 
ENGR 225 Thermo, Fluids, Transport 
(404) 
MATH 224 Differential Equations 
(303) 
EECS 245 Electronic Circuits 
(324) 
EECS 309 Electromagnetic Fields I 
(303) 
Total 
(16217) 
Junior Year 
ClassLabCredit Hours 
Fall
HM/SS Sequence II 
(303) 
ENGR 200 Statics & Strength of Materials 
(303) 
EECS 246 Signals & Systems 
(324) 
STAT 332 Statistics of Signal Processingc 
(303) 
Approved Tech. Elective d 
(303) 
Total 
(15216) 
Spring
HM/SS Sequence III 
(303) 
EECS 321 Semiconductor Elect. Devices 
(324) 
Applied Statistics Req.e 
(303) 
Approved technical elective d 
(303) 
Approved technical elective d 
(303) 
Total 
(15216) 
Senior Year
Fall
EECS 398L Senior Project Lab I f, g 
(084) 
ENGL 398N Professional Communications 
(303) 
Open Elective 
(303) 
Approved technical elective d 
(303) 
Approved technical elective d 
(303) 
Total 
(12816) 
Spring
HM/SS elective 
(303) 
HM/SS elective 
(303) 
EECS 399L Senior Project Lab II 
(084) 
Open elective 
(303) 
Approved technical elective d 
(303) 
Total 
(12816) 
Graduation Requirement: 128 hours total
a. Although not required students may elect to take ENGR 101 Freshman Engineering Field Service Project as their open elective in the freshman year.
b. Selected students may be invited to take PHYS 123, 124 in place of PHYS 121 and PHYS 122.
c. Students may replace this class with STAT 333 Uncertainty in Engineering and Science if approved by their advisor.
d. Technical electives will be chosen to fulfill the depth requirement and otherwise increase the student’s understanding of electrical engineering. Courses used to satisfy the depth requirement must come from the department’s list of depth areas and related courses. Technical electives not used to satisfy the depth requirement are more generally defined as any course related to the principles and practice of electrical engineering. This includes all EEAP courses at the 200 level and above and can include courses from other programs. All nonEEAP technical electives must be approved by the student’s advisor.
e. This course must utilize statistics in electrical engineering applications and is typically EEAP 352 Digital Communications or EEAP 355 RF Communications. Other courses possible with approval of advisor.
f. Coop students may obtain design credit for one semester of Senior Project Lab if their coop assignment included significant design responsibility; however, the student is still responsible for such course obligations as reports, presentations and ethics assignments. Design credit and fulfillment of remaining course responsibilities are arranged through the senior project instructor.
g. B.S./M.S. students may also utilize EEAP 398/399 to fulfill eight credits of M.S. thesis provided their thesis has adequate design content to meet the requirements of EEAP 398/399. B.S./M.S. students should see their thesis advisor for details.
BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN COMPUTER ENGINEERING
Freshman Year 
ClassLabCredit Hours 
Fall
Open elective or HM/SS elective a 
(303) 
CHEM 111 Chemistry I 
(404) 
MATH 121 Calculus I 
(404) 
ENGR 131 Elementary Computer Programming 
(303) 
ENGL 150 Expository Writing 
(303) 
PHED 101 Physical Education 
(030) 
Total 
(17317) 
Spring
HM/SS elective or open elective a 
(303) 
ENGR 145 Chemistry of Materials 
(404) 
PHYS 121 Physics I: Mechanics 
(404) 
MATH 122 Calculus II 
(404) 
PHED 102 Physical Education 
(030) 
Total 
(15315) 
Sophomore Year
Fall
HM/SS Sequence I 
(303) 
PHYS 122 Physics II: Electricity & Magnetism 
(404) 
MATH 223 Calculus III 
(303) 
ENGR 200 Statics & Strength of Materials 
(303) 
EECS 233 Introduction to Data Structures 
(324) 
Total 
(16217) 
Spring
HM/SS Sequence II 
(303) 
MATH 224 Differential Equations 
(303) 
ENGR 210 Circuits and Instrumentation 
(324) 
Technical Elective b 
(303) 
EECS 281 Comp. Organization Logic Design 
(324) 
Total 
(15417) 
Junior Year 
ClassLabCredit Hours 
Fall
HM/SS Sequence III 
(303) 
MATH 304 Discrete Mathematics 
(303) 
EECS 337 Systems Programming 
(324) 
ENGR 225 Thermodynamics, Fluids, Transport 
(404) 
Technical elective b 
(303) 
Total 
(16217) 
Spring
ENGL 398N Prof. Communications 
(303) 
EECS 301 Digital Laboratory 
(042) 
EECS 314 Computer Architecture 
(303) 
EECS 315 Digital Systems Design 
(324) 
EECS 338 Intro to Operating Systems d 
(324) 
or 

Technical elective d 
(303) 
Total 
(12816) or (12615) 
Senior Year
Fall
HM/SS elective 
(303) 
EECS 318 VLSI/CAD d 
(324) 
or 

Technical elective c 
(303) 
Technical elective b 
(303) 
Statistics elective 
(303) 
Open elective 
(303) 
Total 
(15216) or (15015) 
Spring
HM/SS elective 
(303) 
EECS 399M Comp. Eng. Design Project 
(063) 
Technical elective b 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(12615) 
Graduation Requirement: 129 hours total
a. One of these must be a humanities/social science course
b. Technical electives are more generally defined as any course related to the principles and practice of computer engineering. This includes all EECS courses at the 200 level and above and can include courses from other programs. All nonEECS technical electives must be approved by the student’s advisor.
c. The student must take either EECS 318 VLSI/CAD (Fall Semester) or EECS 338 Intro. to Operating Systems (Spring Semester), AND a three credit hour technical elective.
d. Chosen from MATH 380 Introduction to Probability, STAT 312 Basic Statistics for Engineering and Science, STAT 313 Statistics for Experimenters, STAT 332 Statistics for Signal Processing, STAT 333 Uncertainty in Engineering and Science.
BACHELOR OF SCIENCE DEGREE
MAJOR IN COMPUTER SCIENCE
Freshman Year 
ClassLabCredit Hours 
Fall
Open elective or HM/SS elective 
(303) 
CHEM 111 Chemistry I 
(404) 
MATH 121 Calculus I 
(404) 
ENGR 131 Elementary Computer Programming 
(303) 
ENGL 150 Expository Writing 
(303) 
PHED 101 Physical Education 
(030) 
Total 
(17317) 
Spring
HM/SS elective or open elective 
(303) 
ENGR 145 Chemistry of Materials 
(404) 
PHYS 121 Physics I: Mechanics 
(404) 
MATH 122 Calculus II 
(404) 
PHED 102 Physical Education 
(030) 
Total 
(15315) 
Sophomore Year
Fall
HM/SS Sequence I 
(303) 
PHYS 122 Physics II Electricity & Magnetism 
(404) 
MATH 223 Calculus III 
(303) 
Technical elective b 
(303) 
ECES 281 Comp. Organization Logic Design 
(324) 
Total 
(16217) 
Spring
HM/SS Sequence II 
(303) 
MATH 224 Differential Equations 
(303) 
Technical Elective c 
(303) 
MATH 304 Discrete Mathematics 
(303) 
EECS 233 Intro Data Structures 
(324) 
Total 
(15216) 
Junior Year 
ClassLabCredit Hours 
Fall
HM/SS Sequence III 
(303) 
EECS 340 Algorithms and Data Structures 
(303) 
EECS 337 Systems Programming 
(324) 
Statistics elective c 
(303) 
Technical elective c 
(303) 
Total 
(15216) 
Spring
HM/SS elective 
(303) 
EECS 345 Programming Language Concepts 
(303) 
EECS 343 Theoretical Computer Science 
(303) 
EECS 314 Computer Architecture 
(303) 
EECS 338 Intro to Operating Systems 
(324) 
Total 
(15216) 
Senior Year
Fall
ENGL 398N Professional Communication 
(303) 
EECS 398M Software Engineering 
(303) 
Technical elective e 
(303) 
Open elective 
(303) 
Open elective d 
(303) 
Total 
(15015) 
Spring
HM/SS elective 
(303) 
EECS 341 Intro. to Database Systems 
(303) 
EECS 391 Intro. to Artificial Intelligence 
(303) 
Technical elective e 
(303) 
Open elective 
(303) 
Total 
(15015) 
Graduation Requirement: 127 hours total
a. One of these must be a humanities/social science course.
b. ENGR 210 is recommended because it provides flexibility in choice of major and advanced EECS courses.
c. Chosen from MATH 380 Introduction to Probability, STAT 312 Basic Statistics for Engineering and Science, STAT 313 Statistics for Experimenters, STAT 332 Statistics for Signal Processing, STAT 333 Uncertainty in Engineering and Science.
d. Course other than mathematics or computer science.
e. Technical electives are more generally defined as any course related to the principles and practice of computer science. This includes all EESC and MATH courses at the 200 level and above and can include courses from other programs. All technical electives which are not EECS or MATH courses must be approved by the students advisor.
BACHELOR OF ARTS DEGREE
COMPUTER SCIENCE
Freshman Year 
ClassLabCredit Hours 
Fall
Open elective 
(303) 
MATH 125 Mathematics I 
(404) 
ENGR 131 Elementary Computer Programming 
(303) 
GER course 
(303) 
GER course 
(303) 
PHED 101 Physical Education 
(030) 
Total 
(16316) 
Spring
ENGL 150 Expository Writing 
(303) 
MATH 126 Mathematics II 
(404) 
GER course 
(303) 
GER course 
(303) 
Open elective 
(303) 
PHED 102 Physical Education 
(030) 
Total 
(16316) 
Sophomore Year
Fall
EECS 281 Comp. Organization Logic Design (324) 

GER course 
(303) 
Logic Design technical elective a 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(15216) 
Spring
GER course 
(303) 
MATH 304 Discrete Mathematics 
(303) 
EECS 233 Intro Data Structures 
(324) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(15216) 
Junior Year 
ClassLabCredit Hours 
Fall
EECS 337 Systems Programming 
(324) 
GER course 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(12213) 
Spring
EECS 338 Intro to Operating Systems 
(324) 
EECS 341 Intro to Database Systems 
(303) 
EECS 314 Computer Architecture 
(303) 
Open elective 
(303) 
Total 
(12213) 
Senior Year
Fall
EECS 340 Algorithms and Data Structures 
(303) 
Technical elective a 
(303) 
GER course 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(15015) 
Spring
Technical elective a 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Open elective 
(303) 
Total 
(15015) 
Graduation Requirement: 120 hours total
a. One technical elective must be a computer science course. The other two technical electives may be computer science, MATH or STAT courses.
BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN SYSTEMS AND CONTROL ENGINEERING
Freshman Year 
ClassLabCredit Hours 
Fall
HM/SS elective 
(303) 
CHEM 111 Chemistry I 
(404) 
MATH 121 Calculus I 
(404) 
ENGR 131 Elementary Computer Programming 
(303) 
ENGL 150 Expository Writing 
(303) 
PHED 101 Physical Education 
(030) 
Total 
(17317) 
Spring
Open elective a 
(303) 
ENGR 145 Chemistry of Materials 
(404) 
PHYS 121 Physics I: Mechanics b 
(404) 
MATH 122 Calculus II 
(404) 
PHED 102 Physical Education 
(030) 
Total 
(15315) 
Sophomore Year
Fall
PHYS 122 Physics II: Electricity & Magnetism b 
(404) 
MATH 223 Calculus III 
(303) 
ENGR 210 Circuits and Instrumentation 
(324) 
EECS 281 Computer Organization 
(324) 
Total 
(13415) 
Spring
HM/SS Sequence I 
(303) 
ENGR 225 Fluid and Thermodynamics 
(404) 
MATH 224 Differential Equations 
(303) 
STAT xxx Statistical Methods Course c 
(303) 
ENGR 200 Statics & Strength of Materials 
(303) 
Total 
(16016) 
Junior Year 
ClassLabCredit Hours 
Fall
HM/SS Sequence II 
(303) 
EECS 246 Signals and Systems 
(324) 
EECS 342 Introduction to Global Systems 
(303) 
EECS 324 Simulation Methods 
(303) 
Approved technical elective e 
(303) 
Total 
(15216) 
Spring
HM/SS Sequence III 
(303) 
EECS 304 Control Engineering I 
(303) 
EECS 305 Control Lab I 
(021) 
EECS 346 Engineering Optimization 
(303) 
Approved technical elective 
(303) 
Open elective 
(303) 
Total 
(15216) 
Senior Year
Fall
HM/SS elective 
(303) 
EECS 398N Senior Project Lab d 
(084) 
ENGL 398N Professional Communications 
(303) 
EECS 352 Eng. Econ. & Dec. Analysis 
(303) 
Approved technical elective e 
(303) 
Total 
12816 
Spring
HM/SS elective 
(303) 
EECS 399N Engineering Projects Lab II 
(084) 
Approved Technical Elective e 
(303) 
Approved Technical Elective e 
(303) 
Approved Technical Elective e 
(303) 
Total 
(12816) 
Graduation Requirement: 127 hours total
a. Although not required, students may elect to take ENGR 101, Freshman Engineering Service Project, as their open elective during the freshman year.
b. Selected students may be invited to take PHYS 124 in place of PHYS 121 and 122.
c. Choose from STAT 312, STAT 332, STAT 333.
d. Coop students may obtain credit for the first semester of Senior Project Lab if their coop assignment includes significant design responsibility. This credit can be obtained by submitting a suitable written report and making an oral presentation on the coop work in coordination with the senior project instructor.
e. Technical electives from an approved list.
Degree Program in Engineering, Undesignated
312 Glennan Building (7220)
Phone 2163686482; Fax 2163686939
James D. Cawley, Associate Dean
email jxc41@po.cwru.edu
The Undesignated Engineering program prepares students who seek a technological background but do not wish to pursue pure engineering careers. For example, some needs in the public sector, such as pollution remediation, transportation, lowcost housing, elective medical care, and crime control could benefit from engineering expertise. To prepare for careers in fields that address such problems, the Undesignated Engineering program allows students to acquire some engineering background, and combine it with a minor in such programs as management, history of technology and science, or economics.
Undergraduate Program
A student electing an undesignated degree must submit both a proposed course schedule and a clear statement of career goals and of the way in which the proposed program will meet those goals. These documents are to be submitted to the office of the associate dean for undergraduate programs of The Case School of Engineering. The program must be approved by the dean of engineering or designate in consultation with representatives of the major and minor departments. A total of at least 128 semester credits are required for graduation.
Since each student’s program is unique, no typical curriculum can be shown. Every program must fulfill the requirements described below.
1. Engineering Core
2. A minimum of two engineering electives courses selected from two of the following four groups
a. Thermodynamics or Physical Chemistry (EMAE 150, EMAC 171 and 172, CHEM 301 and 302, or ECHE 363)
b. Signals, systems or control (EECS 212, EECS 304, ECHE 367)
c. Materials science (EMSE 201, EMAC 270, EMSE 314, EBME 306, or EECS 321)
d. Economics, production systems or decision theory (EECS 350, EECS 352, OPRE 345)
MAJOR
The major must contain a minimum of 24 semester credit hours of work in one of the following engineering fields
• Biomedical engineering
• Chemical engineering
• Civil engineering
• Computer engineering
• Electrical engineering
• Fluid and thermal engineering sciences
• Materials science and engineering
• Polymer science and engineering
• Systems and control engineering
This work includes a senior projects laboratory (3 credits) and usually a course with a physical measurements laboratory.
MINOR
The minor program requires a minimum of 15 semester credit hours. Suggested minors for students pursuing the undesignated degree program in engineering are the following. Other minors are available with approval of the Office of Undergraduate Studies.
Engineering
A minor program may be chosen in any engineering field that differs from the major and that, when combined with the major, fulfills a specific purpose or career plan. The purpose of a minor program is to allow more breadth, with less depth in any one engineering area. For example, such a program may appeal to the student who prefers a broad designoriented background or the student who wishes to couple knowledge in systems and control engineering with knowledge in a field such as civil engineering, chemical processing, or computer engineering. Other major and minor combinations that may be of interest are the coupling of a civil engineering major with a metallurgy or materials minor or a combination of electrical and materials science and engineering.
Science
A minor field may be chosen in any field of science wherein the majorminor combination fulfills a unique purpose. Many engineering majors and science minors can be successfully combined. For example, a major in civil engineering coupled with a minor in geology leads to a program aimed at geophysical sciences or oceanography. The student with electrical engineering interests in lasers, optics, solid state, plasmas, and the like may profit by coupling an electrical engineering major with a physics minor. In particular, an engineering major coupled with a minor in biological sciences or in biomedical engineering (plus chemistry) leads to a biomedical engineering background for the student interested in premedicine, predentistry, prenursing, or prebiomedical engineering. This combination also provides a unique background for a student interested in biomaterials or a student who wishes to explore the bioelectronics area or biomechanics, systems biology, or a combination that deals with information processing and the computer in biomedical applications.
Management
Many students enter the engineering program at Case Western Reserve in preparation for industrial management careers. Generally, their plan is to work in an engineering capacity and gradually assume management responsibilities. Some of these students plan to take a graduate program in management, such as the Master of Business Administration degree. However, others rely on a combination of undergraduate elective courses, job experience, and industrial training programs for this career preparation.
To serve engineering students whose career goals involve management, a minor program has been developed in cooperation with the Weatherhead School of Management. This program gives the student the options of direct entry into industry in either an engineering or a management tracking program or entry into graduate school to earn the Master of Science degree in engineering or the Master of Business Administration degree.
A management minor requires the following courses
• ACCT 303, Survey of Accountancy (3)
• BAFI 355, Corporation Finance (3)
• OPMT 350, Operations Management (3)
plus any two of the following
• LHRP 251, Industrial Relations and Administrative Practices (or LHRP 311, Labor Problems (3) )
• MIDS 308, Management Information Systems I (3)
• MKMR 301, Marketing Management(3)
• OPRE 201, Introduction to Operations Research I (3)
• ORBH 250, Introduction to Organizational Behavior and Management (3)
History of Technology and Science
The purpose of coupling an engineering major with a minor in the history of technology and science is primarily to prepare for entry into the field of history of technology. Beyond this, however, knowledge of the history of technology may be invaluable to engineers who take decisionmaking roles during their careers. This minor provides a much needed emphasis on the consequences of technology and technological decisions on society and the importance of historical insight in such decisions.
The minor program can be tailored to individual interests, based on the following offerings
• HSTY 266, The Engineer in America (3)
• HSTY 306, Engineering in History (3)
• HSTY 307, Development of Chemistry and Chemical Engineering (3)
• HSTY 366, Science, Technology, and Government (3)
• HSTY 377, Nuclear Weapons and Arms Control (3)
Economics
The field of economics is moving rapidly toward a more quantitative approach and is an important field for engineers. The economics minor requires the following courses
• ECON 103, Principles of Macroeconomics (3)
• ECON 102, Principles of Microeconomics (3)
The following electives in economics are suggested
• ECON 341, Money and Banking (3)
• ECON 326, Econometrics (3)
• ECON 342, Public Finance (3)
• ECON 369, Economics of Industrial Production and Technology (3)
• ECON 386, Urban Economics (3)
• ECON 361, Managerial Economics (3)
BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN ENGINEERING (UNDESIGNATED)
Freshman Year 
ClassLabCredit Hours 
Fall
Open elective or Humanities/Social Science a 
(303) 
CHEM 111 Principles of Chemistry for Engineers 
(404) 
ENGR 131 Elementary Computer Programming 
(223) 
ENGL 150 Expository Writing 
(303) 
MATH 121 Calculus for Science and Engineering I 
(404) 
PHED 101 Physical Education Activities 
(030) 
Total 
(16517) 
Spring
Humanities/Social Science or open elective a 
(303) 
ENGR 145 Chemistry of Materials 
(404) 
MATH 122 Calculus for Science and Engineering II 
(404) 
PHED 102 Physical Education Activities 
(030) 
PHYS 121 General Physics I 
(404) 
Total 
(15315) 
Sophomore Year
Fall
Humanities or Social Science Sequence I 
(303) 
ENGR 200 Statics and Strength of Materials 
(303) 
MATH 223 Calculus for Science and Engineering III 
(303) 
ECES 251 Numerical Methods 
(223) 
PHYS 122 General Physics II 
(404) 
Total 
(15216) 
Spring
Humanities or Social Science Sequence II 
(303) 
ENGR 225 Thermodynamics, Fluid Mechanics, Heat and Mass Transfer 
(404) 
ENGR 210 Introduction to Circuits and Instrumentation 
(324) 
MATH 224 Elementary Differential Equations 
(303) 
PHYS 221 General Physics III, Modern Physics 
(303) 
Total 
(16217) 
Junior Year 
ClassLabCredit Hours 
Fall
Humanities or Social Science Sequence III 
(303) 
Major Concentration Course 
(303) 
Major Concentration Course 
(303) 
Minor Concentration Course 
(303) 
Engineering elective 
(303) 
Open elective 
(303) 
Total 
(18018) 
Spring
ENGL 398N Professional Communications 
(303) 
Major Concentration Course 
(303) 
Major Concentration Course 
(303) 
Minor Concentration Course 
(303) 
Engineering elective 
(303) 
Total 
(15015) 
Senior Year
Fall
Humanities or Social Science elective 
(303) 
Exxx 398 Engineering Senior Project 
(063) 
Major Concentration Course 
(303) 
Minor Concentration Course 
(303) 
Minor Concentration Course 
(303) 
Total 
(12615) 
Spring
Humanities or Social Science elective 
(303) 
Major Concentration Course 
(303) 
Major Concentration Course 
(303) 
Minor Concentration Course 
(303) 
Open elective 
(303) 
Total 
(15015) 
Hours required for graduation: 128
a. One of these courses must be a humanities/social science course.