1 COURSE DESCRIPTIONS Revised November 18, 1994 181 (CS 181). Introduction to Computer Systems. Prerequisite: None. I and II. (4). A student can receive credit for only one of, EECS 181, Engr. 103 or Engr. 104. Introduces students to computers. Focuses on software, hardware, and social impact of computers. Elementary programming concepts, software packages and applications, word processing, data communications, information management, input-output, data entry, computer hardware components and storage devices, microcomputers, and ethics in computing. Programming assignments using a personal computer. Term paper required. 183 (CS 183). Elementary Programming Concepts Prerequisite: None. I, II and IIIa (4). Not intended for CS or Computer Engineering majors. Introduction to a high level programming language, top down analysis, and structured programming. Basic searching and sorting techniques. No previous experience in computers or programming is assumed. Students will write and debug several computer programs. 216 (CS 216). Circuit Analysis. Prerequisite: Preceded or accompanied by Math 215. I and II. (4). Resistive circuit elements; mesh and node analysis; network theorems; network graphs and independence; energy storage elements; one- and two-time-constant circuits; phasors and a.c. steady-state analysis; complex frequency and network functions; frequency response and resonance. Lecture and laboratory. 250 (Naval Sci. 202). Electronic Sensing System. Prerequisite: preceded or accompanied by Physics 240. II. (3). Introduction to properties and behavior of electromagnetic energy as it pertains to naval applications of communication, radar, and electro-optics. Additional topics include sound navigation and ranging (SONAR), tracking and guidance systems, and computer controlled systems. Several laboratory demonstrations will illustrate applications of the theories and concepts learned in the classroom. 270 (CS 270). Introduction to Logic Design. Prerequisite: None. I, II, IIIa. (4). Binary and non-binary systems, Boolean algebra digital design techniques, logic gates, logic minimization, standard combinational circuits, sequential circuits, flip-flops, synthesis of synchronous sequential circuits, PLA's, ROM's, RAM's, arithmetic circuits, computer-aided design. Laboratory includes hardware design and CAD experiments. 280 (CS 280). Programming and Introductory Data Structures. Prerequisite: Math 115 & (EECS 183 or EECS 284 or Eng. 104 or passing a placement test in PASCAL). I and II. (4). Allow two credits for students who have already taken EECS 283. Techniques and algorithm development and effective programming, top-down analysis, structured programming, testing, and program correctness. Program language syntax and static and run-time semantics. Scope, procedure instantiation, recursion, abstract data types, and parameter passing methods. Structured data types, pointers, linked data structures, stacks, queues, arrays, records, and trees. 284 (CS 284). Introduction to a Programming Language or System Prerequisite: Some programming knowledge. I and II. (1). A 14-week mini course covering the fundamentals of a high level programming language or a system such as UNIX. Programming problems will be assigned. Specific languages or systems to be offered will be announced in advance. Credit will not be given for the C mini course to students who have taken EECS 280 303 (CS 303). Discrete Structures Prerequisite: Math. 115. I and II. (4). Fundamental concepts of algebra; partially ordered sets, lattices, Boolean algebras, semi-groups, rings, polynomial rings. Graphical representation of algebraic systems; graphs, directed graphs. Application of these concepts to various areas of computer engineering. . 314. Circuit Analysis and Electronics. Prerequisite: Math. 216 and Physics 240. I, II and IIIa. (3). Not open to electrical engineering or engineering science students. A survey of electrical and electronic circuits for non-electrical engineering students. Formulation of circuit equations; equivalent circuits; frequency response ideas; steady-state and transient response; introduction to amplifiers; operational amplifiers; survey of electronic devices and circuits. Use of computer simulations for analysis of more advanced circuits. 315. Circuit Analysis and Electronics Laboratory. Prerequisite: preceded or accompanied by EECS 314. I and II. (1). Not open to electrical engineering or engineering science students. Lecture and laboratory designed to illustrate the principles developed in EECS 314 with application to other engineering disciplines. AC and DC measurements; steady-state and transient response; amplifiers and filters. Transducers for the measurement of strain, position and velocity, temperature. Design of a simple thermostat and electric motor speed control circuit. 316. Signals and Systems. Prerequisite: EECS 216. I , II and IIIa. (4). Basic concepts in linear system theory, and their use in analyzing signals and linear systems. Topics include: superposition; convolution and impulse response; Fourier series and transform; Laplace transforms; transfer functions; Bode plots and stability. Systems concepts. Discrete Fourier series and Fourier transform methods. Introduction to z-transforms. 317. Solid-State Devices and Digital Electronics. Prerequisite: EECS 216 and EECS 270. I, II and IIIa.. (4). Circuit models for diodes, bipolar-junction and field-effect transistors; piecewise linear and nonlinear analysis; logic circuits; memory circuits (flip-flops, RAM, ROM); computer analysis of electronic circuits. 318. Analog Electronics. Prerequisite: EECS 316 and EECS 317. I, II. (4). Operation and small-signal models of diodes, junction and field-effect transistors; basic single-stage and multi-stage amplifiers: gain, biasing, and frequency response; feedback; op-amp circuits: amplifiers, rectifiers, oscillators, filters. Design problems. Lectures and laboratory. 320. Introduction to Semiconductor Device Theory. Prerequisite: Physics 242, and either EECS 216 or EECS 314. I, II and IIIa. (4). Introduction to semiconductors in terms of atomic bonding and electron energy bands. Equilibrium statistics of electrons and holes. Carrier dynamics; continuity, drift and diffusion currents, generation and recombination processes. Introduction to: PN junctions, metal-semiconductor junctions, bipolar junction transistors, junction and insulated-gate field-effect transistors. 331 Electromagnetics I. Prerequisite: Physics 240 and Math. 216. I and II. (4). Gauss's law and the static electric field; boundary value problems in electrostatics. Dielectric and magnetic media. Magnetostatics; Faraday's law and applications. Maxwell's equations; wave equation; plane waves. 332. Electromagnetics II. Prerequisite: EECS 331. I and II. (3). Theory and applications of electromagnetic waves; reflection refraction, and attenuation in various media. Antennas and radiating systems. Introduction to radio and optical transmission including waveguides, striplines, optical fibers, and the earth's atmosphere. 359. Measurements and Instrumentation. Prerequisite: EECS 316. I. (3). Measurements of circuit parameters, electric and magnetic fields, characteristics of discrete and integrated devices. Basic concepts of modern instrumentation. Two lectures and laboratory. 360. Dynamic Systems and Modeling. Prerequisite: EECS 316. I and II. (3). Introduction to the fundamentals of modeling and analysis of real world systems using mathematical techniques. Examples drawn from engineering, biology, economics, medicine, politics and sociology to illustrate linear, nonlinear, differential and/or difference equations, lumped parameter and distributed parameter models, state space and input-output systems, discrete-event systems. Basic systems concepts: superposition, time-invariance, causality, stability. Simulation on modern CAE packages. Group project. 361. Automotive Electronic Systems. Prerequisite: EECS 316 or ME/AM 360. II, even years. (3). Theory and practice of electronic systems on automobiles. Detailed qualitative, quantitative, and performance analyses are made of automotive electronic systems including: digital engine/drivetrain control, instrumentation, vehicle multiplexing, diagnostics, suspension, steering antilock braking/traction control, communication and safety subsystems. 370 (CS 370). Introduction to Computer Organization. Prerequisite: EECS 270 and EECS280. I and II. (4). Computer organization will be presented as a hierarchy of virtual machines representing the different abstractions from which computers can be viewed. These include the logic level, microprogramming level, and assembly language level. Lab experiments will explore the design of a microprogrammed computer. . 373. Design of Microprocessor Based Systems. Prerequisite: EECS 270 and junior standing. I, II. (3). Principles of hardware and software microcomputer interfacing; digital logic design and implementation. Experiments with specially designed laboratory facilities. Introduction to digital development equipment and logic analyzers. Assembly language programming. Lecture and laboratory. 380 (CS 380). Data Structures and Algorithms. Prerequisite: EECS 280 and EECS 303 I and II. (4). Abstract data types. Recurrence relations and recursions. Advanced data structures: sparse matrices, generalized lists, strings. Tree-searching algorithms, graph algorithms general searching and sorting. Dynamic storage management. Analysis of algorithms O-notation. Complexity. Top-down program development: design, implementation, testing modularity. Several programming assignments. 381 (CS 381). Systems Programming. Prerequisite: EECS 380. I and II. (4). Design and implementation of basic systems programming tools and infrastructure. Topics to be covered include assembly language programming, assemblers, macro processors, linkers and loaders, and I/O drivers, etc., and programming projects will involve the design and implementation of such systems. Students will also write some programs in assembly language. 398. Special Topics. Prerequisite: Permission of instructor. (1-4). Topics of current interest selected by the faculty. Lecture, seminar or laboratory. 400 (Math. 419). Linear Spaces and Matrix Theory. Prerequisite: 4 semesters of college mathematics beyond Math. 110. I and II. (3). Not open to students with credit for Math 417 or 513. Finite dimensional linear spaces and matrix representations of linear transformations. Bases, subspaces, determinants, eigenvectors, and canonical forms. Structure of solutions of systems of linear equations. Applications to differential and difference equations. The course provides more depth and content than Math. 417. Math. 513 is the proper election for students contemplating research in mathematics. 401 (Aero. Eng. 452). Probabilistic Methods in Engineering. Prerequisite: Junior standing. I, II and IIIa. (3). Basic concepts of probability theory. Random variables: discrete, continuous, and conditional probability distributions; averages; independence. Introduction to discrete and continuous random processes: wide sense stationarity, correlation, spectral density. 411. Microwave Circuits I. Prerequisite: EECS 332. I. (3). Transmission-line theory, microstrip and coplanar lines S-parameters, signal-flow graphs, matching networks, directional couplers, low-pass and band-pass filters, diode detectors. Design, fabrication and measurements (1-10GHz) of microwave integrated-circuits using CAD tools and network analyzers. 413. Monolithic Amplifier Circuits. Prerequisite: EECS 318 and EECS 320. I. (4). Analysis and design of BJT and MOS multi-transistor amplifiers. Feedback theory and application to feedback amplifiers. Stability considerations, pole-zero cancellation, root locus techniques in feedback amplifiers. Detailed analysis and design of BJT and MOS integrated operational amplifiers. Lectures and laboratory. 415 (Mech Eng. 424). Engineering Acoustics. Prerequisite: Math 216 and Physics 240. II. (3). Vibrating systems; acoustic wave equation; plane and spherical waves in fluid media; reflection and transmission at interfaces; propagation in lossy media; radiation and reception of acoustic waves; pipes, cavities, and waveguides; resonators and filters; noise; selected topics in physiological, environmental and architectural acoustics. 417 (Bioeng. 417). Electrical Biophysics. Prerequisite: EECS 216 or 314, and p/a by EECS 316. I. (3). Electrical biophysics of muscle, nerve, and synapse; electrical conduction in excitable tissue; models for nerve, muscle, and sensory receptors, including the Hodgkin Huxley equations; biopotential mapping, cardiac electrophysiology, and biological noise. 421. Properties of Transistors. Prerequisite: EECS 320 and EECS 331. I. (3). DC, small and large signal AC, switching and power-limiting characteristics, and derivation of equivalent circuit models of: PN junctions, metal-semiconductor and metal-insulator semiconductor diodes, bipolar junction transistors, junction and insulated-gate field-effect transistors, and thyristors. 422. Electronic Properties of Semiconductor Materials. Prerequisite: EECS 320. I. (3). Free electron theory for transport, crystal structure and X-ray diffraction, Bloch theorem, band structure and effective mass; donors and acceptors and carrier statistics; phonons; transport in electric field; heterostructure concepts. 423. Solid-State Devices Laboratory. Prerequisite: EECS 320. I. (3). Semiconductor material and device fabrication and evaluation: diodes, bipolar and field-effect transistors, passive components. Semiconductor processing techniques: oxidation, diffusion, deposition, etching, photolithography. Lecture and laboratory. 424. Integrated Circuit Process Technology and Process Integration. Prerequisite: EECS 320 and EECS 317. II. (3). Integrated circuit processing techniques: oxidation, diffusion, ion implantation, epitaxy, deposition, etching, process integration for silicon CMOS and bipolar technologies. Relationship between processing, device design, and device performance. 425. Integrated Circuits Laboratory. Prerequisite: EECS 320 and EECS 427. II. (3). Integrated circuit fabrication; mask design, photographic reduction; photoresist application, exposure, development, and etching, oxidation; diffusion; metal film deposition by evaporation and sputtering; die bonding, wire bonding, and encapsulation; testing of completed integrated circuits. 426. (CS 426). Fundamentals of Electronic Computer-Aided Design. Prerequisite: EECS 280 and Senior level standing. I. (3) Course will address, in roughly equal proportion: (1) modeling, simulation, and verification at various abstraction levels; (2) behavioral and logic synthesis; and (3) placement and routing. Emphasis will be on understanding the underlying techniques and algorithms of these various CAD areas rather than on the use of specific CAD tools. 427. VLSI Design I. Prerequisite: EECS 317. I and II. (4). Design techniques for rapid implementations of very large scale integrated (VLSI) circuits, MOS technology and logic. Structured design. Design rules, layout procedures. Design aids: layout, design rule checking, logic and circuit simulation. Timing. Testability. Architectures for VLSI. Projects to develop and lay out circuits. 429. Semiconductor Optoelectronic Devices. Prerequisite: EECS 320. II. (4). Materials for optoelectronics, optical processes in semiconductors, absorption and radiation, transition rates and carrier lifetime. Principles of LEDs, lasers, photodetectors, modulators and solar cells. Optoelectronic integrated circuits. Designs, demonstrations and projects related to optoelectronic device phenomena. 430. Radiowave Propagation and Link Design. Prerequisite: EECS 332. II. (3). Fundamentals of electromagnetic wave propagation in the ionosphere, the troposphere, and near the Earth. Student teams will develop practical radio link designs and demonstrate critical technologies. Simple antennas, noise, diffraction, refraction, absorption, multi-path interference, and scattering are studied. 431. Fields and Optics Laboratory. Prerequisite: Preceded or accompanied by EECS 332. I. (2). Experiments and lectures to demonstrate the behavior and practical aspects of electromagnetic fields at microwave and optical frequencies. Microwave experiments involving transmission lines, waveguides, antennas, sources, and detectors. Fiber optics and lasers. 432 (Bioeng. 432). Fundamentals of Ultrasonics with Medical Applications. Prerequisite: EECS 331. II. (3). Basic principles; waves, propagation, impedance, reflection, transmission, attenuation, power levels. Generation of ultrasonic waves; transducers, focusing, Fraunhofer and Fresnel zones. Instrumentation; display methods, Doppler techniques, signal processing. Medical applications will be emphasized. 433. Principles of Optics. Prerequisite: Physics 240 & Math 216. I. (3). A student cannot receive credit for both Phys. 402 and EECS 433. Basic principles of optics: generation and propagation of light; interaction of light and matter; geometrical optics, ray tracing and introduction to aberration theory; superposition of waves, coherence and interference; Fresnel and Fraunhofer diffraction. Special topics such as lasers and holography. 434. Principles of Photonics. Prerequisite: EECS 332. II. (3). Wave propagation in crystals; propagation of Gaussian beams; optical resonators; optical waveguides; interaction of radiation and atomic systems; theory of laser operation; the modulation of optical radiation; the detection of optical radiation; noise in optical detection and generation; nonlinear optical phenomena. 435. Fourier Optics. Prerequisite: EECS 316, preceded or accompanied by EECS 433. I, odd years. (3). Basic physical optics treated from the viewpoint of Fourier analysis. Fourier-transform relations in optical systems. Theory of image formation and Fourier transformation by lenses. Frequency response of diffraction-limited and aberrated imaging systems. Coherent and incoherent light. Comparison of imagery with coherent and with incoherent light. Resolution limitations. Optical information processing, including spatial matched filtering. 436. Optical Radiation and Detector Technology. Prerequisite: Physics 240 & Math 216. I, odd yrs. (3). Theory and instrumentation for sensing and measuring visible and infrared radiation. Topics include: blackbody radiation; radiometric concepts; radiative transfer through the atmosphere; basic optics of semiconductors; photon detectors (photoconductive, photovoltaic, and photoemissive); thermal detectors; detector noise sources and figures of merit; imaging detectors (pyroelectric arrays and CCD's); LED's and diode laser sources. 437. Coherent Optics Laboratory. Prerequisite: EECS 433. II. (2). Experimental aspects and techniques of coherent optics. Lasers, alignment techniques for optical systems, characteristics of photographic recording materials, spatial filtering, coherent imaging, interferometry and coherence measurement of light sources, holography. Lecture and laboratory. 442 (CS 442). Computer Vision. Prerequisite: EECS 303 and 380. I. (3). Computational methods for the recovery, representation, and application of visual information. Topics from image formation, binary images, digital geometry, similarity and dissimilarity detection, matching, curve and surface fitting, constraint propagation and relaxation labeling, stereo, shading texture, object representation and recognition, dynamic scene analysis, and knowledge based techniques. Hardware/software techniques. 450 (NR 543). Imaging Radar as a Remote Sensor. Prerequisite: NR 541 or senior standing in Elec. or Comp. Eng. II. (3). Descriptive treatment of imaging radar systems, theoretical and operational performance and limitations, reflection from terrestrial and vegetal surfaces, interpretation of imagery; application to topics of student's interest (e.g., geology, oceanography, forestry). Special topics include holographic radar, passive microwave systems, synthetic aperture radar, and imaging sonar. 451. Digital Signal Processing and Analysis. Prerequisite: EECS 316. I, II, IIIa. (4). Introduction to digital signal processing of continuous and discrete signals. The family of Fourier transforms including the Discrete Fourier Transform (DFT). Development of the Fast Fourier Transform (FFT). Signal sampling and reconstruction. Design and analysis of digital filters. Correlation and spectral estimation. Laboratory experiences exercise and illustrate the concepts presented. 452. Digital Signal Processing Design Laboratory. Prerequisite: EECS 316. II. (3). Architectural features of single-chip DSP processors are introduced in lecture. Laboratory exercises using two different state-of-the-art fixed-point processors include sampling, A/D and D/A conversion, digital wave form generators, real-time FIR and IIR filter implementations, and a six-week real-time DSP project of the student's choice. 453. Analog Communication Signals and Systems. Prerequisite: EECS 316. I. (3). Mathematical analysis of the signals and signal processing used in analog communication systems; spectral analysis, signal transmission; amplitude, phase, frequency and pulse modulation; modulation and demodulation techniques; frequency and time multiplexing; analysis of signal to noise ratio; application to radio and television. 455. Digital Communication Signals and Systems. Prerequisite: EECS 316 and 401. II. (3). Digital transmission techniques in data communications, with application to computer and space communications; design and detection of digital signals for low error rate; forward and feedback transmission techniques; matched filters, modems, block and convolutional coding, Viterbi decoding. 458 (Bioeng. 458). Biomedical Instrumentation and Design. Prerequisite: permission of instructor. I and II. (4). Measurement and analysis of biopotentials and biomedical transducer characteristics; electrical safety; applications of FETs, integrated circuits, operational amplifiers for signal processing and computer interfacing; signal analysis and display on the laboratory minicomputer. Lectures and laboratory. 459. Advanced Electronic Instrumentation. Prerequisite: EECS 360 or 359 or 453 or 458. II, odd years. (3). Systematic design of optimum measuring instruments which give maximum confidence in results. Analog and digital signal processing, transducer modeling, A/D and D/A conversion, survey of modern instrumentation components. 460 Fundamentals of Control Systems. Prerequisite: Mech. Eng. 240 and EECS 316 or EECS 360 and senior standing. I, II and IIIa. (3). Concept and importance of control systems. Control system descriptions: state variable and transfer function representations. System performance and design criteria: stability, sensitivity, time response. Concept of feedback. Time response of linear control systems. Use of Hurwitz, root-locus, Nyquist, and Bode methods for analysis and synthesis of linear control systems. 463. Modern Control Systems Design. Prerequisite: EECS 460. II. (3). Introduction to concepts and techniques of modern control in the context of control system design. Topics include: state variable feedback, optimal control, nonlinear control, state estimators, and adaptive control. Both analog and digital control design techniques are presented. Design application is emphasized through use of selected case studies. 467(Mech Eng. 467)(Manu. 467). Robotics: Theory, Design and Application Prerequisite: Mech. Eng. 360 or EECS 360; and senior standing. I and II. (3). Basic concepts underlying the design and application of computer-controlled manipulators: Manipulator geometry, work volume, sensors, feedback control of manipulator linkages, kinematics, trajectory planning, programming, robot system architecture, design and application. Lab experiments cover kinematics, dynamics, trajectory planning, control of manipulators, and motion by fixed robots and mobile robots. 470 (CS 470). Computer Architecture. Prerequisite: EECS 370. I and II. (4). Basic concepts of computer architecture and organization. Computer evolution. Design methodology. Performance evaluation. Elementary queueing models. CPU architecture. Introductions sets. ALU design. Hardwired and microprogrammed control. Nanoprogramming. Memory hierarchies. Virtual memory. Cache design. Input-output architectures. Interrupts and DMA. I/O processors. Parallel processing. Pipelined processors. Multiprocessors. 473 (CS 473). Advanced Digital System Design. Prerequisite: EECS 373, or permission of instructor. II. (3). This course introduces advanced digital system design concepts, such as timing analysis, reliability, and testability. These concepts are then applied to a semester-long design project of the student's choice. The result of this project will be a highly testable, highly reliable digital system. 476 (CS 476). Foundations of Computer Science. Prerequisite: EECS 280 and EECS 303 or equivalent. I and II. (4). An introduction to computation theory: finite automata, regular languages, pushdown automata, context-free languages, Turing machines, recursive languages and functions, and computational complexity. 477 (CS 477). Introduction to Algorithms. Prerequisite: EECS 380. I. (3). Fundamental techniques for designing efficient algorithms and basic mathematical methods for analyzing their performance. Paradigms for algorithm design: divide-and-conquer, greedy methods, graph search techniques, dynamic programming. Design of efficient data structures and analysis of the running time and space requirements of algorithms in the worst and average cases. 478 (CS 478). Switching and Sequential Systems. Prerequisite: EECS 303 and EECS 270 and senior or graduate standing. I and II. (3). An introduction to the theory of switching networks and sequential systems. Switching functions and realizations, threshold logic, fault detection, connectedness and distinguishability, equivalence and minimality, state identification, system decomposition. 481 (CS 481). Software Engineering. Prerequisite: EECS 380. I and II. (4). Pragmatic aspects of the production of software systems, dealing with structuring principles, design methodologies and informal analysis. Emphasis is given to development of large complex software systems. A term project is usually required. 482 (CS 482). Introduction to Operating System. Prerequisite: EECS 370 and EECS 381. I and II. (4). Operating system functions and implementations: multi-tasking; concurrency and synchronization; deadlock; scheduling; resource allocation; real and virtual memory management; input/output; file systems. Students write several substantial programs dealing with concurrency and synchronization in a multitask environment. 483 (CS 483). Compiler Construction. Prerequisite: EECS 370 and 381. I and II. (4). Introduction to compiling techniques including parsing algorithms, semantic processing, and optimization. Students implement a compiler for a substantial programming language using a compiler generating system. 484 (CS 484)(I.&O.E. 484). Database Management Systems. Prerequisite: EECS 380 or I&OE 473. I and II. (3). Concepts and methods in the definition and management of large integrated databases for organizational information systems. Functions and objectives of existing file and data management systems will be considered and methods of analyzing proposals for new data management software will be studied; database administration, database design, and data security problems. 486 (CS 486). Object-Based Software Development. Prerequisite: EECS 380. II. (3). Object-based programming concepts such as data and program abstraction, decomposition of large systems into reusable objects, and inheritance. Programming projects will be done in an object-based language such as Ada. Comparative studies will be made of languages such as C++, Objective C, Eiffel, and Smalltalk that support object-based programming. 487 (CS 487)(I.&O.E. 478). Interactive Computer Graphics. Prerequisite: EECS 380 or I&OE 373, and senior standing. I and II. (3). Graphics devices and fundamentals of operation. Two dimensional and three dimensional transformations. Interactive graphical techniques and applications. Three dimensional graphics, perspective transformation, hidden line elimination. Data structures and languages for graphics. Interactive graphical programming. 489 (CS 489). Computer Networks. Prerequisite: EECS 482. II. (3). Hardware and software architectures employed in building modern computer networks. Emphasis is placed on architectural and design considerations over actual implementation issues. Tradeoffs in network architectures and in understanding what choices are available. Software problems assigned. 492 (CS 492). Introduction to Artificial Intelligence. Prerequisite: EECS 380. I and II. (4). Basic artificial intelligence methods using LISP. Topics covered include search, rule-based systems, logic, constraint satisfaction, and knowledge representation. 493 (CS 493)(I.& O.E. 437). User Interface Design and Analysis. Prerequisite: EECS 481. I. (3). Current theory and design techniques concerning how user interfaces for computer systems should be designed to be easy to learn and use. Focus on cognitive factors, such as the amount of learning required, and the information-processing load imposed on the user, rather than ergonomic factors. 497. Analysis and Design Projects. Prerequisite: Successful completion of at least two-thirds of the credit hours required for the program subjects. A student may elect this course more than once ONLY with the explicit approval of the Chief Program Advisor. I,II and III. (1-4). Professional problem-solving methods developed through intensive group and individual studies. Normally, two or three significant engineering analysis and design projects will be carefully chosen from devices, software tools, and systems. Use of analytic, computer, design, and experimental techniques where applicable. Lecture and laboratory sessions will be arranged. 498. Special Topics. Prerequisite: Permission of instructor. (1-4). Topics of current interest selected by the faculty. Lecture, seminar or laboratory. 499. Directed Study. Prerequisite: Senior standing in EECS. I, II, III, IIIa and IIIb. (1-6). Individual study of selected topics in Electrical Engineering and Computer Science. May include experimental investigation or library research. Primarily for undergraduates. 500(1-2-3). Tutorial Lecture Series in System Science. Prerequisite: None. I, and II. (1). Students are introduced to the frontiers of System Science research. Sections 01, 02, and 03 are devoted, respectively, to Communications, Control, and Signal Processing. The tutorials are delivered by leaders of the respective research fields, invited from academia and industry. The presentations are self-contained and accessible to all graduate students in System Science. 501 (Aero. Eng. 552). Probability and Random Processes. Prerequisite: EECS 401 or Graduate Standing. I and II. (4). Introduction to probability and random processes. Topics include probability axioms, sigma algebras, random vectors, expectation, probability distributions and densities, Poisson and Wiener processes, stationary processes, autocorrelation, spectral density, effects of filtering, linear least-squares estimation, and convergence of random sequences. 502 (Aero. Eng. 553). Stochastic Processes. Prerequisite: EECS 501. II. (3). Correlations and spectra. Quadratic mean calculus, including stochastic integrals and representations, wide-sense stationary processes (filtering, white noise, sampling, time averages, moving averages, autoregression). Renewal and regenerative processes, Markov chains, random walk and run, branching processing, Markov jump processes, uniformization, reversibility, and queueing applications. 505 (I.&O.E.511, Math. 562, Aero. Eng. 577). Continuous Optimization Methods. Prerequisite: Math 217, Math 417 or Math 419. I. (3). Survey of continuous optimization problems. Unconstrained optimization problems: unidirectional search techniques; gradient, conjugate direction, quasi-Newton methods. Introduction to constrained optimization using techniques of unconstrained optimization through penalty transformations, augmented Lagrangians and others. Discussion of computer programs for various algorithms. 506 (CS 506). Computing System Evaluation. Prerequisite: EECS 280 or 283, and 370 and 501. II, odd yrs. (3). Theory and application of analytic methods for evaluating the performance of reliability of computing systems. Measures of performance, reliability, and performability. Reliability evaluation: classification and representation of faults, stochastic process models, coherent systems. Performance evaluation: Markovian queueing models, networks of queues. Unified performance-reliability evaluation. 510. Intelligent Vehicle-Highway Systems Research Topics. Prerequisite: Two IVHS-Certificate courses (may be taken concurrently). II. (3) Topics include driver-highway interactions (traffic modeling, analysis and simulation), driver-vehicle interactions (human factors), vehicle-highway interactions (computer/communications systems architecture), collision prevention, IVHS technologies (in-vehicle electronic sensors, etc.), socioeconomic aspects (user acceptance and liability), and system integration (comprehensive modeling and competitive strategy). 511. Microwave Circuits II Prerequisite: EECS 411. II, odd years. (3). General theory of waveguides; inhomogeneously filled waveguides. Surface waveguides. Circuit theory of waveguiding systems. Passive microwave devices, directional couplers, filters, isolators, circulators. 514. Intelligent Vehicle-Highway Systems Technologies. Prerequisite: Graduate standing, College Calculus and Physics. I. (3). This course covers various technologies relevant to IVHS, including systems architecture, communications, and computers. Intended for students with a variety of backgrounds appropriate for the study of IVHS. Followed by EECS 515, the laboratory portion. 515. Intelligent Vehicle-Highway Technologies Laboratory. Prerequisite: EECS 514. II. (1). This is the laboratory portion of IVHS Technologies. Experience applying the concepts and techniques learned in the lecture part of the course. 516. Medical Imaging Systems. Prerequisite: EECS 451. I. (3). Principles of modern medical imaging systems. For each modality the basic physics is described leading to a systems model of the imager. Fundamental similarities between the imaging equations of different modalities will be stressed. Modalities covered include radiography, x-ray computed tomography (CT), NMR imaging (MRI) and real-time ultrasound. 517. Physical Processes in Plasmas. Prerequisite: EECS 332. II, even years. (3). Plasma physics applied to electrical gas discharges used for materials processing. Gas kinetics; atomic collisions; modeling RF and microwave discharges; ambipolar diffusion; transport coefficients; sheaths; distribution function calculation by Boltzmann equation and Monte Carlo methods; plasmas in dc magnetic fields; analysis of plasma tools; plasma diagnostics. 518 (A.&O.S. 595). Magnetosphere and Solar Wind. Prerequisite: Graduate standing. I, even years. (3). General principles of magnetohydrodynamics; theory of expanding atmosphere; properties of solar winds, interaction of solar wind with the magnetosphere of the Earth and other planets; bow shock and magnetotail, trapped particles, auroras. 519 (Nuc. Eng. 575). Plasma Dynamics and Particle Optics Lab. Prerequisite: Preceded or accompanied by a course in plasmas or physical electronics. II. (3). Experimental techniques for plasma dynamics, electron and ion beam technology, and vacuum technology. Experiments on microwave and probe diagnostics of plasmas, plasma instabilities, vacuum systems, plasma generation, electron and ion beam generation and optics, and other topics of current interest. Lectures given for background material. 520. Theoretical Methods for Solid-State Electronics. Prerequisite: EECS 422. II, odd years. (4). Bandstructure in semiconductors; strain dependence of bandstructure; perturbation approaches to scattering; transport in semiconductors; optical properties of semiconductors. 521. High-Speed Transistors. Prerequisite: EECS 421 or EECS 422. II. (3). Detailed theory of high-speed digital and high-frequency analog transistors. Carrier injection and control mechanisms. Limits to miniaturization of conventional transistor concepts. Novel submicron transistors including MESFET, heterojunction and quasi-ballistic transistor concepts. 522. Analog Integrated Circuits. Prerequisite: EECS 413 . II. (4). Review of integrated circuit fabrication technologies and BJT and MOS transistor models. Detailed analysis and design of analog integrated circuits, including power amplifiers, voltage references, voltage regulators, rectifiers, oscillators, multipliers, mixers, phase detectors, and phase-locked loops. Design projects. Lectures and discussion. 523. Digital Integrated Circuits. Prerequisite: Either EECS 320 and EECS 317, or EECS 424. I. (4). Device technologies for LSI circuits. Approaches to logic implementation, including gate arrays, master-slices, PLAs. Non-volatile semiconductor memory structures, including ROM, PROM, EPROM, and EAROM. Static and dynamic random access memory and microcomputers. Relationship of terminal performance to the design, layout, and fabrication techniques used. Circuit layout and computer simulation. 524. Field-Effect-Transistors and Microwave Monolithic Integrated Circuits Technology. Prerequisite: Graduate standing and EECS 421 and either EECS 525 or EECS 528. II. (3). Physical and electrical properties of III-V materials, epitaxy and ion-implantation, GaAs and InP based devices (MESFET's , HEMT's varactors) and Microwave Monolithic Integrated Circuits (MMIC's). Cleaning, Photolithography, metal and dielectric deposition, wet and dry etching. Device isolation, ohmic and Schottky contacts, dielectrics, passive component technology, interconnects, via holes, dicing and mounting. Study of the above processes by DC characterization. 525. Solid State Microwave Circuits. Prerequisite: EECS 411 and EECS 421. I. (3). General properties and design of nonlinear solid-state microwave networks, including: negative resistance oscillators and amplifiers, frequency convertors and resistive mixers, transistor amplifiers, power combiners, and harmonic generators. 526. High Performance Dynamic Device Models and Circuits. Prerequisite: EECS 413, or both EECS 318 and EECS 320. II. (4). Models for devices (BJTs, FETs, and integrated circuits), with primary emphasis on large-signal dynamic charge-control models. Mathematics and physics fundamentals for measurement concepts and methods. Mathematical and computer analysis and design of high speed dynamic circuits. Dynamic circuit functional blocks, level detection/comparison circuits; sweep/ramp, multivibrator, and logic gate circuits. 527. Computer-Aided Design for VLSI System. Prerequisite: EECS 478. II. (3). Theory of circuit layout partitioning and placement algorithms. Routing algorithms, parallel design automation on shared memory and distributed memory multiprocessors, simulated annealing and other optimization techniques and their applications in CAD, layout transformation and compaction, fault-repair algorithms for RAM's & PLA's hardware synthesis from behavioral modeling, artificial intelligence based CAD. 528. Principles of Microelectronics Process Technology. Prerequisite: EECS 422 and 424. I. (3). Theoretical analysis of the chemistry and physics of process technologies used in microelectronics fabrication. Topics include: semiconductor growth, material characterization, lithography tools, photo-resist models, thin film deposition, chemical etching, plasma etching, electrical contact formation, microstructure processing, and process modeling. 529. Semiconductor Lasers and LEDs. Prerequisite: EECS 429. I. (3). Optical processes in semiconductors, spontaneous emission, absorption gain, stimulated emission. Principles of light emitting diodes, including transient effects, spectral and spatial radiation fields. Principles of semiconducting lasers; gain-current relationships, radiation fields, optical confinement and transient effects. 530 (Appl.Phys. 530). Electromagnetic Theory I. Prerequisite: EECS 332 or Physics 438. I. (3) Maxwell's equations, constitutive relations and boundary conditions. Potentials and the representation of electromagnetic fields. Uniqueness, duality, equivalence, reciprocity and Babinet's theorems. Plane, cylindrical and spherical waves. Waveguides and elementary antennas. The limiting case of electro- and magneto-statics. 531. Antenna Theory and Design. Prerequisite: EECS 332. II. (3). Theory of transmitting and receiving antennas. Reciprocity. Wire antennas: dipoles, loops and traveling-wave antennas. Analysis and synthesis of linear arrays. Phased arrays. Input impedance and method of moments. Mutual impedance. Aperture antennas: slot, Babinet's principle. Microstrip antennas. Horns, reflector and lens antennas. 532. Microwave Remote Sensing I: Radiometry. Prerequisite: EECS 332 and graduate standing. I. (3). Radiative transfer theory: blackbody radiation; microwave radiometry; atmospheric propagation and emission; radiometer receivers; surface and volume scattering and emission; applications to meteorology, oceanography, and hydrology. 533. Microwave Measurements Laboratory. Prerequisite: EECS 332 and graduate standing. II. (3). Advanced topics in microwave measurements: power spectrum and noise measurement, introduction to state-of-the-art microwave test equipment, methods for measuring the dielectric constant of materials, polarimetric radar cross section measurements, radar field antenna pattern measurements, electromagnetic emission measurement (EM compatibility). Followed by a project that will include design, analysis, and construction of a microwave subsystem. 534. Design and Characterization of Microwave Devices and Monolithic Circuits. Prerequisite: EECS 421 or EECS 525 and graduate standing. I. (4). Theory and design of passive and active microwave components and monolithic integrated circuits including: microstrip, lumped inductors and capacitors, GaAs FETs, varactor and mixer diodes, monolithic phase shifters, attenuators, amplifiers and oscillators. Experimental characterization of the above components using network analyzer, spectrum analyzer, power and noise meters. Lecture and laboratory and project. 535. Optical Information Processing. Prerequisite: EECS 300, 433. II, odd years. (4). Theory of image formation with holography; applications of holography; white light interferometry; techniques for optical digital computing; special topics of current research interest. 536. Classical Statistical Optics. Prerequisite: EECS 433 or 434, and EECS 401 or Math 425. II. (3). Applications of random variables to optics; statistical properties of light waves. Coherence theory, spatial and temporal. Information collecting interferometers; stellar, intensity, and speckle. Phase retrieval; imaging through inhomogeneous media; noise processes in imaging and interferometric systems. 537. Integrated and Guided Wave Optics. Prerequisite: EECS 332. I. (3). Theory of guided light wave propagation; planar and channel waveguides; optical fibers. Waveguide excitation and coupling; integrated devices; directional couplers, gratings, filters, and modulators. Materials issues; dispersion and attenuation; aspects of waveguide and device fabrication. Introduction to nonlinear optical phenomena in waveguide structures. 538 (Appl. Phys. 550, Physics 650). Optical Waves in Crystals. Prerequisite: EECS 434. I. (3). Propagation of laser beams: Gaussian wave optics and the ABCD law. Manipulation of light by electrical, acoustical waves: Crystal properties and the dielectric tensor; electro-optic, acousto-optic effects and devices. Introduction to nonlinear optics; harmonic generation, optical rectification, four-wave mixing, self-focusing, and self-phase modulation. 539 (Appl. Phys. 551, Physics 651). Lasers. Prerequisite: EECS 433 or 434. II. (3). Complete study of laser operation: the atom-field interaction; homogeneous and inhomogeneous broadening mechanisms; atomic rate equations; gain, amplification and saturation; laser oscillation; laser resonators, modes, and cavity equations; Gaussian beams; laser dynamics, Q-switching and modelocking. Special topics such as femtosecond lasers and ultra high power lasers. 540 (Appl. Phys. 540). Applied Quantum Mechanics I. Prerequisite: EECS 300 or Math 404, Phys. 242. I. (3). Introduction to nonrelativistic quantum mechanics. Summary of classical mechanics; one dimensional quantum problems including the quantum wells, WKB approximation, tunneling and the harmonic oscillator; introduction to angular momentum; the hydrogen atom; molecular orbitals; the rigid rotator and diatomic molecules; spin and identical particles, and time independent perturbation theory. 541 (Appl. Phys. 541). Applied Quantum Mechanics II. Prerequisite: EECS 540. II. (3). Advanced theory of angular momentum, time dependent perturbation theory, quantization of fields, the second quantization for bosons & fermions, scattering theory, the density matrix, reservoir theory. 542 (CS 542). Vision Processing. Prerequisite: EECS 442. I. (3). Details of image formation theory, including the consideration of dynamic image sequences. The theoretical frameworks for edge detection, feature extraction, and surface description are presented. The relationship between image formation and object features is examined in detail. Programming required. 543 (CS 543). Knowledge-Based Systems. Prerequisite: EECS 492 and permission of instructor. II, even yrs. (3). Techniques and principles for developing application software based on explicit representation and manipulation of domain knowledge, as applied to computer vision, robotic control, design and manufacturing, diagnostics, autonomous systems, etc. Topics include: identifying and representing knowledge, integrating knowledge-based behavior into complex systems, reasoning, and handling uncertainty and unpredictability. 545 (CS 545). Machine Learning. Prerequisite: EECS 492. II, odd yrs. (3). Survey of recent research on learning in artificial intelligent systems. Topics include learning based on examples, instructions, analogy, discovery, experimentation, observation, problem solving and explanation. The cognitive aspects of learning will also be studied. 546. Ultrafast Optics. Prerequisite: EECS 434. I. (3). Propagation of ultrashort optical pulses in linear and nonlinear media, and through dispersive optical elements. Laser mode-locking and ultrashort pulse generation. Chirped-pulse amplification. Experimental techniques for high time resolution. Ultrafast Optoelectronics. Survey of ultrafast high field interactions. 547 (CS 547). Cognitive Architectures. Prerequisite: EECS 492. II. (3). Survey of architectures of symbolic systems in artificial intelligence. Architectures such as blackboards, production systems, logic systems, reflective systems, discovery systems and learning systems. Also integrated cognitive architectures such as ACT*, SOAR, MRS, and EURISKO. 550. Information Theory. Prerequisite: EECS 501. I. (3). The concepts of source, channel, rate of transmission of information. Entropy and mutual information. The noiseless coding theorem. Noisy channels; the coding theorem for finite state zero memory channels. Channel capacity. Error bounds. Parity check codes. Source encoding. 551. Deterministic Signal Processing. Prerequisite: P/A EECS 451. I. (3). Fundamentals of deterministic signal processing are introduced: Signal representation, linear vector spaces, parametric representations, time-frequency distributions, time-varying models; least-squares filtering; adaptive signal processing. Principles presented in lecture are investigated through open laboratory projects. 552. Fiber Optical Communications. Prerequisite: EECS 332 and EECS 320. II, odd yrs. (3). Fundamentals of lightwave communication systems. Introduction to calculus of variations and geometrical optics; propagation in step-index and graded-index fibers; intra- and inter-modal dispersion; optoelectronic devices: LEDs, lasers, PIN and APD detectors; direct detection and heterodyne receiver structures; noise calculations; and basic statistical communication theory for optical channels. 554. Introduction to Digital Communication and Coding. Prerequisite: EECS 316 and EECS 401. I. (3). Digital transmission of information across discrete and analog channels. Sampling; quantization; noiseless source codes for data compression: Huffman's algorithm and entropy; block and convolutional channel codes for error correction; channel capacity; digital modulation methods: PSK, MSK, FSK, QAM; matched filter receivers. Performance analysis: power, bandwidth, data rate and error probability. 555. Digital Communication Theory. Prerequisite: EECS 501 and 554. I. (3). Theory of digital modulation and coding. Optimum receivers in Gaussian noise. Signal space and decision theory. Signal design. Bandwidth and dimensionality. Fundamental limits in coding and modulation. Capacity and cutoff rate. Block, convolutional and trellis coding. Continuous phase modulation. Filtered channels and intersymbol interference. Equalization. Spread-spectrum. Fading channels. Current topics. 556. Image Processing. Prerequisite: EECS 551 and EECS 501. II. (3). Theory and application of digital image processing. Random field models of images. Sampling, quantization, image compression, enhancement, restoration, segmentation, shape description, reconstruction of pictures from their projections, pattern recognition. Applications include biomedical images, time-varying imagery, robotics, and optics. 557. Communication Networks. Prerequisite: Graduate standing and preceded by EECS 401 or accompanied by EECS 501. I. (3). System architectures. Data link control; error correction, protocol analysis, framing. Message delay: Markov processes, queueing, delays in statistical multiplexing, multiple users with reservations, limited service, priorities. Network delay: Kleinrock independence, reversibility, traffic flows, throughput analysis, Jackson networks. Multiple access networks: ALOHA and splitting protocols, carrier sensing, multi-access reservations. 558. Stochastic Control. Prerequisite: EECS 501 and 560. I. (3). Analysis and optimization of controlled stochastic systems. Models: linear and nonlinear stochastic controlled systems, controlled Markov chains. Optimization of systems described by Markov processes: dynamic programming under perfect and imperfect information, finite and infinite horizons. System identification: off-line, recursive. Stochastic adaptive control: Markov chains, self tuning regulators, bandit problems. 559. Advanced Signal Processing. Prerequisite: EECS 551 and 501. II. (3). Estimators of second order properties of random processes: nonparametric and model-based techniques of spectral estimation, characterization of output statistics for nonlinear systems, time-frequency representations. Performance evaluation using asymptotic techniques and Monte Carlo simulation. Applications include speech processing, signal extrapolation, multidimensional spectral estimation, and beamforming. 560 (Aero. Eng. 550). Linear Systems Theory. Prerequisite: Graduate standing. I. (4). Linear spaces and linear operators. Bases, subspaces, eigenvalues and eigenvectors, canonical forms. Linear differential and difference equations. Mathematical representations: state equations, transfer functions, impulse response, matrix fraction and polynomial descriptions. System-theoretic concepts: causality, controllability, observability, realizations, canonical decomposition, stability. 561 (Aero. Eng. 571). Digital Control Systems. Prerequisite: EECS 460/Aero. Eng. 471/Mech. Eng. 461. I. (3). Sampling and data reconstruction in computer control systems. z-transforms and state equations to describe discrete and mixed data systems. Analysis of digital feedback systems using root locus, Nyquist and Jury tests. Design of digital feedback systems using frequency domain techniques and state space techniques. Nonlinear digital feedback systems. 562 (Aero. Eng. 551). Nonlinear Systems and Control Prerequisite: Graduate standing. II. (3). Introduction to the analysis and design of nonlinear systems and nonlinear control systems. Stability analysis using Liapunov, input-output and asymptotic methods. Design of stabilizing controllers using a variety of methods: linearization, absolute stability theory, vibrational control, sliding modes and feedback linearization. 563 (Aero. Eng. 576). Optimal Control. Prerequisite: EECS 560/Aero. Eng. 550. II. (3). Definition of optimal control problems. Formulation of discrete time optimal control problems as constrained mathematical programming problems. Formulation of continuous time optimal control problems as variational problems. The Pontryagin necessary conditions. Application to a variety of specific optimal control problems from diverse disciplines. Introduction to computational methods in optimal control. 564 (Aero. Eng. 578). Estimation, Filtering, and Detection. Prerequisite: EECS 501. II. (3). Principles of estimation, linear filtering and detection. Estimation: linear and nonlinear minimum mean squared error estimation, and other strategies. Linear filtering: Wiener and Kalman filtering. Detection: simple, composite, binary and multiple hypotheses. Neyman-Pearson and Bayesian approaches. 565 (Aero. Eng. 580). Linear Feedback Control Systems. Prerequisite: EECS 460/Aero. Eng. 471/ Mech. Eng. 461 and EECS 560/Aero. Eng. 550. II. (3). Control design concepts for linear multivariable systems. Review of single variable systems and extensions to multivariable systems. Purpose of feedback. Sensitivity, robustness, and design tradeoffs. Design formulations using both frequency domain and state space descriptions. Pole placement/ observer design. Linear quadratic Gaussian based design methods. Design problems unique to multivariable systems. 567 (Mfg. 567). Introduction to Robotics: Theory and Practice. Prerequisite: EECS 380. II. (3). Introduction to robots considered as electro-mechanical computational systems performing work on the physical world. Data structures representing kinematics and dynamics of rigid body motions and forces and controllers for achieving them. Emphasis on building and programming real robotic systems and on representing the work they are to perform. 569 (Bioeng. 569). Signal Analysis in Biosystems. Prerequisite: EECS 451 or 501 or permission of instructor. II (3). This course will present a variety of techniques for the analysis and understanding of biological signals and biosystems. Both signals of biological nature and images will be discussed. Techniques will include signal representation, time frequency and wavelet analysis, nonlinear filtering (median and rank order) and pattern recognition including neural networks. 570 (CS 570). Parallel Computer Architecture. Prerequisite: EECS 470. I or II. (3). Pipelining and operation overlapping, SIMD and MIMD architectures, numeric and non-numeric applications, VLSI, WSI architectures for parallel computing, performance evaluation. Case studies and term projects. 571 (CS 571). Principles of Real-Time Computing. Prerequisite: EECS 470 and 482 or permission of instructor. II. (3). Principles of real-time computing based on high performance, ultra reliability and environmental interface. Architectures, algorithms, operating systems and applications that deal with time as the most important resource. Real-time scheduling, communications and performance evaluation. 572 (CS 572). Digital Computer Arithmetic. Prerequisite: EECS 470 or 370, and 478. I. (3). Classification and structure of finite number systems and arithmetic including weighted, redundant and signed digit classes of number systems. Theory of modern high-speed computer arithmetic including fast carry logic, multiplier recoding and SRT division. Case studies of general and special purpose arithmetic processors. 574 (CS 574). Theoretical Computer Science I. Prerequisite: EECS 476. I. (4). Fundamentals of the theory of computation and complexity theory. Computability, undecidability, and logic. Relations between complexity classes, NP-completeness, and randomized computation. Applications in selected areas such as cryptography, logic programming, theorem proving, approximation of optimization problems, or parallel computing. 575 (CS 575). Theoretical Computer Science II. Prerequisite: EECS 574. II. (4). Advanced computational complexity, intractability, classical probability and information theory, algorithmic information theory, and special topics such as computational algebra, concurrency, semantics, and verification. 577 (CS 577). Reliable Computing Systems. Prerequisite: EECS 478 and 280. I. (3). An introduction to models and methods used in the analysis and design of reliable hardware systems, software systems and computing systems. Aspects of reliability considered include fault tolerance, fault detection and diagnosis, reconfiguration, design verification and testing, and reliability evaluation. 579 (CS 579). Digital System Testing. Prerequisite: EECS 478. II. (3). Overview of fault-tolerant computing. Fault sources and models. Testing process. Combinational circuit testing. D-Algorithm and PODEM. Sequential circuit testing. Checking experiments. RAM and microprocessor testing. Fault simulation. Design for testability. Testability measures. Self-testing circuits and systems. 581 (CS 581). Software Engineering Tools. Prerequisite: EECS 481 or equiv. programming experience. II. (3). Fundamental areas of software engineering including life cycle paradigms, metrics, and tools. Information hiding architecture, modular languages, design methodologies, incremental programming, and very high level languages. 582 (CS 582). Advanced Operating Systems. Prerequisite: EECS 482. II. (4). Course discusses advanced topics and research issues in operating systems. Topics will be drawn from a variety of operating systems areas such as distributed systems and languages, networking, security, and protection, real-time systems, modelling and analysis, etc. 583 (CS 583). Programming Languages. Prerequisite: EECS 483 and 476. I. (4). Various programming languages are compared to understand general principles. To do this systematically and ignore inessential details, a formal specification method is introduced. Current programming paradigms are examined; their potentials and compatibility are assessed. For example, the question why functional languages become imperative when they "go public" is discussed. 584 (CS 584). Distributed Database Concepts. Prerequisite: EECS 484. II. (3). Database design methodologies, distributed database technology, and developments in heterogeneous systems. Distributed database design and implementation issues such as transaction management, concurrency control, security, and query optimization. Database design includes semantic data modeling, transformation to SQL, normalization theory, physical design and data allocation strategies. 585 (CS 585). Object-Oriented Databases. Prerequisite: EECS 484 and Permission of Instructor. I. (3). Basic principles of object-oriented data models: classes, encapsulation, object identity. Advanced research issues such as schema evolution, views, and authorization. OODB implementation technology: transaction and secondary storage management. Case study of popular OODB systems and their use for advanced scientific and engineering applications. Programming projects generally required. 586 (CS 586). Design and Analysis of Algorithms. Prerequisite: EECS 380. II. (3). Design of algorithms for nonnumeric problems involving sorting, searching, scheduling, graph theory, and geometry. Design techniques such as approximation, branch-and-bound, divide-and- conquer, dynamic programming, greed, and randomization applied to polynomial and NP-hard problems. Analysis of time and space utilization. 587 (CS 587). Parallel Algorithms. Prerequisite: EECS 380 and graduate standing. I. (3) The design and analysis of efficient algorithms for parallel computers. Fundamental problem areas, such as sorting, matrix multiplication, and graph theory, are considered for a variety of parallel architectures. Simulations of one architecture by another. 588 (CS 588)(I.&O.E. 578)(ME 551)(MFG. 588). Geometric Modelling. Prerequisite: EECS 487 (IOE 478) or ME454 or P/I. II. (3). Individual or group study of topics in geometric modelling and computer graphics. Geometric data structures for curves, surfaces, and volume parameterization, and topological data structures for vertices, edges, faces, and bodies. Algorithms for set operations, Euler operations and deformations. Design and experimentation with geometric modelling facilities. 589 (CS 589). Raster Graphics-Principles and Applications. Prerequisite: EECS 487. I. (3). A detailed account of modern raster-based computer graphics. Topics include solid area scan conversion, color theory and application, hidden surface elimination, shading, highlights, animation, painting, and standardized graphics software. 590. EECS Introductory Seminar. Prerequisite: Senior standing. I. (1). Introduction to the technical areas of graduate study and research in the EECS Dept. Discussion of the policies and practices of graduate study. 592 (CS 592). Advanced Artificial Intelligence. Prerequisite: EECS 492 or P/I. II. (4). Advanced topics in artificial intelligence. Issues in knowledge representation, knowledge based systems, problem solving, planning and other topics will be discussed. Students will work on several projects. 593 (CS 593). The Human as an Information Processing System. Prerequisite: Graduate standing and permission of instructor. I, odd yrs. (3). Basic human information handling processes such as perception, learning cognitive map information, and problem solving are analyzed in an evolutionary context. Emphasis is largely theoretical. Includes the application to the human-computer interface of the principles that emerge. 594(CS 594). Introduction to Adaptive Systems. Prerequisite: EECS 303 & Math. Stat. 425. II. (3). Programs and automata that "learn" by adapting to their environment; programs that utilize genetic algorithms for learning. Samuel strategies, realistic neural networks, connectionist systems, classifier systems, and related models of cognition. Artificial intelligence systems, such as NETL and SOAR, are examined for their impact upon machine learning and cognitive science. 595 (CS 595)(Ling. 541). Natural Language Processing. Prerequisite: Senior standing. I. (3). A survey of syntactic and semantic theories for natural language processing, including unification-based grammars, methods of parsing, and a wide range of semantic theories from artificial intelligence as well as from philosophy of language. Programming will be optional, though a project will normally be required. 596. Master of Engineering Team Project. Prerequisite: Enrollment in the M. Eng. program in EECS. I, II, IIIa, IIIb, III. (1-6). To be elected by EECS students purusing the Master of Engineering degree. Students are expected to work in project teams. May be taken more than once up to a total of 6 credit hours. 598 (CS 598). Special Topics in Electrical Engineering and Computer Science. Prerequisite: permission of instructor or counselor. I, II, III, IIIa and IIIb. (1-4). Topics of current interest in electrical engineering and computer science. Lectures, seminar, or laboratory. Can be taken more than once for credit. 599. Directed Study. Prerequisite: prior arrangement with instructor. I, II, III, IIIa, and IIIb. (1-4). Individual study of selected advanced topics in electrical engineering and computer science. May include experimental work or reading. Primarily for graduate students. To be graded on a satisfactory/unsatisfactory basis ONLY. 600 (Aero. Eng. 651). Function Space Methods in System Theory. Prerequisite: EECS 400. II. (3). Introduction to the description and analysis of systems using function analytic methods. Metric spaces, normed linear spaces, Hilbert spaces, resolution spaces. Emphasis on using these concepts in systems problems. 623. Integrated Sensors and Sensing Systems. Prerequisite: EECS 413, and either EECS 423 or EECS 424, or EECS 425, or EECS 523. I. (4). Fundamental principles and design of integrated solid-state sensors and sensing systems. Micromachining and wafer bonding. Microstructures for the measurement of visible and infrared radiation, pressure, acceleration, temperature, gas purity, and ion concentrations. Merged process technologies for sensors and circuits. Data acquisition circuits, microactuators and integrated microsystems. 627. VLSI Design II. Prerequisite: EECS 427. I (4). Advanced very large scale integrated (VLSI) circuit design: VLSI CAD tools and techniques. IC failure modes: testing. Design for testability. Self-checking circuits. Automated layout. Design verification; placement and compaction; routing. Gate arrays; silicon compilers. Advanced projects in chip design and CAD tool development. Testing of chips fabricated in EECS 427. (3 hours lecture plus design laboratory.) 631. Electromagnetic Scattering. Prerequisite: EECS 530 and graduate standing. I, even years. (3). Boundary conditions, field representations. Low and high frequency scattering. Scattering by half plane (Wiener-Hopf method) and wedge (Maliuzhinets method); edge diffraction. Scattering by a cylinder and sphere: Watson transformation, Airy and Fock functions, creeping waves. Geometrical and physical theories of diffraction. 632. Microwave Remote Sensing II: Radar. Prerequisite: EECS 532 and graduate standing. II, even years. (3). Radar equation; noise statistics; resolution techniques; calibration; synthetic aperture radar; scatterometers; scattering models; surface and volume scattering; land and oceanographic applications. 633. Numerical Methods in Electromagnetics. Prerequisite: EECS 530. I, odd years. (3). Numerical techniques for antennas and scattering; integral representation; solutions of integral equations: method of moments, Galerkin's technique, conjugate gradient FFT; finite element methods for 2D and 3D simulations; hybrid finite element/boundary integral methods; applications: wire, patch and planar arrays; scattering by composite structures. 634 (Physics 611). Nonlinear Optics. Prerequisite: EECS 537 or EECS 538 or EECS 530. II. (3). Formalism of wave propagation in nonlinear media; susceptibility tensor; second harmonic generation and three-wave mixing; phase matching; third order nonlinearities and four-wave mixing processes; stimulated Raman and Brillouin scattering. Special topics: nonlinear optics in fibers, including solitons and self-phase modulation. 638 (Physics 542)(Appl. Phys. 609). Quantum Theory of Light Prerequisite: One graduate level course in quantum mechanics. I, even yrs. (3). The atom-field interaction; density matrix; quantum theory of radiation including spontaneous emission; optical Bloch equations and theory of resonance fluorescence; coherent pulse propagation; dressed atoms and squeezed states; special topics in nonlinear optics. 650. Channel Coding Theory. Prerequisite: EECS 501 and 400. II, alter yrs. (3). The theory of channel coding for reliable communication and computer memories. Error correcting codes; linear, cyclic and convolutional codes; encoding and decoding algorithms; performance evaluation of codes on a variety of channels. 651. Source Coding Theory. Prerequisite: EECS 501. II, odd years. (3). Introduction to a variety of source coding techniques such as quantization, block quantization; and differential, predictive, transform, and tree coding. Introduction to rate-distortion theory. Applications include speech and image coding. 658. Fast Algorithms for Signal Processing. Prerequisite: EECS 451, EECS 501. I, odd years. (3). Introduction to abstract algebra with applications to problems in signal processing. Fast algorithms for short convolutions and the discrete Fourier transform; number theoretic transforms; multi-dimensional transforms and convolutions; filter architectures. 659. Adaptive Signal Processing. Prerequisite: EECS 559. I, even years. (3). Theory and applications of adaptive filtering in systems and signal processing. Iterative methods of optimization and their convergence properties: transversal filters; LMS (gradient) algorithms. Adaptive Kalman filtering and least-squares algorithms. Specialized structures for implementation; e.g., least-squares lattice filters, systolic arrays. Applications to detection, noise cancelling, speech processing, and beam forming. 661. Discrete Event Systems. Prerequisite: EECS 560 or EECS 476 or equivalent. I, even years. (3). Modeling, analysis, and control of discrete event dynamical systems. Modeling formalisms considered include state machines, Petri nets, and recursive processes. Supervisory control theory; notions of controllable and observable languages. Analysis and control of Petri nets. Communicating sequential processes. Applications to database management, manufacturing, and communication protocols. 670. Advanced Topics in Computer Architecture. Prerequisite: EECS 570, Grad. Standing & permission of Instr. I or II. (3). Advanced concepts and specialized areas in computer system design are discussed and analyzed in depth. Topics chosen by instructor. Examples are database machines, highly reliable systems, computers for artificial intelligence, architectural support for operating system functional, high-level language architectures, object oriented architecture, other special purpose architecture (vision, dataflow). 681. Advanced Software Engineering. Prerequisite: EECS 481 and either 581, 582, 583 or 584. I, even yrs. (3). Problems of current research interest in software engineering such as software environments, program transformations, application generators, and very high level languages. A term project will be required. 682. Advanced System Programming. Prerequisite: EECS 482 or 582. I or II. (3). This course introduces the student to the more difficult problems and techniques of system programming. Such areas as dynamic storage allocation and relocation, interaction between central and peripheral hardware units, etc. will be discussed. The main emphasis of the course is a group project and the handling of the problems that are involved in all aspects of system design and final implementation. 691. Advanced Natural Language Processing Prerequisite: EECS 595 or consent of the instructor. II. (3). An in-depth look at state-of-the-art systems for natural language understanding, processing, and generation. Content will vary from year to year. Example topics: integrated syntactical and semantic systems; implementation of new semantic paradigms; learning systems. 695 (Psych. 640). Neural Models and Psychological Processes. Prerequisite: permission of instructor. II. (3). Consideration of adaptively and biologically oriented theories of human behavior. Emphasis on both the potential breadth of application and intuitive reasonableness of various models. There is a bias toward large theories and small simulations. 698. Master's Thesis. Prerequisite: Election of an EECS Master's Thesis Option. (May be elected for a maximum of 6 credit hours.) I, II, IIIa, IIIb, III. (1-6). To be elected by EE and EES students pursuing the Master's Thesis Option. May be taken more than once up to a total of 6 credit hours. To be graded on a satisfactory/unsatisfactory basis only. 699. Research Work in Electrical Engineering & Computer Science. Prerequisite: Graduate standing and permission of instructor. I, II, III, IIIa, and IIIb. (1-6). Students working under the supervision of a faculty member plan and execute a research project. A formal report must be submitted. May be taken for credit more than once, up to a total of 6 credit hours. To be graded satisfactory/unsatisfactory only. 700. Special Topics in System Theory. Prerequisite: Permission of instructor. To be arranged. 720. Special Topics in Solid-State Devices, Integrated Circuits and Physical Electronics. Prerequisite: Permission of instructor. (1-4). Special topics of current interest in solid-state devices, integrated circuits, microwave devices, quantum devices, noise, plasmas. This course may be taken for credit more than once. 730. Special Topics in Electromagnetics. Prerequisite: permission of instructor. (1-4). To be arranged. 731(AOSS 731). Space Terahertz Technology & Applications. Prerequisite: Permission of instructor. I. (1) Study and discussion of various topics related to high frequency applications in space exploration. Topics will be chosen from the following areas: Planetary Atmospheres and Remote Sensing, Antennas, Active and Passive Circuits, Space Instrumentation. 735. Special Topics in the Optical Sciences. Prerequisite: Graduate Standing and permission of instructor. Term to be arranged. (1-4). Key topics of current research interest in ultrafast phenomena, short wavelength lasers, atomic traps, integrated optics, nonlinear optics and spectroscopy. This course may be taken for credit more than once under different instructors. 750. Special Topics in Communication and Information Theory. Prerequisite: Permission of instructor. To be arranged. 755. Special Topics in Signal Processing. Prerequisite: Permission of Instructor. To be arranged. (1-4). 760. Special Topics in Control Theory. Prerequisite: Permission of instructor. To be arranged. 765. Special Topics in Stochastic Systems and Control. Permission of Instructor. (3). Advanced topics on stochastic systems such as stochastic calculus, nonlinear filtering, stochastic adaptive control, decentralized control, and queueing networks. 770. Special Topics in Computer Systems. Prerequisite: Permission of instructor. To be arranged. 820. Seminar in Solid-State Electronics. Prerequisite: Graduate standing and permission of instructor. I. (1). Advanced graduate seminar devoted to discussing current research topics in areas of solid-state electronics. Specific topics vary each time the course is offered. Course may be elected more than once. 874. Seminar in Theory of Computing. Prerequisite: EECS 574. I and II. (2). Advanced graduate seminar devoted to new development in theory of computing. Topics may include theory of programming languages, complexity, algorithms, AI, and applications of logic and mathematics to computer science. 880. Software Research Seminar. Prerequisite: Grad. Stdg. in EECS or P/I. May be taken more than once since topics will vary each term. I, II. (1-3). Seminar and current research in programming languages, operating systems, distributed computing, software engineering, databases, graphics, and other software topics. Each week a different speaker will describe his/her own research or report on a recent published paper. Exact topics varied each term. Occasional speakers from other universities. 892. Seminar in Artificial Intelligence. Prerequisite: EECS 592 or equivalent. I and II. (2). Advanced graduate seminar devoted to discussing current research papers in artificial intelligence. The specific topics vary each time the course is offered. 990. Dissertation/Pre-Candidate. I, II and III (2-8); IIIa and IIIb (1-4). Election for dissertation work by a doctoral student not yet admitted to Candidate status. 995. Dissertation/Candidate. Prerequisite: Graduate School authorization for admission as a doctoral Candidate. I, II and III (8); IIIa and IIIb (4). Election for dissertation work by a doctoral student who has been admitted to Candidate status.