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

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

COMPUTER SCIENCE

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

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

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

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

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

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 non-technological systems. Problems relating to modeling, decision-making, control, and optimization are studied. Some examples of systems problems which are studied include: computer control of industrial plants, development of world models for studying environmental policies, and optimal planning and management in large-scale systems. In each case, the relationship and interaction among the various components of a given system must be modeled. This information is used to determine the best way of coordinating and regulating their individual contributions to achieve the overall goal of the system. What may be best for an individual component of the system may not be the best for the system as a whole.

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

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

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

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

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

Minor Program in Systems and Control Engineering
A total of five courses (15 credit hours) are required to obtain a minor in systems and control engineering.

At least nine credit hours must be selected from:

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

B.S./M.S. Program
The department encourages students with at least a 3.2 grade point average to apply for admission to the five-year bachelors/master’s program in the junior year. This integrated program, which permits substitution of M.S. thesis work for the senior design project, provides a high level of fundamental training and in-depth advanced training in the student’s selected specialty. It also offers the opportunity to complete both the Bachelor of Science in Engineering and Master of Science degrees within five years.

Control Engineering and Signal Processing
EECS 306 Control Engineering II

EECS 396 Hybrid Systems

EECS 401 Digital Signal Processing

EECS 404 Digital Control

EECS 409 Discrete Event Systems

EECS 417 Introduction to Stochastic Control

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

EECS 416 Engineering Optimization

EECS 429 Risk and Decision Analysis

OPRE 432 Simulation

OPRE 426 Stochastic Processes in Operations Research

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

EECS 360 Manufacturing and Integrated Systems

OPMT 351 Logistical Systems

OPMT 353 Quality Control and Management

EECS 450 Production and Operational Systems

EECS 460 Manufacturing and Integrated Systems

OPRE 424 Scheduling

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

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 computer-aided design. Academic requirements for graduate degrees in engineering are as specified for The Case School of Engineering in this bulletin, however, some exceptions are noted below. All current rules and regulations for this department are detailed in a graduate student handbook, available from the department office, which supersedes any rules contained here. A number of teaching and research assistantships are available, on a competitive basis, for the full support of qualified students. In addition, a limited number of tuition assistantships are also available for partial support of graduate students.

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 (multi-objective and large scale system theory), control of industrial and manufacturing systems (facilities layout, flexible manufacturing), biomedical control system design and analysis (control of neural prostheses, automatic control of therapeutic drug delivery), energy systems (power distribution and production planning, load forecasting), and global and environmental system analysis and control.(resource constraints: water, energy etc., carrying capacity and global climate change).

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

ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (EECS)

Undergraduate Courses

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

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

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

EECS 233. Introduction to Data Structures (4)
The programming language C++; pointers, files, variant records, and recursion. Representation and manipulation of data: one-way and circular linked lists, doubly linked lists; the available space list. Different representations of stacks and queues. Representation of binary trees, trees and graphs. Hashing; searching and sorting. Laboratory. Prereq: ENGR 131.

EECS 245. Electronic Circuits (4)
Analysis of time-dependent electrical circuits. Dynamic waveforms and elements: inductors, capacitors, and transformers. First- and second-order circuits, passive and active. Analysis of sinusoidal steady state response using phasors. Laplace transforms and pole-zero diagrams. S-domain circuit analysis. Two-port networks, impulse response, and transfer functions. Introduction to nonlinear semiconductor devices: diodes, BJTs, and FETs. Gain-bandwidth product, slew-rate and other limitations of real devices. SPICE simulation and laboratory exercises reinforce course materials. Prereq: ENGR 210. Coreq: MATH 224.

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

EECS 251. Numerical Methods (3)
Introduction to basic concepts and algorithms used in the numerical solution of common problems including solving non-linear equations, solving systems of linear equations, interpolation, fitting curves to data, integration and solving ordinary differential equations. Computational error and the efficiency of various numerical methods are discussed in some detail. Most homework requires the implementation of numerical methods on a computer. Prereq: ENGR 131 and MATH 122.

EECS 281. Logic Design and Computer Organization (4)
Fundamentals of digital systems in terms of both computer organization and logic level design. Organization of digital computers; information representation; boolean algebra; analysis and synthesis of combinational and sequential circuits; datapaths and register transfers; instruction sets and assembly language; input/output and communication; memory. Prereq: ENGR 131.

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

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

EECS 301. Digital Logic Laboratory (2)
This course is an introductory experimental laboratory for digital networks. The course introduces students to the process of design, analysis, synthesis and implementation of digital networks. The course covers the design of combinational circuits, sequential networks, registers, counters, synchronous/asynchronous Finite State Machine, register based design, and arithmetic computational block. Prereq: EECS 281.

EECS 304. Control Engineering I with Laboratory (3)
Analysis and design techniques for control applications. Linearization of nonlinear systems. Design specifications. Classical design methods: root locus, bode, nyquist. PID, lead, lag, lead-lag controller design. State space modeling, solution, controllability, observability and stability. Modeling and control demonstrations and experiments single-input/single-output and multivariable systems. Control system analysis/design/implementation software. Prereq: EECS 212.

EECS 305. Control Engineering I Laboratory (1)
A laboratory course based on the material in EECS 304. Modeling, simulation, and analysis using MATLAB. Physical experiments involving control of mechanical systems, process control systems, and design of PID controllers. Prereq: EECS 212 or equivalent. Coreq: EECS 304.

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

EECS 309. Electromagnetic Fields I (3)
Maxwell’s integral and differential equations, boundary conditions, constitutive relations, energy conservation and Pointing vector, wave equation, plane waves, propagating waves and transmission lines, characteristic impedance, reflection coefficient and standing wave ratio, in-depth analysis of coaxial and strip lines, electro- and magneto-quasistatics, simple boundary value problems, correspondence between fields and circuit concepts, energy and forces. Prereq: MATH 223 and PHYS 122. Coreq: MATH 224.

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

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

EECS 313. Signal Processing (3)
Fourier series and transforms. Analog and digital filters. Fast-Fourier transforms, sampling, and modulation for discrete time signals and systems. Consideration of stochastic signals and linear processing of stochastic signals using correlation functions and spectral analysis. Prereq: EECS 246.

EECS 314. Computer Architecture (3)
This course provides students the opportunity to study and evaluate a modern computer architecture design. The course covers topics in fundamentals of computer design, performance, cost, instruction set design, processor implementation, control unit, pipelining, communication and network, memory hierarchy, computer arithmetic, input-output, and an introduction to RISC and super-scalar processors. Prereq: EECS 281.

EECS 315. Digital Systems Design (4)
This course gives students the ability to design modern digital circuits. The course covers topics in logic level analysis and synthesis, digital electronics: transistors, CMOS logic gates, CMOS lay-out, design metrics space, power, delay. Programmable logic (partitioning, routing), state machine analysis and synthesis, register transfer level block design, datapath, controllers, ASM charts, microsequencers, emulation and rapid protyping, and switch/logic-level simulation. Prereq: EECS 281.

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

EECS 318. Computer-Aided Design (4)
With Very Large Scale Integration (VLSI) technology there is an increased need for Computer-Aided Design (CAD) techniques and tools to help in the design of large digital systems that deliver both performance and functionality. Such high performance tools are of great importance in the VLSI design process, both to perform functional, logical and behavioral modeling and verification to aid the testing process. This course discusses the fundamentals in behavioral languages, both VHDL and Verilog, with hands-on experience with state-of-the-art computer-aided design tools. Prereq: EECS 281 and EECS 321.

EECS 321. Semiconductor Electronic Devices (4)
Energy bands and charge carriers in semiconductors and their experimental verifications. Excess carriers in semiconductors. Principles of operation of semiconductor devices that rely on the electrical properties of semiconductor surfaces and junctions. Development of equivalent circuit models and performance limitations of these devices. Devices covered include: junctions, bipolar transistors, Schottky junctions, MOS capacitors, junction gate and MOS field effect transistors, optical devices such as photodetectors, light-emitting diodes, solar cells and lasers. Laboratory experiments to characterize some of the above devices. Prereq: EECS 309.

EECS 322. Integrated Circuits and Electronic Devices (3)
Technology of monolithic integrated circuits and devices, including crystal growth and doping, photolithography, vacuum technology, metalization, wet etching, thin film basics, oxidation, diffusion, ion implantation, epitaxy, chemical vapor deposition, plasma processing, and micromachining. Basics of semiconductor devices including junction diodes, bipolar junction transistors, and field effect transistors. Prereq: EECS 321.

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

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

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

EECS 338. Introduction to Operating Systems (4)
CPU scheduling, memory management, concurrent processes, semaphores, monitors, deadlocks, secondary storage management, file systems, protection, UNIX operating system, fork, exec, wait, UNIX System V IPCs, sockets, remote procedure calls, threads. Must be proficient in "C" programming language. Prereq: EECS 337.

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

EECS 341. Introduction to Database Systems (3)
Relational model, ER model, relational algebra and calculus, SQL, OBE, security, views, files and physical database structures, query processing and query optimization, normalization theory, concurrency control, object relational systems, multimedia databases, Oracle SQL server, Microsoft SQL server. Prereq: EECS 233.

EECS 342. Introduction to Global Issues (3)
This systems course is based on the paradigm of the world as a complex system. Global issues such as population, world trade and financial markets, resources (energy, water, land), global climate change, and others are considered with particular emphasis put on their mutual interdependence. A reasoning support computer system which contains extensive data and a family of models is used for future assessment. Students are engaged in individual, custom-tailored, projects of creating conditions for a desirable or sustainable future based on data and scientific knowledge available. Students at Case Western Reserve will interact with students from fifteen universities that have been strategically selected in order to give global coverage to UNESCO’S Global-problematique Education Network Initiative (GENIe) in joint, participatory scenario analysis via the internet.

EECS 343. Theoretical Computer Science (3)
Introduction to mathematical logic, different classes of automata and their correspondence to different classes of formal languages, recursive functions and computability, assertions and program verification, denotational semantics. Prereq: MATH 304. Cross-listed as MATH 343.

EECS 344. Electronic Analysis and Design (3)
The design and analysis of real-world circuits. Topics include: junction diodes, non-ideal op-amp models, characteristics and models for large and small signal operation of bipolar junction transistors (BJTs) and field effect transistors (FETs), selection of operating point and biasing for BJT and FET amplifiers. Hybrid-pi model and other advanced circuit models, cascaded amplifiers, negative feedback, differential amplifiers, oscillators, tuned circuits, and phase-locked loops. Computers will be extensively used to model circuits. Selected experiments and/or laboratory projects. Prereq: EECS 245.

EECS 345. Programming Language Concepts (3)
This course studies important concepts underlying the design, definition, implementation and use of modern programming languages including syntax, semantics, names/scopes, types, expression, assignment, subprograms, data abstraction, and inheritance. Imperative, object-oriented, concurrent, functional, and logic programming paradigms are discussed. Illustrative examples are drawn from a variety of popular languages, such as C++, Java, Ada, Lisp, and Prolog. Prereq: EECS 233, EECS 337.

EECS 346. Engineering Optimization (3)
Optimization techniques including linear programming and extensions; transportation and assignment problems; network flow optimization; quadratic, integer, and separable programming; geometric programming; and dynamic programming. Nonlinear optimization topics: optimality criteria, gradient and other practical unconstrained and constrained methods. Computer applications using engineering and business case studies. Prereq: MATH 201.

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

EECS 348. Communication Electronic Cir (4)

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

EECS 351. Communications and Signal Analysis (3)
Fourier transform analysis and sampling of signals. AM, FM and SSB modulation and other modulation methods such as pulse code, delta, pulse position, PSK and FSK. Detection, multiplexing, performance evaluation in terms of signal-to-noise ratio and bandwidth requirements. Prereq: EECS 246 or equivalent.

EECS 352. Engineering Economics and Decision Analysis (3)
Economic analysis of engineering projects, focusing on financial decisions concerning capital investments. Present worth, annual worth, internal rate of return, benefit/cost ratio. Replacement and abandonment policies, effects of taxes, and inflation. Decision making under risk and uncertainty. Decision trees. Value of information.

EECS 354. Digital Communications (3)
Fundamental bounds on transmission of information. Signal representation in vector space. Optimum reception. Probability and random processes with application to noise problems, speech encoding using linear prediction. Shaping of base-band signal spectra, correlative coding and equalization. Comparative analysis of digital modulation schemes. Concepts of information theory and coding. Applications to data communication. Prereq: EECS 351 recommended.

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

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

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

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

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

EECS 381. Hybrid Systems (3)
Today, the most interesting computer code and microprocessor designs are "embedded" and hence interact with the physical world, producing a mixture of digital and analog domains. The class studies an array of tools for understanding and designing these "hybrid systems." Topics include: basics of language and finite state automata theory, discrete-event dynamic systems, Petri nets, timed and hybrid automata, and hybrid dynamical systems. Simulation, verification, and control concepts and languages for these models. Prereq: MATH 224 and either EECS 246 or MATH 304.

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

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

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

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

EECS 394X. Senior Project I (3)

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

EECS 396M. Special Topics: Computer Science (1-9)

EECS 396N. Special Topics (1-18)

EECS 397L. Special Topics in Electrical Engineering (1-6)
(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 discrete-time signals and systems. Fourier analysis: the Discrete-time Fourier Transform, the Discrete-time Fourier series, the Discrete Fourier Transform and the Fast Fourier Transform. Continuous-time signal sampling and signal reconstruction. Digital filter design: infinite impulse response filters, finite impulse response filters, filter realization and quantization effects. Random signals: discrete correlation sequences and power density spectra, response of linear systems. Prereq: EECS 313.

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

EECS 405. Data Structures and File Management (3)
Fundamental concepts: sequential allocation, linked allocation, lists, trees, graphs, internal sorting, external sorting, sequential, binary, interpolation search, hashing file, indexed files, multiple level index structures, btrees, hashed files. Multiple attribute retrieval; inverted files, multi lists, multiple-key hashing, hd trees. Introduction to data bases. Data models. Prereq: EECS 233 and MATH 304.

EECS 408. Introduction to Linear Systems (3)
Analysis and design of linear feedback systems using state-space techniques. Review of matrix theory, linearization, transition maps and variations of constants formula, structural properties of state-space models, controllability and observability, realization theory, pole assignment and stabilization, linear quadratic regulator problems, observers, and the separation theorem. Prereq: EECS 304.

EECS 409. Discrete Event Systems (3)
A broad range of system behavior can be described using a discrete event framework. These systems are playing an increasingly important role in modeling, analyzing, and designing manufacturing systems. Simulation, automata, and queuing theory have been the primary tools for studying the behavior of these logically complex systems; however, new methods and techniques as well as new modeling frameworks have been developed to represent and to explore discrete event system behavior. The class will begin by studying simulation, the theory of languages, and finite state automata, and queuing theory approaches and then progress to examining selected additional frameworks for modeling and analyzing these systems including Petrinets, perturbation analysis, and Min-Max algebras.

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

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

EECS 412. Electromagnetic Fields III (3)
Maxwell’s equations, macroscopic versus microscopic fields, field interaction with materials in terms of polarization vectors P and M. Laplace’s and Poisson’s equations and solutions, scalar and vector potentials. Wave propagation in various types of media such as anisotropic and gyrotropic media. Phase and group velocities, signal velocity and dispersion. Boundary value problems associated with wave-guide and cavities. Wave solutions in cylindrical and spherical coordinates. Radiation and antennas.

EECS 413. Nonlinear Systems I (3)
This course will provide an introduction to techniques used for the analysis of nonlinear dynamic systems. Topics will include existence and uniqueness of solutions, phase plane analysis of two dimensional systems including Poincare-Bendixson, describing functions for single-input single-output systems, averaging methods, bifurcation theory, stability, and an introduction to the study of complicated dynamics and chaos. Coreq: EECS 408.

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

EECS 415. Integrated Circuit Technology I (3)
Review of semiconductor technology. Device fabrication processing, material evaluation, oxide passivation, pattern transfer technique, diffusion, ion implantation, metallization, probing, packaging, and testing. Design and fabrication of passive and active semi-conductor devices. Prereq: EECS 322.

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

EECS 417. Introduction to Stochastic Control (3)
Analysis and design of controllers for discrete-time stochastic systems. Review of probability theory and stochastic properties, input-output analysis of linear stochastic systems, spectral factorization and Weiner filtering, minimum variance control, state-space models of stochastic systems, optimal control and dynamic programming, statistical estimation and filtering, the Kalman-Bucy theory, the linear quadratic Gaussian problem, and the separation theorem. Prereq: EECS 408.

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

EECS 419. Computer System Architecture (3)
Interaction between computer systems hardware and software. Pipeline techniques - instruction pipelines - arithmetic pipelines. Instruction level parallelism. Cache mechanism. I/O structures. Examples taken from existing computer systems. Prereq: EECS 338.

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

EECS 421. Optimization of Dynamic Systems (3)
Fundamentals of dynamic optimization with applications to control. Variational treatment of control problems and the Maximum Principle. Structures of optimal systems; regulators, terminal controllers, time-optimal controllers. Sufficient conditions for optimality. Singular controls. Computational aspects. Selected applications. Prereq: EECS 408. Cross-listed as MATH 434.

EECS 422. Solid State Electronics II (3)
Advanced physics of semiconductor devices. Review of current transport and semiconductor electronics. Surface and interface properties. P-N junction. Bipolar junction transistors, field effect transistors, solar cells and photonic devices.

EECS 423. Distributed Systems (3)
Introduction to distributed systems; system models; network architecture and protocols; interprocess communication; client-server model; group communication; TCP sockets; remote procedure calls; distributed objects and remote invocation; distributed file systems; file service architecture; name services; directory and discovery services; distributed synchronization and coordination; transactions and concurrency control; security; cryptography; replication; distributed multimedia systems. Prereq: EECS 338.

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

EECS 426. MOS Integrated Circuit Design (3)
Design of digital and analog MOS integrated circuits. IC fabrication and device models. Logic, memory, and clock generation. Amplifiers, comparators, references, and switched-capacitor circuits. Characterization of circuit performance with/without parasitics using hand analysis and SPICE circuit simulation. Prereq: EECS 344 and EECS 321.

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

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

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

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

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

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

EECS 433. Database Systems (3)
Basic issues in file processing and database management systems. Physical data organization. Relational databases. Database design. Relational Query Languages, SQL. Query languages. Query optimization. Database integrity and security. Object-oriented databases. Object-oriented Query Languages, OQL. Prereq: EECS 341 and MATH 304.

EECS 434. Microfabricated Silicon Electromechanical Systems (3)
Topics related to current research in microelectromechanical systems based upon silicon integrated circuit fabrication technology: fabrication, physics, devices, design, modeling, testing, and packaging. Bulk micromachining, surface micromachining, silicon to glass and silicon-silicon bonding. Principles of operation for microactuators and microcomponents. Testing and packaging issues. Prereq: EECS 322 or EECS 415.

EECS 435. Data Mining (3)
Data Mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Topics to be covered includes: Data Warehouse and OLAP technology for data mining, Data Preprocessing, Data Mining Primitives, Languages, and System Architectures, Mining Association Rules from Large Databases, Classification and Prediction, Cluster Analysis, Mining Complex Types of Data, and Applications and Trends in Data Mining. Prereq: EECS 341 or equivalent.

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

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

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

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

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

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

EECS 452. Random Signals (3)
Fundamental concepts in probability. Probability distribution and density functions. Random variables, functions of random variables, mean, variance, higher moments, Gaussian random variables, random processes, stationary random processes, and ergodicity. Correlation functions and power spectral density. Orthogonal series representation of colored noise. Representation of bandpass noise and application to communication systems. Application to signals and noise in linear systems. Introduction to estimation, sampling, and prediction. Discussion of Poisson, Gaussian, and Markov processes.

EECS 454. Analysis of Algorithms (3)
This course presents and analyzes a number of efficient algorithms. Problems are selected from such problem domains as sorting, searching, set manipulation, graph algorithms, matrix operations, polynomial manipulation, and fast Fourier transforms. Through specific examples and general techniques, the course covers the design of efficient algorithms as well as the analysis of the efficiency of particular algorithms. Certain important problems for which no efficient algorithms are known (NP-complete problems) are discussed in order to illustrate the intrinsic difficulty which can sometimes preclude efficient algorithmic solutions. Prereq: MATH 304 and (EECS 340 or EECS 405). Cross-listed as OPRE 454.

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

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

EECS 458. Introduction to Bioinformatics (3)
Fundamental algorithmic methods in computational molecular biology and bioinformatics discussed. Sequence analysis, pairwise and multiple alignment, probabilistic models, phylogenetic analysis, folding and structure prediction emphasized. Prereq: EECS 340, EECS 233.

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

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

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

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

EECS 466. Computer Graphics (3)
Theory and practice of computer graphics: object and environment representation including coordinate transformations image extraction including perspective, hidden surface, and shading algorithms; and interaction. Covers a wide range of graphic display devices and systems with emphasis in interactive shaded graphics. Laboratory. Prereq: EECS 233.

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

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

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

EECS 478. Computational Neuroscience (3)
Computer simulation of neurons and neural circuits, and the computational properties of nervous systems. Students are taught a range of models for neurons and neural circuits, and are asked to implement and explore the computational and dynamic properties of these models. The course introduces students to dynamical systems theory for the analysis of neurons and neural circuits, as well as to cable theory, passive and active compartmental modeling, numerical integration methods, models of plasticity and learning, models of brain systems, and their relationship to artificial neural networks. Term project required. Two lectures per week. Cross-listed as BIOL 478, EBME 478, and NEUR 478.

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

EECS 483. Data Acquisition and Control (3)
Data acquisition (theory and practice), digital control of sampled data systems, stability tests, system simulation digital filter structure, finite word length effects, limit cycles, state-variable feedback and state estimation. Laboratory includes control algorithm programming done in assembly language.

EECS 484. Computational Intelligence I: Basic Principles (3)
This course is concerned with learning the fundamentals of a number of computational methodologies which are used in adaptive parallel distributed information processing. Such methodologies include neural net computing, evolutionary programming, genetic algorithms, fuzzy set theory, and "artificial life." These computational paradigms complement and supplement the traditional practices of pattern recognition and artificial intelligence. Functionalities covered include self-organization, learning a model or supervised learning, optimization, and memorization.

EECS 485. VLSI Systems (3)
Basic MOSFET models, inverters, steering logic, the silicon gate, nMOS process, design rules, basic design structures (e.g., NAND and NOR gates, PLA, ROM, RAM), design methodology and tools (spice, N.mpc, Caesar, mkpla), VLSI technology and system architecture. Requires project and student presentation, laboratory.

EECS 486. Research in VLSI Design Automation (3)
Research topics related to VLSI design automation such as hardware description languages, computer-aided design tools, algorithms and methodologies for VLSI design for a wide range of levels of design abstraction, design validation and test. Requires term project and class presentation.

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

EECS 488. Embedded Systems Design (3)
Objective: to introduce and expose the student to methodologies for systematic design of embedded system. The topics include, but are not limited to, system specification, architecture modeling, component partitioning, estimation metrics, hardware software codesign, diagnostics.

EECS 489. Robotics I (3)
(See EMAE 489.) Prereq: EMAE 181. Cross-listed as EMAE 489.

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

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

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

EECS 500T. Graduate Teaching II (0)
This course will provide the Ph.D. candidate with experience in teaching undergraduate or graduate students. The experience is expected to involve direct student contact but will be based upon the specific departmental needs and teaching obligations. This teaching experience will be conducted under the supervision of the faculty member who is responsible for the course, but the academic advisor will assess the educational plan to ensure that it provides an educational experience for the student. Students in this course may be expected to perform one or more of the following teaching related activities: grading homeworks, quizzes, and exams, having office hours for students, running recitation sessions, providing laboratory assistance. Prereq: Ph.D. student in EECS department.

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

EECS 516. Large Scale Optimization (3)
Concepts and techniques for dealing with large optimization problems encountered in designing large engineering structure, control of interconnected systems, pattern recognition, and planning and operations of complex systems; partitioning, relaxation, restriction, decomposition, approximation, and other problem simplification devices; specific algorithms; potential use of parallel and symbolic computation; student seminars and projects. Prereq: EECS 416.

EECS 518. Nonlinear Systems: Analysis and Control (3)
Mathematical preliminaries: differential equations and dynamical systems, differential geometry and manifolds. Dynamical systems and feedback systems, existence and uniqueness of solutions. Complicated dynamics and chaotic systems. Stability of nonlinear systems: input-output methods and Lyapunov stability. Control of nonlinear systems: gain scheduling, nonlinear regulator theory and feedback linearization. Prereq: EECS 408 and EECS 421.

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

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

EECS 526. Integrated Mixed-Signal Systems (3)
Mixed-signal (analog/digital) integrated circuit design. D-to-A and A-to-D conversion, applications in mixed-signal VLSI, low-noise and low-power techniques, and communication sub-circuits. System simulation at the transistor and behavioral levels using SPICE. Class will design a mixed-signal CMOS IC for fabrication by MOSIS. Prereq: EECS 426.

EECS 527. Advanced Sensors: Theory and Techniques (3)
Sensor technology with a primary focus on semiconductor-based devices. Physical principles of energy conversion devices (sensors) with a review of relevant fundamentals: elasticity theory, fluid mechanics, silicon fabrication and micromachining technology, semiconductor device physics. Classification and terminology of 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, CAD-based vision and range image processing. Neural-net image processing. Model-based computer vision for scene interpretation and autonomous systems. Prereq: EECS 490 or equivalent.

EECS 550. Neuromechanics Seminar (0)
(See EBME 550.) Cross-listed as EBME 550.

EECS 583. Implementation of Non-linear 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, on-line planning, high-speed control, man/machine interfaces, robot learning, sensory processing for real-time control. Primarily a project-based lab course in which students design real-time software executing on multi-processors to control an industrial robot. Prereq: EECS 489.

EECS 591. Intelligent Systems II (3)

EECS 600. Special Topics (1-18)

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 (1-18)

EECS 602. Advanced Projects Laboratory (1-18)

EECS 620. Special Topics (1-18)

EECS 621. Special Projects (1-18)

EECS 649. Project M.S. (1-9)

EECS 651. Thesis M.S. (1-18)

EECS 701. Dissertation Ph.D. (1-18)

EECS 702. Appointed Dissertation Fellow (9)

BACHELOR OF SCIENCE IN ENGINEERING DEGREE
MAJOR IN ELECTRICAL ENGINEERING

Freshman Year

Class-Lab-Credit Hours

Fall

HM/SS elective

(3-0-3)

CHEM 111 Chemistry I

(4-0-4)

MATH 121 Calculus I

(4-0-4)

ENGR 131 Elementary Computer Programming

(3-0-3)

ENGL 150 Expository Writing

(3-0-3)

PHED 101 Physical Education

(0-3-0)

Total

(17-3-17)

Spring

Open elective a

(3-0-3)

ENGR 145 Chemistry of Materials

(4-0-4)

PHYS 121 Physics I: Mechanics b

(4-0-4)

MATH 122 Calculus II

(4-0-4)

PHED 102 Physical Education

(0-3-0)

Total

(15-3-15)

Sophomore Year

Fall

PHYS 122 Physics II Electricity & Magnetism

(4-0-4)

MATH 223 Calculus III

(3-0-3)

ENGR 210 Circuits and Instrumentation

(3-2-4)

EECS 281 Computer Organization, Logic Design

(3-2-4)

Total

(13-4-15)

Spring

HM/SS Sequence I

(3-0-3)

ENGR 225 Thermo, Fluids, Transport

(4-0-4)

MATH 224 Differential Equations

(3-0-3)

EECS 245 Electronic Circuits

(3-2-4)

EECS 309 Electromagnetic Fields I

(3-0-3)

Total

(16-2-17)

Junior Year

Class-Lab-Credit Hours

Fall

HM/SS Sequence II

(3-0-3)

ENGR 200 Statics & Strength of Materials

(3-0-3)

EECS 246 Signals & Systems

(3-2-4)

STAT 332 Statistics of Signal Processingc

(3-0-3)

Approved Tech. Elective d

(3-0-3)

Total

(15-2-16)

Spring

HM/SS Sequence III

(3-0-3)

EECS 321 Semiconductor Elect. Devices

(3-2-4)

Applied Statistics Req.e

(3-0-3)

Approved technical elective d

(3-0-3)

Approved technical elective d

(3-0-3)

Total

(15-2-16)

Senior Year

Fall

EECS 398L Senior Project Lab I f, g

(0-8-4)

ENGL 398N Professional Communications

(3-0-3)

Open Elective

(3-0-3)

Approved technical elective d

(3-0-3)

Approved technical elective d

(3-0-3)

Total

(12-8-16)

Spring

HM/SS elective

(3-0-3)

HM/SS elective

(3-0-3)

EECS 399L Senior Project Lab II

(0-8-4)

Open elective

(3-0-3)

Approved technical elective d

(3-0-3)

Total

(12-8-16)

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 non-EEAP 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. Co-op students may obtain design credit for one semester of Senior Project Lab if their co-op assignment included significant design responsibility; however, the student is still responsible for such course obligations as reports, presentations and ethics assignments. Design credit and fulfillment of remaining course responsibilities are arranged through the 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

Class-Lab-Credit Hours

Fall

Open elective or HM/SS elective a

(3-0-3)

CHEM 111 Chemistry I

(4-0-4)

MATH 121 Calculus I

(4-0-4)

ENGR 131 Elementary Computer Programming

(3-0-3)

ENGL 150 Expository Writing

(3-0-3)

PHED 101 Physical Education

(0-3-0)

Total

(17-3-17)

Spring

HM/SS elective or open elective a

(3-0-3)

ENGR 145 Chemistry of Materials

(4-0-4)

PHYS 121 Physics I: Mechanics

(4-0-4)

MATH 122 Calculus II

(4-0-4)

PHED 102 Physical Education

(0-3-0)

Total

(15-3-15)

Sophomore Year

Fall

HM/SS Sequence I

(3-0-3)

PHYS 122 Physics II: Electricity & Magnetism

(4-0-4)

MATH 223 Calculus III

(3-0-3)

ENGR 200 Statics & Strength of Materials

(3-0-3)

EECS 233 Introduction to Data Structures

(3-2-4)

Total

(16-2-17)

Spring

HM/SS Sequence II

(3-0-3)

MATH 224 Differential Equations

(3-0-3)

ENGR 210 Circuits and Instrumentation

(3-2-4)

Technical Elective b

(3-0-3)

EECS 281 Comp. Organization Logic Design

(3-2-4)

Total

(15-4-17)

Junior Year

Class-Lab-Credit Hours

Fall

HM/SS Sequence III

(3-0-3)

MATH 304 Discrete Mathematics

(3-0-3)

EECS 337 Systems Programming

(3-2-4)

ENGR 225 Thermodynamics, Fluids, Transport

(4-0-4)

Technical elective b

(3-0-3)

Total

(16-2-17)

Spring

ENGL 398N Prof. Communications

(3-0-3)

EECS 301 Digital Laboratory

(0-4-2)

EECS 314 Computer Architecture

(3-0-3)

EECS 315 Digital Systems Design

(3-2-4)

EECS 338 Intro to Operating Systems d

(3-2-4)

or

 

Technical elective d

(3-0-3)

Total

(12-8-16) or (12-6-15)

Senior Year

Fall

HM/SS elective

(3-0-3)

EECS 318 VLSI/CAD d

(3-2-4)

or

 

Technical elective c

(3-0-3)

Technical elective b

(3-0-3)

Statistics elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-2-16) or (15-0-15)

Spring

HM/SS elective

(3-0-3)

EECS 399M Comp. Eng. Design Project

(0-6-3)

Technical elective b

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(12-6-15)

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 non-EECS 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

Class-Lab-Credit Hours

Fall

Open elective or HM/SS elective

(3-0-3)

CHEM 111 Chemistry I

(4-0-4)

MATH 121 Calculus I

(4-0-4)

ENGR 131 Elementary Computer Programming

(3-0-3)

ENGL 150 Expository Writing

(3-0-3)

PHED 101 Physical Education

(0-3-0)

Total

(17-3-17)

Spring

HM/SS elective or open elective

(3-0-3)

ENGR 145 Chemistry of Materials

(4-0-4)

PHYS 121 Physics I: Mechanics

(4-0-4)

MATH 122 Calculus II

(4-0-4)

PHED 102 Physical Education

(0-3-0)

Total

(15-3-15)

Sophomore Year

Fall

HM/SS Sequence I

(3-0-3)

PHYS 122 Physics II Electricity & Magnetism

(4-0-4)

MATH 223 Calculus III

(3-0-3)

Technical elective b

(3-0-3)

ECES 281 Comp. Organization Logic Design

(3-2-4)

Total

(16-2-17)

Spring

HM/SS Sequence II

(3-0-3)

MATH 224 Differential Equations

(3-0-3)

Technical Elective c

(3-0-3)

MATH 304 Discrete Mathematics

(3-0-3)

EECS 233 Intro Data Structures

(3-2-4)

Total

(15-2-16)

Junior Year

Class-Lab-Credit Hours

Fall

HM/SS Sequence III

(3-0-3)

EECS 340 Algorithms and Data Structures

(3-0-3)

EECS 337 Systems Programming

(3-2-4)

Statistics elective c

(3-0-3)

Technical elective c

(3-0-3)

Total

(15-2-16)

Spring

HM/SS elective

(3-0-3)

EECS 345 Programming Language Concepts

(3-0-3)

EECS 343 Theoretical Computer Science

(3-0-3)

EECS 314 Computer Architecture

(3-0-3)

EECS 338 Intro to Operating Systems

(3-2-4)

Total

(15-2-16)

Senior Year

Fall

ENGL 398N Professional Communication

(3-0-3)

EECS 398M Software Engineering

(3-0-3)

Technical elective e

(3-0-3)

Open elective

(3-0-3)

Open elective d

(3-0-3)

Total

(15-0-15)

Spring

HM/SS elective

(3-0-3)

EECS 341 Intro. to Database Systems

(3-0-3)

EECS 391 Intro. to Artificial Intelligence

(3-0-3)

Technical elective e

(3-0-3)

Open elective

(3-0-3)

Total

(15-0-15)

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

Class-Lab-Credit Hours

Fall

Open elective

(3-0-3)

MATH 125 Mathematics I

(4-0-4)

ENGR 131 Elementary Computer Programming

(3-0-3)

GER course

(3-0-3)

GER course

(3-0-3)

PHED 101 Physical Education

(0-3-0)

Total

(16-3-16)

Spring

ENGL 150 Expository Writing

(3-0-3)

MATH 126 Mathematics II

(4-0-4)

GER course

(3-0-3)

GER course

(3-0-3)

Open elective

(3-0-3)

PHED 102 Physical Education

(0-3-0)

Total

(16-3-16)

Sophomore Year

Fall

EECS 281 Comp. Organization Logic Design (3-2-4)

GER course

(3-0-3)

Logic Design technical elective a

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-2-16)

Spring

GER course

(3-0-3)

MATH 304 Discrete Mathematics

(3-0-3)

EECS 233 Intro Data Structures

(3-2-4)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-2-16)

Junior Year

Class-Lab-Credit Hours

Fall

EECS 337 Systems Programming

(3-2-4)

GER course

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(12-2-13)

Spring

EECS 338 Intro to Operating Systems

(3-2-4)

EECS 341 Intro to Database Systems

(3-0-3)

EECS 314 Computer Architecture

(3-0-3)

Open elective

(3-0-3)

Total

(12-2-13)

Senior Year

Fall

EECS 340 Algorithms and Data Structures

(3-0-3)

Technical elective a

(3-0-3)

GER course

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-0-15)

Spring

Technical elective a

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-0-15)

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

Class-Lab-Credit Hours

Fall

HM/SS elective

(3-0-3)

CHEM 111 Chemistry I

(4-0-4)

MATH 121 Calculus I

(4-0-4)

ENGR 131 Elementary Computer Programming

(3-0-3)

ENGL 150 Expository Writing

(3-0-3)

PHED 101 Physical Education

(0-3-0)

Total

(17-3-17)

Spring

Open elective a

(3-0-3)

ENGR 145 Chemistry of Materials

(4-0-4)

PHYS 121 Physics I: Mechanics b

(4-0-4)

MATH 122 Calculus II

(4-0-4)

PHED 102 Physical Education

(0-3-0)

Total

(15-3-15)

Sophomore Year

Fall

PHYS 122 Physics II: Electricity & Magnetism b

(4-0-4)

MATH 223 Calculus III

(3-0-3)

ENGR 210 Circuits and Instrumentation

(3-2-4)

EECS 281 Computer Organization

(3-2-4)

Total

(13-4-15)

Spring

HM/SS Sequence I

(3-0-3)

ENGR 225 Fluid and Thermodynamics

(4-0-4)

MATH 224 Differential Equations

(3-0-3)

STAT xxx Statistical Methods Course c

(3-0-3)

ENGR 200 Statics & Strength of Materials

(3-0-3)

Total

(16-0-16)

Junior Year

Class-Lab-Credit Hours

Fall

HM/SS Sequence II

(3-0-3)

EECS 246 Signals and Systems

(3-2-4)

EECS 342 Introduction to Global Systems

(3-0-3)

EECS 324 Simulation Methods

(3-0-3)

Approved technical elective e

(3-0-3)

Total

(15-2-16)

Spring

HM/SS Sequence III

(3-0-3)

EECS 304 Control Engineering I

(3-0-3)

EECS 305 Control Lab I

(0-2-1)

EECS 346 Engineering Optimization

(3-0-3)

Approved technical elective

(3-0-3)

Open elective

(3-0-3)

Total

(15-2-16)

Senior Year

Fall

HM/SS elective

(3-0-3)

EECS 398N Senior Project Lab d

(0-8-4)

ENGL 398N Professional Communications

(3-0-3)

EECS 352 Eng. Econ. & Dec. Analysis

(3-0-3)

Approved technical elective e

(3-0-3)

Total

12-8-16

Spring

HM/SS elective

(3-0-3)

EECS 399N Engineering Projects Lab II

(0-8-4)

Approved Technical Elective e

(3-0-3)

Approved Technical Elective e

(3-0-3)

Approved Technical Elective e

(3-0-3)

Total

(12-8-16)

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. Co-op students may obtain credit for the first semester of Senior Project Lab if their co-op assignment includes significant design responsibility. This credit can be obtained by submitting a suitable written report and making an oral presentation on the co-op 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 216-368-6482; Fax 216-368-6939
James D. Cawley, Associate Dean
e-mail 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, low-cost 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 design-oriented 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 major-minor 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 pre-medicine, pre-dentistry, pre-nursing, or pre-biomedical 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 decision-making 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

Class-Lab-Credit Hours

Fall

Open elective or Humanities/Social Science a

(3-0-3)

CHEM 111 Principles of Chemistry for Engineers

(4-0-4)

ENGR 131 Elementary Computer Programming

(2-2-3)

ENGL 150 Expository Writing

(3-0-3)

MATH 121 Calculus for Science and Engineering I

(4-0-4)

PHED 101 Physical Education Activities

(0-3-0)

Total

(16-5-17)

Spring

Humanities/Social Science or open elective a

(3-0-3)

ENGR 145 Chemistry of Materials

(4-0-4)

MATH 122 Calculus for Science and Engineering II

(4-0-4)

PHED 102 Physical Education Activities

(0-3-0)

PHYS 121 General Physics I

(4-0-4)

Total

(15-3-15)

Sophomore Year

Fall

Humanities or Social Science Sequence I

(3-0-3)

ENGR 200 Statics and Strength of Materials

(3-0-3)

MATH 223 Calculus for Science and Engineering III

(3-0-3)

ECES 251 Numerical Methods

(2-2-3)

PHYS 122 General Physics II

(4-0-4)

Total

(15-2-16)

Spring

Humanities or Social Science Sequence II

(3-0-3)

ENGR 225 Thermodynamics, Fluid Mechanics, Heat and Mass Transfer

(4-0-4)

ENGR 210 Introduction to Circuits and Instrumentation

(3-2-4)

MATH 224 Elementary Differential Equations

(3-0-3)

PHYS 221 General Physics III, Modern Physics

(3-0-3)

Total

(16-2-17)

Junior Year

Class-Lab-Credit Hours

Fall

Humanities or Social Science Sequence III

(3-0-3)

Major Concentration Course

(3-0-3)

Major Concentration Course

(3-0-3)

Minor Concentration Course

(3-0-3)

Engineering elective

(3-0-3)

Open elective

(3-0-3)

Total

(18-0-18)

Spring

ENGL 398N Professional Communications

(3-0-3)

Major Concentration Course

(3-0-3)

Major Concentration Course

(3-0-3)

Minor Concentration Course

(3-0-3)

Engineering elective

(3-0-3)

Total

(15-0-15)

Senior Year

Fall

Humanities or Social Science elective

(3-0-3)

Exxx 398 Engineering Senior Project

(0-6-3)

Major Concentration Course

(3-0-3)

Minor Concentration Course

(3-0-3)

Minor Concentration Course

(3-0-3)

Total

(12-6-15)

Spring

Humanities or Social Science elective

(3-0-3)

Major Concentration Course

(3-0-3)

Major Concentration Course

(3-0-3)

Minor Concentration Course

(3-0-3)

Open elective

(3-0-3)

Total

(15-0-15)

Hours required for graduation: 128

a. One of these courses must be a humanities/social science course.