Ee by nuhman10

VIEWS: 10 PAGES: 11

									                          Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 1
                                              RETURN PROOF TO rob.hirtz@duke.edu
                    ________________________________________________________________________________

This a proof for the 2011-2012 Bulletin of Undergraduate Instruction. This file should be in track-change mode (if it isn’t, please
type [Ctrl]+[Shift]+e). Please do not change the title of this file, or turn off the track-change setting.

The only changes permissible on this proof are to:
        Change faculty listings (in both the beginning of this proof, and in course listings)
        Indicating courses that have been processed by the University Registrar's office that are missing from the proof
        Correct misspellings
        Indicating curriculum changes that have been officially approved by the Arts and Sciences Curriculum Committee
         through Dean Walther's office that are missing from the proof;

Also, PLEASE IGNORE WHAT MAY LOOK LIKE INCORRECT OR AWKWARD FORMATTING (e.g., font sizes and
styles, indents, spacing between paragraphs or words). Formatting irregularities are a function of the translation of the document
from publishing software into Word format. All formatting will be finalized and proofed before publication. This proof is
supplied to revise only content, not formatting or layout. For questions, contact rob.hirtz@duke.edu.




Electrical and Computer Engineering (ECE)
Professor Collins, Chair; Associate Professor Board, Associate Chair; Associate Professor of the Practice Huettel,
Director of Undergraduate Studies; Professors Brady, Brown, Carin, Chakrabarty, Fair, Glass, Joines, Jokerst,
Krolik, Liu, Massoud, Nolte, Smith, and Trivedi; Associate Professors Brooke, Cummer, Kedem, Sorin, and
Teitsworth; Assistant Professors Dwyer, Kim, Reynolds, Roy Choudhury, Stiff-Roberts, Willet, and Yoshie;
Professors Emeriti Casey, George, Marinos, Owen, Wang, and Wilson; Professor of the Practice Ybarra; Associate
Professor of the Practice Gustafson; Assistant Research Professors Morizio, Tantum, and Wolter; Adjunct
Professors Derby and Guenther; Adjunct Associate Professors Janet and Ozev; Adjunct Assistant Professor Remus;
Visiting Professors Kaiser and McCumber
     The educational mission of the Department of Electrical and Computer Engineering is to facilitate the
development of graduates who are highly technically skilled, well rounded, productive, and ethical individuals
versed in social, economic, political, and environmental issues. Our goals are to develop within each student a robust
repertoire of professional skills, to provide each with avenues for exploring diverse interests, and to launch each
successfully into one of a variety of careers offering lifelong learning, service, and leadership within their own local,
national, and global communities. To achieve our mission, the department puts forth the following educational
objectives for the extremely capable students entering the ECE program.
     Our graduates
     1) will be prepared to enter careers in academia, industry, or government with problem solving and
         technical skills that will facilitate their advancement into leadership roles in the profession of electrical
         and computer engineering or related areas;
     2) will utilize their analytical skills, knowledge of modern engineering tools, and interdisciplinary
         project-based learning to function effectively in positions that require creative solutions, involve
         coordination of multiple disciplines, and concern for positive societal outcomes; and
     3) will be prepared to solve problems based upon fundamental knowledge of electrical and computer
         engineering, abilities to engage in life-long learning, and in-depth exposure to the humanities and
         social sciences.
     The Electrical and Computer Engineering (ECE) program is fully accredited by the Engineering Commission of
the Accreditation Board for Engineering and Technology (ABET)1 and leads to a Bachelor of Science in
Engineering (BSE) degree. The ECE curriculum provides a solid foundation in mathematics, physical and life
sciences, computer science, and humanities and social sciences that complements a set of 12 theme-based ECE
courses.
     The Department of Electrical and Computer Engineering has designed its curriculum based on the theme of
Integrated Sensing and Information Processing (ISIP). The ISIP theme capitalizes on the collective research
expertise of the ECE faculty and provides a coherent, overarching framework that links principles of ECE to each


1
 Engineering Accrediation Commission of the Accrediation Board for Engineering and Technology (ABET) 111 Market Place, Suite
1050, Baltimore, MD 21202, telephone (410) 347-7700
                         Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 2
                                             RETURN PROOF TO rob.hirtz@duke.edu
                   ________________________________________________________________________________

other and to real-world engineering problems. The cornerstone of the new ECE curriculum is the first course
Fundamentals of Electrical and Computer Engineering, which has been designed to provide students with a holistic
view of ECE by introducing concepts spanning how to interface sensors and systems with the physical world, how
to transfer/transmit energy/information, and how to extract, manipulate, analyze and interpret information. The
integrated design challenge in this first course introduces students to team problem solving and motivates in-depth
study of ECE concepts in subsequent terms. Each of four follow-on core courses focuses on a specific subfield of
ECE (Digital Systems, Microelectronics, Sensing and Waves, Signals and Systems), and integrates lateral and
vertical connections to other courses through the use of thematic examples. Following the five core courses are
seven ECE technical electives that include a culminating engineering design course where teams of students address
a significant real-world problem or opportunity.
     The ECE curriculum emphasizes creative problem solving through open-ended design challenges in many
courses. Working in teams, students collaborate to utilize and develop their individual and collective technical,
management, and leadership skills to design, simulate, build, and test components and systems to meet a set of
specifications, often defined by industry standards.
     Students have the option to pursue two or three areas of concentration, depending on personal interests. The
upper-level technical electives, which extend the breadth and depth of the ECE core curriculum, provide a firm
foundation for future technical accomplishment and for effective problem solving in the diverse fields that our
graduates pursue.
     The flexibility of the ECE curriculum enables students and their faculty advisors to tailor a unique educational
experience for every student. This may include a semester abroad; a second major, minor, or certificate program;
and/or a research experience with a faculty member; The most popular second majors are computer science and
biomedical engineering. Other popular second majors include mathematics, economics, physics, and public policy.
Interests such as premedicine, prelaw, art, music, psychology, and social sciences can be accommodated through
individually designed programs. Students are encouraged to take more than the minimum required courses in the
sciences and the liberal arts, as is fitting at an engineering school in a university with a strong liberal arts tradition.
27L. Fundamentals of Electrical and Computer Engineering. Students learn core ECE concepts, providing a
foundation on which subsequent courses build. These concepts include techniques for analyzing linear circuits,
semiconductor and photonic devices, frequency representation, filtering, and combinational and sequential logic.
Central to the course is an extensive design challenge that requires students to integrate knowledge across topics
while honing practical design and project management skills. The course culminates in an exciting competition in
which teams of robots race to overcome challenging obstacles using sensor data acquisition and processing.
Prerequisite: Engineering 53L. Corequisite: MATH 32. Instructor: Huettel or Ybarra. One course.
51L. Introduction to Microelectronic Devices and Circuits. Hands-on, laboratory driven introduction to
microelectronic devices, sensors, and integrated circuits. Student teams of 3-4 students/team compete in a design,
assembly, testing, characterization and simulation of an electronic system. Projects include microelectronic devices,
sensors, and basic analog and digital circuits. Classroom portion designed to answer questions generated in
laboratory about understanding operation of devices and sensors, and the performance of electronic circuits. Student
evaluation based on project specification, prototyping, integration, testing, simulation and documentation.
Prerequisites: Engineering 53L, and either Electrical and Computer Engineering 27L or Biomedical Engineering
153L. Instructor: Brooke or Massoud. One course.
52L. Introduction to Digital Systems. Techniques for the analysis and design of combinational and sequential
networks via manual and automated methods. Introduction to hardware description languages. Introduction to
simple computer systems, including their lower-level architecture, assembly language programming, and computer
arithmetic. Lab stresses simulation of target circuits and physical realization with both discrete and high-complexity
programmable components. Final design project. Prerequisite: Engineering 53L, and either Electrical and Computer
Engineering 27L or Biomedical Engineering 153L. Instructor: Board, Dwyer, or Sorin. One course.
53L. Introduction to Electromagnetic Fields. Fundamentals and application of transmission lines and
electromagnetic fields and waves, antennas, field sensing, and signal transmission. Transmission line transients and
digital signal transmission; transmission lines in sinusoidal steady state, impedance transformation, and impedance
matching; electrostatics and magnetostatics, including capacitance and inductance; electromagnetic waves in
uniform media and their interaction with interfaces; antennas and antenna arrays. Alternating laboratories and
recitations. Laboratory experiments include transmission line transients, impedance matching, static and dynamic
electromagnetic fields, and antennas. Prerequisites: Engineering 53L, Mathematics 107 and either Electrical and
Computer Engineering 27L or Biomedical Engineering 153L. Instructor: Carin, Cummer, Joines, Liu, or Smith. One
course.
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 3
                                            RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

54L. Introduction to Signals and Systems. Continuous and discrete signal representation and classification;
system classification and response; transfer functions. Fourier series; Fourier, Laplace, and z transforms.
Applications to Integrated Sensing and Information Processing; networks, modulation, sampling, filtering, and
digital signal processing. Laboratory projects using digital signal processing hardware and microcontrollers.
Computational solutions of problems using Matlab and Maple. Prerequisite: Engineering 53L, and either Electrical
and Computer Engineering 27L or Biomedical Engineering 153L. Instructor: Collins, Gustafson, or Huettel. One
course.
122L. Optics and Photonics. NS One course. C-L: see Physics 185L; also C-L: Visual and Media Studies 123A
123. Photonic and Electronic Design Projects. Photonic and electronic design problems obtained from industry
are solved by teams of students. Required student response includes: formulation and written presentation of
proposed problem solution, execution and evaluation of approved design solution, and written and oral presentation
of final design performance, all for faculty review. Completed design must consider cost, performance,
manufacturability. Students must address design solution impact on: environment, health, safety, society, and public
policy as appropriate. Ethical issues as well as proper handling of intellectual property are discussed and used to
guide the design process. Prerequisites: Electrical and Computer Engineering 163L and Electrical and Computer
Engineering 122. Instructor: Guenther. One course.
135. Opto-Electronic Design Projects. Teams of students design an opto-electronic board-level system to a
published specification. The system is built, tested, and compared to the design specifications. Optical, analog,
digital, and radio frequency (RF) components are used to complete the projects. Group tasks include resource
planning and management using GANTT charts, project budgeting, estimating product Bill of Materials costs,
background study of the standard specification and component characteristics, testing of an evaluation board,
interaction with component vendors, design of the team's board, submission of that design to a quick-turnaround
board fabrication foundry, assembly of the purchased components onto the fabricated board, and board-level system
test. The opto-electric board design incorporates considerations such as cost, economic viability, environmental
impact, ethical issues, manufacturability, and social and political impact. Prerequisite: Senior standing in ECE OR
ECE 122L OR ECE 162L OR ECE 163L. Instructor: Brooke, Jokerst. One course.
141. Linear Control Systems. Analysis and design of feedback control systems. Block diagram and signal flow
graph system models. Servomechanism characteristics, steady-state errors, sensitivity to parameter variations and
disturbance signals. Time domain performance specifications. Stability. Root locus, Nyquist, and Bode analysis;
design of compensation circuits; closed loop frequency response determination. Introduction to time domain
analysis and design. Prerequisite: Electrical and Computer Engineering 54L or consent of instructor. Instructor:
Gustafson. One course.
142. Introduction to Robotics and Automation. Fundamental notions in robotics, basic configurations of
manipulator arm design, coordinate transformations, control functions, and robot programming. Applications of
artificial intelligence, machine vision, force/torque, touch and other sensory subsystems. Design for automatic
assembly concepts, tools, and techniques. Application of automated and robotic assembly costs, benefits, and
economic justification. Selected laboratory and programming assignments. Prerequisites: Electrical and Computer
Engineering 54L. Instructor: Janet. One course. C-L: Mechanical Engineering and Materials Science 142
148L. Electrical Energy Systems. Electrical systems including energy distribution, static, linear, and rotary energy
conversion, and control functions, linear and discrete, for energy conversion. DC and steady-state AC circuits.
Transmission lines for distribution and signal transfer. Studies of static transformers, linear transducers, and rotary
machines. Control theory applied to system operation. Laboratory. Prerequisites: Physics 62L and Mathematics 107.
Instructor: George. One course.
149L. Electric Vehicle Project. Analysis, design, and construction of electrical and mechanical components found
in electric vehicles. Traction motors, controllers, batteries and chargers, and metering. Hybrid and fuel cell vehicle
systems. Project includes building electrical devices and wiring of traction, control, lighting, and other components
along with construction of adapters and devices necessary for the conversion of a vehicle to electric drive.
Prerequisite: Physics 62L, Electrical and computer Engineering 27L or Engineering 119L. Instructor: Klenk. One
course. C-L: Mechanical Engineering and Materials Science 149L
152. Introduction to Computer Architecture. Architecture and organization of digital computer systems.
Processor operation, computer arithmetic, instruction set design. Assembly language programming. Selected
hardware and software exercises culminating in the design, simulation, and implementation in FPGA technology of
the major components of a complete computer system. Not open to students who have taken Computer Science 104.
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 4
                                            RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

Prerequisite: Electrical and Computer Engineering 52L and Computer Science 100E. Instructor: Board or Sorin. One
course. C-L: Modeling Biological Systems
153. Introduction to Operating Systems. Basic concepts and principles of multiprogrammed operating systems.
Processes, interprocess communication, CPU scheduling, mutual exclusion, deadlocks, memory management, I/O
devices, file systems, protection mechanisms. Also taught as Computer Science 110. Prerequisites: Computer
Science 100 and 104. Instructor: Chase or Ellis. One course.
154. Introduction to Embedded Systems. An introduction to hardware/software codesign of embedded computer
systems. Structured programming techniques for high and low level programs. Hardware interfacing strategies for
sensors, actuators, and displays. Detailed study of Motorola 68HC11 and 68HC12 microcomputers as applied to
embedded system development. Hardware and simulation laboratory exercises with 68HC11 and 68HC12
development boards. Major design project. Prerequisite: Electrical and Computer Engineering 152 or equivalent and
consent of instructor. Instructor: Board. One course. C-L: Modeling Biological Systems
156. Computer Network Architecture. The architecture of computer communication networks and the hardware
and software required to implement the protocols that define the architecture. Basic communication theory,
transmission technology, private and common carrier facilities. International standards. Satellite communications
and local area networks. Performance analysis and modeling of communication networks. Prerequisite: Electrical
and Computer Engineering 52L. Instructor: Chakrabarty. One course.
157. Computer Network Analysis and Design. Graph representation of networks. Network design problems as
graph optimization problems; related graph algorithms. Elementary queuing models and formulae. Network
performance issues. Modern high-speed computer-communication networks. Packet switching. Network protocols.
Broadband integrated services networks (B-ISDN) and the asynchronous transfer mode (ATM). Network admission
and congestion controls. Instructor: Staff. One course.
158. Web Technologies. Introduction to the programming languages, authoring tools, and other technologies
needed to design and implement effective sites on the World Wide Web. Topics include HTML, Javascript, cgi-bin,
multimedia, and security. Students lead many class sessions; course project is to design or redesign a web site of
interest to the Duke or Durham communities. Satisfactory/Unsatisfactory grading only. Prerequisite: knowledge of
at least one programming language at level of Computer Science 1. Instructor: Board. Half course.
159. Discrete Mathematics. Mathematics as applied to finite and infinite collections of discrete objects, including
techniques for solving engineering problems involving finite and infinite sets, permutations and combinations of
elements, discrete numeric functions, finite and infinite sums. Mathematical methods needed to tackle real-world
problems in computer engineering, applied mathematics, computer science, and engineering. Instructor: Staff. One
course.
162. Fundamentals of Microelectronic Devices. Fundamentals of semiconductor physics and modeling
(semiconductor doping technology, carrier concentrations, carrier transport by drift and diffusion, temperature
effects, semiconductor device models). Principles of semiconductor device analysis (current-voltage and
capacitance-voltage characteristics). Static and dynamic operation of semiconductor contacts, PN junction diodes,
MOS capacitors, MOS field-effect transistors (MOSFETs), and bipolar-junction transistors (BJTs). SPICE models
and parameter extraction. Prerequisite: Electrical and Computer Engineering 51L. Instructor: Massoud. One course.
163L. Introduction to Electronics: Integrated Circuits. Analysis and design of electronic circuits in bipolar and
MOS technologies, with emphasis on both large-signal and small-signal methods. Circuits for logic gates, latches,
and memories. Single-stage and multistage amplifiers and op amps. Circuits with feedback, including stability and
frequency response considerations. Analog and mixed analog/digital circuit applications. Extensive use of SPICE for
circuit simulation. Prerequisite: Electrical and Computer Engineering 51L. Instructor: Derby, Dwyer, or Fair. One
course.
164L. Electronic Design Projects. Electronics/photonics project laboratory in which multidisciplinary teams of
students build and test custom designed circuits or electronic/photonic systems. Students gain experience in the
design/build/test/demonstrate process. Requirements include: a design plan incorporating engineering standards and
realistic constraints, a timeline indicating project milestones, a written project report, and oral presentations to the
class. The completed design must consider most of the following: cost, environmental impact, manufacturability,
ethics, health and safety, social and political impact. Prerequisites: Electrical and Computer Engineering 163L (or
Biomedical Engineering 154L with consent of instructor) and at least one of 52L, 141 or 180. Instructor: Brooke,
George, Jokerst, Ybarra. One course.
171. Applications of Electromagnetic Fields and Waves. Solution techniques applied to static and dynamic field
problems. Discussions and example applications include the following topics: waves and transmission lines,
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 5
                                            RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

waveguides and resonators, antennas and radiation, and electromagnetic forces and energy. Prerequisite: Electrical
and Computer Engineering 53L. Instructor: Carin or Joines. One course.
176. Thermal Physics. Thermal properties of matter treated using the basic concepts of entropy, temperature,
chemical potential, partition function, and free energy. Topics include the laws of thermodynamics, ideal gases,
thermal radiation and electrical noise, heat engines, Fermi-Dirac and Bose-Einstein distributions, semiconductor
statistics, kinetic theory, and phase transformations. Also taught as Physics 176. Prerequisites: Mathematics 103 or
equivalent and Physics 51L, 62L or equivalent. Instructor: Staff. One course.
180. Fundamentals of Digital Signal Processing. An introduction to theory and applications of digital signal
processing. Concepts, analytical tools and design techniques to process signals in digital form. Signal sampling and
reconstruction, discrete-time transforms including the z-transform, discrete-time Fourier transform, and discrete
Fourier transform. Discrete systems including the analysis and design of FIR and IIR filters. Introduction to
applications of digital signal processing such as image processing, and optimal detection of signals in noise. Discrete
system simulations using MATLAB. Prerequisite: Electrical and Computer Engineering 54L and Statistics 113 or
Mathematics 135 or Electrical and Computer Engineering 255 or permission of instructor. Instructor: Huettel or
Nolte. One course. C-L: Modeling Biological Systems
182L. Sound in the Sea: Introduction to Marine Bioacoustics. NS, R, STS One course. C-L: see Environment
124L; also C-L: Marine Sciences, Marine Science and Conservation
184. Introduction to Digital Communication Systems. Introduction to the design and analysis of modern digital
communication systems. Communication channel characterization. Baseband and passband modulation techniques.
Optimal demodulation techniques with performance comparisons. Key information-theoretic concepts including
entropy and channel capacity. Channel-coding techniques based on block, convolutional and Trellis codes.
Equalization techniques. Applications to design of digital telephone modems, compact discs and digital wireless
communication systems. Prerequisite: Electrical and Computer Engineering 54L and Statistics 113 or equivalent.
Instructor: Krolik. One course.
186. Wireless Communication Systems. Fundamentals of wireless system analysis and design; channel
assignment, handoffs, trunking efficiency, interference, frequency reuse and capacity planning. Path loss models
including large and small scale, multipath interference, diffraction, and scattering. Signal manipulation and
conditioning including modulation/demodulation, equalization and speech coding. Air interference standards and
multiple access techniques including CDMA, TDMA and OFDM. Prerequisites: ECE 54L and one of STAT 113,
ECE 255 or MATH 135. Instructor: Ybarra. One course.
189. Digital Image and Multidimensional Processing. Introduction to the theory and methods of digital image and
video sampling, denoising, coding, reconstruction, and analysis. Both linear methods (such as 2- and 3-D Fourier
analysis) and non-linear methods (such as wavelet analysis). Key topics include segmentation, interpolation,
registration, noise removal, edge enhancement, halftoning and inverse halftoning, deblurring, tomographic
reconstruction, superresolution, compression, and feature extraction. While this course covers techniques used in a
wide variety of contexts, it places a strong emphasis on medical imaging applications. Prerequisites: Electrical and
Computer Engineering 54L and Statistics 113 or Mathematics 135 or Electrical and Computer Engineering 255 or
permission of instructor. Instructor: Willett. One course. C-L: Modeling Biological Systems
191. Undergraduate Research in Electrical and Computer Engineering. For juniors only. Half course or one
course each. Instructor: Staff. Variable credit.
192. Undergraduate Research in Electrical and Computer Engineering. For juniors only. Half course or one
course each. Instructor: Staff. Variable credit.
193. Undergraduate Research in Electrical and Computer Engineering. For seniors only. Half course or one
course each. Instructor: Staff. Variable credit.
194. Undergraduate Research in Electrical and Computer Engineering. For seniors only. Half course or one
course each. Instructor: Staff. Variable credit.
195. Special Topics in Electrical and Computer Engineering. Study of selected topics in electrical engineering
tailored to fit the requirements of a small group. Consent of instructor and director of undergraduate studies
required. Half course or one course each. Instructor: Staff. Variable credit.
196. Special Topics in Electrical and Computer Engineering. Study of selected topics in electrical engineering
tailored to fit the requirements of a small group. Consent of instructor and director of undergraduate studies
required. Half course or one course each. Instructor: Staff. Variable credit.
197. Projects in Electrical and Computer Engineering. A course which may be undertaken only by seniors who
are enrolled in the graduation with distinction program or who show special aptitude for individual project work.
                         Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 6
                                             RETURN PROOF TO rob.hirtz@duke.edu
                   ________________________________________________________________________________

Elective for electrical and computer engineering majors. Consent of director of undergraduate studies required. Half
course to two courses each. Instructor: Staff. Variable credit.
198. Projects in Electrical and Computer Engineering. A course which may be undertaken only by seniors who
are enrolled in the graduation with distinction program or who show special aptitude for individual project work.
Elective for electrical engineering majors. Consent of director of undergraduate studies required. Half course to two
courses each. Instructor: Staff. Variable credit.
211. Quantum Mechanics. Discussion of wave mechanics including elementary applications, free particle
dynamics, Schrödinger equation including treatment of systems with exact solutions, and approximate methods for
time-dependent quantum mechanical systems with emphasis on quantum phenomena underlying solid-state
electronics and physics. Prerequisite: Mathematics 107 or equivalent. Instructor: Brady, Brown, or Stiff-Roberts.
One course.
212. Introduction to Micro-Electromechanical Systems (MEMS). Design, simulation, fabrication, and
characterization of micro-electromechanical systems (MEMS) devices. Integration of non-conventional devices into
functional systems. Principles of fabrication, mechanics in micrometer scale, transducers and actuators, and issues in
system design and integration. Topics presented in the context of example systems. Lab covers design, simulation,
and realization of MEMS devices using commercially available foundry process. Prerequisite: ECE 51L or ME
125L or equivalent. Instructor: Kim. One course.
214. Introduction to Solid-State Physics. Discussion of solid-state phenomena including crystalline structures, X-
ray and particle diffraction in crystals, lattice dynamics, free electron theory of metals, energy bands, and
superconductivity, with emphasis on understanding electrical and optical properties of solids. Prerequisite: quantum
physics at the level of Physics 143L or Electrical and Computer Engineering 211. Instructor: Teitsworth. One
course.
215. Semiconductor Physics. A quantitative treatment of the physical processes that underlie semiconductor device
operation. Topics include band theory and conduction phenomena; equilibrium and nonequilibrium charge carrier
distributions; charge generation, injection, and recombination; drift and diffusion processes. Prerequisite: Electrical
and Computer Engineering 211 or consent of instructor. Instructor: Staff. One course.
216. Semiconductor Devices for Integrated Circuits. Basic semiconductor properties (energy-band structure,
effective density of states, effective masses, carrier statistics, and carrier concentrations). Electron and hole behavior
in semiconductors (generation, recombination, drift, diffusion, tunneling, and basic semiconductor equations).
Current-voltage, capacitance-voltage, and static and dynamic models of PN Junctions, Schottky barriers,
Metal/Semiconductor Contacts, Bipolar-Junction Transistors, MOS Capacitors, MOS-Gated Diodes, and MOS
Field-Effect Transistors. SPICE models and model parameters. Prerequisites: ECE 162. Instructor: Massoud. One
course.
217. Analog Integrated Circuits. Analysis and design of bipolar and CMOS analog integrated circuits. SPICE
device models and circuit macromodels. Classical operational amplifier structures, current feedback amplifiers, and
building blocks for analog signal processing, including operational transconductance amplifiers and current
conveyors. Biasing issues, gain and bandwidth, compensation, and noise. Influence of technology and device
structure on circuit performance. Extensive use of industry-standard CAD tools, such as Analog Workbench.
Prerequisite: Electrical Engineering 216. Instructor: Richards. One course.
218. Integrated Circuit Engineering. Basic processing techniques and layout technology for integrated circuits.
Photolithography, diffusion, oxidation, ion implantation, and metallization. Design, fabrication, and testing of
integrated circuits. Prerequisite: Electrical and Computer Engineering 162 or 163L. Instructor: Fair. One course.
219. Digital Integrated Circuits. Analysis and design of digital integrated circuits. IC technology. Switching
characteristics and power consumption in MOS devices, bipolar devices, and interconnects. Analysis of digital
circuits implemented in NMOS, CMOS, TTL, ECL, and BiCMOS. Propagation delay modeling. Analysis of logic
(inverters, gates) and memory (SRAM, DRAM) circuits. Influence of technology and device structure on
performance and reliability of digital ICs. SPICE modeling. Prerequisites: Electrical and Computer Engineering 162
and 163L. Instructor: Massoud. One course.
225. Nanophotonics. Theory and applications of nanophotonics and sub-wavelength optics. Photonic crystals, near-
field optics, surface-plasmon optics, microcavities, and nanoscale light emitters. Prerequisite: Electrical and
Computer Engineering 53L or equivalent. Instructor: Yoshie. One course.
226. Optoelectronic Devices. Devices for conversion of electrons to photons and photons to electrons. Optical
processes in semiconductors: absorption, spontaneous emission and stimulated emission. Light-emitting diodes
                         Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 7
                                             RETURN PROOF TO rob.hirtz@duke.edu
                   ________________________________________________________________________________

(LEDs), semiconductor lasers, quantum-well emitters, photodetectors, modulators and optical fiber networks.
Prerequisite: Electrical and Computer Engineering 216 or equivalent. Instructor: Stiff-Roberts. One course.
227. Quantum Information Science. Fundamental concepts and progress in quantum information science.
Quantum circuits, quantum universality theorem, quantum algorithms, quantum operations and quantum error
correction codes, fault-tolerant architectures, security in quantum communications, quantum key distribution,
physical systems for realizing quantum logic, quantum repeaters and long-distance quantum communication.
Prerequisites: ECE 211 or Physics 211 or equivalent. Instructor: Kim. One course. C-L: Physics 272
241. Linear System Theory and Optimal Control. Consideration of system theory fundamentals; observability,
controllability, and realizability; stability analysis; linear feedback, linear quadratic regulators, Riccati equation, and
trajectory tracking. Prerequisite: Electrical and Computer Engineering 141. Instructor: P. Wang. One course.
243. Pattern Classification and Recognition Technology. Theory and practice of recognition technology: pattern
classification, pattern recognition, automatic computer decision-making algorithms. Applications covered include
medical diseases, severe weather, industrial parts, biometrics, bioinformation, animal behavior patterns, image
processing, and human visual systems. Perception as an integral component of intelligent systems. This course
prepares students for advanced study of data fusion, data mining, knowledge base construction, problem-solving
methodologies of "intelligent agents" and the design of intelligent control systems. Prerequisites: Mathematics 107,
Statistics 113 or Mathematics 135, Computer Science 6, or consent of instructor. Instructor: Collins or P. Wang. One
course.
245. Digital Control Systems. Review of traditional techniques used for the design of discrete-time control
systems; introduction of ''nonclassical'' control problems of intelligent machines such as robots. Limitations of the
assumptions required by traditional design and analysis tools used in automatic control. Consent of instructor
required. Instructor: Staff. One course.
246. Optimal Control. Review of basic linear control theory and linear/nonlinear programming. Dynamic
programming and the Hamilton-Jacobi-Bellman Equation. Calculus of variations. Hamiltonian and costatic
equations. Pontryagin's Minimum Principle. Solution to common constrained optimization problems. This course is
designed to satisfy the need of several engineering disciplines. Prerequisite: Electrical and Computer Engineering
141 or equivalent. Instructor: Staff. One course. C-L: Mechanical Engineering and Materials Science 232
250. Computer Networks and Distributed Systems. QS, R One course. C-L: see Computer Science 214
251. Advanced Digital System Design. This course covers the fundamentals of advanced digital system design, and
the use of a hardware description language, VHDL, for their synthesis and simulation. Examples of systems
considered include the arithmetic/logic unit, memory, and microcontrollers. The course includes an appropriate
capstone design project that incorporates engineering standards and realistic constraints in the outcome of the design
process. Additionally, the designer must consider most of the following: Cost, environmental impact,
manufacturability, health and safety, ethics, social and political impact. Each design project is executed by a team of
4 or 5 students who are responsible for generating a final written project report and making an appropriate
presentation of their results to the class. Prerequisite: Electrical and Computer Engineering 52L and Senior/graduate
student standing. Instructor: Derby. One course.
252. Advanced Computer Architecture I. QS, R One course. C-L: see Computer Science 220; also C-L: Modeling
Biological Systems
253. Parallel System Performance. Intrinsic limitations to computer performance. Amdahl's Law and its
extensions. Components of computer architecture and operating systems, and their impact on the performance
available to applications. Intrinsic properties of application programs and their relation to performance. Task graph
models of parallel programs. Estimation of best possible execution times. Task assignment and related heuristics.
Load balancing. Specific examples from computationally intensive, I/O intensive, and mixed parallel and distributed
computations. Global distributed system performance. Prerequisites: Computer Science 110; Electrical and
Computer Engineering 152. Instructor: Staff. One course.
254. Fault-Tolerant and Testable Computer Systems. Technological reasons for faults, fault models, information
redundancy, spatial redundancy, backward and forward error recovery, fault-tolerant hardware and software,
modeling and analysis, testing, and design for test. Prerequisite: Electrical and Computer Engineering 152 or
equivalent. Instructor: Sorin. One course. C-L: Computer Science 225
255. Probability for Electrical and Computer Engineers. Basic concepts and techniques used stochastic modeling
of systems with applications to performance and reliability of computer and communications system. Elements of
probability, random variables (discrete and continuous), expectation, conditional distributions, stochastic processes,
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 8
                                            RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

discrete and continuous time Markov chains, introduction to queuing systems and networks. Prerequisite:
Mathematics 107. Instructor: Trivedi. One course. C-L: Computer Science 226, Modeling Biological Systems
256. Wireless Networking and Mobile Computing. Theory, design, and implementation of mobile wireless
networking systems. Fundamentals of wireless networking and key research challenges. Students review pertinent
journal papers. Significant, semester-long research project. Networking protocols (Physical and MAC, multi-hop
routing, wireless TCP, applications), mobility management, security, and sensor networking. Prerequisites:
Electrical and Computer Engineering 156 or Computer Science 114. Instructor: Roy Choudhury. One course. C-L:
Computer Science 215
257. Performance and Reliability of Computer Networks. Methods for performance and reliability analysis of
local area networks as well as wide area networks. Probabilistic analysis using Markov models, stochastic Petri nets,
queuing networks, and hierarchical models. Statistical analysis of measured data and optimization of network
structures. Prerequisites: Electrical and Computer Engineering 156 and 255. Instructor: Trivedi. One course.
258. Artificial Neural Networks. Elementary biophysical background for signal propagation in natural neural
systems. Artificial neural networks (ANN) and the history of computing; early work of McCulloch and Pitts, of
Kleene, of von Neumann and others. The McCulloch and Pitts model. The connectionist model. The random neural
network model. ANN as universal computing machines. Associative memory; learning; algorithmic aspects of
learning. Complexity limitations. Applications to pattern recognition, image processing and combinatorial
optimization. Instructor: Staff. One course.
259. Advanced Computer Architecture II. QS One course. C-L: see Computer Science 221; also C-L: Modeling
Biological Systems
261. CMOS VLSI Design Methodologies. Emphasis on full-custom chip design. Extensive use of CAD tools for
IC design, simulation, and layout verification. Techniques for designing high-speed, low-power, and easily-testable
circuits. Semester design project: Groups of four students design and simulate a simple custom IC using Mentor
Graphics CAD tools. Teams and project scope are multidisciplinary; each team includes students with interests in
several of the following areas: analog design, digital design, computer science, computer engineering, signal
processing, biomedical engineering, electronics, photonics. A formal project proposal, a written project report, and a
formal project presentation are also required. The chip design incorporates considerations such as cost, economic
viability, environmental impact, ethical issues, manufacturability, and social and political impact. Prerequisites:
Electrical and Computer Engineering 52L and Electrical and Computer Engineering 163L. Some background in
computer organization is helpful but not required. Instructor: Chakrabarty. One course.
262. Analog Integrated Circuit Design. Design and layout of CMOS analog integrated circuits. Qualitative review
of the theory of pn junctions, bipolar and MOS devices, and large and small signal models. Emphasis on MOS
technology. Continuous time operational amplifiers. Frequency response, stability and compensation. Complex
analog subsystems including phase-locked loops, A/D and D/A converters, switched capacitor simulation, layout,
extraction, verification, and MATLAB modeling. Projects make extensive use of full custom VLSI CAD software.
Prerequisite: Electrical and Computer Engineering 162 or 163L. Instructor: Morizio. One course.
263. Multivariable Control. One course. C-L: Civil Engineering 263, Mechanical Engineering and Materials
Science 263
264. CAD For Mixed-Signal Circuits. The course focuses on various aspects of design automation for mixed-
signal circuits. Circuit simulation methods including graph-based circuit representation, automated derivation and
solving of nodal equations, and DC analysis, test automation approaches including test equipments, test generation,
fault simulation, and built-in-self-test, and automated circuit synthesis including architecture generation, circuit
synthesis, tack generation, placement and routing are the major topics. The course will have one major project, 4-6
homework assignments, one midterm, and one final. Prerequisites: ECE 163L. Permission of instructor required.
Instructor: Staff. One course.
266. Synthesis and Verification of VLSI Systems. Algorithms and CAD tools for VLSI synthesis and design
verification, logic synthesis, multi-level logic optimization, high-level synthesis, logic simulation, timing analysis,
formal verification. Prerequisite: Electrical and Computer Engineering 52L or equivalent. Instructor: Chakrabarty.
One course.
267. Radiofrequency (RF) Transceiver Design. Design of wireless radiofrequency transceivers. Analog and digital
modulation, digital modulation schemes, system level design for receiver and transmitter path, wireless
communication standards and determining system parameters for standard compliance, fundamentals of synthesizer
design, and circuit level design of low-noise amplifiers and mixers. Prerequisites: Electrical and Computer
Engineering 54L and Electrical and Computer Engineering 163L or equivalent. Instructor: Staff. One course.
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 9
                                            RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

269. VLSI System Testing. Fault modeling, fault simulation, test generation algorithms, testability measures,
design for testability, scan design, built-in self-test, system-on-a-chip testing, memory testing. Prerequisite:
Electrical and Computer Engineering 52L or equivalent. Instructor: Chakrabarty. One course.
271. Electromagnetic Theory. The classical theory of Maxwell's equations; electrostatics, magnetostatics,
boundary value problems including numerical solutions, currents and their interactions, and force and energy
relations. Three class sessions. Prerequisite: Electrical and Computer Engineering 53L. Instructor: Carin, Joines,
Liu, or Smith. One course.
272. Electromagnetic Communication Systems. Review of fundamental laws of Maxwell, Gauss, Ampere, and
Faraday. Elements of waveguide propagation and antenna radiation. Analysis of antenna arrays by images.
Determination of gain, loss, and noise temperature parameters for terrestrial and satellite electromagnetic
communication systems. Prerequisite: Electrical and Computer Engineering 53L or 271. Instructor: Joines. One
course.
273. Optical Communication Systems. Mathematical methods, physical ideas, and device concepts of
optoelectronics. Maxwell's equations, and definitions of energy density and power flow. Transmission and reflection
of plane waves at interfaces. Optical resonators, waveguides, fibers, and detectors are also presented. Prerequisite:
Electrical and Computer Engineering 53L or equivalent. Instructor: Joines. One course.
275. Microwave Electronic Circuits. Microwave circuit analysis and design techniques. Properties of planar
transmission lines for integrated circuits. Matrix and computer-aided methods for analysis and design of circuit
components. Analysis and design of input, output, and interstage networks for microwave transistor amplifiers and
oscillators. Topics on stability, noise, and signal distortion. Prerequisite: Electrical and Computer Engineering 53L
or equivalent. Instructor: Joines. One course.
277. Computational Electromagnetics. Systematic discussion of useful numerical methods in computational
electromagnetics including integral equation techniques and differential equation techniques, both in the frequency
and time domains. Hands-on experience with numerical techniques, including the method of moments, finite
element and finite-difference time-domain methods, and modern high order and spectral domain methods.
Prerequisite: Electrical and Computer Engineering 271 or consent of instructor. Instructor: Carin or Liu. One course.
278. Inverse Problems in Electromagnetics and Acoustics. Systematic discussion of practical inverse problems in
electromagnetics and acoustics. Hands-on experience with numerical solution of inverse problems, both linear and
nonlinear in nature. Comprehensive study includes: discrete linear and nonlinear inverse methods, origin and
solution of nonuniqueness, tomography, wave-equation based linear inverse methods, and nonlinear inverse
scattering methods. Assignments are project oriented using MATLAB. Prerequisites: Graduate level acoustics or
electromagnetics (Electrical and Computer Engineering 271), or consent of instructor. Instructor: Liu. One course.
279. Waves in Matter. Analysis of wave phenomena that occur in materials based on fundamental formulations for
electromagnetic and elastic waves. Examples from these and other classes of waves are used to demonstrate general
wave phenomena such as dispersion, anisotropy, and causality; phase, group, and energy propagation velocities and
directions; propagation and excitation of surface waves; propagation in inhomogeneous media; and nonlinearity and
instability. Applications that exploit these wave phenomena in general sensing applications are explored.
Prerequisites: Electrical and Computer Engineering 53L. Instructor: Cummer. One course.
281. Random Signals and Noise. Introduction to mathematical methods of describing and analyzing random
signals and noise. Review of basic probability theory; joint, conditional, and marginal distributions; random
processes. Time and ensemble averages, correlation, and power spectra. Optimum linear smoothing and predicting
filters. Introduction to optimum signal detection, parameter estimation, and statistical signal processing.
Prerequisite: Mathematics 135 or Statistics 113. Instructor: Collins or Nolte. One course.
282. Digital Signal Processing. Introduction to fundamental algorithms used to process digital signals. Basic
discrete time system theory, the discrete Fourier transform, the FFT algorithm, linear filtering using the FFT, linear
production and the Wierner filter, adaptive filters and applications, the LMS algorithm and its convergence,
recursive least-squares filters, nonparametric and parametric power spectrum estimation minimum variance and
eigenanalysis algorithms for spectrum estimation. Prerequisite: Electrical and Computer Engineering 281 or
equivalent with consent of the instructor. Instructor: Collins, Krolik, Nolte, Tantum, or Willett. One course. One
course.
283. Digital Communication Systems. Digital modulation techniques. Coding theory. Transmission over
bandwidth constrained channels. Signal fading and multipath effects. Spread spectrum. Optical transmission
techniques. Prerequisite: Electrical and Computer Engineering 281 or consent of instructor. Instructor: Staff. One
course.
                        Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 10
                                             RETURN PROOF TO rob.hirtz@duke.edu
                  ________________________________________________________________________________

284. Acoustics and Hearing (GE, IM). One course. C-L: see Biomedical Engineering 235
285. Signal Detection and Extraction Theory. Introduction to signal detection and information extraction theory
from a statistical decision theory viewpoint. Subject areas covered within the context of a digital environment are
decision theory, detection and estimation of known and random signals in noise, estimation of parameters and
adaptive recursive digital filtering, and decision processes with finite memory. Applications to problems in
communication theory. Prerequisite: Electrical and Computer Engineering 281 or consent of instructor. Instructor:
Nolte. One course.
286. Digital Processing of Speech Signals. Detailed treatment of the theory and application of digital speech
processing. Modeling of the speech production system and speech signals; speech processing methods; digital
techniques applied in speech transmission, speech synthesis, speech recognition, and speaker verification. Acoustic-
phonetics, digital speech modeling techniques, LPC analysis methods, speech coding techniques. Application case
studies: synthesis, vocoders, DTW (dynamic time warping)/HMM (hidden Markov modeling) recognition methods,
speaker verification/identification. Prerequisite: Electrical and Computer Engineering 182 or equivalent or consent
of instructor. Instructor: Staff. One course.
287. Information Theory. This class provides an introduction to information theory. The student is introduced to
entropy, mutual information, relative entropy and differential entropy, and these topics are connected to practical
problems in communications, compression, and inference. The class is appropriate for beginning graduate students
who have a good background in undergraduate electrical engineering, computer science or math. Instructor: Carin.
One course.
288. Sensor Array Signal Processing. An in-depth treatment of the fundamental concepts, theory, and practice of
sensor array processing of signals carried by propagating waves. Topics include: multidimensional frequency-
domain representations of space-time signals and linear systems; apertures and sampling of space-time signals;
beamforming and filtering in the space-time and frequency domains, discrete random fields; adaptive beamforming
methods; high resolution spatial spectral estimation; optimal detection, estimation, and performance bounds for
sensor arrays; wave propagation models used in sensor array processing; blind beamforming and source separation
methods; multiple-input-multiple-output (MIMO) array processing; application examples from radar, sonar, and
communications systems. Instructor: Staff. One course.
289. Adaptive Filters. Adaptive digital signal processing with emphasis on the theory and design of finite-impulse
response adaptive filters. Stationary discrete-time stochastic processes, Wiener filter theory, the method of steepest
descent, adaptive transverse filters using gradient-vector estimation, analysis of the LMS algorithm, least-squares
methods, recursive least squares and least squares lattic adaptive filters. Application examples in noise canceling,
channel equalization, and array processing. Prerequisites: Electrical and Computer Engineering 281 and 282 or
consent of instructor. Instructor: Krolik. One course.
298. Advanced Topics in Electrical and Computer Engineering. Opportunity for study of advanced subjects in
electrical and computer engineering. Instructor: Staff.
299. Advanced Topics in Electrical and Computer Engineering. Opportunity for study of advanced subjects
related to programs within the electrical and computer engineering department tailored to fit the requirements of a
small group. Instructor: Staff. One course.
322. Quantum Electronics. Quantum theory of light-matter interaction. Laser physics (electron oscillator model,
rate equations, gain, lasing condition, oscillation dynamics, modulation) and nonlinear optics (electro-optic effect,
second harmonic generation, phase matching, optical parametric oscillation and amplification, third-order
nonlinearity, optical bistability.) Prerequisite ECE 211, Physics 211, or equivalent. Instructors: Stiff-Roberts or
Yoshie. One course. One course.
375. Optical Imaging and Spectroscopy. Wave and coherence models for propagation and optical system analysis.
Fourier optics and sampling theory. Focal plane arrays. Generalized and compressive sampling. Impulse response,
modulation transfer function and instrument function analysis of imaging and spectroscopy. Code design for optical
measurement. Dispersive and interferometric spectroscopy and spectral imaging. Performance metrics in optical
imagine systems. Prerequisite: Electrical and Computer Engineering 53L and 54L. Instructor: Brady. One course.
376. Lens Design. Paraxial and computational ray tracing. Merit functions. Wave and chromatic aberrations. Lenses
in photography, microscopy and telescopy. Spectrograph design. Emerging trends in lens system design, including
multiple aperture and catadioptric designs and nonimaging design for solar energy collection. Design project
management. Each student must propose and complete a design study, including a written project report and a
formal design review. Prerequisite: Electrical and Computer Engineering 122 or 274. Instructor: Brady. 3 units. One
course.
                       Proof for the 2011-2012 Duke University Bulletin of Undergraduate Instruction, p. 11
                                            RETURN PROOF TO rob.hirtz@duke.edu
                 ________________________________________________________________________________

THE MAJOR
    The requirements for the Electrical Engineering (EE) and Electrical and Computer Engineering (ECE) majors
are included in the minimum total of 34 courses listed under the general requirements and departmental
requirements. The program of study must include an approved engineering design course taken in the junior or
senior year of the program.

								
To top