EE 520 - Purdue University
EE 520 TOPICS IN BIOMEDICAL ENGINEERING
Credit: 1
Area: Biomedical Engineering (BE)
PIC: Geddes
Prerequisite: Admission by consent of instructor. (May be repeated for credit.)
Description:
This course is designed to present a broad spectrum of examples illustrating how electrical engineering is used in the basic life and medical sciences. Each week, life-science research and medical-practice problems are presented by a speaker who is an authority in his/her field. The speakers are drawn from engineering, veterinary medicine, biology, and industry. This is an interdisciplinary course of value to the undergraduates and graduates. Three reports are required on topics selected from those presented by the speakers.
EE 522( PROBLEMS IN THE MEASUREMENT OF PHYSIOLOGICAL EVENTS (BIOL 563, VPH 522)
Credit: 4
Area: Biomedical Engineering (BE)
PIC: Biomedical Engineering Staff
Prerequisite: Consent of instructor must be obtained at least one year prior to registering for the course.
Description:
Lectures devoted to the methods used to measure physiological events with demonstrations and laboratory exercises to emphasize the practical aspects of quantitative measurements on living subjects. The systems covered are cardiovascular, respiratory, central and peripheral nervous, gastrointestinal and renal.
Text:
L.A. Geddes and L.E. Baker, Principles of Applied Biomedical Instrumentation, John Wiley, 3rd Edition, 1989. (0-471-60899-8)
Outline:
Weeks
1. Events of the Cardiac Cycle 1.0
2. Mechanical Aspects of Respiration 1.0
3. Indirect Blood Pressure
4. The Strength-Duration Curve of Excitable Tissue 1.0
5. Record Analysis and Examination 1.0
6. Nerve Propagation Velocity 1.0
7. Pulse Wave Velocity 1.0
8. Electrocardiographic Trainer 1.0
9. The Electrocardiogram of the Turtle 1.0
10. Anesthesia and Blood Pressure in Dog 1.0
11. Heart Sounds in the Experimental Animal 1.0
12. Record Analysis and Examination 1.0
13. Cardiac Output 1.0
14. Cardiac Monitoring and Myocardial Infarction 1.0
EE 532 COMPUTATIONAL METHODS FOR POWER SYSTEM ANALYSIS
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Ong
Prerequisite: EE 432
Prerequisite by Topic:
Familiarity with three-phase circuit calculations, matrix manipulations, and Fortran, C, or MATLAB programming
Description:
System modeling and matrix analysis of three-phase power networks. Applications of numerical methods and computers to the solution of a variety of problems related to the planning, design and operation of electric power systems.
Objective:
An introduction to modern power system analysis and computer methods used in planning and operating electric power systems.
Course Outcomes:
A student who successfully completes this course will be able to:
1. use network matrices in power system analysis (4, k),
2. perform short circuit studies on a small network (4,b),
3. perform load flow calculations on a small network (4,b),
4. understand automatic generation control of a power system (4, e, j,)and
5. understand economic dispatch of a power system (4, e, j,)
Text:
Modern Power Systems Control and Operations, Atif S. Debs, Decision Systems International
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EE 532 COMPUTATIONAL METHODS FOR POWER SYSTEM ANALYSIS
Outline:
Weeks
1. Introduction: Network models and matrices 1.0
2. Symmetrical components, sequence networks 2.0
3. Symmetrical and unsymmetrical fault studies 2.5
4. Low flow formulations, solution techniques, programming 3.0
aspects, transformer and phase shifter representation,
tie-line control.
5. Load Flow Studies 2.0
6. Economic dispatch 1.5
7. Automatic generation control 3.0
EE 538 DIGITAL SIGNAL PROCESSING I
Credit: 3
Area: Communications and Signal Processing (CS)
PIC:
Prerequisite: EE 301 and EE 302 or equivalent
Description:
Theory and algorithms for processing of deterministic and stochastic signals. Topics include discrete signals, systems, and transforms, linear filtering, fast Fourier transform, nonlinear filtering, spectrum estimation, linear prediction, adaptive filtering, and array signal processing.
Objective:
Provide the student with a broad, yet strong background in the traditional topics associated with processing of deterministic digital signals, e.g., discrete-time transforms, and linear filtering. Provide student with a strong background in traditional topics associated with processing of stochastic signals, e.g., spectrum estimation and linear prediction. Introduce the student to some of the more recent developments that promise to have a broad impact on digital signal processing, e.g., nonlinear filtering and adaptive filtering.
Text:
J. G. Proakis and D.G. Manolakis, Introduction to Digital Signal Processing, 3rd edition MacMillan, NY, 1992.
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EE 538 DIGITAL SIGNAL PROCESSING I
Outline:
Lectures
1. Discrete signals, systems, and transforms 7.0
A. Discrete linear system
B. Discrete-time Fourier transform (DTFT)
C. 2-sided Z transfor
D. Discrete Fourier transform (DFT)
2. Linear filtering 5.0
A. Finite impulse response filters
1. Windowed designs (Kaiser)
2. Equiripple designs
B. Infinite impulse response filters
1. Bilinear Z transform
2. Computer-aided techniques
3. Fast Fourier transform (FFT) algorithms 3.0
A. Decimation in time
B. Decimation in frequency
C. Chirp Z-Transform
D. Sectioned convolution
4. Nonparametric methods of power spectrum estimation 3.0
A. Estimation of the autocorrelation sequence for random signals
B. Smoothing the periodogram: the Blackman-Turkey method
5. Model-based power spectrum estimation 6.0
A. Autoregressive (AR) spectral estimation
B. Lattice filter: Burg's method
C. Signal subspace methods
D. Applications
6. Adaptive signal processing 9.0
A. Applications
B. Least mean square (LMS) adaptive algorithm
C. Recursive least squares (RLS) lattice filters
D. Adaptive beamforming
7. Nonlinear filtering 9.0
A. Rank Order filters
B. Deterministic and statistical analysis of median filters
C. Threshold decomposition -- stock filters
D. Applications
8. Exams 3.0
EE 544 DIGITAL COMMUNICATIONS
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Communications and Signal Processing Staff
Prerequisite: EE 440 or graduate standing
Description:
Introduction to digital communication systems and spread spectrum communications. Topics include analog message digitization, signal space representation of digital signals, binary and M-ary signalling methods, detection of binary and M-ary signals, comparison of digital communication systems in terms of signal energy and signal bandwidth requirements. The principal types of spread spectrum systems are analyzed and compared. Application of spread spectrum to multiple access systems and to secure communication systems is discussed.
Text:
Proakis, J.G., Digital Communications, 3rd edition, McGraw-Hill, 1995. ISBN (0070517266)
Outline:
Lectures
1. Fundamentals
A. Channel Models 1.0
B. Narrowband Gaussian Noise Repr. 1.0
C. Matched Filters and Correlators 2.0
D. Message Digitization 2.0
E. Digital Modulation Methods 1.0
2. Detection of Binary Signals
A. Baseband Signal Detection 1.0
B. Detection on Nonwhite Noise 1.0
C. Effects of Timing Errors 1.0
D. Intersymbol Interference 1.0
E. Phase-Shift Keying (PSK) 1.0
F. Differential PSK 1.0
G. Frequency-Shift Keying (FSK) 1.0
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EE 544 DIGITAL COMMUNICATIONS
Outline:
Lectures
3. M-ary Signal Detection
A. Signal Space Representation 2.0 B. Coherent Detection of M-ary Signals 1.0
C. Union Bound for Orthogonal Signals 1.0
D. Detection of Nonorthogonal Signals 3.0
E. Biorthogonal
F. Simplex
G. Polyphase
H. Quadriphase (QPSK)
I. Offset QPSK
J. Differential QPSK
K. Noncoherent Detection of M-ary Signals 1.0
L. Amplitude Shift Keying (ASK)
M. Amplitude and Phase Shift Keying (APSK)
4. Comparison of Digital Communication Systems
A. Theoretical Limits of Performance 1.0
B. Bandwidth Expansion Factor 1.0
C. Energy and Bandwidth Comparison 1.0
5. Fundamentals of Spread Spectrum
A. General Concepts 1.0
B. Direct Sequence Spread Spectrum 1.0
C. Frequency Hopping Spread Spectrum 1.0
D. Hybrid Spreading Methods 1.0
6. Analysis of Direct Sequence Systems
A. Properties of PN Sequences 1.0
B. Partial Correlation 1.0
C. Direct Sequence Performance 1.0
D. Interference Rejection and Antijam 1.0
E. Interception 1.0
7. Analysis of Frequency Hop Systems
A. Frequency Hopping Patterns 1.0
B. Frequency Hopping Performance 1.0
C. Interference Rejection and Antijam 1.0
8. Applications of Spread Spectrum
A. Multiple Access 1.0
B. Jam Resistance 1.0
C. Low Probability of Intercept 3.0
9. Exams 3.0
EE 546 DIGITAL COMPUTATIONAL TECHNIQUES FOR ELECTRONIC CIRCUITS
Credit: 3
Area: VLSI and Circuit Design (VC)
PIC: Ogborn
Prerequisite: EE 255 and 301 or graduate standing.
Description:
Digital computer methods for dc, ac, and transient analyses of electronic circuits. Linear, nonlinear and piecewise linear dynamic circuits are considered. Actual usage of programs ECAP, SPICE, CORNAP, SNAP and MECA in coursework. Algorithms used in these programs are studied.
Text:
Vlach and Singhal, Computer Methods for Circuit Analysis and Design; Van Nostrand Reinhold, 1994. 0-442-01194-6
Outline:
Lectures
1. Overview of circuit simulation programs. 1.0
2. DC and AC analysis of linear networks. Explicit form of nodal
Equations Gaussian elimination and LU factorization. 5.0
3. DC analysis of nonliner resistive networks. Newton- Raphson
algorithm, companion model. 2.0
4. Transient analysis of nonlinear resistive networks.
Implicit integration, discretized circuit models for capacitors
and inductors. 5.0
5. Exam #1 1.0
6. Circuit models for semiconductor devices (diodes, BJT, FET).
Piecewise-linear models and solution techniques.
Macromodel for op amps. 8.0
7. Computer formulation of Kirchoff's Laws.
Computer generation of fundamental loop and cutset matrices.
8. Exam #2 1.0
9. Tableau equations and modified nodal equations. The element stamps. 5.0
10. Sparse matrix techniques. Fill-ins and ordering algorithms. Packed
vector implementation. 5.0
11. Exam #3 1.0
12. The stability region of numerical integration algorithms. 2.0
13. Backward differentiation formulas. Predictor formulas. 3.0
14. Transient analysis by BDF with variable step size and variable order. 2.0
15. Final Exams
EE 547‡ INTRODUCTION TO COMPUTER COMMUNICATION NETWORKS
Credit: 3
Area: Communications and Signal Processing (CS)
Computer Engineering (CE)
PIC: Shroff
Prerequisite: EE 302 or equivalent
Description:
A qualitative and quantitative study of the issues in design, analysis and operation of computer communication and telecommunication networks as they evolve towards the integrated networks of the future employing both packet and circuit switching technology. The course covers packet and circuit switching, the OSI standards architecture and protocols, elementary queueing theory for performance evaluation, random access techniques, local area networks, reliability and error recovery, and integrated networks.
Objective:
To introduce students to the design, analysis and performance evaluation of computer communication and telecommunication networks through an understanding of their architectures and protocols.
Text:
M Schwartz, Telecommunication Networks: Protocols, Modeling and Analysis, Addison Wesley, 1987.(0-201-16423-X)
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EE 547‡ INTRODUCTION TO COMPUTER
COMMUNICATION NETWORKS
Outline:
Lectures
1. Introduction 1.0
A. Circuit and Packet Switching
B. Layered Communication Architectures
2. Layered Architectures in Data Networks 3.0
A. OSI Standards Architecture and Protocols
B. X.25 Protocol
C. Systems Network Architecture (SNA)
3. Elementary Queueing Theory 6.0
4. Data Link Layer: Examples and Performance Analysis 3.0
A. Stop-and-Wait Protocol
B. Go-Back-N Protocol
C. High-level data Link Control (HDLC)
5. Network Layer: Flow Control and Congestion Control 5.0
A. Window-Flow Control
B. SNA Path Control
C. Input-buffer Limiting3
D. Centralized Routing Algorithms
E. Virtual Circuit and Datagram Networks
F. Distributed Routing Algorithms
7. Transport Layer 3.0
A. OSI Transport Protocol
B. Transmission Control Protocol (TCP)
8. Polling and Random Access in Data Networks 6.0
A. Polling?
B. Pure Aloha, slotted Aloha, CSMA/CD
9. Local Area Networks and Design Issues 6.0
A. CSMA/CD
B. Token Ring
C. Network Control
D. Reliability, Availability, and Survivability
10. Introduction to Circuit Switching 4.0
A. Circuit and Packet Switching Compared
B. Digital Switching Networks3
C. Integrated Services Digital Networks (ISDN)
D. Broadband ISDN
11. Exams 2.0
EE 551 APPLIED MAGNETICS
Credit: 3
Area: Fields and Optics (FO)
PIC: Friedlaender/Nyenhuis
Prerequisite: EE 311.
Description:
The elements of magnetics are reviewed and applied to a variety of technologically important devices. Traditional applications covered include permanent magnets, transformers, and saturable reactors. Time is spent on the elements of magnetic information technology -- mostly digital and analog recording; and also on other memory technologies such as magnetic bubbles and magneto-optic recording. A discussion of high frequency magnetic devices is included.
Objective:
To introduce the students to the concepts required to solve engineering problems involving the use of magnetic materials from dc to the megahertz range. Examples will be introduced ranging from power applications of magnetics (transformers, motors) to information technology such as magnetic recording or signal processing employing magnetic materials. Great emphasis will be placed on developing the students' problem-solving skills.
Outline:
Weeks
1. Magnetic circuit and magnetic field concepts with applications
to soft and hard magnetic materials (partly review though for
many students this will be the first introduction).
(Ampere's Circuital Law concepts) 2.5
2. Magnetic circuits with "windings". Faraday's Law concepts
Equivalent circuit representation and philosophy (modeling) 2.5
3. Applications to magnetic design and analysis. Typical examples:
transformers, saturable reactors, permanent magnet devices.
Discussions of B-H properties, simple dynamic concepts, hysteresis
and coercive force (losses) introduced through the problems. 3.0
4. Magnetic Information Technology. Digital and analog magnetic
recording, mostly. Other memory devices such as bubble memories. 5.0
5. Some high frequency applications of magnetics (e.g.,magneto-optics). 2.0
EE 552‡ INTRODUCTION TO LASERS
Credit: 3
Area: Solid State Devices and Materials (SS)
Fields and Optics (FO)
PIC: Elliott
Prerequisite: EE 311
Description:
An introduction to lasers and laser applications which does not require a knowledge of quantum mechanics as a prerequisite. Topics include: the theory of laser operation, some specific laser systems, non-linear optics, optical detection, and applications to optical communications, holography, laser-driven fusion, and integrated optics.
Text:
J. Verdeyen, Laser Electronics, Prentice-Hall, 1995.(0-13-706666-X)
Outline:
Weeks
1. Introduction: Review of Some Aspects of Classical Optics Elementary
Optical Cavity Analysis. Gaussian Beams. Laser Cavity Modes 3.0
2. Interactions of Radiation with Matter. Absorption, Emission, Scattering,
Line Broadening. Laser Media. 2.0
3. Introduction to Laser Theory. Rate Theory. Gain Saturation.
Homogeneous and Inhomogeneous Broadening. Gain Narrowing.
Output Coupling. Optimization. Hole Burning. Threshold Behavior.
Pulse Generation. 4.0
4. Specfific Laser Systems: Solid State lasers. Tunable Lasers. Gas Lasers. Semiconductior Lasers.
5. Special Topics (Detectors, Coherence, etc.....) 1.0
6. Exams 1.0
EE 554‡ ELECTRONIC INSTRUMENTATION AND CONTROL CIRCUITS
Credit: 3
Area: Energy Sources and Systems (ES)
VLSI and Circuit Design (VC)
PIC: Ogborn
Prerequisite: EE 255, EE 301
Description:
Analysis and design of special amplifiers, pulse circuits, operational circuits, DC amplifiers and transducers used in instrumentation, control and computation.
Objective:
To provide a general background in electronic circuit analysis and design for senior and beginning graduate students. To develop an understanding of the limitations of present solid state devices that are used in electronic circuit design. To aid students in experimental research, develop the specialized electronic circuits required for the acquisition and processing of experimental data.
Text:
Class notes.
Outline:
Weeks
1. Review of models for unipolar and bipolar transistors,
machine aids for analysis, ECAP. 1.0
2. High frequency limitations, compensation, practical
design techniques. 3.0
3. Power gain limitations maximum frequency of oscillation,
optimum terminations, narrow band high frequency amplifiers,
computer aided design techniques. 3.0
4. Driving point impedance limitations, the return difference
algorithm, applications to feedback pairs, negative imitance
circuits, electrometers. 2.0
5. Noise limitations, noise figure, four terminal network noise
models, noise measurements. 1.0
6. Low frequency limitations, the sag algorithm, DC amplifiers,
thermal limitations, the analog switch, applications to
regulators and power amplifiers. 2.0
7. Switching limitations, the charge control model, digital
examples, the power switch. 1.0
8. Selected circuit examples (e.g. operational amplifiers,
multiplier, logarithmic amplifiers, INIC's gyrator,
DC/AC converter, etc.) 2.0
EE 556( FUNDAMENTALS OF MICROELECTRONICS PROCESSING (VLSI) (CHE 556)
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Neudeck
Prerequisite: EE 305 or equivalent, or consent of instructor, or graduate standing.
Text:
Campbell, Stephen A., The Science and Engineering of Microelectronic Fabrication, Oxford Press, 1996. ISBN: 0-19-510508-7.
Description:
The study of basic principles and practical aspects of the most advanced state of electronics processing. Emphasis is placed on crystal growth, epitaxy, lithography, and dry etching. Process-property relations are also presented.
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EE 556( FUNDAMENTALS OF MICROELECTRONICS PROCESSING (VLSI) (CHE 556)
Outline:
Hours
0. Introduction
A. Overview of Microelectronics 2.0
B. Semiconductor Devices 3.0
1. Crystal Growth and Epitaxy
A. Crystal Growth 3.0
B. Chemical Vapor Deposition - Vapor Phase Epitaxy
- Molecular Beam Epitaxy 6.0
C. Silicon in Insulators 3.0
D. Doping Profiles in Epi-layers 3.0
2. Dielectric and Polysilicon Film Deposition
A. Deposition Processes and Reactor Design 2.0
B. Polysilicon and Silicon Dioxide 2.0
3. Ion Implantation
A. Ion Implant System - Dose Control 1.0
B. Impurity Profiles of Implanted Ions 1.0
C. Process Considerations
4. Lithography
A. Pattern Generation - Mask Making 2.0
B. Printing and Engraving 2.0
C. Resists 3.0
5. Dry Etching
A. Selectivity - Feature Size Control 2.0
B. Fundamentals of Dry Etching 2.0
C. Process Considerations 2.0
6. Other Processes - Device and Circuit Fabrication
A. Oxidation - Diffusion – Metallization 3.0
B. Fabrication Considerations 1.0
EE 557 INTEGRATED CIRCUIT FABRICATION LABORATORY
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Janes
Prerequisite: EE 305 or graduate standing (limited registration).
Consent of instructor required.
Description:
Laboratory exercises in the fabrication and testing of silicon integrated circuits. Both bipolarand MOS integrated circuit test chips are fabricated and tested. Laboratory technique, the technology of integrated circuit fabrication, and electrical characterization are emphasized.
Design Project:
A process design project, utilizing the SUPREM-3 process simulator, will be completed. Simulation of all major unit processes will be included.
Text:
S. A. Campbell, The Science and Engineering of Microelectronic Fabrication, Oxford Univ. Press, 1996, ISBN 0-19-510508-7.
Outline:
LECTURES:
1. Course/Lab Intro & Organization, Chemical Safety
2. Overview, Unit Processes
3. Diffusion
4. Thermal Oxidation
5. Ion Implantation
6. Optical Lithography & Resists
7. SUPREM Process Simulation & Design Project
8. Vacuum Science & Etching
9. Physical & Chemical Deposition
10. Crystal Growth & Epitaxy
12. Process Technologies: MOS/CMOS
13. Process Technologies: Bipolar
14. Manufacturing & Yield
15. Review
16. Exam
Laboratory Exercises:
1. 3-mask Diode Test Chip
2. 6-mask Bipolar/CMOS/BiCMOS device and circuit chip
EE 558‡ INTEGRATED-CIRCUIT LAYOUT AND DESIGN
Credit: 3
Area: Solid States Devices and Materials (SS)
VSLI and Circuit Design (VC)
PIC:
Prerequisite: EE 305 or EE 455 or graduate standing.
Description:
A project course to design, layout on a graphics terminal, and evaluate MSI and LSI circuits. Bipolar layout design rules are studied and linear and digital circuits are layed out; I2L gates and logic functions. A bipolar linear I-C is designed and fabricated using the masterslice approach, typically of the op-amp complexity. NMOS layout rules are presented and logic gates and functions are layed out and designed into the graphics system library. Using the "library" a MSI NMOS circuit is designed and simulated, and a layout completed. CMOS circuits and layout design rules are applied to several layouts.
Objective:
The principle objective is to have the student design with real I-C layout rules and integrate circuit design with layout design and the fabrication process. The use of computer graphics allows the student to make complex design drawings with a minimum of effort, while Spice2 simulations are used to prove the circuit design information.
Text:
W. Maly, Atlas of IC Technologies: An Introduction to VLSI Processes, Addison-Wesley, 1987. (0-805-36850-7)
Outline:
Weeks
1. Learn the graphics package MASK, bipolar processing, review 1.0
2. Bipolar layout design rules and simulations 2-3
3. Design layouts for bipolar linear and digital circuits 4.0
4. N-MOS design rules and fabrication process for silicon gate
depletion load MOSFETS 5.0
5. N-MOS layout examples and pad protection. 6.0
6. N-MOS gates and logic function layout 7.0
7. NMOS models and Spice2 simulation; NMOS circuit design for speed 8.0
8. CMOS circuit design, layout rules 9-10
9. CMOS Examples of Layouts 11-12
10. CMOS layout and design of logic functions 13-14
11. Exams 15.0
EE 559‡ MOS VLSI DESIGN
Credit: 3
Area: VLSI and Circuit Design (VC)
Computer Engineering (CE)
PIC: Roy
Prerequisite: EE 365
Description:
An introduction to most aspects of large-scale MOS integrated circuit design including: device fabrication and modeling; useful circuit building blocks; system considerations; and algorithms to accomplish common tasks. Most circuits discussed are treated in detail with particular attention given those circuits, whose regular and/or expandable structures, are primary candidates for integration. All circuits will be digital and will be considered in the context of the Silicon-gate MOS enhancement-depletion technology. Homework will require the use of existing IC mask layout software and term projects will be assigned.
Text:
Weste, Principles of CMOS VLSI Design, Addison-Wesley, 2nd edition, 1993. (0-201-53376-6).
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EE 559† MOS VLSI DESIGN
Outline:
Lectures
1. Device Fabrication and Modeling: Si-MOS enhancement and
depletion devices, fabrication, interconnection levels,
interconnection resistivites, sheet capacitance, design rules,
device models. 2.5
2. Inverters: Logic voltage levels, logic threshold levels, transfer
characteristics, power dissipation, transient response, layout,
the all-enhancement inverter. 2.0
3. Buffers and Gates: NAND and NOR gates, buffers multistage
capacitor drivers. 3.0
4. Transmission Gating: Transient response vs. number of stages,
level restoring, poly/active area cross-unders. 1.0
5. Registers: Dynamic registers with two-phase clocking,
static registers, sampling of intermediate voltage levels,
LIFO stacks, FIFO queues. 2.0
6. Optimum cross-under widths. 0.5
7. Combinational Logic: PLA's, PLA folding, finite state machines 1.5
8. Power Distribution 1.0
9. Clock Generation and Distribution 0.5
10. Pad Receivers and Drivers 1.0
11. Procedures for orderly layout of I.C.'s 3.0
12. Ram Design 2.5
13. Design of Masterslices: Cell types and interconnection provisions. 1.0
14. Data Path Chip: ALU's, bus-precharging, shifters, general logic
cell, two-port register, microcoding. 4.0
15. Switching: Crosspoint switches and circuit switching networks. 0.5
16. Other Digital Circuits: Serial-in-parallel-out registers, a string
compacitor, parity circuits, multipliers, counters, power-up
resetting, Buss Arbitrators, REQ/ACK ?Arbiters 6.0
17. Calculator Algorithms: The GORDIC technique for ?multiplication,
division, and trigonometric function evaluation. 3.5
18. Structures for sorting. 1.5
19. Array Processing Structures: Inner products, matrix-vector
multiplication, matrix multiplication 2.0
20. Term Project Discussions 6.0
EE 562 INTRODUCTION TO DATA MANAGEMENT
Credit: 3
Area: Computer Engineering (CE)
PIC: Ghafoor
Prerequisite: Graduate standing or consent of instructor
Description:
Emphasis is on the design of systems that can manipulate and retrieve data from large databases using high level formal languages. Topics covered are: data models and data independence, normalization in relational databases, development of high level query languages for relational and hierarchical models, visual query languages, object oriented systems and object oriented databases. The course includes a project that accounts for about twenty percent of the grade for the course.
Objective:
To acquaint graduate students with up-to-date knowledge of the theory of relational and object oriented data bases and the development of database systems for applications.
Text:
C.J. Date, An Introduction to Data Base Systems, Volume I, 5th edition, Addison Wesley. (0-201-54329-X)
Outline:
Weeks
1. Knowledge and data and information 0.5
2. Representation of knowledge in conventional date base system 1.0
3. Semantic modelling: entity relationship 0.5
4. Overview of data base management 0.5
5. Relational data structure 1.0
6. Relational integrity rules 0.5
7. Relational algebra 1.0
8. First order predicate calculus 1.0
9. Relational calculus 1.0
10. QBE 1.0
11. Data manipulation by SQL on Oracle DBMS 1.0
12. Normal forms (paper by Salzburg) and formal design theory of data bases 1.0
13. Knowledge intensive data models in engineering:
Object oriented framework 1.0
Visual query language for graphical interaction 1.0
14. Association algebra: a mathematical foundation for object
oriented databases 0.5
15. Object oriented intelligent computer integrated design,
process planning and inspection. 0.5
16. Intelligent computer integrated manufacturing 0.5
17. Computer supported cooperative work 0.5
EE 563 PROGRAMMING PARALLEL MACHINES
Credit: 3
Area: Computer Engineering (CE)
PIC: Eigenmann
Prerequisite: EE 264 and EE 463
Description:
Examines how to program parallel processing systems. Various parallel algorithms are presented to demonstrate different techniques for mapping tasks onto parallel machines. Parallel architectures to be considered are: SIMD (synchronous), MIMD (asynchronous), and mixed-mode (SIMD/MIMD hybrid). Machines that represent these classes to be used in the course are: the MasPar MP-1 (SIMD); nCUBE 2 (MIMD); and PASM (mixed-mode). There will be three programming projects, one on each machine. The similarities and differences among the machines and their languages will be discussed.
Objective:
To give students the background needed to be able to map computational tasks onto a variety of parallel processing systems in an effective manner.
Text:
Reprints of research papers describing parallel machines and their use, as well as appropriate programming manuals and related documentation, will be distributed or made available for purchase.
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EE 563 PROGRAMMING PARALLEL MACHINES
Outline:
Lectures
1. Introduction models of parallelism, networks 1-2
2. Parallel algorithm case studies 3-7
3. MasPar architecture 8.0
4. Programming model for MasPar 9-11
5. MasPar project description 12.0
6. MasPar SIMD algorithms 13-14
7. Advanced programming concepts for MasPar 15-17
8. Exam #1 18.0
9. nCUBE 2 architecture 19.0
10. Programming model for nCUBE 2 20-22
11. nCUBE 2 project description 23.0
12. nCUBE 2 MIMD algorithms 24-25
13. Discussion of MasPar project 26.0
14. Advanced programming concepts for nCUBE 2 27-29
15. Exam #2 30.0
16. PASM architecture 31.0
17. Programming model for PASM 32-34
18. PASM project description 35.0
19. PASM mixed-mode SIMD/MIMD algorithms 36-37
20. Discussion of nCUBE 2 project 38.0
21. Advanced programming concepts for PASM 39-41
22. Comparisons of languages and machines 42-43
23. Discussion of PASM project 44.0
24. Exam #3 45.0
EE 565 COMPUTER ARCHITECTURE
Credit: 3
Area: Computer Engineering (CE)
PIC: Fortes
Prerequisite: EE 365 or graduate standing
Description:
An introduction to the problems involved in designing and analyzing current machine architectures. Major topics include performance and cost analysis, pipeline processing, vector machines and numerical applications, hierarchical memory design, and multiprocessor architectures. A quantitative approach allowing a computer system designer to determine the extent to which a design meets design goals is emphasized.
Text:
Patterson and J. Hennessy, Computer Architecture - A Quantitative Approach, second edition, Morgan Kaufmann (1-55860-329-8).
Outline:
Weeks
1. Introduction 0.5
2. Performance and Cost 1.0
3. Pipelining 3.0 A. Implementation
B. Hazards
C. Performance Evaluation
D. Advanced Techniques
4. Vector Processors 2.0
A. Fundamentals
B. Case Study
5. Memory Hierarchy 3.0
A. Program Characteristics
B. Cache Design
C. Main Memory
D. Virtual Memory
6. Input/Output 2.0
A. Performance Prediction
B. I/O Devices
7. Multiprocessors 2.5
A. Models
B. Interconnection Techniques
8. Exams 1.0
EE 566 CISC MICROPROCESSOR SYSTEM DESIGN
Credit: 3
Area: Computer Engineering (CE)
PIC: Meyer
Prerequisite: EE 365 or equivalent
Description:
An overview of advanced architecture CICS microprocessors and their associated support components, with emphasis on incorporating these devices into both general purpose and embedded board level designs for multi-microprocessor systems utilizing open architecture system buses. Topics include a survey of 32-bit CISC microprocessors, memory management, floating point support, advanced peripherals, PLD-based "glue logic" design, performance evaluation, IEEE standard open architecture system buses, and various pertinent interface and networking standards. Design experience will be gained through a comprehensive, semester-long project.
Objective:
To introduce students to the design of computer systems using advanced architecture CISC microprocessors along with their related support chips as building blocks. Also, to provide students experience with state machine design, analysis of signal timing, analysis of gate electrical characteristics, and use of modern programmable logic devices to implement interface logic.
Text:
D. Tabak, Advanced Microprocessors, 2nd edition, McGraw-Hill, 1995 (0-07-062-843-2)
Continued next page
EE 566 CISC MICROPROCESSOR SYSTEM DESIGN
Outline:
Weeks
1. Review of microprocessor system design fundamentals, introduction to salient characteristics of complex instruction set (CISC) "advanced architecture" microprocessors.
2. Representative advanced peripherals: serial communication controllers, counter/timers, parallel I/O, dual-port RAMs, DMA controllers.
3. Memory management: segmentation, paging, memory protection, Progress
Progress Report #1 due (serial interface module).
4. Multiprogramming and multitasking, motivation for "multi-microprocessor" open architecture bus systems.
5. Intel "86" family survey.
6. Intel "86" family survey, floating point support.
Progress Report #2 due (counter/timer, parallel I/O module).
7. Motorola "68000" family survey.
8. Motorola "68000" family survey, floating point support.
9. National "32x32" family survey, floating point support.
Midterm Exam
10. Analysis of timing and electrical interfacing considerations, synthesis of desired timing relationships, use of programmable logic devices to implement interface logic.
11. Benchmarking and performance evaluation, comparison criteria for choosing a microprocessor for a specific application (embedded vs. general purpose).
12. IEEE standard open architecture system buses: Multibus I, VME; Progress
Progress Report #3 due (CPU/RAM/ROM module)
13. Open architecture system buses: VME, Multibus II
14. Open architecture system buses: summary comparison.
15. Interface and networking standards (GPIB, SDLC/HDLC, SCSI Ethernet);
Progress Report #4 due (bus interface module)
16. Videotaped Project Summary Presentations, Final Project Report due.
EE 568 RISC & DSP MICROPROCESSOR SYSTEM DESIGN
Credit: 3
Area: Computer Engineering (CE)
PIC: Meyer
Prerequisite: EE 365 or equivalent
Description:
This course provides an overview of reduced instruction set (RISC) microprocessors and digital signal processing (DSP) microprocessors, with emphasis on incorporating these devices in general purpose and embedded system designs, respectively. The first half of the course emphasizes design considerations for RISC microprocessor based computer systems; a half-semester design project focuses on principles that could be utilized in a general-purpose computer system (e.g., an engineering workstation). The second half of the course emphasizes design considerations for DSP microprocessor based computer systems: a half-semester design project focuses on analog I/O interfacing techniques and use of these devices for embedded applications (e.g., spectrum analyzer, digital audio equalizer).
Objective:
To introduce students to the unique system design considerations associated with RISC and DSP microprocessors. Also, to provide students experience with designing support circuitry for board level systems incorporating these devices.
Text:
Tabak, E. Advanced Microprocessors, Second Edition, McGraw-Hill, 1995.
(0-07-062843-2); various technical reference and data books.
Continued next page
EE 568 RISC & DSP MICROPROCESSOR SYSTEM DESIGN
Outline:
Weeks
1. Introduction to salient characteristics of reduced instruction set
microprocessors; overview of Design Project I; early
generation RISCs: Berkely RISC I & II, MIPS 1.0
2. Intel i860, Motorola M88000 family 2.0
3. AMD 29000, Sun SPARC 3.0
4. MIPS Rx000, HP PA-RISC 4.0
5. PowerPC (IBM RS/6000), DEC Alpha 5.0
6. Bus timing analysis and high-speed system design considerations 6.0
7. Memory system design considerations 7.0
8. RISC performance evaluation and processor selection criteria 8.0
Midterm Exam
9. The CISC vs. RISC debate,
Design Project I due (core processor/memory/cache design for
a general-purpose RISC computer system) 9.0
10. Introduction to salient characteristics of digital signal processing
microprocessors, overview of typical embedded applications,
overview of Design Project II 10.0
11. TMS 320 family overview 11.0
12. Motorola 56000/96000 family overview 12.0
13. Analog interfacing techniques, overview of both conventional
and oversampling A/D and D/A converters, survey of practical
oversampling converters suitable for high-performance signal
processing applications 13.0
14. Embedded DSP system design case study 14.0
15. DSP performance evaluation and processor selection criteria,
Design Project II due (DSP system with analog I/O targeted for
a specific embedded application) 15.0
16. Videotaped Project Summary Presentations 16.0
EE 569( INTRODUCTION TO ROBOTIC SYSTEMS (CS569)
Credit: 3
Area: Automatic Control (AC)
PIC: Koivo
Prerequisite: EE 382 and basic knowledge of vector-matrix manipulations, or
EE 483, or consent of instructor
Description:
The topics to be covered include: basic components of robotic systems; selection of coordinate frames; homogeneous transformations; solutions to kinematic equations; velocity and force/torque relations; manipulator dynamics in Lagrange's formulation; digital simulation of manipulator motion; motion planning; obstacle avoidance; controller design using the computed torque method; and classical controllers for manipulators.
Text:
A.J. Koivo, Fundamentals for Control of Robotic Manipulators, J. Wiley & Sons, Inc (1989). (0-471-85714-9)
Outline:
Weeks
1. Introduction to Robotic Systems
A. Uses of robotic manipulators 0.5
B. Components of robotic systems 0.5
2. Kinematics of Manipulators
A. Selection of coordinate frames 0.5
B. Transformation matrices 1.5
C. Calculation of inverse solutions 1.0
3. Velocities, Forces, Torques in Joint and Base Coordinates
A. Differential motion 1.0
B. Velocity relations 0.5
C. Determination of forces, torques in a manipulator 0.5
4. Dynamic Modelling
A. Lagrange's energy expressions for a manipulator 1.0
B. Lagrange's equation of motion 1.0
C. Digital simulation of manipulator model
5. Trajectory Planning
A. Joint trajectory 1.0
B. Cartesian path 1.0
6. Path Control of Manipulator
A. Classical system design 0.5
B. PID-controller design 0.5
C. Force-torque control 1.0
7. Special Topics 2.0
8. Exams
EE 570 ARTIFICIAL INTELLIGENCE
Credit: 3
Area: Computer Engineering (CE)
PIC: Brodley
Prerequisite: Data Structures (EE 368), Probabilistic Methods (EE 302)
Prerequisite by description:
Basic understanding of data structures including the proper use of arrays, lists, trees and queues. Understanding of searching and sorting concepts. Basic understanding of probability and statistics, including Bayes rule, statistical tests of significance, and the normal distribution.
Description:
Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how in some cases it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamentals issues in all aspects of artificial intelligence.
Objective:
To provide students with a comprehensive overview of the principles of artificial intelligence.
Text:
Russell, S & Norvig, P., Artificial Intelligence: A Modern Approach, Prentice Hall, 1995, ISBN: (0-13-103805-2)
Continued next page
EE 570 ARTIFICIAL INTELLIGENCE
Outline:
Weeks
1. What is AI?; Blind search 1.0
2. Heuristic search 2-4
3. Game Playing 5-6
4. Knowledge and reasoning 7.0
5. First-order logic and theorem proving 8-9
6. Logical Reasoning Systems 10.0
7. Planning 11.0
8. Advanced topics chosen from:
Learning, Computer Vision, Natural Language
Understanding, and reasoning Under Uncertainty 12-15
Computer Usage:
Throughout the course homework assignments will require the use of computer skills developed in previous courses. Knowledge of algorithms and data structures will be used to develop programs capable of solving artificial intelligence problems in areas such as general search, game theory, deduction, and planning. Students will be encouraged to use the programming languages LISP and Prolog for their projects.
EE 573 COMPILERS & TRANSLATOR WRITING SYSTEMS
Credit: 3
Area: Computer Engineering (CE)
PIC: Eigenmann
Prerequisite: Proficiency in C language and basic understanding of compilers.
Description:
This course presents the concepts needed to efficiently design and implement translators. Basic compiler/translation theory and technology are briefly reviewed, after which the course focuses on software tools for the automatic construction of translators, as well as more complex concepts involving the construction of compiler symbol tables, etc. Using C on ECN UNIX, each student will construct a simple lexical-recognizer generator, parser generator, and code-generator generator.
Objective:
To give students a better understanding of translation systems, stressing tools for compiler construction.
Text:
Fischer and LeBlanc, Crafting a Compiler with C, Benjamin/Cummings, 1991; course notes and research papers will be used. (0-8053-2166-7)
Continued next page
EE 573 COMPILERS & TRANSLATOR WRITING
SYSTEMS
Outline:
Weeks
1. Translation/Compilation Goals: Compilers; Assemblers;
Interpreters; Natural Language 1.0
2. Organization of a Translator 0.5
3. Grammars: Chomsky Hierarchy; Ambiguities; Determinism;
BNF and Syntax Diagrams 1.0
4. Syntax Analysis (Parsing): Recursive Descent,
Precedence, and Shift/Reduce Parsing; Parser-Generators;
Error Detection & Recovery 2.0
5. Lexical Analysis: Separation from Syntax; String
Comparison, Scanning, NFA, DFA, and Atomic 2.0
Techniques; Lexical Recognizer Generators
6. Symbol Tables: Linear, Tree, and Hashed Tables;
Simple and Complex Data Types; Type Coercion/Casting;
Data Allocation 2.0
7. Intermediate Parse Representations: Pseudo-code Models;
Tuples; Parse/Expression Trees; Interpreters 1.0
8. Code Generation: Techniques; Template-Driven Generators 2.0
9. Introduction To Code Optimization (Improvement) 0.5
10. Project discussions (dispersed throughout term) 2.0
11. Exams 1.0
EE 574 SOFTWARE ENGINEERING METHODOLOGY
Credit: 3
Area: Computer Engineering (CE)
PIC: Mowle
Prerequisite: EE 461 or graduate standing with a good working knowledge of C programming, UNIX tools and data structures.
Description:
Topics include: Life cycle models, software planning, software analysis, software design including data flow and data structure design, software testing methods, and software documentation. A software design project is a part of the course requirements.
Objective:
To introduce students to current software process and life cycle models. To introduce students to software management methods for controlling and managing software projects. Topics include: Life cycle models, requirements gathering, software planning, software quality, risk management, software inspections, software metrics, software management concepts. Team project work is part of the course requirements. Students are expected to use their programming skills and knowledge of data structures to design and test software generated during their team project activities.
Text:
1. Robert S. Pressman, Software Engineering: A Practitioner's Approach, 4th edition, McGraw-Hill, Inc., 1992. (0-07-052183-2)
2. M. Shaw and D. Garlan, Software Architecture, Perspectives on an Emerging Discipline, Prentice Hall. (0-13-182957-2)
Outline:
Lectures
1. Life Cycle Models 6.0
2. Requirements 5.0
3. Software Risk Management 5.0
4. Software Quality Management 4.0
5. Software Inspections 3.0
6. Software System Testing 7.0
7. Software Metrics 3.0
8. Software Management 5-6*
9. Projects Work 4.0
10. Exams 2.0
*5 if M,W,F; 6 if T,Th
EE 576 IMAGE SYNTHESIS
Credit: 3
Area: Computer Engineering (CE)
PIC: Maciejewski
Prerequisite: EE 301 or graduate standing
Description:
The purpose of this course is to introduce techniques for producing synthetic photo-realistic images by simulating the interaction of light with 3D geometric models. The emphasis will be on computationally expensive techniques centered around ray tracing and radiosity as opposed to approximate algorithms amenable to real-time display.
Text:
A. Watt and M. Watt, Advanced Animation and Rendering Techniques, Addison-Wesley, 1992.
Outline:
Weeks
1. Introduction (Ch.1 of text) 1.0
2. Local Illumination Models (Ch.2) 2.0
3. Basic Ray Tracing (Ch.8) 1.0
4. Accelerated Ray Tracing (Ch.9) 1.0
5. Aliasing (Ch.4) 2.0
6. Texture Mapping (Ch.6) 1.0
7. Distributed Ray Tracing (Ch.10) 2.0
8. Shadows (Ch.5) 1.0
9. Radiosity (Ch.11) 2.0
10. Global Illumination Models (Ch.12) 1.0
11. Exams and Project 1.0
EE 577 ENGINEERING ASPECTS OF REMOTE SENSING
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Landgrebe
Prerequisite:
EE 301, EE 302, or equivalent, or graduate standing. Description: Introduction to the concepts of optical and microwave multispectral image generation and analysis. Fundamentals of imaging sensor design and image analysis for complex scenes. Application of signal processing and signal design principles, and of statistical pattern recognition to these problems. Spatial image processing, cartographic and geographic information systems techniques, as appropriate to land scene data.
Objective:
To provide primarily graduate students with an introduction to modern remote sensing techniques. The course is intended to prepare the student to undertake research in remote sensing and related areas, as preparation for post graduation professional activities in remote sensing, or as a means of further broadening one's background in the general field of image generation and processing. For students interested in image processing in general, it provides a look at the combined optimization of image design and image analysis, and an introduction to techniques applicable for scenes of high complexity.
Text:
J.A. Richards, Remote Sensing Digital Image Analysis: An Introduction, Springer-Verlag, 1986. (3-540-54840-8)
Continued next page
EE 577 ENGINEERING ASPECTS OF REMOTE SENSING
Outline:
Weeks
1. Introduction 1.0
A. How information is conveyed in remote sensing data
B. The nature of multivariate images
C. Image space and feature (measurement) space
D. Introduction to pattern recognition
2. Statistical Pattern Recognition in Remote Sensing 4.0
A. Decision-making in the face of uncertainty
B. Fundamental steps in pattern recognition
C. Supervised and unsupervised classification
D. Multitemporal and multitype data analysis
3. How Scenes Become Signals Become Images 6.0
A. Electromagnetic energy interactions with the atmosphere
B. Characteristics of natural scene reflectance and emittance
C. Signal design as applied to sensor design
E. Modern optical and microwave sensor systems
E. Ancillary sources (digital) terrain maps, map digitizing,
non-imaging sensors, etc.
4. Spatial Image Processing and Analysis 3.0
A. Registration and rectification
B. Texture
C. Image segmentation
D. Map (GIS) analysis techniques
5. Exams 1.0
EE 580 OPTIMIZATION METHODS FOR SYSTEMS AND CONTROL
Credit: 3
Area: Automatic Control (AC)
PIC: Chong
Prerequisite: Elements of linear algebra and calculus of several variables; some experience with MATLAB helpful.
Description:
Introduction to optimization theory and methods, with applications in systems and control. Nonlinear unconstrained optimization, linear programming, nonlinear constrained optimization, various algorithms and search methods for optimization, and their analysis. Examples from various engineering applications are given.
Text:
E.K.P. Chong and S.H. Zak, An Introduction to Optimization, New York, NY, John Wiley & Sons, Inc., 1996, (0471-08949-4)
Continued next page
EE 580 OPTIMIZATION METHODS FOR SYSTEMS AND CONTROL
Outline:
Weeks
1. Introduction
A. Motivating examples; mathematical preliminaries (Chapters 1-5) 1.0
B. Unconstrained optimization 1.5 First and second order conditions (Chapter 6)
2. Algorithms for unconstrained optimization 4.0
A. One dimensional search methods (Chapter 7)
B. Gradient methods (Chapter 8)
C. Newton methods (Chapter 9)
D. Conjugate direction methods (Chapter 10)
E. Quasi-Newton methods (Chapter 11)
3. Least squares analysis (Chapter 12) 1.0
Examples; basic properties, recursive least squares algorithm
5. Linear programming (Chapters 15-17) 2.5
Examples; basic properties; simplex method; duality.
6. Nonlinear constrained optimization (Chapters 19-20) 3.0
Equality and inequality constrainst; Lagrange conditions;
Karush-Kuhn-Tucker conditions; Second order conditions
7. Convex optimization (Chapter 21) 1.0
Convexity; Optimality conditions
EE 589 STATE ESTIMATION AND PARAMETER IDENTIFICATION OF STOCHASTIC SYSTEMS
Credit: 3
Area: Automatic Control (AC)
PIC: Kashyap
Prerequisite: EE 302
Description:
Introduction to point estimation, least squares, Bayes risk and maximum likelihood. Optimum mean-square recursive estimation for non-dynamic stochastic systems. State estimation for discrete-time and continuous-time dynamic systems. Parameter identification of stochastic systems using maximum likelihood. Stochastic approximation, least squares and random search algorithms.
Text:
A. Gelb, Applied Optimal Estimation, MIT Press, Cambridge, Mass., 1974. (0-262-20027-9)
Outline:
Weeks
1. Review of Probability Theory 1.0
2. Point Estimation - Optimal and Acceptable Estimators 1.0
4. Recursive Estimation - Optimal Mean Square Estimator 2.0
(Project 1)
5. Stochastic Approximation Methods 1.0
6. State Estimation - Discrete-Time Kalman Filter 2.0
6. State Estimation - Continuous-Time Kalman Filter 2.0
(Project 2)
8. System Identification Using Maximum Likelihood 1.0
9. System Identification Using Stochastic Approximation 1.0
10. System Identification Using Least Squares 1.0
(Project 3)
11. System Identification Using Random Search 1.0
12. Exams 1.0
EE 595 SELECTED TOPICS IN ELECTRICAL ENGINEERING
Credit: Variable
Area: N/A
Prerequisite: Admission by consent of instructor.
Description:
Formal classroom or individualized instruction on advanced topics of current interest.
EE 600‡ RANDOM VARIABLES AND SIGNALS
Credit: 3
Area: Communications and Signal Processing (CS)
Biomedical Engineering (BE)
PIC: Graduate Committee
Prerequisite: Graduate Standing
Description:
Engineering applications of probability theory. Problems on events, independence, random variables, distribution and density functions, expectations, and characteristic functions. Dependence, correlation, and regression; multi-variate Gaussian distribution. Stochastic processes, stationarity, ergodicity, correlation functions, spectral densities, random inputs to linear systems; Gaussian processes.
Suggested Background: EE 440 or 483 or
Objective:
This course is one of the required core courses and, as such is intended to provide breadth in a students program and also to serve as a prerequisite for more advanced courses in communications and control. The emphasis is on applications of probability to engineering problems and the major objective is to train the student to formulate such problems within the framework of probability theory. It is not intended to be a course in mathematical probability and is no substitute for such a course for those students planning research in areas requiring probability theory.
Text:
Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd edition, McGraw Hill, 1984. (0-07-048468-6)
Continued next page
EE 600‡ RANDOM VARIABLES AND SIGNALS
Outline:
Weeks
1. The Meaning of Probability 1.0
A. Preliminary Remarks
B. The Various Definitions of Probability
C. Determinism versus Probability
2. The Axioms of Probability
A. Set Theory
B. Probability Space
C. Conditional Probabilities and Independent Events
D. Summary
3. The Concept of a Random Variable 2.0
A. Random Variables; Distributions; Densities
B. Examples of Distribution and Density Functions
C. Conditional Distributions and Densities
D. Bayes' Theorem in Statistics (re-examined)
4. Functions of One Random Variable 3.0
A. The Concept of a Function of One Random Variable
B. Determination of the Distribution and Density of y=g(x)
C. Applications
D. Expected Value; Dispersion; Moments
E. Characteristic Functions
5. Two Random Variables
A. Joint Distribution and Density Functions
B. Conditional Distributions and Densities
C. Independent Random Variables
D. Jointly Normal Random Variables
6. Functions of Two Random Variables 4.0
A. One Function of Two Random Variables
B. Two Functions of Two Random Variables
C. Expected Value; Moments; Characteristic Functions
D. Mean-square Estimation; the Orthogonality Principle
E. More on Normal Random Variables
7. Sequences of Random Variables 5.0
A. General Concepts
B. Mean; Mean-square Estimation; Moments;
Characteristic Functions
8. Sequences of Random Variables 6.0
A. Applications
B. Normal Random Variables
C. Convergence Concepts and the Law of Large Numbers
D. The Central-limit Theorem
Continued next page
EE 600‡ RANDOM VARIABLES AND SIGNALS
Outline:
Weeks
RANDOM PROCESSES
9. General Concepts 7.0
A. Introductory Remarks
B. Special Processes
C. Definitions
D. Stationary Processes
E. Transformation of Stochastic Processes (Systems)
F. Stochastic Continuity and Differentiation
G. Stochastic Differential Equations
H. Stochastic Integrals; Time Averages; Ergodicity
10. Correlation and Power Spectrum of Stationary Processes 8,9
A. Correlation
B. Power Spectrum
C. Linear Systems
D. Hilbert Transforms; Shot Noise; Thermal Noise
E. Mean-square Periodicity and Fourier Series
F. Band-limited Processes
G. An Estimate of the Variation of a Band-Limited Process
11. Linear Mean-square Estimation 10.0
A. Introductory Remarks
B. The Orthogonality Principle in Linear Mean-square Estimation
C. The Wiener-Kilmogoroff Theory
12. Linear Mean Square Estimation 11,12
A. The Filtering Problem
B. The Prediction Problem
C. Wide-sense Markoff Sequences and Recursive Filtering
13. Nonstationary Processes; Transients in Linear Systems
with Stochastic Inputs 13.0
A. Transients in Linear Systems with Stochastic Inputs
B. Two-dimensional Fourier Transforms
C. Time Averages
14. Harmonic Analysis of Stochastic Processes 14.0
A. Series Expansions
B. Approximate Fourier Expansion with Uncorrelated Coefficients
C. Fourier Transforms of Stochastic Processes
D. Generalized Harmonic Analysis
Three one-hour Exams plus Final Exam.
EE 602 LUMPED SYSTEM THEORY
Credit: 3
Area: Automatic Control (AC)
PIC: Graduate Committee
Prerequisite: Graduate Standing and MA 511
Description:
An investigation of the basic theory and techniques of modern system theory, emphasizing linear state model formulations of continuous and discrete time systems in the time domain and frequency domain. Coverage includes notions of linearity, time invariance, discrete and continuous times state models, canonical forms, associated transfer functions and impulse response models, the state transition matrix, the Jordan form, controllability, observability, and stability.
Suggested Background: EE 301
Objective:
This course is part of the core curriculum for graduate students receiving the masters degree. As such, this offering is intended to provide breadth in the student's program and to serve as prerequisites for more advanced courses.
Text:
P.J. Antsoklis and A. Michel, Linear Systems, McGraw Hill, N.Y. (0-07-041433-5 ISBN)
Continued next page
EE 602 LUMPED SYSTEM THEORY
Outline:
Lectures
1. Basic Concepts, Vocabulary, and Notation of Systems and State Models 3.0
2. Formal Definition and Examples of Linear Time Invariant and
Time Varying State Models for Lumped Systems 2.0
3. Canonical State Models from Ordinary Differential Equations 4.0
4. Newton Raphson Technique and Numerical Simulation of State Models 2.0
5. Linear Discrete Time State Models:
Basics and Parallels with Continuous Time Case 2.0
6. Existence and Uniqueness of Solutions 2.0
7. State Transition and Fundamental Matrices of Linear Time Varying
State Model 4.0
8. Closed Form Solution to Linear Time Varying State Model 2.0
9. Eigenvalue-Eigenvector Techniques for Computing e At 3.0
10. Discrete Time State Models 3.0
11. Impulse Response Matrices and Transfer Function Matrices 4.0
12. Controllability of Linear Time Invariant State Models 4.0
13. Observability of Linear Time Invariant State Models 3.0
14. BIBS and BIBO Stability 4.0
15. Exams 3 0
EE 604 ELECTROMAGNETIC FIELD THEORY
Credit: 3
Area: Fields and Optics (FO)
PIC: Graduate Committee
Prerequisite: Graduate Standing
Description:
Review of general concepts (Maxwell's equations, materials interaction, boundary conditions, energy flow); statics (Laplace's equation, Poisson's equation); distributed parameter systems (classification of solutions, transmission lines, and waveguides); radiation and antennas (arrays, reciprocity, Huygen's principle); a selected special topic (e.g. magnetostatics, waves in anisotropic media and optical fibers).
Suggested Background: EE 311 or
Text:
Ramo, Whinnery, Van Duzer, Fields and Waves in Communication Elec., 3rd Edition, (1984). (0-471-87130-3)
Outline:
Weeks
1. Electrostatics and dielectric materials 1 1/3
2. Magnetostatics and magnetic materials 1 1/3
3. Maxwell's equations 1.0
4. Propagation and reflection of plane waves 1.0
5. Numerical methods, and product-solution method. 2/3
6. TE and TM mode, transmission lines and wave guides 2.0
7. Resonant cavities 1.0
8. Antennas 2.0
9. Fresnel and Fraunhofer diffraction 1.0
10. Gaussian beams 2/3
11. Special topic selected by the instructor 2.0
12. Review and Exams 1.0
EE 606‡ SOLID-STATE DEVICES
Credit: 3
Area: Solid State Devices and Materials (SS)
VLSI and Circuit Design (VC)
PIC: Graduate Committee
Prerequisite: Graduate Standing.
Description:
A relatively-broad moderate-depth coverage of semiconductor devices and related topics. The first portion of the course presents and examines semiconductor fundamentals required in the operational analysis of solid state devices. A detailed examination of the PN junction diode and PN junction devices follows. The final portion of the course treats heterojunction surface devices including the Schottky diode, the MOS capacitor and MOSFET.
Suggested Background: An undergraduate introduction to semiconductor physics and devices would be helpful but not essential.
Objective:
The objectives of this course are to acquaint students with the semiconductor fundamentals which underlie the operation of devices in general as well as with specific devices now in widespread use. The topics are developed from the physical approach with an emphasis on the mechanisms that control device operation.
Text:
1. S.M Sze, Physics of Semiconductor Devices, Wiley-Interscience, 2nd edition, 1981. (0-471-05661-8)
2. R. F. Pierret, Advanced Semiconductor Fundamentals, Volume VI in the Modular Series on Solid State Devices, Addison-Wesley, 1987. (0-201-05338-1)
Continued next page
EE 606‡ SOLID-STATE DEVICES
Outline:
Weeks
1. Semiconductor materials properties and crystal structure 1.0
2. Elements of quantum mechanics 2.0
3. Energy band theory 3.0
4. Equilibrium carrier statistics 4.0
5. Recombination-generation processes 5.0
6. Carrier transport 6.0
Exam #1
7. PN homojunction diodes 7.0
8. Junction breakdown: impact ionization and tunneling 8.0
9. Transient and a.c. behavior 9.0
10. Bipolar transistors and PNPN devices 10.0
Exam #2
11. Semiconductor heterojunctions 11.0
12. MS diodes 12.0
13. MOS and modulation-doped capacitors: ideal characteristics 13.0
14. MOS capacitors: non-ideal characteristics 14.0
15. MOSFET's 15.0
Final Exam
EE 608‡ COMPUTATIONAL MODELS AND METHODS
Credit: 3
Area: Computer Engineering (CE)
VLSI and Circuit Design (VC)
PIC: Graduate Committee
Prerequisite: Graduate Standing
Description:
Computation models and techniques for the analysis of algorithm complexity. The design and complexity analysis of recursive and non-recursive algorithms for searching, sorting, set operations, graph algorithms, matrix multiplication, polynomial evaluation and FFT calculations. NP-complete problems.
Suggested Background: EE 368 and EE 369
Objective:
This is one of the graduate core courses and, as such, is intended to add breadth to a student's program and also to provide fundamental knowledge regarding algorithm design that is needed in more advanced courses in the computer engineering area. The course emphasizes understanding the classes of problems that can be solved by computers and quantifying the performance of algorithms used to solve such problems.
Text:
T. Cormen, C. Leiserson and R. Rivest, Introduction to Algorithms, McGraw-Hill (0-07-013143-0)
Outline:
Weeks
1. Time and space complexity; analysis methods 1.0
2. Models of computation Turing machine 2.5
3. Recurrence formulas, discrete mathematics 2.5
4. Sorting 1.5
5. Search; Set Operations 1.5
6. Graph Algorithms 2.0
7. Polynomial, matrix and FFT algorithms 1.0
8. NP-complete problems 2.0
EE 610 ENERGY CONVERSION
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Graduate Committee
Prerequisite: EE 321
Description:
Basic principles of static and electromechanical energy conversion. Control of static power converters. Reference frame theory applied to the analysis of rotating devices. Analysis and dynamic characteristics of induction and synchronous machines. State variable analysis of electromechanical devices and converter supplied electromechanical drive systems.
Objective:
Provide an entering MSEE student with a basic background in static and electromechanical conversion devices. This course is intended for students with interests in the control of electrical and electromechanical systems with applications in robotics and electric energy systems.
Text:
1. P. C. Krause, Analysis of Electric Machinery, McGraw-Hill, 1986, ISBN (0-7803-1101-9)
2. N. Mohan, T. M. Undeland and W.P. Robbins, Power Electronics, John Wiley, 1989. (0-471-61342-8)
Outline:
Weeks
1. Introduction - Ideal Switching Networks Rectifier Circuits, Transients 3.0
2. AC Control, Single Phase,ÊBridge Converter 3.0
3. Bridge Converter, Switch Models (Thyristors, etc.) Commutation 3.0
4. Converter Control, Converter System Example Design and Analysis 4.0
Exam #1
5. Magnetic Circuits and Electromechanical Energy Conversion Principles 6.0
6. Reference Frame Theory. 3.0
7. Induction Machines 6.0
Exam #2
8. Introduction to Synchronous Machines 3.0
9. Brushless DC Machines 6.0
10. Analysis of Stepper and Servomotors 6.0
EE 613 DIFFRACTIVE OPTICS
Credit: 3
Area: Fields and Optics (FO)
PIC:
Prerequisite: Basic knowledge of fields
Description:
An introduction to the modern theory of diffraction is developed from the viewpoints of scalar theory as well as electromagnetic theory. Consideration is given to the limits of validity of scalar theory. Topics to be covered include computer generated holograms, the electromagnetic theory of gratings, and numerical methods of solution. Discussions will also include a study of those devices that have been fabricated as diffractive optical elements such as the intra-ocular lens implant, and microwave holograms.
Objective:
Developments in the areas of Device Fabrication and Computer Technology have allowed for the fabrication of optical components not possible 5 years ago. These diffractive optical components have found application in optical testing, integrated optics, optical display for both commercial and military applications. A common example is the compact optical disc. This course highlights the theoretical analysis and fabrication issues for these optical an microwave components.
Text:
Material from assorted textbooks and research papers will be collected into class notes to be distributed.
Continued next page
EE 613 DIFFRACTIVE OPTICS
Outline:
Lectures
1. Maxwell's Eqns. 2.0
2. Scalar Diffraction
A. Kirchoff 1.0
B. Rayleigh-Summerfeld 1.0
C. Fresnel Diffraction 1.0
D. Fraunhofer Diffraction 1.0
3. Diffractive Analysis of Optical Systems
A. Thin Elements 3.0
B. Thick Elements & Off Axis Systems 6.0
4. Exam 1.0
5. Vector Theory Revisited/Polarization/Relationship to
Scalar Theory/The Jones Calculus/ Computational Issues 6.0
6. Reflection from Conductors 1.0
7. Reflection from Dielectrics 1.0
8. Reflection from Metals (not perfect conductors) 1.0
9. Diffraction from Semi-infinite Conducting Sheets 2.0
10. Reflection from Corrugated Conducting Surfaces 6.0
11. Diffraction & Reflection through & from Corrugated Dielectrics 6.0
12. Computer-Generated Diffractive Elements 6.0
EE 614 INTEGRATED & FIBER OPTICS
Credit: 3
Area: Fields and Optics (FO)
PIC: Chen
Prerequisite: EE 604
Description:
The propagation and loss characteristic of various guided wave structures are treated. Emphasis is on the fundamental concepts crucial in the understanding and the basic techniques useful in analyzing integrated and fiber optic devices and components. Also presented are current research topic in optical waveguides.
Objective:
To provide the student with a firm theoretical base for understanding and analyzing guided wave components and to introduce the student to the research topic so interest.
Text:
N. Nishihara, M. Harana and T. Suhara, Optical Integrated Circuits, McGraw Hills Book Company (1989).
Outline:
Lectures
1. Introduction to thin-film waveguides, modes and cutoff 5.0
2. Types of optical waveguides and their fabrication 4.0
3. Prism and end-fire coupling 3.0
4. Directional couplers 5.0
5. Current topics in integrated optics 3.0
6. Step-index and graded index optical fibers 6.0
7. Characterization of single mode filters 6.0
8. Birefringence in single mode fibers 6.0
9. Current topics in optical fibers 4.0
10. Exams 2.0
EE 615 NONLINEAR OPTICS
Credit: 3
Area: Fields and Optics (FO)
PIC: Elliott
Prerequisite: EE 552 and EE 604, or consent of instructor
Description:
An in-depth study of nonlinear optics. After a review of linear effects, several nonlinear optical processes an applications are discussed. These include electro-optic switches and modulators, harmonic light generators, sum and difference frequency mixing, parametric amplifiers and oscillators, and phase conjugate mirrors. Discussions of nonlinear spectroscopy include topics such as two-photon absorption, saturation spectroscopy, Raman spectroscopy and double-optical resonance measurements. Photon-echoes and other transient effects, and surface effects are also discussed.
Objective:
The purpose of this advanced level course is to introduce the student to principles, techniques, and applications of nonlinear optics. The student should be able to calculate nonlinear intensities and transition rates, to design nonlinear devices and to apply nonlinear optical techniques to solve problems.
Text:
Boyd, R. W., Nonlinear Optics, Academic Press, 1992, ISBN 0-12-121680-2
Outline:
Lectures
1. Introduction 1.0
2. Linear Optics 2-5
3. Nonlinear Polarization 6-8
4. Electro-optic Effect, Modulators 9-12
5. Second harmonic generation, 13-14
6. Sum and Difference Frequencies
Phase Matching (General, Crystals, Gases) 15-18
7. Parametric Amplifiers and Oscillators 19-20
8. Two-photon Absorption 21-23
9. High Resolution Spectroscopy 24-28
10. Four Wave Mixing 29-32
11. Nonlinear Refractive Index effects 33-34
12. Coherent Optics 35-39
13. Strong Interactions 40-43
EE 616 ULTRAFAST OPTICS
Credit: 3
Area: Fields and Optics (FO)
PIC: Weiner
Prerequisite: EE 552 or consent of instructor.
Description:
A study of the physics, technology, and applications of ultrashort laser pulses. Topics to be covered include the following: Methods for generating and measuring ultrafast laser pulses. Basic physical processes affecting ultrashort pulses. Devices for manipulating ultrashort pulses. Ultrafast nonlinear optics, including nonlinear optics in fibers, nonlinear refractive index effects, pulse compression, solitons, and all-optical switching. Time-resolved spectroscopy of ultrafast materials processes. Applications to ultrafast optoelectronics. In addition, each student will select a specific topic for in-depth study.
Text:
G.P. Agrawal, Nonlinear Fiber Optics, 2nd edition, Academic Press, 1995.
Continued next page
EE 616 ULTRAFAST OPTICS
Outline:
Lectures
1. Introduction 1.0
2. Ultrashort Pulse Generation Techniques 10.0
A. Mole-locking
B. Physical processes affecting ultrashort pulses
C. Types of ultrashort pulse lasers
D. Amplifiers
3. Measurement AND Manipulation 10.0
A. Techniques for measuring ultrashort pulses
B. Dispersion and dispersion compensation
C. Pulse shaping
4. Ultrafast Nonlinear Optics 9.0
A. Nonlinear optics in optical fibers
B. Self-phase-modulation
C. Pulse compression
D. Solitons
E. All-optical switching
5. Time-Resolved Spectroscopy 8.0
A. Pump-probe measurements
B. Transient grating measurements
C. Orientational effects
D. Photon echoes
E. Examples
6. Ultrafast Optoelectronics 3.0
7. Student Presentations 3.0
EE 617 ANTENNAS: DESIGN AND APPLICATIONS
Credit: 3
Area: Fields and Optics (FO)
PIC: Webb
Prerequisite: EE 441: prerequisite or co-requisite: EE 604
Description:
Electrically small antennas; Arrays; wire antennas and feeding arrangements; aperture antennas, such as slots, horns, and parabolic reflectors; antennas for multiple frequencies, including log-periodic and other frequency independent types; receiving antennas and the concept of antenna temperature; antenna measurements and evaluation.
Objective:
The objective of the course is to present a summary of both the basic theory and practice of modern antenna technology. Specifically, the student learns to carry out simple designs of several different types of antennas and in the process, becomes familiar with the requirements of different applications for antennas.
Text:
Balanis, Antenna Theory: Analysis and Design, Harper & Rowe, 1982 (0-06-040458-2)
Continued next page
EE 617 ANTENNAS: DESIGN AND APPLICATIONS
Outline:
Lectures
1. Antenna history, review of field equations and methods
of solutions, field of elemental current and superposition. 3.0
2. Small antennas: Impedance, gain efficiency, images effects
of actual ground, transmission line loaded antennas,
Microstrip Antennas. 6.0
3. Arrays: Array factor, polynomial representations,
Fourier representation, Dolph-Chebyshev arrays, Tolerances,
Signal processing arrays. 6.0
4. Wire antennas: Patterns and impedance of straight wires,
bent and curved wires, feeding arrangements, impedance
matching, baluns, mutual effects. 5.0
5. Theory of biconical and cylindrical antennas. 3.0
6. Digital Computation of Antenna Performance Parameters. 6.0
7. Aperture antennas: Slot antennas, slotted cylinders horns,
circular aperture theory and practice. 5.0
8. Antennas for Multiple Frequencies: Spot band antennas,
log-periodic dipoles and other frequency independent antennas. 3.0
9. Receiving antennas: Power transfer, effective area,
noise considerations, antenna temperature, diversity. 3.0
EE 618 NUMERICAL ELECTROMAGNETICS
Credit: 3
Area: Fields and Optics (FO)
PIC: Webb
Prerequisite: EE 604
Description:
The numerical solution of Maxwell's equations is studied. Numerical methods such as the Finite Element Method and the Finite Difference Method are presented for the solution of both differential and integral equations. Applications studied include: waveguides (microstrip, VLSI interconnects, optical, discontinuities), scattering (frequency selective surfaces, arbitrary scatterers), antennas, magnetics, semiconductor devices, and inverse scattering. Papers in the current literature are used.
Objective:
To develop fundamental numerical modeling skills with a view to current research applications in the areas of electromagnetic fields, optics and semiconductor devices.
Text:
P. P. Silvester and R. L. Ferrari, Finite Elements for Electrical Engineers, Cambridge 1986. (0-521-25321-7)
Outline:
Weeks
1. Maxwell's equations, wave eqns., boundary conditions,
equivalence principle, reciprocity 2.0
2. Finite difference solution of Maxwell's eqns., wave eqns. 1.0
3. Variational formulations 1.0
4. Finite element method - solution of differential equations arising
in statics, waveguides, scattering (radiation boundary condition) 3.0
5. Green's identities and development of integral equations 2.0
6. Spectral domain formulation for microstrip, frequency selective
surfaces, printed antennas 2.0
7. Waveguide discontinuities 1.0
8. Inverse scattering 1.0
9. Computer hardware and software issues 1.0
10. Exams 1.0
EE 622 ENGINEERING IN MEDICINE:
NERVOUS SYSTEM
Credit: 3
Area: Biomedical Engineering (BE)
PIC: Biomedical Engineering Staff
Prerequisite: Graduate Standing
Description:
Introduction to the use of engineering in clinical medicine with special emphasis on neurology. Topics include: fundamental properties of excitable tissues, anatomy of the nervous system, hearing and vision, stimulation, electroencephalographic and electromyographic analysis, spinal cord and motor evoked potentials, disorders of the nervous system, novel clinical instrumentation.
Text:
Geddes and Baker, Principles of Applied Biomedical Instrumentation, third edition, John Wiley, 1990 (0-471-60889-8).
Outline:
Weeks
1. Fundamental Properties of Excitable Tissues 2.0
2. Anatomy of the Nervous System 2.0
3. Hearing and Vision 1.0
4. Stimulation
A. Electrical 1.0
B. Magnetic 1.0
5. EEG and EMG Measurement and Analysis 2.0
6. Spinal Cord
A. Structure and Function 1.0
B. Evoked Potentials 1.0
7. Disorders of the Nervous System 1.0
8. Novel Clinical Instrumentation 2.0
EE 624 MULTIMEDIA SYSTEMS
Credit: 3
Area: Computer Engineering (CE)
PIC: Ghafoor
Prerequisite: EE 547 and 562 or equivalent, or consent of instructor.
Description:
This course provides a general coverage of three major areas that include multimedia data management (logical and physical modeling), broadband network architectures and protocols for distributed multimedia communication, and user interface environments. Various models and specification methodologies in these areas are introduced. The discussion is augmented with various case studies.
Text:
None
Outline:
Weeks
1. Introduction to multimedia applications 1.5
2. Multimedia Database Management: Logical Modeling 2.5
3. Case Studies: Oracle Media Server
4. Multimedia Database Management: Physical Management 2.0
5. Broadband multimedia communication 3.5
6. Distributed multimedia systems 2.0
7. User Interface, tools and methodologies 1.5
8. Design on Oracle Web Server
9. Tests and presentation of term papers* 2.0
*Term Paper project can be done based on Oracle Media Server
and broadband networks (ATM, FDDI and 100 Base-T)
EE 625 ANALYSIS OF ELECTROMECHANICAL SYSTEMS II
Credit: 3
Area: Energy Sources and Systems (ES)
Pic: Wasynczuk
Prerequisite: EE 525
Description:
Extension of EE 525. Electric propulsion systems including presentation of cycloconverter and rectifier-inverter drive systems. Dynamic and steady-state analysis of machine performance with series controlled rectifiers in the stator or rotor phases. MMF space harmonic analysis.
Text:
P. C. Krause, Analysis of Electric Machinery, McGraw-Hill, 1986 and class notes. (0-070-35436-7).
Objective:
To prepare students for research in electromechanical systems.
Outline:
Weeks
1. Advanced Analysis of Electromechanical Devices
a. Linearized equations, transfer functions 2
b. Reduced order equations 1
c. Unbalanced operation 2
d. Computer simulation 2
e. Finite element design 2
2. Analysis and Simulation of Electric Drive Systems
a. Rectifier inverter drives 2
b. Electronically commutated machines 2
c. Special 2
EE 626 ADAPTIVE SIGNAL PROCESSING
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Gelfand
Prerequisite: EE 600 or co-requisite: EE 638
Description:
Adaptive transversal and lattice filter based on mean square and least squares error criteria will be discussed. This includes the least-mean square and recursive least squares transversal filters, the gradient lattice filter, the least squares lattice filter, and the fast transversal filter. The course will conclude with a selection of advanced topics such as QR decomposition adaptive filters and systolic implementations, finite precision effects, nonlinear adaptive filters, blind adaptive filters and infinite impulse response adaptive filters. Applications will be considered throughout.
Objectives:
To introduce students to adaptive filters, which have become standard components in communications and signal processing systems.
Text:
S.T. Alexander, Adaptive Signal Processing, Theory and Applications, Springer-Verlag, 1986.
Continued next page
EE 626 ADAPTIVE SIGNAL PROCESSING
Outline:
Lectures
1. Introduction 2.0
Signal processing in unknown environment, some examples
2. Minimum Mean Square Error Estimation 2.0
Statement of optimal filtering problem, normal equations,
orthogonality principle
3. Linear Prediction and the Lattice Structure 3.0
Durbin algorithm, lattice derivation
4. The Least Mean Squares (LMS) Algorithm 5.0
Method of steepest descent, derivation and analysis of the
LMS algorithm
5. Applications of LMS Algorithm 3.0
6. Linear Least Squares Error Estimation 2.0
Statement of linear least squares filtering problem,
deterministic normal equations, orthogonality principle
7. The Recursive Least Squares (RLS) Algorithm 4.0
Matrix inversion lemma, derivation and analysis of the
RLS algorithm
8. Applications of RLS Algorithm 2.0
Adaptive prediction, equalization, system identification
9. Vector Space Approach to Least Squares Filters 4.0
Linear vector spaces, least squares filters and projection
matrices, general update relations
10. The Least Squares Lattice (LSL) Algorithm 4.0
Forward and backward prediction filters, the LS lattice structure,
LSL order and time updates
11. The Fast Transversal Filter (FTF) Algorithm 4.0
Additional vector space relations, FTF time updates
Advanced Topics:
12. QR Decomposition RLS and LSL Algorithms
QR decomposition, Givens rotation, systolic arrays
13. Finite-Precision Effects
Quantization errors, square root vs. square root free algorithms
14. Nonlinear Adaptive Filtering
Wiener and Volterra filters, median filters, neural net filters
15. Blind Adaptive Filtering
Bussgang,polyspectral methods
16. Infinite Impulse Response Adaptive Filtering
Output error, equations error methods 8.0
17. Exams 2.0
EE 627 INTRODUCTION TO CRYPTOGRAPHY AND SECURE COMMUNICATION
Credit: 3
Area: Communication and Signal Processing (CS)
PIC: Delp
Prerequisite: EE 600 or equivalent
Description:
This course introduces the basic concepts of cryptography. Various cipher systems Various cipher systems are presented including transposition and substitution systems, Block ciphers, RSA & Knapsack. Methods used to attack ciphers are discussed with emphasis on complexity. Case studies of use of cryptographic methods in communication systems are presented with some consideration given to privacy issues.
Text:
D. Stinson, Cryptography: Theory and Practice, CRC Press, 1995 (0-8493-8521-0)
Outline:
Lectures
1. Historical Overview of Cryptography 2.0
2. Privacy 1.0
3. Mathematical Overview 4.0
4. What did Shannon say about cryptography 1.0
5. Transposition and Substitution Ciphers 3.0
6. Rotor Machine and Polyalphabetic Ciphers 2.0
7. Block Ciphers: DES 3.0
8. Can DES be attacked 1.0
9. Public Key Systems 5.0
10. Knapsack System 1.0
11. The Knapsack System Bites the Dust! 2.0
12. RSA System 2.0
13. Key Management 1.0
14. Digital Signatures and Authentication 4.0
15. Stream Ciphers 3.0
16. Linear Shift Registers 3.0
17. Non-Linear Shift Register 2.0
18. Privacy and Cryptography 4.0
19. Exam 2.0
EE 628 COMPUTER GRAPHIC SIMULATION AND VISUALIZATION
Credit: 3
Area: Computer Engineering (CE)
PIC: Maciejewski
Prerequisite: EE 569 or ME 573 or knowledge of homogeneous transformations.
Description:
An introduction to techniques for visually simulating multi-dimensional systems that evolve over time. The emphasis will be on the numerical issues that arise as a result of the various approximations that are performed when modeling a time-varying system by a set of mathematical equations. The issue of abstract mappings between simulation variables and visual parameters will also be discussed. Students will demonstrate their command of these topics by implementing a complete computer graphic simulation of a suitable time-varying system.
Objective:
To introduce students to techniques for visually simulating multi-dimensional systems that evolve over time.
Text:
L. Baker and J. P. Gollub, Chaotic Dynamics: An Introduction, Cambridge University Press, 1990. (0-521-38897-X)
Continued next page
EE 628 COMPUTER GRAPHIC SIMULATION AND VISUALIZATION
Outline:
Lectures
1. Overview of Computer Graphic Fundamentals 3.0
2. Specifying Changes in View (Camera Motion) 4.0
A. Position Specification and Interpolation
B. Rotational Interpolation (Quaternions)
3. Equations of Motion 2.0
A. Linear
B. Nonlinear
4. Kinematic Simulations 8.0
A. Singularities
B. Ill-conditioning
5. Numerical Integration 2.0
A. Runge-Kutta
B. Bulirsch-Stoer
6. Dynamic Simulations 7.0
A. Numerical Stability
B. Best Approximate Solutions
7. Visualizing System Evolution 4.0
A. Phase Portraits
B. Poincarre Maps
8. Chaotic Systems 5.0
A. Fractals
B. Strange Attractors
9. Visualizing Multi-Dimensional Results 6.0
A. Projections
B. Parallel Coordinates
C. Principal Component Analysis
10. Aliasing 2.0
A. Temporal
B. Time-varying Spatial
11. Exams 2.0
EE 629‡ INTRODUCTION TO NEURAL NETWORKS
Credit: 3
Area: Communication and Signal Processing (CS) Computer Engineering (CE)
PIC: Ersoy
Prerequisite or
Co-Requisite: EE 600 or consent of instructor
Description:
Information processing with neural networks, biological and engineering implications, learning algorithms, current neural network models and architectures, implementational topics, applications in areas such as signal/image processing, pattern recognition, optimization, simulation, system identification, nonlinear prediction, communications and control.
Objective:
To introduce fundamental concepts in the field of neural networks as an approach to the design of distributed intelligent and adaptive systems.
Text:
S. Haykin, Neural Networks, A Comprehensive Foundation, Prentice College, New York. (0-02-352761-7)
Continued next page
EE 629‡ INTRODUCTION TO NEURAL NETWORKS
Outline:
Weeks
1. Introduction to Artificial Neural Networks 2.0
A. Overview and Fundament Concepts
B. Historical Development
C. Basic Models and Learning Rules of ANNS
D. Distributed Representations
E. Linear Threshold Elements and the Perceptron
2. Feedforward and Multistage Networks 1.0
A. One Stage and Multistage Feedforward Networks
B. Introduction to Sigmoidal, Radial Basis, and Other
Activation Functions
3. Supervised Learning of Feedforward Networks 3.0
A. Discriminant Functions
B. The Perceptron Learning Algorithm
C. The Least Mean Square (LMS) Algorithm and the Adaline
D. The Backpropogation Leaning Algorithm
E. Convergence Analysis
F. Optimal Choices of Leaning Parameters
G. Generalization Properties
4. Recurrent Networks 3.0
A. Hopfield Networks
B. Associative Memory
C. Relations to Liapunov Functions and Stability of Nonlinear Systems
D. Optimization Problems
E. Recurrent Backpropogation Networks
F. Avalanche Networks
F. Reinforcement Learning
5. Unsupervised Learning Networks 3.0
A. Competitive Learning and Other Unsupervised Learning Rules
B. Self-organizing Feature Maps
C. Adaptive Resonance Theory and the Leader Algorithm
D. Principal Component Analysis
E. Other Unsupervised Learning Networks
6. Applications 2.0
A. Signal/Image Processing and Recognition
B. System Identification
C. Nonlinear Prediction
D. Control Applications
E. Intelligence Applications
F. Communications Applications
G. Fault Diagnosis
H. Optimization
7. Exams 1.0
EE 630 TOPICS IN ENERGY SOURCES AND SYSTEMS ENGINEERING
Credit: 1-3
Area: Energy Sources and Systems (ES)
PIC: Energy Sources and Systems Staff
Prerequisite: Admission by consent of instructor
Description:
Coverage of selected topics in Energy Sources and Systems Engineering. Topics may change from semester to semester and will be announced one semester in advance. Possible topics include: machine modeling, power electronics, HVDC transmission, alternative energy systems, or power system topics.
EE 631 DIRECT CURRENT TRANSMISSION SYSTEMS
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Ong
Prerequisite: EE 432
Description:
Fundamental analysis of line-commutated, three-phase bridge converters, as applied to HVDC transmission systems. Methods of control, system protection, abnormal behavior, harmonics.
Objective:
To familiarize the graduate student with the operation of HVDC transmission systems, and to introduce methods for analysis of such systems.
Text:
E. W. Kimbark, Direct Current Transmission, Vol. 1, Wiley, 1971. (0-471-47580-7)
Continued next page
EE 631 DIRECT CURRENT TRANSMISSION SYSTEM
Outline:
Lectures
1. Historical sketch 1.0
2. Present-day operating systems 1.0
3. Future applications 1.0
4. Basic configurations 1.0
5. Comparison with AC transmission 2.0
6. Transformer and valve arrangements 1.0
7. Three-phase bridge circuit 1.0
8. Analysis for commutation angles less than 60° 3.0
9. Rectifier characteristics 3.0
10. Inverter characteristics 1.0
11. Power factor 1.0
12. Compensation 1.0
13. Analysis for commutation angles greater than 60° 3.0
14. Exam #1 1.0
15. Basic control requirements 1.0
16. Converter valve control pulses 1.0
17. Constant current control 1.0
18. Constant extinction angle control 1.0
19. Combined characteristics/power reversal 1.0
20. Tap changer control/constant power control 1.0
21. System stability analysis 2.0
22. Computer simulation of a DC system (Demonstration) 1.0
23. Bypass operation 1.0
24. Arcback in rectifiers 1.0
25. Commutation failure 1.0
26. Analysis for disturbances on DC lines 1.0
27. Selecting line reactors 1.0
28. Preventing commutation failure 1.0
29. Intermittent current 1.0
30. Exam #2 1.0
31. Line discharge through bypass valve 1.0
32. Valve oscillation 1.0
33. Line oscillations/faults 1.0
34. Multiterminal operation/DC breakers 1.0
35. DC surge arresters 1.0
36. AC harmonics & filtering 1.0
37. DC harmonics & filtering 1.0
38. Positive & zero sequence fundamental 1.0
39. Analysis of standing harmonics on DC lines 1.0
EE 632 MACHINE LEARNING AND DATA MINING
Credit: 3
Area: Computer Engineering (CE)
PIC: Brodley
Prerequisite: EE 302 or consent of instructor
Description:
Machine learning is concerned with computer programs that automatically improve their performance through experience. Knowledge discovery in databases is concerned with extracting useful patterns or deviations from data using "data mining" methods. This course introduces students to the primary approaches to machine learning and data mining from a variety of fields, including inductive inference of decision trees, neural network learning, statistical learning methods, reinforcement learning, clustering, and discovery. In addition this course will introduce theoretical concepts such as inductive bias and the PAC (Probably Approximately Correct) learning framework.
Objective:
To introduce students to current machine learning and data mining methods. This course is intended to prepare graduate students with the background with which to undertake research in this area or to apply machine learning and data mining techniques to other areas of research. A course project will be required.
Text:
Selected reading from specialized books and professional publications.
Outline:
1. Introduction: What is machine learning? Concept formation
2. Decision trees: test selection, pruning, MDLP, Increment versus Bach
3. Instance-based learning; logically weighted regression
4. Neural networks: Perceptrons and gradient descent, backpropagation
5. Bayesian approaches: Basics, EM, hidden Markov models
6. Knowledge discovery in databases
7. Empirical evaluation of learning systems
8. Boosting, feature selection
9. Computational learning theory
10. Scientific discovery; deviation detection
11. Clustering
12. Reinforcement learning; Q-learning; TD-learning
13. Learning from time series
14. In Class presentation of course projects
15. In class presentation of course projects, exams
EE 633 MODELING AND SIMULATION OF
POWER SYSTEM COMPONENTS
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Ong
Prerequisite: EE 425 or EE 432 or equivalent
Description:
This course is recommended for those interested in learning to use computer simulation to investigate the dynamic and controlled behavior of electrical power components. Beginning with an introduction to MATLAB/SIMULINK, the course goes through the key steps of modeling, implementing and verifying the simulation of single and three-phase transformers, single and three-phase induction machines, three-phase wound field synchronous machines and permanent magnet machines useful in power applications, each case amply illustrated in projects around some interesting topics. Students are expected to implement and verify about ten simulation projects, and also discuss observed behaviors on topics such as inrush current in transformers, motoring, generating and braking operation of machines, and pulsating torque from subsynchronous resonance.
Objective:
To acquaint the graduate student with the analysis and simulation techniques of power system components.
Text:
Chee-Mun Ong, Dynamic Simulation of Electric Machinery, Prentice-Hall, 1997.
Continued next page
EE 633 MODELING AND SIMULATION OF
POWER SYSTEM COMPONENTS
Outline:
Weeks
1. Modeling philosophy for dynamic simulation of power system 1.0
components
2. Introduction to MATLAB/SIMULINK and numerical techniques. 2.0
Exercises on the use of MATLAB/SIMULINK
3. Modeling and simulation of electromagnetic transients in 2.0
transmission lines using distributed and lumped parameter models.
Discuss simulation techniques and verification methods of
assigned projects.
4. Modeling and simulation of single and three-phase transformers 2.0
including core saturation. Discuss simulation techniques and
verification methods of assigned projects.
5. Modeling and simulation of single and three-phase induction 4.0
machines. Derivation of linearized models. Discuss simulation
techniques and verification methods of assigned projects.
6. Modeling and simulation of sychronous machines. Derivation of 4.0
linearized models. Discusses machine models used in transient
stability studies. Modeling of excitation system, power system
stabilizer, torsional shaft, permanent magnet field machines and
higher order models with mutual leakages in the rotor and stator
windings. Discuss simulation techniques and verification methods
of assigned projects.
EE 635 OPTIMIZATION AND ECONOMIC OPERATON OF INTEGRATED POWER SYSTEMS
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Energy Sources and Systems Staff
Prerequisite: EE 633
Description:
Theory of optimization under equality and inequality constraints, computational methods, and applications to generation scheduling in integrated power systems.
Objective:
To introduce the graduate student to the problems of resource scheduling in power systems and acquaint him with its different aspects:
1. Choice of goals and constraints,
2. Theory, existence and uniqueness of solution,
3. Computational methods and convergence,
4. State-of-the-art and possible future development.
Text:
Allen Wood, Bruce Wollenberg, Power Generation, Operation, and Control, John Wiley & Sons, 1984. (0-471-09182-0)
Outline:
Weeks
1. Motivation, factors pertinent to energy sources scheduling,
factors in system operation 2.0
2. Constrained extrema of functions of a finite number of variables 1.0
3. Transmission loss formulas and coordination equations 3.0
4. Student presentations (Each student will be assigned a relevant
project to study in four to six weeks) 3.0
5. Computational methods 2.0
A. Gradient methods
B. Mathematical programming
C. Simulation and search techniques
6. Optimum load flow 4.0
A. Problem definition
B. Extensions of coordination equations approach
C. Approaches based on Newton Method
D. Approaches based on Linear Programming
E. Approaches based on Nonlinear Programming
EE 636 DYNAMICS AND CONTROL OF INTEGRATED POWER SYSTEMS
Credit: 3
Area: Energy Sources and Systems (ES)
PIC: Wasynczuk
Prerequisite: EE 532
Description:
Description of a variety of transient and control problems associated with interconnected power systems and techniques for their analysis and solution. Practical methods for dynamic analysis of large systems are stressed.
Objective:
To provide an awareness of the significance of dynamic analysis in power systems, to stress the need for careful numerical analysis as well as problem simplification when working with large systems, to show how modern control theory can be applied to improve system security, to stimulate interest in this aspect of systems engineering as well as introducing relevant research topics in the area.
Text:
P. C. Krause, Analysis of Electric Machinery, McGraw-Hill, 1986, and Class notes.
(0-070-35436-7)
Outline:
Weeks
1. Modeling and simulation of synchronous and induction machines 2.0
2. Transmission line dynamics and simulation 2.0
3. Computer representation of excitation systems 1.0
4. Governor and prime mover dynamics 1.0
5. Interconnected system dynamics 1.0
6. Theory of neglecting electromagnetic transients - Time scale
separation. 1.0
7. Transient stability Studies - Simulation methods 2.0
8. Dynamic stability analysis 2.0
9. Heroic measures for transient stability enhancement 1.0
10. Special topics 2.0
EE 637 DIGITAL IMAGE PROCESSING I
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Allebach
Prerequisite: EE 302 and EE 638, or equivalent
Description:
Introduction to digital image processing techniques for enhancement, compression, restoration, reconstruction, and analysis. 2-D signals and systems, sampling and scanning, random fields, discrete cosine transform, discrete Karhunen-Loeve transform, grayscale transformations, linear, ranked order, and morphological filters, human vision, printing and display of images, entropy-based compression, vector quantization, block truncation coding, transform coding, predictive coding, image degradation models, Wiener filter, constrained deconvolution, computed tomography, edge detection, shape representation, segmentation.
Objective:
To develop a sound mathematical foundation in the analysis of 2-D signals and systems as applied to image processing, to introduce a wide range of current image processing techniques, and to illustrate the application of the analytical tools and image processing techniques to some specific image processing problems.
Text:
A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989.
(0-13-336165-9)
Outline:
Weeks
1. 2-D signals and systems 4.0
Continuous-Space Fourier Transform (CSFT)
Imaging systems and convolution
Sampling and scanning
Discrete-Space Fourier Transform (DSFT)
2-D Discrete Fourier Transform (DFT)
2-D digital filtering
2. Discrete picture transforms 1.0
Hadamard transform
Discrete cosine transform (DCT)
Continued next page
EE 637 DIGITAL IMAGE PROCESSING I
Outline:
Weeks
3. Random fields (continued)
2-D random processes
2-D linear systems with stochastic inputs
Discrete Karhunen-Loeve transform (KLT)
4. Image enhancement 2.0
Grayscale transformations
Linear filtering
Ranked order filtering
Morphological filtering
5. Human vision and printing and display of images 1.0
Relative luminous efficiency
Weber's law
Spatial and temporal contrast sensitivity
Channel models
Masking
Calibration
Quantization
Halftoning
6. Image compression 2.0
Overview
Entropy-based compression
Vector quantization
Feature selection
A. Block truncation coding
B. Predictive coding
C. Transform coding
D. Pyramid-based coders
E. Subband coders
7. Image restoration and reconstruction 2.0
Image degradation models
Wiener filter
Constrained deconvolution
Computed tomography
A. Algebraic reconstruction
B. Fourier slice theorem
C. Filtered-back projection
8. Image analysis 1.0
A. Edge detection
B. Edge linking
C. Shape representation
D. Segmentation
9. Exams 1.0
EE 639 ERROR CONTROL CODING
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Lehnert
Prerequisite: EE 600 (or co-requisite)
Description:
The theory and practice of error control coding is examined. The study includes the arithmetic of Galois fields as well as linear block, cyclic, and convolutional codes. Some applications of codes in digital communication systems and in computer systems are presented.
Objective:
To present the theory and methodology of error control coding in digital communication systems and computers.
Text:
S. Wicker, Error Control Systems for Digital Communication and Storage, Prentice Hall, Engelwood Cliffs, N.J., 1995 (ISBN 0-13-200809-2).
Continued next page
EE 639 ERROR CONTROL CODING
Outline:
Weeks
1. Linear Block Codes 8.0
A. Shannon Channel Coding Theorem
B. Basics: Matrix Descriptions, Hamming Distance, Hamming Codes
C. Syndrome Decoding
D. BSC Performance and Performance Bounds
E. Finite Field Algebra
F. Cyclic Codes (+ implementation circuits)
G. BCH and Reed-Solomon Codes
H. Peterson-Massey-Berlekamp Decoding Algorithm
I. Other Decoding Algorithms: Majority Logic and Meggit
2. Binary Convolutional Codes 4.0
A. Basics (state diagram, trellis, etc.)
B. The Viterbi Algorithm (+ register exchange & back tracing)
C. Sequential Decoding
D. The Union Bound and the Transfer-Function Bound
3. Coded Modulation 3.0
A. Shannon's Channel Coding Theorem Revisited
(bandwidth efficiency)
C. Set-Partition Trellis Coding
D. Continuous Phase Modulation
EE 640 TOPICS IN COMMUNICATION ENGINEERING
Credit: 1-3
Area: Communications and Signal Processing (CS)
PIC: Communications and Signal Processing Staff
Prerequisite: Admission by consent of instructor
Description:
A series of one credit hour courses offering an introductory treatment of topics of special interest. Topics change from semester to semester and will be announced in advance. Possible topics include spectral estimation, orthogonal representation of signals, adaptive signal processing, array signal processing, optical communication, nonlinear signal processing, ill-posed inverse problems, satellite communications, advanced coding topics.
EE 641 DIGITAL IMAGE PROCESSING II
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Bouman
Prerequisite: EE 600 and EE 637
Description:
An advanced treatment of selected topics in digital image processing. Image models, color, digital video, synthetic aperture radar, magnetic resonance imaging, stack filters, morphological filters, inverse problems in computational vision, multiscale techniques.
Objective:
To bring the student up to the level of current research in selected areas of active research in digital image processing.
Text:
None
Continued next page
EE 641 DIGITAL IMAGE PROCESSING II
Outline:
Weeks
1. Image models 3.0
Random field models
Autoregressive
Markov
Hierarchical models
2. Color 2.0
Radiometry and photometry
Trichromatic theory
Physiological basis
Computational color
3. Time-varying imaging 2.0
Digital video
Motion compensation
Interframe interpolation
High definition TV (HDTV)
Video teleconferencing
4. Inverse problems in imaging 2.0
Computed tomography
Synthetic aperture radar
Magnetic resonance imaging
5. Nonlinear filtering 2.0
Stack filters
Morphological filters
6. Inverse problems in computational vision 3.0
Motion estimation
Surface reconstruction
Shape
Edge detection
Multiscale approaches
Neural network approaches
7. Exams 1.0
EE 642 INFORMATION THEORY AND SOURCE CODING
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: CS Area Staff
Prerequisite: EE 600
Description:
A treatment of the basic concepts of Information Theory. Determination of channel capacity and its relation to actual communication systems. Rate distortion theory is introduced and the performance of various source codes is presented.
Text:
R. G. Gallager, Information Theory and Reliable Communication, John Wiley & Sons. (0-471-29048-3)
Continued next page
EE 642 INFORMATION THEORY AND SOURCE CODING
Outline:
Lectures
1. Introduction
A. A Prototypical Communications System 1.0
B. Discrete information source 1.0
C. Typical sequences 2.0
D. Weak Law of Large Numbers 1.0
E. Entropy 1.0
2. Noiseless Source Coding
A. Shannon's First Theorem (including converse) 2.0
B. Entropy and its Properties 1.0
C. Variable Length Codes 2.0
3. Channel Coding for Discrete Memoryless Channels
A. Jointly typical sequences 1.0
B. Mutual Information and its properties 2.0
C. Shannon's Second Theorem (including converse) 2.0
D. Combined source and channel coding theorem 1.0
E. Channel Capacity 2.0
F. Continuous Sources and Channels 2.0
4. Source Coding for Discrete Sources with Memory
A. Typical sequences 1.0
B. Markov Sources 1.0
C. Entropy of Markov Sources 1.0
D. Coding theorems revisited 1.0
5. Source Coding with a Fidelity Criterion
A. Distortion measures 1.0
B. The rate-distortion function 3.0
C. Block Codes 1.0
D. Quantizers 2.0
E. Transform Coding 2.0
F. Sliding Block Codes 2.0
G. Predictive Coding (DPCM) 1.0
H. Universal source coding (Ziv-Lempel Alogrithm) 2.0
I. Vector Quantizers 2.0
6. Exams 3.0
EE 643 STOCHASTIC PROCESSES IN INFORMATION SYSTEMS
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Communications and Signal Processing Staff
Prerequisite: EE 600
Description:
Many different communication and information systems will be examined, with the emphasis on determining the fundamental questions each one poses. For instance, an examination of computer networks and computer systems will be shown to lead to questions about conditions which guarantee stable operation, while an examination of optical communication systems will lead to questions about communications in the presence of impulsive noise. Other examples that will be used include estimation in dynamic environments, speech modeling, signal detection, and the modeling of neural processes. The stochastic processes that will be developed for the modeling and analysis of these systems include: Markov Chains and Processes; Point Processes: Brownian Motion; and Martingales.
Text:
S. Ross, Stochastic Processes, Wiley & Sons, N.Y., 0-471-12062-6
Continued next page
EE 643 STOCHASTIC PROCESSES IN INFORMATION SYSTEMS
Outline:
Lectures
1. Fundamentals
A. Probability spaces, measures, and random variables 1.0
B. Convergence concepts for events and random variables 2.0
C. Conditional Expectations and probabilities 1.0
D. Stochastic processes - definition, continuity concepts 1.0
2. Point Processes
A. Context: optical communications and impulsive noise
B. The poisson process 4.0
C. Compound poisson processes and stochastic calculus 3.0
D. Doubly stochastic poisson processes 4.0
3 . Markov Chains and Processes
A. Context: Stability questions in networks and speech modeling.
B. Markov Chains
1. Definition and types of states 2.0
2. Recurrence concepts 3.0
3. Limiting behavior 3.0
4. Random walks and networks 1.0
C. Markov Processes
1. Definition and types 1.0
2. Continuous time chains
4. Gaussian processes
A. Context: recursive estimation and limits
B. Weiner and Orstein-Ulenbeck processes 3.0
C. Stochastic Calculus 2.0
5. Martingales
A. Context: recursive estimation and limits
B. Histories and stopping times 1.0
C. Definition and properties of martingales 2.0
D. Predictability and the Doob-Meyer decomposition 2.0
E. Recursive estimation and limit theorems 3.0
6. Exams 2.0
EE 645 ESTIMATION THEORY
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Communications and Signal Processing Staff
Prerequisite: EE 600
Description:
This course presents the basics of estimation and detection theory that are commonly applied in communications and signal processing systems. Applications in communications and signal processing will be considered throughout.
Objective:
This course is intended to provide a solid foundation for further study and research in the Communications Sciences Area of Electrical Engineering. The level is aimed at the second semester graduate student who has completed EE 600.
Text:
M.D. Srinath, P.K. Rajasekarau, R. Viswanatha, Introduction to Statistical Signal Processing With Applications, Prentice-Hall 1996, (ISBN 0-13-125295X).
Continued next page
EE 645 ESTIMATION THEORY
Outline:
Weeks
1. Concepts of Estimation and Detection (continued)
A. Maximum Likelihood (ML) Estimation
1. The Maximum Likelihood Principle and Maximum
Likelihood Estimation
2. Invariance Principle
3. The Fisher Information Matrix and the Cramer-Rao
Lower Bound
4. Efficiency
B. Bayesian Estimation
1. Priors, likelihood functions and posterior distributions
2. Bayes Risk and Bayesian estimators
3. Admissibility and risk analysis
4. Noninformative priors, maximum entropy priors and
exponential families
2. Linear Estimation 5.0
A. Least Squares Estimation
1. Ordinary least squares
2. Covariance factorization and generalized least squares
B. Discrete-Time Kalman Filtering
1. System and measurements models
2. Derivations
3. Numerical considerations and square root algorithms
C. Continuous-Time MMSE Filtering
1. Calculus of Variation approach
2. Applications in Communications:
pretransmission equalizers, random channels, matched filters, multiplicative noise, etc.
3. Causal Filter
3. Special Topics 2.0
EE 647‡ PERFORMANCE MODELING OF COMPUTER COMMUNICATION NETWORKS
Credit: 3
Area: Communications and Signal Processing (CS)
Computer Engineering (CE)
PIC: Coyle
Prerequisite: EE 600
Description:
The mathematical background needed for the performance and stability analysis of computer communication networks is developed. Point processes, Markov processes, and queueing processes are used in the modeling and analysis of queues, interconnected queues such as ARPANET, and random multiple access networks such as Xerox's ETHERNET. Distributed control of random access networks and centralized control of queueing networks is considered. The techniques developed are useful in the design of computer systems as well as computer networks.
Text:
E. Cinlar, Introduction to Stochastic Processes. Prentice-Hall, 1975 (0-13-498089-1)
Outline:
Lectures
1. Point Processes (3 weeks) 6.0
A. Renewal Processes, Poisson Process
B. Performance analysis of Unslotted ALOHA
2. Markov Chains 12.0
Discrete time and continuous time M.C., limiting behavior,
stability and optimal control of slotted ALOHA, analysis of
Carrier Sense Multiple Access (CSMA) protocol.
3. Product-Form Queueing Networks 6.0
Reversibility and quasi-reversibility, Norton's theorem,
insensitivity, optimal flow control in computer
communications networks.
4. Sojour Times and Flows 3.0
Arrival theorem, heavy traffic.
5. Numerical Methods 6.0
Matrix-geometric solutions, Mean Value Analysis.
6. Dynamic Control of Queueing Systems 5.0
7. Routing 5.0
Flow models, optimal routing.
8. Exams 2.0
EE 648 DIGITAL SIGNAL PROCESSING II
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Zoltowski
Prerequisite: EE 638 or consent of the instructor.
Description:
In this course, a number of advanced topics in digital signal processing are covered. The emphasis is on fast transforms and algorithms, adaptive signal processing, multidimensional and multirate signal processing, inverse problems, nonlinear filtering, time-frequency methods, and processing of signals carried by propagating waves.
Text:
Course notes and handouts
Outline:
Lectures
1. Background material in algebra and number theory 3.0
2. Analysis of fast transforms and fast algorithms 4.0
3. Multidimensional (MD) Signal Processing (multidimensional
transforms, design and implementation of MD filters, stability) 4.0
4. Adaptive Signal Processing (LMS-Newton algorithm, Kalman filter,
adaptive lattice structures, multidimensional problems, nonlinear
adaptive systems, multistage adaptive systems) 4.0
5. Multirate Signal Processing (sampling rate conversion, FIR filters for
interpolation and decimation), quadrature-mirror filters,
multistage implementations, signal processing based on
decimation and interpolation) 4.0
6. Wavelets, time-frequency methods 4.0
7. Inverse Problems (iterative signal restoration algorithms,
reconstruction of nonuniformly sampled signals, regularization 4.0
8. Nonlinear Filtering (rank order filters, multistage nonlinear
filters, transform domain nonlinearities 4.0
9. Multidimensional spectral estimation, beaming,
seismic wave migration 4.0
10. High Order Signal Processing (bispectrum, cumulants,
system identification and spectral analysis) 4.0
11. VLSI Signal Processing (mappings, hardware algorithms,
and transformations) 3.0
12. Exams 3.0
EE 649 SPEECH PROCESSING BY COMPUTER
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Jamieson
Prerequisite: EE 638; Programming experience in C or Fortran
Description:
Covers the main aspects of speech processing by computer. Topics include: models of the vocal tract; identification and extraction of speech features; the recognition of speech and speakers by computer; and control of speech synthesizers. Computer projects are required.
Text:
L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition, Prentice Hall, 1993, ISBN 0-13-015157-2; Article reprints.
Outline:
Weeks
1. Speech production and representation articulation, 2.0
classification of phonic units digital representations of speech
short-time Fourier analysis
2. Speech analysis and analysis - synthesis systems vocoders, 5.5
format analysis linear predictive coding cepstrum analysis
and homomorphic processing vector quantization pitch determination
and excitation identification
3. Automatic recognition of speech acoustic-phonetic processig 5.5
word recognition: dynamic time warping, hidden Markov models,
neural nets continuous speech recognition and understanding:
use of syntax and semantics, relation of natural language processing,
example systems - case studies
4. Speaker verification . 0.5
5. Speech synthesis
speech synthesizers, text-to-speech systems 1.0
6. Exams 0.5
EE 650 TOPICS IN SOLID STATE DEVICES AND MATERIALS
Credit: 1-3
Area: Solid State Devices and Materials (SS)
PIC: Materials Science Engineering Staff
Prerequisite: Admission by consent of instructor.
Description:
An introductory treatment of selected device and materials related topics. Topics will change from semester to semester and will be announced in advance. The list of possible topics includes solid state microwave devices, optoelectronics, laser-quantum electronics, magnetics, noise in semiconductor devices, acoustic wave devices, energy conversion, device fabrication, electroceramics, MOS devices, thin-film devices and memory devices.
EE 652 WAVE PHENOMENA IN SOLIDS
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Bagwell
Prerequisite: EE 604 (C, FW, I)
Description:
This course is designed to introduce graduate students to advanced concepts in wave propagation, coupling and excitation. Maxwell's equations in anisotropic media, reflection at interfaces, optical waveguides and fibers, perturbation theory, waveguide couplers, parametric oscillators, special topics.
Objective:
The course is designed to introduce graduate students to advanced concepts in wage propagation, coupling and excitation.
Text:
H.A. Haus, Waves and Fields in Optoelectronics by Prentice-Hall, 1984.
(0-13-946053-5)
Outline:
Weeks
1. Wave propagation
A. Maxwell's equations in anisotropic media (Ch 11) 1-3
B. Reflection at interfaces – scattering matrices –
(Ch. 2, Ch. 3.1-3.3) 4-5
C. Optical waveguides and fibers (Ch. 6.1 - 6.4) 6-7
2. Mode Coupling
A. Mode orthogonality (Ch 6.8) 8.0
B. Waveguide couplers (Ch 7.6-7.8) 9.0
C. Distributed feedback (Ch 8) 10.0
D. Acousto-optic modulators (Ch. 9.1) 11.0
E. Parametric oscillators (Ch 13.5) 12.0
3. Special topic selected by the instructor 13-15
Such as acoustic waves or plasma waves or wave propagation
in random media
EE 653 NANOELECTRONICS
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Datta
Prerequisite: EE 606 or PHYS 545
Description:
Ultra small devices with dimensions less than the mean free path or DeBroglie Wavelength offer new concepts and new device possibilities. These concepts will be discussed along with special phenomena, which accompany current flow in such devices.
The field of 'nanoelectronics' has developed significantly in the last few years. The objective of this course is to communicate these developments to graduate students. This course introduces the new concepts and novel issues that arise when describing current flow in ultrasmall devices whose dimensions are less than the mean free path and/or the DeBroglie wavelength of the carriers.
Text:
S. Datta, Electronic Transport in Mesoscopic Systems, Cambridge University Press (to be published).
Outline:
Weeks
1. Preliminary Concepts 1.0
2. Conductance from transmission 3.0
3. Calculating the transmission function 3.0
4. Midterm Exam 0.5
4. Quantum Hall effect 2.0
5. Localization and fluctuations 2.5
6. Double-barrier tunneling 2.0
7. Optical analogies 1.0
8. Final Exam
EE 654 SOLID STATE DEVICES II
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Cooper
Prerequisite: EE 606
Description:
Introduction to advanced concepts in semiconductor devices as an extension and continuation of EE 606. Topics include charge storage and transfer in deep depletion MOS devices (CCDs and DRAMs); negative differential mobility and transit time effects in microwave oscillators (Gunn and IMPATT diodes); spontaneous and stimulated emission, quantum efficiency, and charge confinement in photonic devices (LEDs and double heterojunction lasers); and quantum efficiency and spectral response in conventional and avalanche photodiodes.
Objective:
To introduce the student to the fundamental concepts of devices beyond those typically covered in EE 606, thereby broadening the student's working knowledge of semiconductor phenomena and stimulating the student's imagination and creativity.
Text:
1. Gower, Optical Commmunication Systems, Prentice-Hall, 1984. (0-13-638065-5)
2. Schroder, Advanced MOS Devices, Addison-Wesley, 1987. (0-201-16506-6)
3. Sze, Physics and Technoloogy of Semiconductor Devices, John Wiley, 1982.
(0-471-05661-8)
Outline:
Weeks
1. MOS Deep Depletion Devices 5.0
2. Microwave Devices 5.0
3. Photonic Devices 5.0
EE 655 SUPERCONDUCTING ELECTRONIC DEVICES
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Bagwell
Prerequisite: EE 606 or consent of Instructor
Description:
A wave equation called the Bogoliubov-de Gennes equation, consisting of two Schrodinger equations for the electron and time-reversed electron coupled by a 'paring potential' Æ(r), is developed and used to describe electronic motion in superconductors. The first half of the course shows how the Bogoliubov-de Gennes equation, combined with a scattering theory of electronic transport, is used to obtain the current-voltage and current-phase relations for various types of superconducting devices. The second half of the course is devoted to a microscopic description of superconductivity, the electron-phonon interaction, experiments which demonstrate the macroscopic phase-coherence of the superconducting state, and defects such as vortex motion which limit the performance of superconducting devices.
Objective:
To provide a framework for physically understanding and calculating supercurrents and normal currents through various engineered structures incorporating superconductors.
Continued next page
EE 655 SUPERCONDUCTING ELECTRONIC DEVICES
Outline:
Week
1. Properties of Superconductors and Phase Transitions, 1.0
Macroscopic Coherence, SQUID Laboratory
2. The Bogoliubov-de Gennes equations (BdG),
Solutions in a Uniform Superconductor, Coherence Factors 2.0
3. Conservation Laws from BdG Equation, 3.0
Self-consistent Pairing Potential Æ(r) and Current Conservation
4. Supercurrent Flow in a Uniform Superconductor, 4.0
Why Current Flows at Zero Voltage, Critical Current
5. SN Junctions, Andreev Reflection and Giaever Tunneling 5.0
Scattering Formulation for Current
6. Multi-terminal NS Junctions, Quasi-Particle Interference Devices 6.0
Short SNS Junctions - Josephson Effect
7. Long SNS Junctions, Scattering Formulation for the Supercurrent 8.0
8. Thermoelectric Properties of Superconductors and NS junctions, 9.0
9. Midterm Exam
10. AC-Josephson Effect, Effect of Self-consistency of the Order 10.0
Parameter on NS and SNS devices, Non-local Order Parameter
11. Microscopic Derivation of Bogoliubov-de Gennes equation, 11.0
Ground State of a Superconductor
12. Theory of Bardeen, Cooper and Schrieffer (BCS) as a Special 12.0
Case of Bogoliubov-de Gennes Theory, Homogeneous Super-
conductor, Motivation for BCS Wavefunction
13. Bardeen-Pines Electron-Phonon Interaction 13.0
14. Ginzburg-Landau Theory for Free Energy, Derivation of 14.0
Ginzburg-Landau Theory from Bogoliubov-de Gennes Equation
15. Applications of Ginzburg-Landau Theory, Parks-Little Experiment, 15.0
Flux-Quantization, Josephson Vortices, Abrikosov Vortices, and
Phase-Slip Centers
16. More Applications of Ginzburg-Landau Theory, AC-Josephson 16.0
Effect, Superconducting Quantum Interference Device (SQUID),
RF-SQUID,
17. Final Exam
EE 656 ELECTRONIC TRANSPORT IN SEMICONDUCTORS
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Lundstrom
Prerequisite: EE 606
Description:
A treatment of the microscopic and phenomenological physics of carrier transport in bulk semiconductors and in semiconductor devices. The Boltzmann transport equation is introduced as are techniques for solving it analytically and numerically. The physics of carrier scattering in common semiconductors is explored. Theoretical treatments of low and high field transport are compared with measured results. Balance equations are derived as moments of the Boltzmann Transport Equation and are applied to the analysis of sub-micron semiconductor devices. Students are expected to be able to apply elementary concepts of quantum mechanics and solid state physics.
Objective:
To provide a firm physical understanding and theoretical base for analyzing semiconductor devices with small dimensions.
Text:
M. Lundstrom, Fundamentals of Carrier Transport, Addison-Wesley, 1990.
(0-201-18436-2).
Outline:
Week
1. Review of Quantum Mechanical Fundamentals 1.0
2. The Boltzmann Transport Equation 2-3
3. Balance Equations 4-5
4. Microscopic Theory of Scattering 6-7
5. Low-Field Transport 8-9
6. Monte Carlo Simulation 10.0
7. High-Field Transport: Bulk Semiconductors 11-12
8. High-Field Transport: Devices 13-15
EE 657 COMPUTER-AIDED MODELING OF SEMICONDUCTOR PROCESSES AND DEVICES
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Gray and Lundstrom
Prerequisite: EE 606 or equivalent and proficiency in FORTRAN
Description:
Computer simulation programs are commonly used by engineers to assist in the development and analysis of semiconductor devices. The models and methods used in state-of-the-art process and device simulators are discussed. Numerical solutions of the classical semiconductor equations with application to specific devices are emphasized.
Text:
Taylor and Francis, Introduction to Semiconductor Device Modeling, World Scientific Publishing Co. Pte. Ltd., 1986. (9-97-15014-22)
Outline:
Lectures
1. Introduction 1.0
2. Finite Differences 2-5
3. 2D and 3D Modeling 6-7
4. Normalization 8.0
5. Solving Poisson's Equation 9-10
6. Finite Elements 11-13
7. Exam 14.0
8. Classical Semiconductor Equations 15-18
9. Solving Systems of Equations 19-21
10. Modeling the Physical Parameters 22-24
11. Modeling Boundary Conditions 25-27
12. Transient Analysis 28-31
13. Small Signal Sinusoidal Steady State Analysis 32-35
14. Exam 36.0
15. Monte Carlo Methods 37-39
16. Process Modeling 40-43
17. Integration of Process, Device & Circuit Modeling 44-45
18. Final Exam 46.0
EE 658 SEMICONDUCTOR MATERIAL AND DEVICE CHARACTERIZATION
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Pierret
Prerequisite: EE 606
Description:
A comprehensive survey of modern characterization techniques routinely used to determine solid-state material and device parameters. Concepts and theory underlying the techniques are examined, and sample experimental results are presented. The coverage includes electrical, optical, chemical, and physical characterization methods.
Objective:
Material and device characterization is invariably performed in evaluating material, establishing device concepts, or as a prelude to accurate device modeling. Many of the same techniques are also employed to monitor quality control during device fabrication. This course exposes the student to the in-use characterization techniques and related concepts.
Text:
D. K. Schroder, Semiconductor Material and Device Characterization, John Wiley and Sons, Inc., New York, 1990.
Outline:
Weeks
1. Resistivity and type measurements 1.0
2. Semiconductor doping measurements/profiling 1.0
3. Contact resistance and barrier height/measurements 1.0
4. Determination of dimensional and circuit parameters
(channel length, series resistance, threshold voltage) 1.0
5. Carrier mobility measurements 1 1/3
6. Characterization of the oxide and interfacial trapped
charge in MOS devices 1 2/3
7. Deep-level transient spectroscopy and related techniques 1 2/3
8. Carrier lifetime measurements 1 2/3
9. Optical characterization (microscopy, ellipsometry,
transmission/reflection, photoluminescence, raman
spectroscopy) 1.0
10. Chemical and physical characterization (electron beam
techniques, ion beam techniques, x-ray/gamma-ray 1 2/3
11. Exams, demonstrations, guest lectures 2.0
EE 659 QUANTUM PHENOMENA IN SEMICONDUCTORS
Credit: 3
Area: Solid State Devices and Materials (SS)
PIC: Datta
Prerequisite: EE 606 and MA 511 or equivalent
Description:
This course is designed for graduate students familiar with semiconductor fundamentals, with engineering electromagnetics and with linear algebra, but having no significant acquaintance with either quantum mechanics or statistical mechanics. The purpose of the course is to introduce the relevant concepts of quantum mechanics and non-equilibrium statistical mechanics as possible using device-related examples. Topics include: preliminary concepts, equilibrium, restoration of equilibrium, transport, effective mass equation, optical properties, advanced concepts.
Objective:
With the miniaturization of solid-state devices, quantum mechanical phenomena are playing an increasingly important role in our understanding of device physics. This course is designed for graduate students familiar with semiconductor fundamentals at the level of EE606, with engineering electromagnetics and with linear algebra, but having no significant acquaintance with either quantum mechanics or statistical mechanics. The purpose of the course is to introduce the relevant concepts of quantum mechanics and non-equilibrium statistical mechanics as concisely as possible using device-related examples.
Text:
S. Datta, Quantum Phenomena, Modular Series on Solid State Devices, Vol. VIII, Addison-Wesley, 1989. (0-201-07956-9)
Outline:
Weeks
1. Preliminary Concepts 1-3
2. Equilibrium 4-5
3. Restoration of equilibrium 6-7
4. Transport 8-9
5. Effective mass equation 10-11
6. Optical properties 12-13
7. Advanced concepts 14-15
EE 660 TOPICS IN COMPUTER ENGINEERING
Credit: 3
Area: Computer Engineering (CE)
PIC: Computer Area Staff
Prerequisite: Admission by consent of instructor
Description:
A series of one-credit-hour courses offering an in-depth study of topics of special interest. Topics change from semester to semester and will be announced in advance. Possible topics include microprogramming, computer networks, parallel and pipeline processing, computer arithmetic, design automation, performance evaluation, associative processing, scheduling in multiprocessing systems, computational complexities, and other topics in the area of computer architecture, software engineering and the theory of computation and artificial intelligence.
Objective:
To provide a treatment of certain specialized topics that might be of interest to students in electrical engineering, or in other branches of engineering or science. This method of presentation enables a student to broaden his knowledge concerning a variety of topics without requiring an extensive prerequisite background or entailing the depth of study usually associated with a three-credit hour course.
EE 661 COMPUTER VISION
Credit: 3
Area: Computer Engineering (CE)
PIC: Tan
Prerequisite: Graduate standing
Description:
This course deals with how an autonomous or a semi-autonomous system can be endowed with visual perception. The issues discussed include: vision psychophysics, image representation, edge detection, region-based segmentation, camera modeling, stereo vision, pose calculation, object recognition, optical flows, visual tracking, color vision, and beginning concepts of computational geometry. Students are expected to implement vision algorithms through programming assignments.
Text:
No required text. Lectures are supplemented with handouts.
Outline:
Major Topics Weeks
1. Vision Psychophysics 1
A. Vision as an inverse problem
B. Assumptions in human visual perception
2. Connectivity and Distance Functions 0.5
3. Image Representation and Data Structure 1
A. Run-length
B. Quadtree
C. MAT
D. Chain-code
E. Crack-code
F. Skeleton
4. Border Following and Thinning 0.5
5. Component Labeling 0.5
Continued next page
EE 661 COMPUTER VISION
6. Edge Detection 1.5
A. Robert's
B. Prewitt
C. Sobel
D. Laplacian
E. LoG Operator
- bias problems
- false edges
- fixes for these problems
F. Edge Thinning
G. Ridge Tracking
7. Hough Transformation 0.5
A. Extraction of straight lines
B. Extraction of circles
8. Region-based Segmentation 1
A. Split-and-merge algorithm
B. Samet's neighbor finding algorithm
9. Camera Modeling 0.5
A. The pin-hole model
B. The two-plane model
10. Stereo Vision 2
A. Epipolar geometry
B. Constraints
C. Rectification
11. Pose Calculation 1
A. Pose estimation from point correspondences
B. Pose estimation using quaternions
12. Object Recognition 1
A. Subgraph isomorphism
B. Range Data
- Segmentation of range maps
13. Optic Flows and Analysis of Time-varying Imagery 1
14. Visual Tracking 0.5
15. Color Vision 1.5
A. The trichromatic theory of color perception
B. Color representation by RGB, HSI, and XYZ spaces
C. Additive (RGB) and subtractive (CMYK) colors
D. Object detection and tracking by color
16. Computational Geometry 1
A. Transformation Groups
- Affine
- Similarity
- Equiaffine
- Euclidean
- Projective
- Relationships of the five groups
B. Binary and greyscale morphology
EE 662( PATTERN RECOGNITION AND
DECISION MAKING PROCESSES (CS 662)
Credit: 3
Area: Computer Engineering (CE)
PIC: Fukunaga
Prerequisite: EE 302 or equivalent
Description:
Introduction to the basic concepts and various approaches of pattern recognition and decision making processes. The topics include various classifier designs, evaluation of classifiability, learning machines, feature extraction and modeling.
Objective:
To introduce students to the mathematical models of decision making in order to prepare them for applying the associated concepts to information processing.
Text:
K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, second edition, 1990. (0-12-269851-7)
Outline:
Weeks
1. Introduction 1.0
A. Problems in decision making processes
B. Mathematical formulation
2. Pattern recognition and learning machines
A. Review of porbability theory and linear algebra 2.0
B. Bayes classification 3.0
C. Parametric classifier design 4.0
D. Nonparametric classifier design 5,6
E. Estimation of clasifiability 7,8
F. Classifier evaluation 9.0
G. Learning algorithms 10.0
3. Data structure analysis
A. Feature extraction for signal representation 11.0
B. Feature extraction for classification 12,13
C. Clustering 14.0
D. Modeling and validity tests 15.0
EE 663 COMPILER CODE GENERATION, OPTIMIZATION, AND PARALLELIZATION
Credit: 3
Area: Computer Engineering (CE)
PIC: Eigenmann
Prerequisite: EE 565 and either EE 468, 573 or CS 502 or equivalent
Description:
This course presents the concepts needed to design and implement production quality code generators for any of the more popular languages and families of computer architecture (including various pipelined and macro-parallel machines). Flow analysis and concurrency detection, as well as optimizations and loop and irregular code parallelizations, are covered in detail. Using C on ECN UNIX, each student will complete a project implementing a simple optimizer/parallelizer.
Objective:
To give students a better understanding of the intimate relationship between compiler techniques and computer architecture.
Text:
Fischer & LeBlanc, Crafting a Compiler with C, Benjamin/Cummings,1991
Course and research papers will be used. (0-8053-2166-7)
Continued next page
EE 663 COMPILER CODE GENERATION,
OPTIMIZATION, AND PARALLELIZATION
Outline:
Weeks
1. How a computer understands a program: manual compilation 3.0
of control constructs, assignments & expressions, calls;
interpreters
2. Embedded code generators: notes 1.5
3. Forward references: backpatching, multiple-passes, and 1.5
SDI problems
4. What kind of code to generate: decomposition concept, 3.0
classical intermediate forms, pseudo- code models, interpretation
5. Simple code improvements: simple insights, where to make 4.0
improvements, constant folding, "peephole optimizations,"
Sethi-Ullman numbering, commutativity, evaluation modes,
library optimizations
6. Compiler's view of computer architecture: 4.0
classical "optimization," use of hardware parallelism
(MIMD, SIMD VLIW, vector/array, pipelined, & systolic).
7. Introduction to flow analysis 1.0
8. Basic block code improvement: classical "optimization," 2.0
use of hardware parallelism
9. Basic block analysis: concepts, value numbering 1.0
10. Implementation of basic block optimizer/parallelizer 3.0
11. "Global" code improvement: the cost of blocks, classical 3.0
"optimizations"
12. "Global" parallelization: loop transformations, transformations 5.0
of irregular code
13. Global" analysis: value/variable bindings, reaching, dependence 2.0
analysis
14. Implementation of "global" optimizer/parallellizer: 2.0
linear nested regions, simplification for structured languages
15. Project discussions (dispersed throughout term) 6.0
16. Exams 3.0
EE 664 FORMAL LANGUAGES, COMPUTABILITY AND COMPLEXITY
Credit: 3
Area: Computer Engineering (CE)
PIC: Kak
Prerequisite: EE 608
Description:
Topics in computability theory and formal languages include recursive function theory, the equivalence of various generic programming languages for numeric calculations and string manipulations, regular languages and finite state automata, and context-free and context-sensitive languages. In complexity theory, emphasis is on the theory of NP-completeness, including proof methods, the distinctions between strong- and weak-sense NP-completeness, NP-hardness, and performance-guaranteed approximation algorithms.
Text:
1. Davis and Weyuker, Computability, Complexity, and Languages, Academic Press, 1983. (0-12-206380-5)
2. Garey and Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Company, 1979. ISBN (0-7167-1044-7)
Outline:
Weeks
1. Recursive Function Theory 1.0
2. Calculation on Strings
3. Deterministic and Non-Deterministic Turing Machines 2.0
4. Regular Languages and Finite Automata 2.0
5. Context-Free Languages 2.0
6. Context-Sensitive Languages 2.0
7. Proof Methods for NP Completeness 2.0
8. Weak and Strong NP-Completeness for Number Problems 1.0
9. NP-Hardness 1.0
EE 666 ADVANCED COMPUTER SYSTEMS
Credit: 3
Area: Computer Engineering (CE)
PIC: Fortes
Prerequisites: EE 565 and EE 608
Description:
This course studies theoretical aspects of advanced computer systems where multiprocessing is used. Topics include the design, architecture and performance evaluation of multiprocessor memories, interconnection networks and computational pipelines. Also included are the topics of scheduling, synchronization, resource allocation, load-balancing, partitioning and deadlock avoidance in multiprocessors. Also covered are the design and analysis of parallel algorithms, programming languages and automatic approaches to parallelism detection/exploitation for concurrent computation.
Objective:
To acquaint graduate students with up-to-date knowledge on the essential aspects of the design, architecture, programming and evaluation of advanced computer systems.
Text:
D. Culler, J. Singh and A. Gupta, Parallel Computer Architecture, A Hardware/Software
Approach, Prentice-Hall. (1-55860-343-3)
Outline:
Weeks
1. Introduction to Multiprocessing 1.0
2. Parallel Algorithms Models and Complexity Measures 1.0
3. Computer Systems Evaluation Techniques 1.0
4. Multiprocessor Memory Systems 2.0
5. Interconnection Networks 1.0
6. Pipeline, Vector and Array Processing 1.0
7. Programming Languages and Parallelism Detection 2.0
8. Partitioning and Scheduling 1.0
9. Synchronisation 1.0
10. Case Studies 1.0
11. Advanced Topics 1.0
12. Exams and Project Discussion 1.0
EE 667 PARALLEL PROCESSING
Credit: 3
Area: Computer Engineering (CE)
PIC: Siegel
Prerequisite: EE 565 or consent of instructor
Description:
The design of large-scale parallel processing systems is studied. Synchronous (SIMD) and asynchronous (MIMD) machine organizations are discussed. Single-stage and multistage interconnection networks for such systems are examined in depth. Case studies of existing and proposed systems are presented.
Text:
H. J. Siegel, Interconnection Networks for Large-Scale Parallel Processing: Theory and
Case Studies, McGraw Hill. (0-07-057561-4)
Also Journal and Conference Papers.
Outline:
Lectures
1. Introduction - Basic Definitions 1.0
2. Single-stage interconnection networks
(Cube, PM2I, Shuffle-Exchange, Illiac) 1.0
3. SIMD algorithms 2.0
4. SIMD masking schemes for enabling and disabling processors 1.0
5. Single-stage network comparisons 5.0
6. Single-stage network partitioning 2.0
7. Multistage cube networks (Generalized Cube, Omega,
indirect binary N-Cube, Flip, Banyan Networks) 5.0
8. Partitioning multistage cube networks 2.0
9. Data manipulator networks 3.0
10. Fault-tolerant networks 7.0
11. SIMD associative processing 4.0
12. Case Studies (PASM, TRAC, Ultracomputer, RP3, MPP,
STARAN, Illiac IV) 10.0
EE 669( NATURAL LANGUAGE PROCESSING
(ENGL 628)
Credit: 3
Area: Computer Engineering (CE)
PIC: Harper
Prerequisite: EE570 (IE 547)
Description:
This course will introduce students to the linguistic concepts needed to effectively build natural language systems. Focus will be on parsing techniques (students will modify several different types of parsers), logical form, semantic analysis, knowledge representation, discourse analysis techniques, and the impact of natural language systems on speech systems.
Objective:
The objective of the course is to give the student a hands-on training in the design of systems for natural language processing. A course project will be required.
Text:
James Allen, Natural Language Understanding, Benjamin/Cummings, 1987, 2nd edition, (0-8053-0330-8)
Continued next page
EE 669( NATURAL LANGUAGE PROCESSING
(ENGL 628)
Outline:
Lectures
1. Human Language Comprehension 1.0
2. An outline of English grammar 1.0
3. Parsing with Context-Free Grammars Introduction 1.0
A. Top-down techniques:
1. Top-down RTNS 1.0
2. Prolog Parser 1.0
3. ATNS
a. The small ATN program 1.0
b. A large ATN program 3 .0
depth-first vs. breadth-first
morphological analysis
lexicon--what is needed
grammar hacking
B. Bottom-up techniques:
1. Bottom-up chart parser 1.0
2. Tomita Parser and parse forests 2.0
3. Bottom-up vs. Top-down and Mixed-mode parsing 1.0
4. Adding features to parsers 3.0
5. Handling passives 1.0
4. Wh-movement 2.0
5. Deterministic Parsing 2.0
6. Semantics and Logical Form
What is it 1.0
Designing a good logical form 2.0
7. Semantic Interpretation
A. Semantic rules and Compositionality 2.0
B. Semantic grammars:
1. Simple semantic grammars 1.0
2. Interleaved Syntactic and Semantic analysis 1.0
3. Semantic Preferences and Interleaving 1.0
4. Rule-by-Rule Semantics 2.0
C. Semantic Ambiguity, how to deal with it 2.0
8. Knowledge Representation 3.0
9. Discourse Structure 2.0
10. Spoken Natural Language 3.0
How can NLP help
Text-based vs. spoken
Constraint Dependency Grammars
11. In class proposals and presentation of Course Project 3.0
12. Exams 1.0
EE 671 EXPERT SYSTEMS
Credit: 3
Area: Computer Engineering (CE)
PIC: Kak
Prerequisite: EE 570 or consent of instructor
Description:
There are essentially three parts to the course. The first part, dealing with software architectures for symbolic reasoning, concentrates mainly on production systems and blackboards. Students are taught about Rete Networks used in production systems and about high-level object-oriented programming for designing blackboards. The second part of the course deals mainly with the different calculii of uncertainty, the emphasis being on the Bayesian Belief Networks. The first two parts of the course are brought together in the third part where students are taught how to embed the different calculii of uncertainty in symbolic reasoning architectures.
Objective:
To give students hands-on training in the design of symbolic reasoning architectures and in the embedding of calculii of uncertainty in such architectures.
Text:
Pearl, Probabilistic Reasoning in Intelligent Networks, Morgan-Kaufmann, 1988.
(0-934613-73-7)
Outline:
Weeks
1. Production Systems 1.0
2. OPS Syntax 1.0
3. Rete Networks 1.0
4. Evidential Reasoning 1.0
5. Bayesian Belief Networks 3.0
6. Dempster-Shafer Calculus 2.0
7. CLOS for Bayesian Networks 1.0
8. Blackboard Programming 2.0
9. Temporal Reasoning 1.0
10. Knowledge-based Planning 1.0
11. Integrated Planning and Control 1.0
EE 672 SYNTHESIS AND DESIGN OF ANALOG FILTERS
Credit: 3
Area: VLSI and Circuit Design (VC)
PIC: DeCarlo
Prerequisite: EE 301 or graduate standing
Description:
Positive real functions. Synthesis of LC, RC, and RLC one-ports. Synthesis of LC two-ports. Synthesis of singly-terminated and doubly-terminated lossless two-ports. Design of equalizers. Design of active filters using operational amplifiers. The sensitivity problem.
Text:
Wai-Kai Chen, Passive and Active Filters, Wiley & Sons, 1986. (0-471-82352-X)
Continued next page
EE 672 SYNTHESIS AND DESIGN OF ANALOG FILTERS
Outline:
1. Positive real functions
1. Definition and tests for PR functions
2. Positive realness of ZRLC (S)
2. Synthesis of LC, RC, and RL 1-ports
1. Pole-zero patterns of immittance functions
2. Foster and Cauer realizations
3. Synthesis of RLCM 1-ports
1. The chop-chop method
2. Brune's synthesis
4. Synthesis of lossless (LCT) 2-ports
1. Ladder realization and zero-shifting
2. Cauer's synthesis
5. The approximation problem
1. Butterworth, Chebychev, elliptic, and Bessel approximations
2. From squared-magnitude to rational functions of s
6. Synthesis of singly-terminated passive filters
1. Derivation of z and y parameters
2. Uses of source transformation, duality, and scaling
7. Synthesis of doubly-terminated passive filters
1. From transduce to immittance parameters
2. Ladder realizations and Darlington Synthesis
8. Synthesis of equalizers
1. Constant resistance networks
2. Magnitude and delay equalization
9. Synthesis of active filters using op amps
1. Leapfrog, multiple feedback, and cascade configurations
2. Direct LC simulation
10. Sensitivity considerations in active filters
EE 673 DISTRIBUTED COMPUTING SYSTEMS
Credit: 3
Area: Computer Engineering (CE)
PIC: Computer Engineering Area Staff
Prerequisite: Introductory course in operating systems
(EE 469 or CS 413 or equivalent)
Description:
This course discusses the design issues of distributed computing systems (DCS). The general theory of distributed transaction management, reliability, and resource management is discussed. Various algorithms and specification methodologies for DCS are introduced. A general coverage of three major areas of DCS, namely: distributed operating systems, distributed databases and distributed AI, is provided. The discussion is augmented with various case studies.
Objective:
The course will enable student to identify critical issues in designing practical distributed computing systems and formulate approaches to solve these issues.
Continued next page
EE 673 DISTRIBUTED COMPUTING SYSTEMS
Outline:
Weeks
1. Network Architecture, ISO reference model, Application Layers, 1.0 Examples will be drawn from Airline Reservation Systems,
Distributed Situation Assessment Systems, and Banking System
(CIRRUS).
2. General Theory of Distributed Computing Systems: 3.0
Distributed Transactions and Nested Transactions, Atomicity
and Reliability, Concurrency Control, Commit Protocols,
Failure Modes and Recovery Protocols, Dynamic Resource
Allocation & Deadlock Handling, Synchronization, Control
Structures, Performance, Security,User Interface and Network
Transparency.
3. Modeling and Specification of Distributed Systems. Guardian, 3.0
Argus, Actor Model, Communicating Sequential Processes (CSP),
Joyce and ADA.
4. Distributed Operating Systems (DOS): Issues, Interprocess 4.0
Communication, Remote Procedure Calls, Distribute File Systems
(Distributed File Server) & File Transfer Protocol,
Crash Recover, Design Philosophy and case studies of various
DOS: LOCUS (UCLA), Cronus, Alpha, VKermal, Mach.
5. Distributed Database Systems: Issues, Homogeneous and 2.5
heterogeneous databases, distributed query processing;
distributed lock management, Update Synchronization, Database
Partitioning Failures, Deterministic & Optimistic
Protocols for Failure Recover, Case studies:
Distributed Hetergeneous System using Oracle,
INGRES and Sybase DBMS.
6. Distributed Artificial Intelligence, Issues in Distributed 2.0
Problem Solving. Control Structures. Knowledge Representation,
Distributed Knowledge Sources, Functional Architecture of a
DAI Node, Case studies: Contact Net Protocol, Distributed
Speech Understanding System (Hearsay II), Scientific
Community Metaphor, Examples of Application of DAI in various
field such as Manufacturing, Medicine etc. are drawn.
7. Exams and Presentation of Term Papers* 2.0
*Term paper may involve development project on Oracle
and INGRES DBases.
EE 674 TOPOLOGICAL METHODS OF NETWORK ANALYSIS
Credit: 3
Area: VLSI and Circuit Design (VC)
PIC: VLSI and Circuit Design Area Staff
Prerequisite: EE 301 or graduate standing
Description:
Fundamentals of graph theory. Signal flow graph method of circuit and system analysis. Network equilibrium equations in explicit form. Formulation of state equations. Topological formulas for network functions. The maximum flow problem. Network reliability analysis.
Objective:
To familiarize the student with the topological aspects of system analysis, the knowledge of which is needed for research in many branches of circuit theory, in particular computer-aided circuit analysis. Besides an in depth study of many general analysis techniques, the student will learn many elegant short-cut methods which make network analysis much easier.
Text:
Wai-Kai Chen, Theory of Nets: Flows in Networks, John Wiley & Sons, 1990
Continued next page
EE 674 TOPOLOGICAL METHODS OF NETWORK ANALYSIS
Outline:
Weeks
1. Signal flow graphs (SFG) for system analysis 3.0
A. Definitions and basic reduction rules.
B. Graph gain evaluation by Mason's rule.
C. Formulation of SFG from circuit diagrams.
D. Application to active and digital filters.
Exam #1
2. Properties of graphs and their associated matrices 4.0
A. Path, circuit, cutset, and tree.
B. Incidence, circuit, and cutset matrices.
C. Relationships among topological matrices.
D. Planar and nonplanar graphs. Dual graphs.
Exam #2
3. Applications to linear network analysis 4.0
A. Explicit forms of node, loop, and cutset analyses.
B. Explicit form of modified nodal analysis.
C. Explicit form of hybrid analyses.
D. Diakoptical analysis of large-scale networks.
E. Explicit form of n-port hybrid matrices.
F. Explicit form of state equations for RLC networks.
Exam #3
4. Graph Theoretic Algorithms 4.0
A. Algorithms for connectedness, tree, and fundamental circuits
B. The depth-first search algorithm.
C. Algorithms for network flows.
D. Algorithms for optimal layout of VLSI circuits.
Final Exam
EE 675 INTRODUCTION TO ANALYSIS OF NON-LINEAR SYSTEMS
Credit: 3
Area: Automatic Control (AC)
PIC: Zak
Prerequisite: EE 602 or consent of instructor
Description:
An introduction to modeling of dynamic control systems. State plane and numerical methods for solving modeling equations. Linearization and describing function techniques. Stability concepts. Controller and state estimator design for nonlinear systems. Variable structure sliding mode control. Vector field techniques. Introduction to chaos.
Objective:
Upon completion of this course the student should have a basic understanding of common nonlinear phenomena, the student should be able to do a frequency response analysis of nonlinear systems and the student should be able to test for Lyapunov stability. The student will also become familiar with several variable structure control and vector field techniques design methods for multivariable nonlinear systems.
Text:
Lecture notes by S.H. Zak and selected readings from journal papers.
Outline:
1. Dynamic system concept
2. Formulation of the control problem
3. Modeling
4. State plane methods
5. Numerical integration
6. Linearization
7. Describing function techniques
8. Stability concepts
9. Variable structure sliding control mode
Conditions for existence of a sliding mode
Switching surface design and construction of switched feedback gains
10. Vector field techniques
11. State estimation of nonlinear systems
12. Combined controller-estimator compensators
13. Neural network based controllers
14. Introduction to chaos
EE 678 RADAR ENGINEERING
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Bell
Prerequisite or
Co-Requisite: EE 600
Description:
An introduction to the system aspects of modern radar engineering. The theoretical basis for radar performance analysis is developed and applied to radars designed for a variety of different applications. Consideration is given to system parameters such as receiver noise, antenna characteristics, operating frequency, target characteristics, transmitted signal modulation and methods of detection. Attention is given to radars for special purposes such as automatic range and angle tracking, moving target indication and resolution enhancement through synthetic aperture techniques.
Objective:
To introduce students to the design, analysis and performance evaluation methods used in modern radar engineering.
Text:
N. Levanon, Radar Principles, John Wiley and Sons, Inc., NY, 1988. (0-471-85881-1)
Outline:
Lectures
1. Radar Fundamentals 1.0
2. Radar Range Equation 3.0
Monostatic radar, bistatic radar, beacon equation
maximum unambiguous range
3. Radar Target Detection 4.0
Noise statistics
Detection and false alarm probability
Integration of pulse trains
Fluctuating targets
CFAR
Continued next page
EE 678 RADAR ENGINEERING
Outline:
Lectures
4. Targets and Interference 4.0
Radar cross-section
Swerling target models
Glint
Clutter
Jamming
5. Radar Antennas 6.0
Antenna fundamentals
Beam shaping
Phased arrays
Beam forming and multiple beams
6. Waveforms and Signal Processing 6.0
Ambiguity function
Matched filters
Pulse compression
Moving-target indication
Performance of MTI systems
7. Radar Propagation 3.0
Atmospheric effects
Surface reflection
Diffraction
Refraction
8. Angle Measurement and Tracking 3.0
Optimum estimators
Conical scan
Monopulse
9. Range and Doppler Measurement 3.0
Optimum range estimators
Split-gate tracker
Doppler measurement
10. Special Topics 9.0
Synthetic Aperture Radar
LIDAR
SONAR
Radar components
11. Exams 3.0
EE 679 ADVANCED DIGITAL COMMUNICATIONS
Credit: 3
Area: Communications and Signal Processing (CS)
PIC: Gelfand
Prerequisite: EE 600, EE 544
Description:
Provides a detailed examination of optimum digital communication principles and introduces three advanced topics critical to the design of digital communication systems: system synchronization; techniques for communication in non-ideal channels (equalization); and communication over fading/multipath channels. Theoretical principles and practical implementations are considered.
Objective:
This course will provide the foundation for advanced work in communication sciences.
Text:
J. G. Proakis, Digital Communications, McGraw-Hill, 1990. (0-07-050937-9)
Continued next page
EE 679 ADVANCED DIGITAL COMMUNICATIONS
Outline:
Weeks
1. Review of Selected Topics 4.0
A. Review of first principles
B. Overview of parameter estimation
C. Optimum detection in additive Gaussian noise
D. Demodulator structures and analysis
2. Synchronization 4.0
A. Levels of synchronization
B. ML Estimation and phase synchronization
C. The phase-locked loop
D. Phase synchronization of digitally modulated signals
E. Symbol time synchronization
F. Bit error performance with synchronization errors
3. Equalization 4.0
A. Introduction
B. ML demodulation
C. Bandlimited ideal channels
D. Nonideal channels
E. Linear equalization
F. Special topics
4. Fading Channels 3.0
A. Characterization of fading channels
B. Channel models
C. Signaling over slow fading channels
D. Frequency selective channels
E. Diversity techniques
EE 680 MODERN AUTOMATIC CONTROL
Credit: 3
Area: Automatic Control (AC)
PIC: Automatic Control Area Staff
Prerequisite: EE 602
Description:
Theoretical methods in optimal control theory. Topics include the calculus of variations and the Pontryagin minimum principle with applications to minimum fuel and minimum energy problems. Geometric methods will be applied to the solution of minimum time problems. Computational methods, singular problems, observer theory and sufficient conditions for existence of solutions are also discussed.
Objective:
This course is to provide an introduction to Optimal Control Methods. The emphasis will be on Linear Quadratic Optimal Control since most engineering applications use this particular method.
Text:
F. Lewis, Optimal Control, John Wiley & Sons, 1986. (0-471-81240-4)
Continued next page
EE 680 MODERN AUTOMATIC CONTROL
Outline:
1. Static Optimization
A. Optimization without Constraints
B. Optimization with Equality Constrains
C. Numerical Solution Methods
Problems
2. Optimal Control of Discrete-Time Systems
A. Solution of the General Discrete Optimization Problem
B. Discrete-Time Linear Quadratic Regulator
C. Digital Control of Continuous-Time Systems
D. Steady-State Closed-Loop Control and Suboptimal Feedback
E. Frequency-Domain Results
F. The Tracking Problem
G. Regulator with Function of Final State Fixed
H. Second-Order Variations in the Performance Index Problems
Problems
3. Optimal Control of Continuous-Time Systems
A. The Calculus of Variations
B. Solution of the General Continuous Optimization Problem
C. Continuous-Time Linear Quadratic Regulator
D. Steady-State Closed-Loop Control and Suboptimal Feedback
E. Frequency-Domain Results
F. The Tracking Problem
G. Regulator with Function of Final State Fixed
H. Second-Order Variations in the Performance Index
I. Final-Time-Free Problems
J. Constrained Input Problems
Problems
4. Dynamic Programming
A. Bellman's Principle of Optimality
B. Discrete-Time Systems
C. Continuous-Time Systems
Problems
5. Optimal Control for Polynomial Systems
A. Discrete Linear Quadratic Regulator
B. Digital Control of Continuous-Time Systems
Problems
EE 681 DISCRETE EVENT SYSTEM
Credit: 3
Area: Automatic Control (AC)
PIC: Chong
Prerequisite: EE 580 and EE 302, or equivalent.
Description:
This course presents models and tools for the design and analysis of discrete event systems, which are dynamic systems that evolve in accordance with the occurrence of events at descrete instants of time. Topics include deterministic and stochastic models of discrete event systems, supervisory control, simulation, gradient estimation, stochastic optimization methods, and hybrid systems. Application examples in communication/computer networks, real-time computer systems, and manufacturing systems are provided. Topics for further research are also considered.
Text:
Class notes, with references from papers in the literature and the following books:
1. C.G. Cassandras, Discrete Event Systems: Modeling and Performance Analysis, Richard D. Irwin, Inc. And Aksen Associates, Inc.,1993.
2. Y.C. Ho and X.R. Cao, Perturbation Analysis of Discrete Event Systems, Kluwer, 1991. P. Glasserman, Gradient Estimation via Perturbation Analysis, Kluwer, 1991.
Continued next page
EE 681 DISCRETE EVENT SYSTEM
Outline:
Weeks
1. Introduction 0.5
A. Beyond traditional control
B. Examples of discrete event systems
C. Historical background
D. Taxonomy of models and approaches
2. Deterministic Models
A. Automata-based Models 3.0
B. Deterministic Timed Automata 1.0
C. Other Deterministic Models: Petri Nets,
(Max+) Algebra, Finitely recursive processes 1.0
3. Stochastic Models
A. Review of Probability and Stochastic Processes 1.0
B. Queueing Models 1.0
C. Simulation and Performance Estimation 2.0
D. Gradient Estimation 1.5
E. Stochastic Optimization 1.5
4. Other Topics
A. Hybrid Systems 1.0
B. Current Research Issues 1.0
5. Exams 0.5
EE 682 CONTROL OF ROBOT MANIPULATORS
Credit: 3
Area: Automatic Control (AC)
PIC: Koivo
Prerequisite: EE 569 and EE 602, or equivalent, or consent of instructor.
Description:
The dynamical models to design controllers for the manipulator motion are discussed in Lagrange's and Newton-Euler's formulations. To make the manipulator motion track a desired trajectory, the system will be designed using PID-controllers, eigenvalue assignment, and adaptive self-tuning control. The design of these controllers for compliant motion of the manipulators is also discussed in detail. The implementations of the foregoing controllers are outlined.
Objective:
To familiarize students with various control algorithms that can be implemented on robot manipulators. The students learn to use manipulator models to design deterministic and adaptive controllers for gross and fine motion. They will learn to test the controlled manipulator system by simulations.
Text:
A.J. Koivo, Fundamentals for Control of Robot Manipulators, J. Wiley and Sons, Inc., 1989. (0-471-85714-9)
Papers from current literature.
Continued next page
EE 682 CONTROL OF ROBOT MANIPULATORS
Outline:
Weeks
1. Dynamic Models for Manipulator Motion
A. Lagrange's formulation 1.0
B. Newton-Euler formulation 1.0
C. State Variable representation and digital simulation 1.0
3.0
2. Primary and Secondary Controller Design for Gross Motion
A. Desired trajectory generation 1.5
B. Design specifications 1.5
C. Controller design by eigenvalue assignment 1.0
D. Optimal controller for manipulator motion 1.0
3.0
3. Adaptive Control of Manipulator Gross Motion
A. Self-tuning controller design by minimizing a
performance criterion 2.0
B. Self-tuning control design by pole-zero placement method 1.0
C. Manipulator control using model reference adaptive controllers 1.0
4.0
4. Compliant Motion Control
A. Modelling and control of generalized forces
exerted by end-effector 1.5
B. PID-controllers for position, velocity, and force control 1.5
C. Adaptive position, velocity and force control 1.0
2.0
5. Special Topics
A. Coordinated multiple manipulators 1.0
B. Non-rigid manipulators 1.0
2.0
6. Exam 1.0
EE 684 LINEAR MULTIVARIABLE CONTROL
Credit: 3
Area: Automatic Control (AC)
PIC: DeCarlo
Prerequisite: Must be preceded by EE 602 or equivalent
Description:
A state space investigation of multi-input multi-output control design problems from the geometric perspective. The course will detail the theory and design algorithms needed for a solution to the state feedback eigenvalue assignment problem, the disturbance decoupling problem with and without internal stability, the output stabilizaton problem, and the tracking (or regulator) problem with internal stability.
Objective:
To familiarize the students with current trends in linear multivariable control while at the same time equipping them with the tools necessary for the solution of advanced feedback design problems.
Text:
W. M. Wonham, Linear Multivariate Control, Springer-Verlag, 1991. (0-387-96071-6)
Continued next page
EE 684 LINEAR MULTIVARIABLE CONTROL
Outline:
Lectures
1. Controllability and spectral assignability 5.0
A. Theory of controllability
B. Spectral assignability by state feedback
C. The singular value decomposition
D. Numerical implementation
2. Disturbance decoupling problem (DDP): Design of state feedback 8.0
to decouple input noise from output.
A. Definition of the DDP
B. (A,B) - invariant subspaces
C. Computation of supremal (A,B) invariant subspaces
D. Constructive solution of DDP
3. Output stabilization problem (OSP): Design of state feedback 6.0
to stabilize specified output variables
A. Factor Spaces
B Definition of the output stabilization problem
C. Constructive solution of the OSP
4. Controllability subspaces 16.0
A. Theory of controllability subspaces
B. Controllability subspaces: assignment of eigenvalues to
subspaces
C. Theory and numerical computation of supremal
controllability subspaces
D. Application to the DDP with internal stability
5. Tracking and regulation 6.0
A. The restricted regulator problem (RRP)
B. The regulator problem with internal stability (RPIS)
C. Solution of RPIS
D. Applications
6. Exams 3.0
EE 686 TOPICS IN ADVANCED DETERMINISTIC CONTROL
Credit: 3
Area: Automatic Control (AC)
PIC: Automatic Control Area Staff
Prerequisite: EE 680 or equivalent
Description:
The course will cover topics to be announced in advance by the instructor and taken from the following list: discrete maximum principle; optimization problems with state and control variable constraints; optimization of time-delay systems; optimization of distributed parameter systems; large scale systems; computer control systems; problems of singular control; chattering control; sensitivity problems; optimization of nonlinear systems; Popov and Tsypkin stability criteria; game theory.
Objective:
To acquaint the students with recent research developments in the general area of deterministic control.
Text:
Recent research papers and class notes.
Continued next page
EE 686 TOPICS IN ADVANCED DETERMINISTIC
CONTROL
Outline:
Weeks
1. Continuous maximum principle 2.0
2. Discrete maximum principle 2.0
3. Linear programming 1.0
4. Dynamic programming 1.0
5. Convex programming 1.0
6. Optimization problems with state and control variables constraints 2.0
7. Nonlinear systems and chattering control 2.0
8. Second order necessary conditions 2.0
9. Computer-control systems 2.0
10. Time-delay systems 3.0
11. Distributed parameter systems 3.0
12. Large scale systems and decomposition 3.0
13. Sensitivity problems 2.0
14. Stability of nonlinear system: Popov and Tsypkin criteria 3.0
15. Controllability and observability of nonlinear systems 2.0
16. Non-cooperative games 2.0
17. Cooperative games 2.0
18. Other (Extra)
Homework may include computer projects and
literature search projects
EE 688 VLSI TESTING AND VERIFICATION
Credit: 3
Area: VLSI and Circuit Design (VC)
PIC: Roy
Prerequisite: EE 266 and EE 559 or consent of instructor
Description:
This course discusses different aspects of VLSI testing and formal verification of designs. Design and manufacturing defect models are introduced along with test generation and fault simulation algorithms targeting the different fault models. Both combinational and sequential logic testing are covered, and different synthesis for testability schemes such as BIST (Built-In-Self-Test), scan path design, etc. are introduced. Other new and emerging test and verification techniques are also discussed.
Text:
M. Abramovici, M. Breuer, and A. Friedman, Digital Systems Testing and Testable Design, IEEE Press.... supplemented by research papers.
Continued next page
EE 688 VLSI TESTING AND VERIFICATION
Outline:
Week
A. Design Flow of VLSI Systems 1
1. Design and manufacturing defect models
2. Simulation based design verification
B. Fault Simulation 1
1. Parallel
2. Deductive
3. Concurrent
C. Functional Testing Methodologies 1
1. Exhaustive testing
2. Pseudo-exhaustive testing
D. Structure Based Testing 5
1. Fault model based testing
a. Stuck-at faults
b. Bridging faults
c. Stuck-open faults
d. Delay faults
2. Fault Grading
3. Automatic Test Pattern Generation Algorithms
a. D-Algorithms
b. PODEM
c. FAN, etc.
E. Sequential Machine Testing 0.5
1. Machine identification experiments
2. Modified PODEM and D-algorithm
F. Quiescent Current Testability Methods 3
deep submicron challenges
G. Design for Testability Methods 3
1. Testable combinational/sequential circuits
2. Scan path design
3. Partial scan
4. Built-in Self Test (BIST)
5. Data compaction techniques
H. Introduction to Formal Design Verification 2
I. On-Line Testing Methods 1
1. Self-checking circuits
2. Error detecting/correcting codes
EE 689 INTRODUCTION TO DECISION AND CONTROL UNDER UNCERTAINTY
Credit: 3
Area: Automatic Control (AC)
PIC: Kashyap
Prerequisite: EE 589 or EE 600 or equivalent.
Description:
The course deals with topics in system identification, stochastic control, stochastic two dimensional systems with a variety of applications in real time control and computer vision. Topics covered include classical identification methods in AR and ARMA models, nonparametric methods, robust estimation methods, Dempster-Shafer and fuzzy logic methods, two dimensional systems and self tuning controllers.
Text:
None
Outline:
Weeks
1. Classical Identification Methods 1.0
A. AR Models
B. ARMA Model
2. Nonparametric Methods of Identifying Impulse Response 1.0
3. Advanced Identification Method 4.0
A.. Concept of Robust Estimation
B. Nonrobustness of Classical Methods
C. Robustness w.r.t. Colored Noise
D. Robustness According to Huber
E. Robust Methods of Identifying Impulse Response
4. Advanced Decision Techniques 2.5
A. Bayesian Decision Methods and Their Limitations
B. The Dempster-Shafer Approach
C. Interval Estimation, Fuzzy Logic, & Multivalued Logic Approaches
5. Stochastic Two Dimensional Systems (Spacial Signals) 4.0
A. Analysis
B. Identification
C. Application to Image Segmentation
6. Feedback Control with Unknown Plant 2.5
A. Identifiable Problems
B. Stochastic Approximation Methods
C. Self Tuning Controllers
7. Exams 1.0
EE 690 NEURAL FUZZY SYSTEMS
Credit: 3
Area: Automatic Control (AC)
PIC: Lee
Prerequisite: EE 369 and EE 569 or consent of the instructor
Description:
This course provides students with insights into the basics of modeling, analysis, design and realization of neural-fuzzy systems. Topics include fuzzy set operations, fuzzy relations, fuzzy measures, fuzzy logic and reasoning; fuzzification, defuzzification, and inference engine in fuzzy logic control and decision systems; connectionist network models, supervised, unsupervised and reinforcement learning of neural networks and finally, structure and learning schemes of neural-fuzzy systems.
Text:
Chin-Teng Lin and C.S. George Lee, Neural Fuzzy Systems: A neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall PTR, 1996 (ISBN 0-13-235169-2).
Continued next page
EE 690 NEURAL FUZZY SYSTEMS
Outline:
Weeks
1. Review of Fuzzy Sets 3.0
A. Basic Concepts of Fuzzy Sets
B. t-norms, t-conorms, and Complement Functions
C. Operations on Fuzzy Sets and Fuzzy Logic
D. Fuzzy Relations and Fuzzy Relations Equations
2. Fuzzy Logic and Approximate Reasoning 1.0
A. Multivalued Logic and Fuzzy Logic
B. Approximate Reasoning
3. Analysis and Design of Fuzzy Logic Control Systems 3.0
A. System Structure and Components of FLC Systems
B. Fuzzification and Defuzzification Strategies
C. Fuzzy Inference Engine
D. Case Studies and Illustrating Examples
4. Connectionist Models 3.0
A. Review of Neural Network Models
B. Multilayer Networks (Supervised Learning)
C. Unsupervised Learning Networks
5. Neural Fuzzy Systems 4.0
A. Comparison of Fuzzy Systems and Neural Networks
B. General Approaches Integrating Fuzzy Systems and
Neural Networks
C. Connectionist Model for Fuzzy Logic Control and
Decision Systems
D. Fuzzy-Logic-Based Neural Network Models
E. Integrated Fuzzy Neural Models for Pattern Recognition
F. Fuzzy Cerebellar Model Articulation Controllers
Two 1 Hour Exams 1.0
EE 693 Advanced Intern Project
Credit: 1
Area: N/A
PIC: Elliott
Prerequisite: None
Description:
Graduate-level project course in ECE based on off-campus intern position. Individual research projects are to be approved by the supervising Purdue ECE faculty member before registering for the course. An approved written report must be filed before credit is accepted. This course cannot be used to satisfy the minimum course requirements for the Master's or Ph.D. degrees.
EE 694 ELECTRICAL ENGINEERING SEMINAR
Credit: 0
Area: N/A
PIC: Faculty
Description:
Seminar presentations by representatives from industry, members of the faculty of the School of Electrical and Computer Engineering, and other staff and faculty of Purdue University. The presentations introduce the student to a wide variety of current topics relevant to the technical and career aspects of electrical and computer engineering. Technical topics span the entire spectrum of electrical and computer engineering. Career topics include the importance of interpersonal communications, opportunities beyond graduate school, interviewing techniques, and description of non-typical jobs. Required of electrical and compute engineering graduate students at Purdue during one of their first two semesters in residence.
EE 695 ADVANCED TOPICS IN ELECTRICAL ENGINEERING
Credit: 3
Area: N/A
Prerequisite: Admission by consent of instructor.
Description:
Formal classroom or individualized instruction on advanced topics of current interest.
EE 696 ADVANCED ELECTRICAL ENGINEERING PROJECTS
Credit: Variable Credit (1-3)
Area: N/A
Description:
Individual research projects to be approved by the supervising faculty member before registering for the course. An approved written report must be filed before credit is accepted. (This course cannot be used on a PhD plan of study for the primary area.)
EE 697 DIRECTED READING IN ELECTRICAL ENGINEERING
Credit: Variable Credit (1-3)
Area: N/A
Description:
Individualized reading course supervised by an appropriate faculty member and pertaining to a topic not intended for a subsequent project or thesis done by that student. Approval for each reading course must be obtained from the department prior to registration. (This course cannot be used on a PhD plan of study for the primary area.)
EE 698 RESEARCH (M.S. THESIS)
Credit: Variable Credit
Area: N/A
Description:
Research for M.S. Thesis.
EE 699 RESEARCH (PH.D. THESIS)
Credit: Variable Credit
Area: N/A
Description:
Research for Ph.D. Thesis.
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