ELECTRICAL & SYSTEMS ENGINEERING (EG) {ESE}
099. Undergraduate Research and/or Independent Study. (C) A maximum of 2 c.u. of ESE 099 may be applied toward the B.A.S. or B.S.E. degree requirements. An opportunity for
the student to become closely associated with a professor
in (1) a research effort to develop research skills
and technique and/or (2) to develop a program of
independent in-depth study in a subject area in which
the professor and student have a common interest.
The challenge of the task undertaken must be consistent
with the student's academic level. To register for
this course, the student and professor jointly submit
a detailed proposal to the undergraduate curriculum
chairman no later than the end of the first week
of the term.
L/L 112. Introduction to Electrical and Systems Engineering - Devices. (C) This is an introduction to the ESE major placing dual emphasis on the experience
of modeling and design issues arising from engineering
a complex physical system along with a coordinated
introduction to programming for engineers. We will
use the example of a legged robotic system as the
motivating complex physical system to be engineered.
There are two components of this class: a recitation
focused on the craft of programming, and a laboratory
experience that uses the empirical world to illustrate
the context in which programming craft subserves
disciplinary goals. Service learning (teaching targeted
concepts to local high classes in prepared settings)
opportunities will be available.
L/L 116. Introduction to C Programming. (C) Prerequisite(s): Knowledge of algorithms and at least one high-level programming language, e.g. ESE 115 or CSE 110. Intended as a connecter course between computer programming on high-level platforms
and more detailed knowledge of processer organization, reading sensors, affecting actuators and understanding
how software is used to control hardware. The final programming project is to control Lego robots.
Details of the C language are covered, including functions, loops, types, characters,
operators, strings, arrays, storage classes, structs,
and pointers. Differences between C and Java are
reviewed.
200. Principles of Digital Design. (A) The course provides an introduction to modern logic design and digital systems.
It starts with an overview of the major building
blocks of a computer. It covers combinational logic
including logic gates, minimization techniques, arithmetic
circuits and modern logic devices such as programmable
logic arrays. The next part deals with sequential
circuits: flip-flops, regist memories, and state
machines. Case studies of real-world applications
are used to illustrate the design of sequential circuits.
The use of hardware description language will be
introduced. There is a companion lab-based course,
ESE 201, required for EE/CTE majors.
201. Principles of Digital Design Lab. (A) This is the companion course for ESE 200 and provides hands-on experience in
modern digital circuit design. It makes use of state-of-the-art
computer-aided design software including schematic
capture, behavioral description, logic-simulation,
minimization and implementation tools. The students
will get familiar with programmable logic devices
and hardware description languages (VHDL). The lab
experiments make use of Xilinx FPGAs which allow
rapid implementation and testing of the designed
circuits. The course consists of weekly 3-hour laboratory
sessions.
205. Electrical Circuits and Systems I Lab. (A) This course is the companion lab for ESE 215 and provides an introduction to
electrical measurements and measuring equipment;
electrical sources; resistive, RL, RC, & RLC
circuits and their non-electrical analogs; op-amp
circuits; transient response and sinusoidal steady
state for linear and nonlinear, e.g. neural/biological
circuits and systems. LabVIEW and the use of data
acquisition boards will be introduced.
206. Electrical Circuits and Systems II Lab. (B) This course is the companion lab for ESE 216. It covers experiments involving
transformers, diodes, and transistors. DC and small
signal model amplifiers, rectification, and non-linear
op amp circuits.
210. Introduction to Dynamic Systems. (B) Prerequisite(s): Math 240. This first course in systems modeling focuses on
linear discrete-time systems. We draw on a set of
examples used throughout the course as the necessary
mathematical tools are developed. The examples demonstrate
the breadth of systems models and are drawn from engineering, the biological sciences, and
economics. MATLAB will be used extensively.
215. Electrical Circuits and Systems I. (A) Prerequisite(s): PHYS 151. Corequisite(s): MATH 240. Common principles of Circuits,
Systems and flows of electron, photons, and other
entities as applied to electrical, and non-electrical
systems such as optical (plasmonic), fluidic,
traffic, neural, electrochemical, and biological
circuits. Class demonstration and computer simulations will be given where applicable
to help in rapid understanding of concepts and
applications.
216. Electrical Circuits and Systems II. (B) Prerequisite(s): ESE 215. The course starts with a discussion of power in electric
systems followed by frequency response of circuits.
Laplace transforms will be used to analyze circuits
and to represent network functions. Next, the course
will focus on modern solid-state devices and electronic
circuits including diodes and their applications,
the metal-oxide-semiconductor(MOSFET) transistor and single stage MOS amplifiers. Use of CAD packages such
as SPICE will be introduced. The associated lab,
ESE 206, is required for EE and CTE majors.
218. Physics and Models of Semiconductor Devices. (B) Prerequisite(s): ESE 215. Physical electronics of semiconductor devices. Energy
bands in solids. Statistics governing the charge
carriers in semiconductors. Equilibrium and nonequilibrium
transport phenomena in semiconductors. Operation
and equivalent circuits for the pn junction, bipolar and field effect transisto, and other
semiconductor devices. Scaling issues in transistors
and introduction to next-generation nanotechnologies.
252. Transportation Systems Engineering. (B) Development of transportation and its impact on society and the economy. Geometric
characteristics of vehicles. Theory of traction and
vehicle dynamic performance. Travel time computations.
Transportation networks. Street design and traffic
engineering. Railroad technology and operations.
High-speed railroads. Air transportation system components
and operations. Air traffic control and navigation.
Transportation system performance and scheduling.
Field trip to major transportation facilities.
301. Engineering Probability. (C) Prerequisite(s): MATH 114. Basic ideas of probability theory. Combinatorics. Random variables and functions
of random variables. Means, moments and generating functions. Order statistics and special distributions.
Inequalities and the central limit theorem.
302. Engineering Applications of Statistics. (C) Prerequisite(s): ESE 301. Principles and engineering applications of statistical inference. The basic
topics covered are parameter estimation, confidence intervals, and hypothesis testing. Additional topics may include
analysis of variance (ANOVA) and/or linear regression. Each method is treated both from theoretical and applied
viewpoints, including software analysis of selected data sets.
303. Stochastic Systems Analysis and Simulation. (A) Prerequisite(s): ESE 301 or equivalent and one computer language. This course provides a study of discrete-event systems simulation.
Some areas of application include: queuing systems,
inventory systems, reliability systems, Markov Chains,
Random Walks and Monte-Carlo systems. The course
examines many of the discrete and continuous probability
distributions used in simulation studies as well
as the Poisson process. Long-run measurements of
performances of queuing systems, steady-state behavior
of infinite and finite-population queuing systems
and network of queues are also examined. Fundamental
to most simulation studies is the ability to generate
reliable random numbers . The course investigates
the basic properties of random numbers and techniques
used for the generation of pseudo-random numbers.
In addition, the course examines techniques used to test pseudo-random numbers
for uniformity and independence. These include the
Kolmogorov-Smirnov and chi-squared tests, runs tests,
gap tests, and poker tests. Random numbers are used
to generate random samples and the course examines
the inverse-transform, convolution, composition and
acceptance/rejection methods for the generation of
random samples for many different types of probability
distributions. Finally, since most inputs to simulation
are probabilistic instead of deterministic in nature,
the course examines some techniques used for identifying
the probabilistic nature of input data. These include
identifying distributional families with sample data,
then using maximum-likelihood methods for parameter
estimating within a given family and then testing
the final choice of distribution using chi-squared
goodness-of-fit tests.
304. Optimization of Systems. (C) Prerequisite(s): MATH 240. Model Building and Linear Programming: Graphical
Methods and The Simplex Method, the LINDO and LINGO
Computer Packages, Degeneracies, Minimization and
the BigM and the Two-Phase Methods, and Goal Programming.
Sensitivity Analysis: Geometric and Algebraic Approaches,
The Computer and Sensitivity Analysis, The Dual of
An LP Problem, The Dual Theorem, Shadow Prices, Complementary
Slackness, The Dual Simplex Method, and The Revised Simplex Method. Integer Programming: The Branch and Bound Method, Enumeration
Methods, and the Cutting Plane Method. Nonlinear
Programming: Review of Differential Calculus, Convex
and Concave Functions, Solving NLP Problems with One Variable, Uncontraint Nonlinear Optimization with
Several Variables, Lagrange Multipliers and Constraint
Nonlinear Optimization with Several Variables, The
Kuhn-Tucker Conditions and Quadratic Programming.
308. Agent Based Modeling and Simulation. (A) Prerequisite(s): Probability, Java or C programming, or equivalent. Agents are a new technique for trying to model, simulate, and understand systems
that are ill-structured and whose mathematics is initially unknown and possibly unknowable. This approach allows
the analyst to assemble models of agents and components where micro-decision rules may be understood; to bring
the agents and components together as a system where macro-behavior then emerges; and to use that to empirically probe
and improve understanding of the whole, the interrelations of the components, and synergies. This approach helps
one explore parametrics, causality, and what-ifs about socio-technical systems (technologies that must support people,
groups, crowds, organizations, and societies). It is applicable when trying to model and understand human behavior
-- consumers, investors , passengers, plant operatoars, patients, voters, political leaders, terrorists, and so on.
This course will allow students to investigate and compare increasingly complex agent based paradigms along three lines - math
foundations, heuristic algorithms/knowledge representations, and empirical science. The student will
gain a toolbox and methodology for attempting to represent and study complex socio-technical systems.
310. Electric and Magnetic Fields I. (A) Prerequisite(s): PHYS 151 and MATH 241. This course examines concepts of electromagnetism, vector analysis, electrostatic
fields, Coulomb's Law, Gauss's Law, magnetostatic fields, Biot-Savart Law, Ampere's Law, electromagnetic induction,
Faraday's Law, transformers, Maxwell equations and time-varying fields, wave equations, wave propagation,
dipole antenna, polarization, energy flow, and applications.
313. Robotics and Bioinspired Systems. (B) Prerequisite(s): ESE 116 (or equivalent) and MATH 240 or by instructor permission. This is a 1.0 cu research-patterned, open-ended, laboratory-focused course addressing
the interface between robotics and integrative biology. The goal is to identify and then explore and possibly
add to a specific corner of the scientific literature wherein it is possible to reach the horizons of knowledge quickly
because the relevant empirical tools have only recently become available for broad use. We will focus attention on the
development of complex adaptive behavior in a legged robotic system with emphasis on such modalities as locomotion,
manipulation, situational awareness, localization and mapping and so on.
L/L 319. Fundamentals of Solid-State Circuits. (A) Prerequisite(s): ESE 216. Analysis and design of basic active circuits involving
semiconductor devices including diodes, bipolar and
field effect transistors. Single stage, differential,
multi-stage, and operational amplifiers will be discussed
including their high frequency response. Oscillators,
wave shaping circuits, filters, feedback, stability,
and power amplifiers will also be covered. A weekly
three-hour laboratory will illustrate concepts and
circuits discussed in the class.
325. Fourier Analysis and Applications in Engineering, Mathematics, and the Sciences.
(B) Prerequisite(s): Math 240, Junior or Senior Standing. This course focuses on
the mathematics behind Fourier theory and a wide
variety of its applications in divers problems in
mathematics, engineering, and the sciences. The course
is very mathematical in content and students signing
up for it should have junior or senior standing.
The topics covered are chosen from: functions and
signals; systems of differential equations; superpositions,
memory, and non-linearity; resonance, eigenfunctions;
the Fourier series and transform, spectra; convergence
theorems; inner product spaces; mean-square approximation;
interpolation and prediction, sampling; random processes,
stationarity; wavelets, Brownian motion; stability
and control, Laplace transforms.
The applications of the mathematical theory that will be presented vary from
year to year but a representative sample includes:
polynomial approximation, Weierstrass's theorem;
efficient computation via Monte Carlo; linear and
non-linear oscillators; the isoperimetric problem;
the heat equation, underwater communication; the
wave equation, tides; testing for randomness, fraud;
nowhere differentiable continuous functions; does
Brownian motion exist?; error-correction; phase conjugate
optics and four-wave mixing; cryptography and secure
communications; how fast can we compute?; X-ray crystallography;
cosmology; and what the diffusion equation has to
say about mathematical finance and risk free investment.
L/L 350. Embedded Systems/Microcontroller Laboratory. (B) Prerequisite(s): Knowledge of C programming or permission of the instructor. An introduction to interfacing real-world sensors
and actuators to embedded microprocessor systems.
Concepts needed for building electronic systems for
real-time operation and user interaction, such as
digital input/outputs, interrupt service routines, serial communications, and analog-to-digital conversion will
be covered. The course will conclude with a final
project where student-designed projects are featured
in presentations and demonstrations.
351. Logistics, Manufacturing and Transportation. (B) Prerequisite(s): ESE 304, Freshmen and Sophmores require instructor permission. Introduction to supply chains -- the production,
distribution, and transportation of goods -- and
the role of engineers and managers in the design
and operation of that system. Supply chain as a physical
process. Transportation service options and design.
Impact of Information Technology (IT) and Intelligent
Transportation Systems (ITS). Basic routing and distribution
strategies. Future trends in transportation and supply
chains in light of sustainability concerns.
360. (CBE 375) Introduction to Environmental Systems. (B) The principles of green design, life cycle analysis, industrial ecology, pollution
prevention and waste minimization, and sustainable
development are introduced to engineers of all disciplines
as a means to identify and solve a variety of emerging
environmental problems. Case studies are used to
assess the problems and devise rational solutions
to minimize environmental consequences.
400. (ESE 540) Engineering Economics. (C) Prerequisite(s): Knowledge of Calculus and Probability. This course investigates
methods of economic analysis for decision making
among alternative courses of action in engineering
applications. Topics include: cost-driven design
economics, break-even anlaysis, money-time relationships,
rates of return, cost estimation, depreciation and
taxes, foreign exchange rates, life cycle analysis, benefit-cost ratios, risk analysis, capital financing and allocation, and financial
statement analysis. Case studies apply these topics
to actual engineering problems.
404. (TCOM500) Introduction to Networks and Protocols. (A) Prerequisite(s): Undergraduate probability and analysis. Course open to Seniors in SEAS and Wharton. All others require instructor
permission. This is an introductory course on packet networks and associated protocols that
form the basis of today's communication infrastructure, with a particular emphasis on IP based networks
such as the Internet. The course introduces the various design and implementation choices that are behind the
development of modern networks, and emphasizes basic analytical understanding in motivating those choices. Topics
are covered in a mostly "bottom-up" approach starting with a brief review of physical layer issues such as digital
transmission, error correction and error recovery strategies . This is then followed by a discussion of link layer aspects,
including multiple access control (MAC) strategies, local area networks (Ethernet, token rings, and 802.11 wireless
LANs), and general store-andforward packet switching. Network layer solutions, including IP addressing,
naming, and routing are covered next, before exploring transport layer and congestion control soutions such as TCP.
Finally, basic approaches for quality-ofservice and network security are examined. Specific applications and aspects
of data compression and streaming may also be covered.
406. (ESE 505, MEAM513) Control of Systems. (B) Prerequisite(s): ESE 325. Basic methods for analysis and design of feedback control in systems. Applications
to practical systems. Methods presented include time response analysis, frequency response analysis, root
locus, Nyquist and Bode plots, and the state-space approach.
408. Data Communications. (B) Prerequisite(s): ESE 301 (Probability) and ESE 325 (Signals and Systems) or equivalent. Overview of the communication process. Review of Fourier transforms, frequency
response of linear channels. Analog modulation (AM, FM) . Random processes and power spectral densities.
Effects of noise in analog demodulation. Pulse amplitude modulation, inter-symbol interference, and equalization.
Digital band-pass modulation (PSK, QAM, FSK, probability of error).
411. Electromagnetic Waves and Applications. (M) Prerequisite(s): ESE 310 or permission of instructor. Key concepts of electromagnetic and optical fields and waves, and their implications
in modern communication systems. Selected topics from areas such as plane waves in lossy media, reflection
and refraction, transmission lines, optical fibers, microwave and photonic waveguides, and antennas and sensors
and their applications in communication systems are discussed.
412. Chaotic Dynamics in Electrical and Biological Systems. (M) Prerequisite(s): MATH 240, PHYS 150 or permission of the instructor. Introduction to chaos, bifurcation, synchronicity,
and strange attractors in nonlinear dynamics and
their use in understanding complexity, self-organization,
self-similarity, and learning in selected man-made
and biological circuits and for understanding the
essentials of biological-clocks, neuron dynamics,
coupled-map lattices, cellular automata and neural
networks.
419. (ESE 572) Analog Integrated Circuits. (A) Prerequisite(s): ESE 319, ESE 570, or permission of the instructor. Design of analog circuits and subsystems using bipolar and MOS technologies
at the transistor and higher levels. Transistor level
design of building block circuits such as op amps,
comparators, sample and hold circuits, voltage and
current references, capacitors and resistor arrays,
and class AB output stages. The course will include
a design project of an analog circuit. This course
is similar to ESE 570, except that it will not require
the use of the physical layout tools associated with
VLSI design and implementation. Students who take
ESE 419 will not later be eligible to take ESE 572
for credit.
441.Senior Design Project I - EE/CTE. (A) Prerequisite(s): Senior standing or permission of the instructor. This is the
first of a two-semester sequence in electrical
engineering senior design. Student work will
focus on project/team definition, systems analysis,
identification alternative design strategies
and determination (experimental or by simualtion)
of specifications necessary for a detailed design.
Students will receive guidance on preparing professinal
written and oral presentations. Each project
team will submit a project proposal and two written
project reports that include coherent technical
presentations, block diagrams and other illustrations
appropriate to the project, a budget and team
schedule with sufficient detal and granularity
to enable close management of the project. Each
student will deliver two formal Powerpoint presentations to an audience comprised of
peers, instructors and project advisors. During
the semester there will be periodic individual-team
project reviews.
442.Senior Design Project II - EE/CTE. (B) Prerequisite(s): ESE 441. This is the second of a two-term sequence in electrical
engineering senior design. Student work will focus
on completing the design undertaken in ESE 441
and successfully implementing the project. Success
will be verified using experimental and/or simualtion
methods appropriate to the project that test the
degree to which project objectives are achieved.
Each project team will prepare a poster to support
a final project presentation and demonstration
to peers, faculty and external judges. The course will conclude with the submission of
a final project written team report. During the semester
there will be periodic project reviews with individual
teams.
444. (ESE 544) Project Management. (A) Prerequisite(s): ESE 304 or equivalent. The course emphasizes a systems engineering
approach to project management including the cycle
costing and analysis, project scheduling, project
organization and control, contract management, project
monitoring and negotiations. In addition, the coure
will also examine management issues in large infrastructure
projects like non recourse or limited recourse project financing. Examples from the logistics
planning process and global software project management
will be used to highlight the course topics.
L/L 460. (ESE 574, MEAM564) Principles of Microfabrication Technology. (A) Prerequisite(s): Any of the following: ESE 218, MSE 321, MEAM 333, CBE 351, CHEM 321/322, PHYS 250 or permission
of the instructor. A laboratory-based course on fabricating
microelectronic and micromechanical devices using
photolithographic processing and related fabrication
technologies. Lectures discuss: clean room procedures;
microelectronic and microstructural materials; photolithography;
diffusion, oxidation; materials deposition; etching
and plasma processes. Basic laboratory processes
are covered for the first two thirds of the course
with students completing structures appropriate to
their major in the final third. Students registering
for ESE 574 will be expected to do extra work (including
term paper and additional project).
490.Senior Design Project I - SSE. (A) Prerequisite(s): Junior Standing. This is the first of a two-semester sequence
in systems engineering senior design. Student
work will focus on project/team definition, systems
analysis, identification alternative design strategies
and determination (experimental or by simualtion)
of specifications necessary for a detailed design.
Students will receive guidance on preparing professional
written and oral presentations. Each project
team will submit a project proposal and two written
project reports that include coherent technical
presentations, block diagrams and other illustrations
appropriate to the project, a budget and team
schedule with sufficient detal and granularity
to enable close management of the project. Each
student will deliver two formal Powerpoint presentations to an audience comprised of
peers, instructors and project advisors. During
the semester there will be periodic individual-team
project reviews.
491.Senior Design Project II - SSE. (B) Prerequisite(s): ESE 490. This is the second of a two-term sequence in systems
engineering senior design. Student work will focus
on completing the design undertaken in ESE 490
and successfully implementing the project. Success
will be verified using experimental and/or simulation
methods appropriate to the project that test the
degree to which project objectives are achieved.
Each project team will prepare a poster to support
a final project presentation and demonstration
to peers, faculty and external judges. The course will conclude with the submission of
a final project written team report. During the semester
there will be periodic project reviews with individual
teams.
500. Linear Systems Theory. (A) Prerequisite(s): Open to graduates and undergraduates who have taken undergraduate courses in linear algebra and differential equations. This graduate level course focuses on linear system theory in time domain based
on linear operators. The course introduces the fundamental mathematics of linear spaces, linear operator theory,
and then proceeds with existence and uniqueness of solutions of differential equations, the fundamental matrix solution
and state transition matrix for time- varying linear systems. It then focuses on the fundamental concepts of stability,
controllability, and observability, feedback, pole placement, observers, output feedback, kalman filtering, linear
quadratic regulator. Special topics such as optimal control, robust, geometric linear control will be considered as time
permits.
502. Introduction to Spatial Analysis. (B) Prerequisite(s): ESE 302 or equivalent. The course is designed to introduce students to modern statistical methods for
analyzing spatial data. These methods include nearest-neighbor analyses of spatial point patterns, variogram and kriging
analyses of continuous spatial data, and autoregression analyses of area data. The underlying statistical theory
of each method is developed and illustrated in terms of selected GIS applications. Students are also given some experience
with ARCMAP, JMPIN, and MATLAB software.
504. (OPIM910) Introduction to Optimization Theory. (A) Prerequisite(s): Linear Algebra. The course provides a detailed inroduction to linear and nonlinear optimization
analysis as well as integer optimization analysis. It discusses methods for the mathematical formulation of linear programming
(LP) integer programming (IP) and nonlinear programming (NLP) problems, as well as methods of computational
tools used for their solutions. In discussions surrounding the solutions to LP problems, the Simplex method and
the Revised Simplex methods are covered in a fairly rigorous fashion along with the LINDO computational computer
package. Sensitivity analysis associated with the optimal solutions to LP problems is also discussed in detail
using both geometric and algebraic methods. In discussions surrounding the solutions to IP problems, the course
covers: (a) branch and bound, (b) enumeration and (c) cutting-plane methods, and these are applied to numerous
classic problems in IP. In discussions surrounding the solutions to NLP problems, the course covers methods involving:
(a) differential Calculus, (b) steepest ascent and decent and (c) Lagrange Multipliers. The Kuhn-Tucker Conditions are
also presented and applied to problems in Quadratic Programming. Many examples are selected from a broad range
of engineering and business problems.
505. (ESE 406, MEAM513) Control of Systems. (B) Basic methods for analysis and design of feedback control in systems. Applications
to practical systems. Methods presented include time
response analysis, frequency response analysis, root
locus, Nyquist and Bode plots, and the state-space
approach.
508. (OPIM660) Info Systems for E-Commerce. (M) Prerequisite(s): A computer programming language course such as CSE 120 (C++), plus ESE 301 (Probability) and ESE 302 (Statistics) or
equivalent. This course looks at the information
systems phenomena that are revolutionizing organizations
(e.g., clicks & mortar shopping, net-centric
value chains, telemedicine, emergent communities,
online democracy, etc). To be effective in this milieu,
organizations must do more than just push new information
technology. They need to determine how to harness
the new technology to manage complexity and to maximize
stakeholder value. Processes need to be systematically
analyzed and redesigned all along the value chain
from supplies and procurement to electronic storefronts
and customer support, from campaign headquarters
to voter booth, etc. This course examines design
principles task and information process modeling
and analysis methodologies, and a range of underlying
information technologies (e.g., webserver design,
transaction processing, warehousing, datamining/knowledge
management, bots and agents, XML, security, information
theory/complexity, and more) that will help the modern
organization or community to maximize its strategic
objectives. We also examine failure case studies
and derive lessons learned.
509. (TCOM503) Waves, Fibers and Antennas for Telecommunications. (A) This course is designed to provide an understanding of the physical aspects
of telecommunications systems. This includes an understanding
of waves and wave propagation, basic optics, the
operation of optical fibers and fiber communication
systems, an introduction to optical networks, free-space
optical communications, and an understanding of simple
antennas and arrays and their use in wireless communication.
510. Electromagnetic and Optical Theory. (A) This course reviews electrostatics, magnetostatics, electric and magnetic materials,
induction, Maxwell's equations, potentials and boundary-value
problems. Topics selected from the areas of wave
propagation, wave guidance, antennas, and diffraction
will be explored with the goal of equipping students
to read current research literature in electromagnetics,
microwaves, and optics.
511. Modern Optics and Image Understanding. (B) Prerequisite(s): ESE 310, graduate standing, or permission of the instructor. The goal of this course is to provide a unified approach to modern optics, image
formation, analysis, and understanding that form the theoretical basis for advanced imaging systems in use today in
science, medicine and technology. The emphasis is on imaging systems that employ electromagnetic energy but the principles
covered can be extended to systems employing other forms of radiant energy such as acoustical.
514.(MSE 570) Physics of Materials I. (A) Prerequisite(s): Undergraduate Physics and Math through modern physics and differential equations. Failures of classical physics and the historical basis for quantum theory. Postulates
of wave mechanics; uncertainty principle, wave packets and wave-particle duality. Shrodinger equation and operators;
eigenvalue problems in 1 and 3 dimensions (barriers, wells, hydrogen atom). Mathematical equivalence to problems
in optics. Perturbation theory; scattering of particles and light. Free electron theory of metals; Drude and
Sommerfeld models, dispersion relations and optical properties of solids. Extensive use of computer-aided self-study
will be made.
515.(MSE 571) Physics of Materials - II. (B) Prerequisite(s): MSE 570/ESE 514 or equivalent. Failures of free electron theory. Crystals and the reciprocal lattice; wave
propagation in periodic media; Bloch's theorem. One-electron band structure models: nearly free electrons, tight binding.
Semiclassical dynamics and transport. Cohesive energy, lattice dynamics and phonons. Dielectric properties
of insulators. Homogeneous semiconductors and p-n junctions. Experimental probes of solid state phenomena:
photo emission, energy loss spectroscopy, neutron scattering. As time permits, special topics selected from
the following: correlation effects, semiconductor alloys and heterostructures, amorphous semiconductors, electroactive
polymers.
517. (BE 517) Optical Imaging. (A) Prerequisite(s): ESE 310 and 325 or equivalent. A modern introduction to the physical principles of optical imaging with biomedical
applications. Propagation and interference of electromagnetic waves. Geometrical optics and the eikonal. Plane-wave
expansions, diffraction and the Rayleigh criterion. Scattering theory and the Born approximation. Introduction
to inverse problems. Multiple scattering and radiative transport. Diffusion approximation and physical optics
of diffusing waves. Imaging in turbid media. Introduction to coherence theory and coherence imaging. Applications
will be chosen from the recent literature in biomedical optics.
521. Semiconductor Device Physics and Technology. (M) Prerequisite(s): ESE 218 or PHYS 240 or MSE 222 or equivalent, or by permission of the instructor. Free electron theory and
density states, band theory of electronic conduction;
review of semiconductor fundamentals and operation
p-n homojunction; multijunction and interface devices;
high-field and hot-electron devices; growth and technology
of heterostructures, quantum wells and related quantum
phenomena, high-frequency and high speed devices;
LEDs and semiconductor lasers.
522. (OPIM221, OPIM656) Process Management in Manufacturing. (C) Prerequisite(s): OPIM 621, OPIM 631, and OPIM 632 or equivalent. This course builds on OPIM 631 and OPIM 632
in developing the foundations of process management,
with applications to manufacturing and supply chain
coordination and integration. This course begins
with a treatment of the foundations of process management,
including quality (e.g. 6-sigma systems) and time
(e.g., cycle time) as building blocks for the sucessful
integration of plant operations with vertical and
horizontal market structures. On the e-manufacturing
side, the course consideres recent advances in enterprise-wide
planning (ERP)systems, supplier management and contract
manufacturing. Industry case studies highlight contrasting
approaches to the integration of manufacturing operations
and risk management with e-Logistics and e-Procurement
providers and exchanges. The course is recommended
for those interested in consulting or operations
careers, and those wishing to understand the role
of manufacturing as a general foundation for economics
value creation.
525. (MSE 525) Nanoscale Science and Engineering. (A) Prerequisite(s): ESE 218 or PHYS 240 or MSE 222 or equivalent, or by permission. Overview of existing device and manufacturing technologies in microelectronics,
optoelectronics, magnetic storage, Microsystems, and biotechnology. Overview of near- and long-term challenges
facing those fields. Near- and long- term prospects of nanoscience and related technologies for the evolutionary
sustension of current approaches, and for the development of revolutionary designs and applications.
529. (MEAM529) RF MEMS and NEMS. (A) Introduction to RF MEMS and NEMS technologies. Need for RF MEMS and NEMS components
in wireless communications. Review of micromachining
techniques and MEMS and NEMS fabrication approaches.
Actuation methods in MEMS and NEMS, MEMS and NEMS
design and modeling. Examples of RF MEMS components
from industry and academia. Case studies: micro and nano switches, tunable capacitors,
inductors, micro and nano resonators, filters, oscillators
and micromachined antennas.
530. Elements of Probability Theory and Random Processes. (A) Prerequisite(s): A solid foundation in undergraduate probability at the level
of STAT 430 or ESE 301 at Penn. Students are expected
to have a sound calculus background as covered in
the first two years of a typcial undergraduate engineering
curriculum. Undergraduates are warned that the course
is very mathematical in nature with an emphasis on
rigour; upperclassmen who wish to take the course
will need to see the instructor for permission to
register. This rapidly moving course provides a formal
framework for the development of fundamental ideas
in probability theory. This course is a prerequisite
for subsequent courses in communication theory and
telecommunications such as ESE 576 and TCOM 501.
The course is also suitable for students seeking
a rigourous and broad graduate-level exposure to
probabalistic ideas and principles with applications
in diverse settings.
Topics covered are taken from: discrete and continuous probability spaces; combinatorial
probabilities; conditional probability and indepence;
Bayes rules and the theorem of total probability;
the inclusion-exclusion principle, Bonferroni's inequalities,
the Poisson paradigm, probability sieves, and the
Lovascz local lemma; arithmetic and lattice distributions;
the central term and the tails of the binomial, Poisson
approximation; densities in one and more dimensions;
characterizations of the uniform, exponential and
normal densities; probability spaces, random variables
and distribution functions; transformations, random
number generation; independent random variables,
Borel's normal law; measures of central tendency---mean,
median, mode, mathematical expectation; the monotone
convergence theorem and its applications; additivity
and monotonicity of expectations; moments; the inequalities
of Markov, Chebyshev, Chernoff, and Talagrand; concentration
phenomena and applications; limit theorems, the weak
and strong laws; generating functions, recurrent
events, Blackwell's theorem; characteristic functions,
the central limit theorem.
531. Digital Signal Processing. (A) This course covers the fundamentals of real-time processing of discrete-time
signals and digital systems. Specific topics covered
are: review of signals and linear system representations;
convolution and discrete Fourier transforms; Z-transforms;
frequency response of lienar discrete-time systems;
sampling and analog/digital conversion; finite and
infinite impulse response filters; digital filter
design; fast Fourier transfers and applications;
adaptive filtering algorithms; wavelet transforms.
Projects requiring implementation of specific digital
signal processing algorithms will also be assigned.
534. Computer Organization. (M) Prerequisite(s): Basic computability and basic digital circuits, VLSI exposure helpful but not required. CSE 371 adequate. Organization and design of physical computational systems, basic building block
for computations, understanding and exploiting structure in computational prob design space, costs, and tradeoffs
in computer organization, common machine abstractions, and implementation/optimization techniques. The course
will develop fundamental issues and tradeoffs which define computer organizational and architectural styles including
RISC, VLIW, Super Scalar, EPIC, SIMD, Vector, MIMD, reconfigur FPGA, PIM, and SoC. Basic topics in the design
of computational units, instruction organization, memory systems, control and data flow, and interconnect will also
be covered.
535.Electronic Design Automation. (M) Prerequisite(s): Digital logic, Programming (need to be comfortable writing ~1-3K lines of code and working with a large, existing base code). Formulation, automation, and analysis of design mapping problems with emphasis
on VLSI and computational realizations. Major themes include: formulating and abstracting problems, figures
of merit (e.g. Energy, Delay, Throughput, Area, Mapping Time), representation, traditional decomposition of
flow (logic optimization, covering, scheduling, retiming, assignment, partitioning, placement, routing), and techniques
for solving problems (e.g., greedy, dynamic programming, search, (integer) linear programming, graph algorithms,
randomization, satisfiability).
539. (BE 539) Neural Networks, Chaos, and Dynamics: Theory and Application. (B) Physiology and anatomy of living neurons and neural networks; Brain organization;
Elements of nonlinear dynamics, the driven pendulum
as paradigm for complexity, synchronicity, bifurcation,
self-organization and chaos; Iterative maps on the
interval, period-doubling route to chaos, universality
and the Feigenbaum constant, Lyapunov exponents,
entropy and information; Geometric characterization
of attractors; Fractals and the Mandelbrot set; Neuron
dynamics: from Hudgkin-Huxley to integrate and fire,
bifurcation neuron; Artificial neural networks and
connectionist models, Hopfield (attractor-type) networks,
energy functions, convergence theorems, storage capacity,
associative memory, pattern classification, pattern
completion and error correction, the Morita network;
Stochastic networks, simulated annealing and the
Boltzmann machine, solution of optimization problems,
hardware implementations of neural networks; the
problem of learning, algorithmic approaches: Perception
learning, back-propagation, Kohonnen's self-organizing
maps and other networks; Coupled-map lattices; Selected
applications including financial markets.
540. (ESE 400) Engineering Economics. (C) This course is cross-listed with an advanced-level undergraduate course (ESE
400). Compared to the undergraduate course, students
will be required to do additional work and will be
graded by a more rigourous performance standard.
Topics include: money-time relationships, discrete
and continuous compoundng, equivalence of cash flows,
internal and external rate of return, design and
production economics, life cycle cost analysis, depreciation,
after-tax cash flow analysis, cost of capital, capital
financing and allocation, parametric cost extemating
models, pricing, foreign exchange rates, stochastic
risk analysis, replacement analysis, benefit-cost
analysis, and analysis of financial statements. Case
studies apply these topics to engineering systems.
544. (ESE 444) Project Management. (A) Prerequisite(s): ESE 304 or equivalent. The course emphasizes a systems engineering
approach to project management including the cycle
costing and analysis, project scheduling, project
organization and control, contract management, project
monitoring and negotiations. In addition, the course
will also examine management issues in large infrastructure
projects like nonrecourse or limited recourse project financing. Examples from the logistics
planning process and global software project management
will be used to highlight the course topics.
552. Transportation Systems Engineering. (B) Development of transportation and its impact on society and the economy. Geometric
characteristics of vehicles. Theory of traction and
vehicle dynamic performance. Travel time computations.
Transportation networks. Street design and traffic
engineering. Railroad technology and operations.
High-speed railroads. Air transportation system components
and operations. Air traffic control and navigation.
Transportation system performance and scheduling.
Field trip to major transportation facilities.
554. Urban Transit Systems and Technology. (A) Role of transportation in founding and growth of cities. Classification, definitions,
theory, and characteristics of urban transportation
modes, their components and performance. Vehicle
propulsion and travel time computations; energy consumption
and its possible reduction. Bus, trolleybus, light
rail, rapid transit, AGT and specialized modes -
vehicles, ways, terminals, operations and costs.
Paratransit modes, and their role in urban transportation.
Theoretical and practical capacities of modes. Present
and potential innovations in vehicle design, propulsion,
automation, including fully automated rapid transit
systems. Visit to a transit system.
555. Cities and Transportation Systems. (B) Transportation systems operations; concepts, scheduling and analyses. Applications
of operations research methods. Rail and bus networks,
lines, branches and feeders. Timed transfer system.
Fares, other revenues and costs. Organization and
management. Transit planning methodology; comparison
of modes. Transit financing and policy. Urban transportation
problems in developed and developing countries: their
origins, causes and solutions. Definition and implementation
of optimal role of cars, transit, bicycles and pedestrians
in cities. Balanced transportation and livable cities.
Field trip.
567. (OPIM261, OPIM761) Risk Analysis and Environmental Management. (C) This course is designed to introduce students to the complexities of making
decisions about threats to human health and the environment
when people's perceptions of risks and their decision-making
processes differ from expert views. Recognizing the
limitations of individuals in processing information
the course explores the role of techniques such as
decision analysis, cost-benefit analysis, risk assessment
and risk perception in structuring risk-management
decisions. We will also examine policy tools such
as risk communication, incentive systems, third party
inspection, insurance and regulation in different
problem contexts.
The problem contexts for studying the interactions between analysis, perceptions,
and communication will include risk-induced stigmatization
of products (e.g. alar, British beef), places (e.g.
Love Canal), and technologies (e.g. nuclear power);
the siting of noxious facilities, radon, managing
catastrophic risks including those from terrorism.
A course project will enable students to apply the
concepts discussed in the course to a concrete problem.
570. Digital Integrated Circuits and VLSI-Fundamentals. (B) Prerequisite(s): ESE 319 (for undergraduates) or permission of the instructor. Explores the design aspects involved in the realization
of an integrated circuit from device up to the register/subsystem
level. It addresses major design methodologies with
emphasis placed on the structured design. The course
includes the study of MOS device characteristics,
the critical interconnect and gate characteristics
which determine the performance of VLSI circuits,
and NMOS and CMOS logic design. Students will use
state-of-the-art CAD tools to verify designs and
develop efficient circuit layouts.
572 (ESE 419) Analog Integrated Circuits. (A) Prerequisite(s): ESE 570 and ESE 319 (for undergraduates) or permission of the instructor. Design of analog circuits and subsystems using bipolar and MOS technologies
at the transistor and higher levels. Transistor level design of building block circuits such as op amps, comparators,
sample and hold circuits, voltage and current references, capacitors and resistor arrays, and class AB output stages.
This graduate course relies heavily on Spice simulation and will require some use of CAD systems to generate integrated
circuit layouts as part of a capstone project.
573 (BE 526) Building Brains in Silicon. (M) Prerequisite(s): Students with advanced knowledge in neurobiology but rudimentary knowledge in electrical engineering or vice versa
are welcome. Biology students should have a course in Cellular Neurobiology and BIOL 451, Systems Neuroscience. Engineering
students should have ESE 218, Physics and Models of Semiconductor Devices and ESE 319, Fundamentals of
Solid-State Circuits.
We model the stucture and function of neural systems in silicon using very large
scale integration (VLSI) complimentary metal-oxide-semiconductor (CMOS) technology. To build these neuromorphic
systems, we proceed from the device level, through the circuit level, to the system level. At the
device level, we mimic electrodiffusion of ions through membrane channels with electrodiffusion of electrons through transistor
channels. At the circuit level, we derive minimal implementation of synaptic interaction, dendritic integration,
and active membrane behavior. At the system level, we synthesize the spatiotemporal dynamics of the cochlea, the
retina, and early stages of cortical processing.
L/L 574. (ESE 460, MEAM564) The Principles and Practice of Microfabrication Technology.
(A)Prerequisite(s): Any of the following courses: ESE 218, MSE 321, MEAM 333, CBE
351, CHEM 321/322, PHYS 250 or permission of the instructor. A laboratory-based course on fabricating microelectronic
and micromechanical devices using photolithographic
processing and related fabrication technologies.
Lectures discuss: clean room procedures; microelectronic
and microstructural materials; photolithography;
diffusion, oxidation; materials deposition; etching
and plasma processes. Basic laboratory processes
are covered for the first two thirds of the course
with students completing structures appropriate to
their major in the final third. Students registering
for ESE 574 will be expected to do extra work (including
term paper and additional project).
575. (TCOM511) Introduction to Wireless Systems. (A) Prerequisite(s): Undergraduate linear systems and elementary probability theory. System/Network Design, cellular concepts, resource management, radio management,
radio channel propagation fundamentals, modulation, fading countermeasure, diversity, coding, spread spectrum,
multiple access techniques.
576. Digital Communication Systems. (B) Prerequisite(s): Undergraduate linear systems, probability, random processes. Sampling, source coding, and capacity. Quantization and coding of speech and
video. Baseband data transmission: line coding, intersymbol interference, equalization, digital modulation schemes,
spectral efficiency. Error control coding; block and convolutional codes, Viterbi Algorithm.
601. Hybrid Systems. (M) Hybrid systems combine discrete state-machines and continuous differential equations,
and have been used as models of a large number of
applications in areas such as real-time software,
embedded systems, robotics, mechatronics, aeronautics,
process control, and biological systems. The course
will cover state-of-the-art modeling, design, and
analysis of hybrid systems. The course is interdisciplinary,
and is aimed at bringing together concepts in control
theory and computer science. Specific topics include
modeling, simulation, stability, reachability, and
controller design for hybrid systems. Computational
tools for the simulation and verification of hybrid
systems will be emphasized with applications to robotics,
avionics, air traffic management systems, and biological
systems. The course consists of lectures, homeworks,
and a final project.
603. Simulation Modeling and Analysis. (B) Prerequisite(s): Probability (undergraduate level) and one computer language. This course provides a study of discrete-event systems simulation.
Some areas of application include: queuing systems,
inventory systems, reliability systems Markov Chains,
Random-Walks and Monte-Carlo systems. The course
examines many of the discrete and continuous probability
distributions used in simulation studies as well
as the Poisson process. Long-run measurements of
performances of queuing systems, steady-state behavior
of infinite and finite-population queuing systems
and network of queues are also examined. Fundamental
to most simulation studies is the ability to generate
reliable random numbers. The course investigates
the basic properties of random numbers and techniques
used for the generation of pseudo-random numbers.
In addition, the course examines techniques used
to test pseudorandom numbers for uniformity and independence.
These include the Kolmogorov-Smirnov and chi-squared
tests, runs tests, gap tests, and poker tests. Random numbers are used to generate random
samples and the course examines the inverse-transform,
convolution, composition and acceptance/rejection
methods for the generation of random samples for
many different types of probability distributions.
Finally, since most inputs to simulation are probabilistic instead of deterministic
in nature, the course examines some techniques used
for identifying the probabilistic nature of input
data. These include identifying distributional families
with sample data, then using maximum-likelihood methods
for parameter estimating within a given family and
then testing the final choice of distribution using
chi-squared goodness-of-fit tests.
605. Modern Convex Optimization. (B) Prerequisite(s): Knowledge of linear algebra and willingness to do programming.
Exposure to numerical computing, optimization, and
application fields is helpful but not required. This
course concentrates on recognizing and solving convex
optimization problems that arise in engineeering.
Topics include: convex sets, functions, and optimization
problems. Basis of convex analysis. Linear, quadratic,
geometric, and semidefinite programming. Optimality
conditions, duality theory, theorems of alternative,
and applications. Interior-point methods, ellipsoid
algorithm and barrier methods, self-concordance.
Applications to signal processing, control, digital
and analog circuit design, computation geometry,
statistics, and mechanical engineering.
608. Intelligent and Animated Software Agents. (M) Prerequisite(s): Undergraduate courses in probability (ESE 301 or equivalent),
optimization (ESE 304 or equivalent), knowledge of
one computer programming language (Fortran, Pascal,
or C), or permission of the instructor.
This course will begin with an introduction to virtual reality personas and
web-based agents, including their usage to assist,
train, and entertain people wherever digital interfaces
exist (on the Web, in e-commerce, in games, in kitchen
appliances, on your dashboard, etc.). What makes
an agent rational? Emotionally appealing? Entertaining?
We will explore mathematical theories of rationality
and behavior, including those from cognitive, behavioral
and decision science. We will then progress into
human behavior, literature, personality and individual
differences studies, and integlligent and emotive
agent designs. We will examine various types of agents
such as web shopping agents, emotive agents, personal
support agents, chatterbots, mobile agents, virtual
reality personas, game-based adversaries, pedagogical
agent coaches, and multi-agent societies. Finally,
students will learn principles about animation, simulated
social interaction and speech generation, knowledge
representation, agent planning and reasoning, agent
communication languages, testing of the use of agent
based systems, and methodologies/toolbenches for
engineering of systems of intelligent and emotive
agents.
610. Electromagnetic and Optical Theory II. (M) This course covers exact, approximate and numerical methods of wave propagation,
radiation, diffraction and scattering with an emphasis
on bringing students to a point of contributing to
the current research literature. Topics are chosen
from a list including analytical and numerical techniques,
waves in complex media and metamaterials, photonic
bandgap structures, imaging, miniaturized antennas,
high-impedance ground plans, and fractal electrodynamics.
617. (CBE 617, CIS 613, MEAM613) Non-Linear Control Theory. (M) Prerequisite(s): Undergraduate Control course. This courses focuses on nonlinear systems, planar dynamical systems, Poincare
Bendixson Theory, index theory, bifurcations, Lyapunov stability, small-gain theorems, passivity, the Poincar
map, the center manifold theorem, geomentric control theory, and feedback linearization.
630. Elements of Neural Computation, Complexity, and Learning. (M) Prerequisite(s): A semester course in probability or equivalent exposure to probability (e.g. ESE 530). Non-linear elements and networks: linear and polynomial threshold elements,
sigmoidal units, radial basis functions. Finite (Boolean) problems: acyclic networks; Fourier analysis and efficient
computation; projection pursuit; low frequency functions. Network capacity: Feedforward networks; Vapnik-Chervnenkis
dimension. Learning theory: Valiant's learning model; the sample complexity of learning. Learning algorithms:
Perception training, gradient descent algorithms, stochastic approximation. Learning complexity: the intractability
of learning; model selection.
632. Random Process Models and Optimum Filtering. (M) Prerequisite(s): ESE 530 or equivalent. Convergence, continuity, stationarity and second order properties of random
processes. Spectral representation. Markov processes, Wiener and Poisson processes. Karhunen-Loeve expansion. Optimum
filtering: matched and Wiener filtering, finite observations, spectral factorization. Kalman filtering.
Basic concepts of parameter estimation and hypothesis testing.
635. Distributed Systems. (M) Prerequisite(s): Basic knowledge of linear systems (ESE 500), linear algebra
(MATH 312 or equivalent), and optimization (ESE 504
or equivalent) and some familiarity with basics of
nonlinear systems (ESE 617 or equivalent). Students
without this background should consult with the instructor
before registering.
This research seminar deals with tools, methods, and algorithms for analysis
and design of distributed dynamical systems. These
are large collections of dynamical systems that are
spatially interconnected to form a collective task
or achieve a global behavior using local interactions.
Over the past decade such systems have been studied
in disciplines as diverse as statistical physics,
computer graphics, robotics, and control theory.
The purpose of this course is to build a mathematical
foundation for study of such systems by exploring
the interplay of control theory, distributed optimization,
dynamical systems, graph theory, and algebraic topology.
Assignments will consist of reading and resesarching
the recent literature in this area. Topics covered
in distributed coordination and consensus algorithms
over networks, coverage problems, effects of delay
in large scale networks. Power law graphs, gossip
and consensus algorithms, synchronization phenomena
in natural and engineered systems, etc.
650. Learning in Robotics. (A) Prerequisite(s): Students will need permission from the instructor. They will
be expected to have a good mathematical background
with knowledge of machine learning techniques at
the level of CIS 520, signal processing techniques
at the level of ESE 531, as weill as have some robotics
experience.
This course will cover the mathematical fundamentals and applications of machine
learning algorithms to mobile robotics. Possible
topics that will be discussed include probabalistic
generative models for sensory feature learning. Bayesian
filtering for localization and mapping, dimensionality
reduction techniques formotor control, and reinforcement
learning of behaviors. Students are expected to have
a solid mathematical background in machine learning
and signal processing, and will be expected to implement
algorithms on a mobile robot platform for their course
projects. Grading will be based upon course project
assignments as well as class participation.
674. Information Theory. (M) Prerequisite(s): ESE 530 or equivalent exposure to probability theory. Deterministic
and probabalistic information. The pigeon-hole principle.
Entropy, relative entropy, and mutual information.
Random processes and entropy rate. The asymptotic
equipartition property. Optimal codes and data compression.
Channel capacity. Source channel coding. The ubiquitous
nature of the theory will be illustrated with a selection of applications drawn from among: universal source coding, vector
quantization, network communication, the stock market,
hypothesis testing, algorithmic computation and Kolmogorov
complexity, and thermodynamics.
680. Special Topics in Electrical and Systems Engineering. (M) Advanced and specialized topics in both theory and application areas. Students
should check Graduate Group office for offerings
during each registration period.
895. Teaching Practicum. (C) Participation of graduate students in the teaching mission of the department
will help to develop teaching, presentation, leadership,
and interpersonal skills while assisting the department
in discharging its teaching responsibilities. All
doctoral students are required to participate under
faculty guidance in the teaching mission of the department.
This requirement will be satisfied by completing
two 0.5 course units of teaching practicum (ESE 895).
Each 0.5 course unit of teaching practicum will consist
of the equivalent of 10 hours of effort per week
for one semester. As a part of the preparation for
and fulfillment of the teaching practicum requirement,
the student will attend seminars emphasizing teaching
and communication skills, lead recitations, lead
tutorials, supervise laborato experiments, develop
instructional laboratories, develop instructional
materiaand grade homeworks, laboratory reports, and
exams. A teacher training seminar will be conducted
the day before the first day of classes of the Fall
semester. Attendance is mandatory for all second-year
students.
As much as possible, the grading aspect of the teaching practicum course will
be such as not to exceed 50% of the usual teaching
assistant commitment time. Some of the recitations
will b supervised and feedback and comments will
be provided to the student by the faresponsible for
the course. At the completion of every 0.5 course
unit of teach, the student will receive a Satisfactory/Unsatisfactory
grade and a written evsigned by the faculty member
responsible for the course. The evaluation will beon
comments of the students taking the course and the
impressions of the facult
899. Independent Study. (C) For students who are studying a specific advanced subject area in electrical
engineering. Students must submit a proposal outlining
and detailing the study area, along with the faculty
supervisor's consent, to the graduate group chair
for approval. A maximum of 1 c.u. of ESE 899 may
be applied toward the MSE degree requirements. A
maximum of 2 c.u.'s of ESE 899 may be applied toward
the Ph.D. degree requirements.
990. Masters Thesis. (C) Register for this only once after all 10 course units are completed. This carries
full time status with no credit.
995. Dissertation. (C) Register for this after completing four years of full-time study including two
course units each Summer Session (and usually equal
to 40 course units).
SM 996. ESE Research Seminar. (C) Research Seminar Requirement. All full-time Masters and PhD students must be
registered every semester for ESE 996 Research Seminar,
a zero credit course. Attendance is required. Students
attend weekly ESE Colloquia organized every semester.
The course is graded on a satisfactory/unsatisfactory
basis. Students will get a satisfactory grade if
they attend any four ESE Colloquia each semester.
PhD students will be required to sign in at the beginning
of each seminar.
999. Thesis/Disseratation Research. (C) For students working on an advanced research program leading to the completion
of master's thesis or Ph.D. dissertation requirements.
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