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## Graduate Course Descriptions

**16:332:501 (F) LINEAR SIGNALS AND SYSTEMS (3)**

Syllabus: 16:332:501 syllabus

Signals and Systems; Linear Time-Invariant Systems; Fourier Series Representation of Periodic Signals; Continuous-Time Fourier Transform; Discrete-Time Fourier Transform; Time and Frequency Characterization of Signals and Systems; Sampling; Communication Systems; LaPlace Transform; z-Transform; Linear Feedback Systems.

**16:332:502 (F) TECHNOLOGY ENTREPRENEURSHIP (3)**

Structure and framework of entrepreneurial endeavors. Phases of a startup, business organization.

intellectual property, financing, financial modeling, and business plan writing.

**16:332:503 (F) PROGRAMMING METHODOLOGY FOR NUMERICAL COMPUTING AND
COMPUTATIONAL FINANCE (3)**

Fundamentals of object-oriented programming ad C++ with an emphasis in numerical computing and

computational finance. Design Oriented. Topics include: C++ basics, objected oriented concepts, data

structures, algorithm analysis and applications.

**16:332:504 (F) SENSOR-BASED SYSTEMS AND APPPLICATIONS (3)**

Corequisite: 16:332:543

Syllabus: 16:332:504 syllabus

The course will develop skills in designing, programming, and testing self-configurable communication

protocols and distributed algorithms for wireless sensor networks enabling environmental, health, and

seismic monitoring, surveillance, reconnaissance, and targeting.

**16:332:505 (S) CONTROL SYSTEM THEORY I (3) **

Prerequisite: 16:332:501.

Review of basic feedback concepts and basic controllers. State space and transfer function approaches

for linear control systems. Concepts of stability, controllability, and observability for time-invariant and

time-varying linear control systems. Pole placement technique. Full and reduced-order observer designs.

Introduction to linear discrete-time systems.

**16:332:506 (F) CONTROL SYSTEM THEORY II (3)**

Prerequisite: 16:332:505.

Review of state space techniques; transfer function matrices; concepts of controllability, observability and

identifiability. Identification algorithms for multivariable systems; minimal realization of a system and its

construction from experimental data. State space theory of digital systems. Design of a three mode

controller via spectral factorization.

**16:332:507 (S) SECURITY ENGINEERING (3)**

Syllabus: 16:332:507 syllabus

Essential principles, techniques, tools, and methods for systems security engineering. Students work in

small collaborative design teams to propose, build, and document a project focused on securing systems.

Students document their work through a series of written and oral proposals, progress reports, and final

reports. Basics of security engineering, usability and psychology, human factors in securing systems,

mobile systems security, intersection of security and privacy, security protocols, access control, password

security, biometrics, and topical approaches such as gesture--based authentication

**16:332:508 (S) DIGITAL CONTROL SYSTEMS (3)**

Prerequisite: 16:332:505.

Review of linear discrete-time systems and the Z-transform. Sampling of continuous-time liner systems

and sampled-data linear systems. Quantization effects and implementation issues. Computer controlled

continuous-time linear systems. Analysis and design of digital controllers via the transfer function and

state space techniques. Linear-quadratic optimal control and Kalman filtering for deterministic and

stochastic discrete-time systems.

**16:332:509 (S) Convex Optimization for Engineering Applications (3)**

The course develops the necessary theory, algorithms and tools to formulate and solve convex optimization problems that seek to minimize cost function subject to constraints. The emphasis of the course is on applications in engineering applications such as control systems, computer vision, machine learning, pattern recognition, financial engineering, communication and networks.

**16:332:510 (S) OPTIMUM CONTROL SYSTEMS (3)**

Prerequisites: 16:332:505 and 16:332:506.

Formulation of both deterministic and stochastic optimal control problems. Various performance indices;

calculus of variations; derivation of Euler-Lagrange and Hamilton-Jacobi equations and their connection

to two-point boundary value problems, linear regulator and the Riccati equations. Pontryagin's maximum

principle, its application to minimum time, minimum fuel and "bang-bang" control. Numerical techniques

for Hamiltonian minimization. Bellman dynamic programming; maximum principle.

**16:332:512 (S) NONLINEAR AND ADAPTIVE CONTROL THEORY (3)**

Prerequisite: 16:332:505.

Nonlinear servo systems; general nonlinearities; describing function and other linearization methods;

phase plane analysis and Poincare's theorem. Liapunov's method of stability; Popov criterion; circle

criterion for stability. Adaptive and learning systems; identification algorithms and observer theory; input

adaptive, model reference adaptive and self-optimizing systems. Estimation and adaptive algorithms via

stochastic approximation. Multivariable systems under uncertain environment.

**16:332:514 (S) STOCHASTIC CONTROL SYSTEMS (3)**

Prerequisite: 16:332:505.

Response of linear and nonlinear systems to random inputs. Determination of statistical character of linear

and nonlinear filter outputs. Correlation functions; performance indices for stochastic systems; design of

optimal physically realizable transfer functions. Wiener-Hopf equations; formulation of the filtering and

estimation problems; Wiener-Kalman filter. Instabilities of Kalman filter and appropriate modifications

for stable mechanization. System identification and modeling in presence of measurement noise.

**16:332:519 (F) Information and Network Security (3) **

Prerequisite: see syllabus

Syllabus: 16:332:519 syllabus

This course is an advanced graduate course that will cover a diverse set of topics related

to information and network security. The class will cover a mix of mathematics and programming, covering

aspects of security from theory to practice. This course is primarily aimed at giving graduate students

the resources needed to follow the state of the art in security research. This class will involve a research

component, and advanced levels of independent study is required.

**16:332:521 (F) DIGITAL SIGNALS AND FILTERS (3)**

Corequisite: 16:332:501.

Sampling and quantization of analog signals; Z-transforms; digital filter structures and hardware

realizations; digital filter design methods; DFT and FFT and methods and their application to fast

convolution and spectrum estimation; introduction to discrete time random signals.

**16:332:525 (F) OPTIMUM SIGNAL PROCESSING (3)**

Prerequisites: 16:332:521 or Permission of instructor.

Block processing and adaptive signal processing techniques for optimum filtering, linear prediction,

signal modeling, and high resolution spectral analysis. Lattice filters for linear prediction and Wiener

filtering. Levinson and Schur algorithms and their split versions. Fast Cholesky factorizations.

Periodogram and parametric spectrum estimation and superresolution array processing. LMS, RLS, and

lattice adaptive filters and their applications. Adaptation algorithms for multilayer neural nets.

**16:332:526 (S) ROBOTIC SYSTEMS ENGINEERING (3)**

Introduction to robotics; robot kinematics and dynamics. Trajectory planning and control. Systems with

force, touch and vision sensors. Telemanipulation. Programming languages for industrial robots. Robotic

simulation examples.

**16:332:527 (S) DIGITAL SPEECH PROCESSING (3)**

Prerequisite: 16:332:521.

Acoustics of speech generation; perceptual criteria for digital representation of audio signals; signal

processing methods for speech analysis; waveform coders; vocoders; linear prediction; differential coders

(DPCM, delta modulation); speech synthesis; automatic speech recognition; voice-interactive information

systems.

**16:332:529 (S) IMAGE CODING AND PROCESSING (3)**

Prerequisites: 16:332:521, 16:642:550, (16:332:535 recommended).

Visual information, image restoration, coding for compression and error control, motion compensation,

advanced television.

**16:332:533 (S) COMPUTATIONAL METHODS FOR SIGNAL RECOVERY (3)**

Prerequisites: 16:332:521 and 16:332:541.

Computational methods for estimating signals in noise, for forecasting trends in noisy data, for clustering

data for the recognition and detection of patterns in data. Kalman filtering, neural networks, support vector

machines, and hidden Markov models. Applications in financial engineering and bioinformatics as well as

in more traditional signal processing areas such as speech, image, and array processing, face recognition.

**16:332:535 (F) MULTIRESOLUTION SIGNAL PROCESSING ALGORITHMS (3)**

Prerequisites: 16:332:521 or Permission of instructor. Corequisite: 16:642:550.

Wavelets and subband coding with applications to audio, image, and video processing. Compression and

communications issues including low-bit-rate video systems. Design of digital filters for systems with 2

or more channels. Matlab and matrix algorithms for analysis, design, and implementation.

**16:332:539 ADVANCED TOPICS IN DIGITAL SIGNAL PROCESSING: INTRODUCTION TO FUNCTIONAL
NEUROIMAGING, METHODS AND DATA ANALYSIS**

Prerequisites: Knowledge of DSP, Matlab Programming and Fundamental Physics

Syllabus: 16:332:539 syllabus

This graduate-level course will offer an introduction to the theoretical and practical aspects of existing functional

neuroimaging techniques, with special focus on Electroencephalography (EEG) and optical imaging modalities.

The course will also demonstrate how advanced signal processing techniques are being employed to study and

interpret brain functionality. The aim of this course is to familiarize students with the applications of engineering

concepts in the field of functional brain imaging, and prepare them to pursue research in neruoscience-related

fields.

**16:332:541 (F) STOCHASTIC SIGNALS AND SYSTEMS (3)**

Corequisite: 16:332:501 and 16:642:550

Axioms of probability; conditional probability and independence; random variables and functions

thereof; mathematical expectation; characteristic functions; conditional expectation; Gaussian random

vectors; mean square estimation; convergence of a sequence of random variables; laws of large numbers

and Central Limit Theorem; stochastic processes, stationarity, autocorrelation and power spectral density;

linear systems with stochastic inputs; linear estimation; independent increment, Markov, Wiener, and

Poisson processes.

**16:332:542 (S) INFORMATION THEORY AND CODING (3)**

Prerequisite: 16:332:541

Syllabus: 16:332:542 syllabus

Noiseless channels and channel capacity; entropy, mutual information, Kullback-Leibler distance and

other measures of information; typical sequences, asymptotic equipartition theorem; prefix codes, block

codes, data compression, optimal codes, Huffman, Shannon-Fano-Elias, Arithmetic coding; memoryless

channel capacity, coding theorem and converse; Hamming, BCH, cyclic codes; Gaussian channels and

capacity; coding for channels with input constraint; introduction to source coding with a fidelity criterion.

**16:332:543 (F) COMMUNICATION NETWORKS I (3)**

Syllabus: 16:332:519 syllabus

Prerequisite: 14:332:226 or equivalent or 16:332:541 or equivalent.

Introduction to telephony and integrated networks. Multiplexing schematics. Circuit and packet

switching networks. Telephone switches and fast packet switches. Teletraffic characterization.. Delay

and blocking analysis. Queueing network analysis.

**16:332:544 (S) COMMUNICATION NETWORKS II (3)**

Prerequisite: 16:332:543

Syllabus 16:332:544 syllabus

Network and protocol architectures. Layered connection management, including network design, path

dimensioning, dynamic routing, flow control, and random access algorithms. Protocols for error control,

signaling, addressing, fault management, and security control. This course is intended to provide an in-depth and practical understanding of modern computer networks that constitute the Internet. The scope includes network architecture, key technologies, layer 2 and layer 3 protocols, and examples of specific systems. Emphasis will be on network protocols and related software implementation. The course includes a hands-on “clean-slate” network prototyping project involving specification, standardization and software implementation.

**16:332:545 (S) DIGITAL COMMUNICATION SYSTEMS (3)**

Prerequisite: 16:332:541

Syllabus: 16:332:545 syllabus

Signal space and Orthonormal expansions, effect of additive noise in electrical communications vector

channels, waveform channels, matched filters, bandwidth and dimensionality. Digital modulation

techniques. Optimum receiver structures, probability of error, bit and block signaling, Intersymbol

interference and its effects, equalization and optimization of baseband binary and M-ary signaling

schemes; introduction to coding techniques.

**16:332:546 (S) WIRELESS COMMUNICATIONS TECHNOLOGIES (3)**

Prerequisite: 16:332:545

Propagation models and modulation techniques for wireless systems, receivers for optimum detection on

wireless channels, effects of multiple access and intersymbol interference, channel estimation, TDMA

and CDMA cellular systems, radio resource management, mobility models.

**16:332:548 (S) ERROR CONTROL CODING (3)**

Prerequisite: 16:332:545

Syllabus 16:332:548 syllabus

Continuation of 16:332:545. Application of information-theoretic principles to communication system

analysis and design. Source and channel coding considerations, rudiments of rate-distortion theory.

Probabilistic error control coding impact on system performance. Introduction to various channel models

of practical interest, spread spectrum communication fundamentals. Current practices in modern digital

communication system design and operation.

**16:332:549 (S) DETECTION AND ESTIMATION THEORY (3)**

Prerequisite: 16:332:541

Syllabus: 16:332:549 syllabus

Statistical decision theory, hypothesis testing, detection of known signals and signals with unknown

parameters in noise, receiver performance and error probability, applications to radar and

communications. Statistical estimation theory, performance measures and bounds, efficient estimators.

Estimation of unknown signal parameters, optimum demodulation, applications, linear estimation,

Wiener filtering, Kalman filtering.

**16:332:553 (S) WIRELESS ACCESS TO INFORMATION NETWORKS (3)**

Prerequisites: 14:332:349 and 14:332:450 or equivalent.

Cellular mobile radio; cordless telephones; systems architecture; network control; switching; channel

assignment techniques; short range microwave radio propagation; wireless information transmission

including multiple access techniques, modulation, source coding, and channel coding.

**16:332:556 (S) MICROWAVE COMMUNICATION SYSTEMS (3)**

Prerequisite: 16:332:580 or equivalent.

Overview of modern microwave engineering including transmission lines, network analysis, integrated

circuits, diodes, amplifier and oscillator design. Microwave subsystems including front-end and

transmitter components, antennas, radar terrestrial communications, and satellites.

**16:332:559 Section 03 (F) ADVANCED TOPICS IN STOCHASTIC PROCESSES (3) **

Prerequisite: see syllabus

Syllabus 16:332:559 Section 03 syllabus

This course provides a graduate level introduction to systems and processes that are described by

sequences of discrete events. Topics to be covered include Poisson processes, the renewal theorem,

discrete and continuous Markov chains, semi-Markov processes, time reversibility, martingales and large

deviations. Applications to queueing, networks, optimization and gambling would be discussec. Understanding

of this subject has become useful for the study of networks. The material is applicable to traffic modeling,

performance analysis, and a variety of network resource allocation problems.

**16:332:560 (F) COMPUTER GRAPHICS (3)**

Computer display systems, algorithms and languages for interactive graphics. Vector, curve, and surface

generation algorithms. Hidden-line and hidden-surface elimination. Free-form curve and surface

modeling. High-realism image rendering.

**16:332:561 (F) MACHINE VISION (3)**

Prerequisite: 16:332:501.

Image processing and pattern recognition. Principles of image understanding. Image formation, boundary

detection, region growing, texture and characterization of shape. Shape from monocular clues, stereo and

motion. Representation and recognition of 3-D structures.

**16:332:562 (S) VISUALIZATION AND ADVANCED COMPUTER GRAPHICS (3)**

Prerequisite: 16:332:560 or permission of instructor.

Advanced visualization techniques, including volume representation, volume rendering, ray tracing,

composition, surface representation, advanced data structures. User interface design, parallel and object-

oriented graphic techniques, advanced modeling techniques.

**16:332:563 (F) COMPUTER ARCHITECTURE I (3)**

Fundamentals of computer architecture using quantitative and qualitative principles. Instruction set design

with examples and measurements of use, basic processor implementation: hardwired logic and microcode,

pipelining; hazards and dynamic scheduling, vector processors, memory hierarchy; caching, main

memory and virtual memory, input/output, and introduction to parallel processors; SIMD and MIMD

organizations.

**16:332:564 (S) COMPUTER ARCHITECTURE II (3)**

Prerequisite: 16:332:563.

Advanced hardware and software issues in main-stream computer architecture design and evaluation.

Topics include register architecture and design, instruction sequencing and fetching, cross-branch

fetching, advanced software pipelining, acyclic scheduling, execution efficiency, predication analysis,

speculative execution, memory access ordering, prefetch and preloading, cache efficiency, low power

architecture, and issues in multiprocessors.

**16:332:565 (F) NEUROCOMPUTER SYSTEM DESIGN (3)**

Prerequisites: 16:332:563.

Principles of neural-based computers, data acquisition, hardware architectures for multilayer, tree and

competitive learning neural networks, applications in speech recognition, machine vision, target

identification and robotics.

**16:332:566 (S) INTRODUCTION TO PARALLEL AND DISTRIBUTED COMPUTING (3)**

Prerequisite: 16:332:563.

Introduction to the fundamental of parallel and distributed computing including systems, architectures,

algorithms, programming models, languages and software tools. Topics covered include parallelization

and distribution models; parallel architectures; cluster and networked meta-computing systems;

parallel/distributed programming; parallel/distributed algorithms, data-structures and programming

methodologies, applications; and performance analysis. A "hands-on" course with programming

assignments and a final project.

**16:332:567 (F) SOFTWARE ENGINEERING I (3)**

Syllabus: 16:332:567 syllabus

Overview of software development process. Formal techniques for requirement analysis, system

specification and system testing. Distributed systems. System security and system reliability. Software

models and metrics. Case studies.

**16:332:568 (S) SOFTWARE ENGINEERING WEB APPLICATIONS (3)**

Prerequisite: 16:332:567.

The course focus is on Web software design with particular emphasis on mobile wireless terminals. The

first part of the course introduces tools; Software component (Java Beans), Application frameworks,

Design patterns, XML, Communication protocols, Server technologies, and Intelligent agents. The

second part of the course presents case studies of several Web applications. In addition, student teams will

through course projects develop components for an XML-Based Web, such as browsers, applets, servers,

and intelligent agents.

**16:332:569 (F) DATABASE SYSTEM ENGINEERING (3)**

Relational data model, relational database management system, relational query languages, parallel

database systems, database computers, and distributed database systems.

**16:332:570 (S) ROBUST COMPUTER VISION (3)**

Prerequisite: 16:332:561

Syllabus: 16:332:570 syllabus

A toolbox of advanced methods for computer vision, using robust estimation, clustering, probabilistic

techniques, invariance. Applications include feature extraction, image segmentation, object recognition,

and 3-D recovery.

**16:332:571 (S) VIRTUAL REALITY TECHNOLOGY (3)**

Prerequisite: 16:332:560.

Syllabus: 16:332:571 syllabus

Introduction to Virtual Reality. Input/Output tools. Computing architectures. Modeling. Virtual Reality

programming. Human factors. Applications and future systems.

**16:332:572 (S) PARALLEL AND DISTRIBUTED COMPUTING (3)**

Prerequisite: 16:332:563, 16:332:564 and 16:332:566.

Study of the theory and practice of applied parallel/distributed computing. The course focuses on

advanced topics in parallel computing including current and emerging architectures, programming models

application development frameworks, runtime management, load-balancing and scheduling, as well as

emerging areas such as autonomic computing, Grid computing, pervasive computing and sensor-based

systems. A research-oriented course consisting of reading, reviewing and discussing papers, conducting

literature surveys, and a final project.

**16:332:573 (S) DATA STRUCTURES AND ALGORITHM (3)**

The objective is to take graduate students in all graduate School of Engineering fields with a good

undergraduate data structures and programming background and make them expert in programming the

common algorithms and data structures, using the C and C++ programming languages. The students will

perform laboratory exercises in programming the commonplace algorithms I C and C++. The students

will also be exposed to computation models and computational complexity.

**16:332:574 (F) COMPUTER-AIDED DIGITAL VLSI DESIGN (3)**

Advanced computer-aided VLSI chip design, CMOS and technology, domino logic, pre-charged busses,

case studies of chips, floor planning, layout synthesis, routing, compaction circuit extraction, multi-level

circuit simulation, circuit modeling, fabrication processes and other computer-aided design tools.

**16:332:575 (S) VLSI ARRAY PROCESSORS (3)**

Prerequisite: 16:332:574

VLSI technology and algorithms; systolic and wavefront-array architecture; bit-serial pipelined

architecture; DSP architecture; transputer; interconnection networks; wafer-cscale integration; neural

networks.

**16:332:576 (S) TESTING OF ULTRA LARGE SCALE CIRCUITS (3)**

Prerequisite: 16:332:563.

Testing of Ultra Large Scale Integrated Circuits (of up to 50 million transistors) determines whether a

manufactured circuit is defective. Algorithms for test-pattern generation for combinational, sequential,

memory, and analog circuits. Design of circuits for easy testability. Design of built-in self-testing circuits.

**16:332:577 (S) ANALOG AND LOW-POWER DIGITAL VLSI DESIGN (3)**

Transistor design and chip layout of commonly-used analog circuits such as OPAMPS, A/D and D/A

converters, sample-and-hold circuits, filters, modulators, phase-locked loops, and voltage-controlled

oscillators. Low-power design techniques for VLSI digital circuits, and system-on-a-chip layout

integration issues between analog and digital cores.

**16:332:578 (S) DEEP SUBMICRON VLSI DESIGN (3)**

Prerequisite: 14:332:574 CAD Digital VLSI Design

Advanced topics in deep submicron and nanotechnology VLSI design and fabrication. Logic and state

machine design for high performance and low power. Tree adders and Booth multipliers. Memory design.

Timing testing for crosswalk faults. Design economics. Emergining nanotechnology devices.

**16:332:579 ADVANCED TOPICS IN COMPUTER ENGINEERING (3)**

Prerequisite: Permission of instructor.

In-depth study of topics pertaining to computer engineering such as microprocessor system design; fault-

tolerant computing; real-time system design. Subject areas may vary from year to year.

**16:332:580 (F) ELECTRIC WAVES AND RADIATION (3)**

Prerequisite: A course in elementary electromagnetics.

Static boundary value problems, dielectrics, wave equations, propagation in lossless and lossy media,

boundary problems, waveguides and resonators, radiation fields, antenna patterns and parameters, arrays,

transmit-receive systems, antenna types.

**16:332:581 (F) INTRODUCTION TO SOLID STATE ELECTRONICS (3)**

Introduction to quantum mechanics; WKB method; perturbation theory; hydrogen atom; identical

particles; chemical bonding; crystal structures; statistical mechanics; free-electron model; quantum theory

of electrons in periodic lattices.

**16:332:583 (F) SEMICONDUCTOR DEVICES I (3)**

Syllabus: 16:332:583 syllabus

Charge transport, diffusion and drift current, injection, lifetime, recombination and generation processes,

p-n junction devices, transient behavior, FET's, I-V, and frequency characteristics, MOS devices C-V, C-f

and I-V characteristics, operation of bipolar transistors.

**16:332:584 (S) SEMICONDUCTOR DEVICES II (3)**

Prerequisite: 16:332:583.

Review of microwave devices, O and M-type devices, microwave diodes, Gunn, IMPATT, TRAPATT,

etc., scattering parameters and microwave amplifiers, heterostructures and III-V compound based BJT's

and FET's.

**16:332:585 (S) SUSTAINABLE ENERGY (3)**

Prerequisite: see syllabus

Syllabus: 16:332:585 syllabus

This course is designed for the student interested in an overview of the technological methods for obtaining

energy from non-renewable and renewable energy sources. The course is divided into three components:

Energy Analysis Toolbox, Non-renewable (Fossil) Energy Sources and Renewable Energy Sources.

**16:332:587 (F) TRANSISTOR CIRCUIT DESIGN (3)**

Design of discrete transistor circuits; amplifiers for L.F., H.F., tuned and power applications biasing;

computer-aided design; noise; switching applications; operational amplifiers; linear circuits.

**16:332:588 (S) INTEGRATED TRANSISTOR CIRCUIT DESIGN (3)**

Prerequisite: 16:332:587

Syllabus: 16:332:588 syllabus

Design of digital integrated circuits based on NMOS, CMOS, bipolar BiCMOS and GaAs FETs;

fabrication and modeling; analysis of saturating and non-saturating digital circuits, sequential logic

circuits, semiconductor memories, gate arrays, PLA and GaAs LSI circuits.

**16:332:589 (S) RF INTEGRATED CIRCUIT DESIGN (3) **

Syllabus: 16:332:589 syllabus

Basic concepts in RF design, analysis of noise, transceiver architectures, analysis and design of RF

integrated circuits for modern wireless communications systems: low noise amplifiers, mixers,

oscillators, phase-locked loops.

**16:332:591 (F) OPTOELECTRONICS I (3)**

Prerequisites: 16:332:580, and 581 or 583.

Syllabus: 16:332:591 syllabus

Waveguides and optical filters, optical resonators, principles of laser action, light emitting diodes,

semiconductor lasers, optical amplifiers, optical modulators and switches, photodetectors, wavelength-

division-multiplexing and related optical devices.

**16:332:592 (S) OPTOELECTRONICS II (3)**

Prerequisite: 16:332:591

Photonic crystals: photonic bandgap, photonic crystal surfaces, fabrication, cavities, lasers, modulators

and switches, superprism devices for communications, sensing and nonlinear optics, channel drop filters;

advanced quantum theory of lasers: Fermi’s golden for laser transition, noise, quantum well lasers,

quantum cascade lasers. Nonlinear optics: parametric amplification, stimulated Raman/Brillouin

scattering, Q-switching, mode-locked lasers.

**16:332:594 (F) SOLAR CELLS (3)**

Prerequisite: 16:332:583 or equivalent.

Photovoltaic material and devices, efficiency criteria, Schottky barrier, p-n diode, heterojunction and

MOS devices, processing technology, concentrator systems, power system designs and storage.

**16:332:597 (S) MATERIAL ASPECTS OF SEMICONDUCTORS (3)**

Prerequisite: 16:332:581.

Preparation of elemental and compound semiconductors. Bulk crystal growth techniques. Epitaxial

growth techniques. Impurities and defects and their incorporation. Characterization techniques to study

the structural, electrical and optical properties.

**16:332:599:01 (F) ADVANCED TOPICS IN SOLID STATE ELECTRONICS: WEARABLE AND IMPLANTABLE ELECTRONIC SYSTEMS**

Prerequisite: 14:332:361 (Electronic Devices)

Syllabus: 16:332:599 Section 01 syllabus

This course will cover the fundamentals of next generation wearable and implantable technologies from the device level to the system level.

**16:332:599:02 ADVANCED TOPICS IN SOLID STATE ELECTRONICS: MOSFET TRANSISTORS**

Prerequisite: 16:332:583 Semiconductor Devices or 14:332:465 Physical Electronics or equivalent

Syllabus: 16:332:599 Section 2 syllabus

Overview of basic MOSFET theory and issues; advanced MOSFET physics; advanced transistor structures, and emerging research devices and applications.

**16:332:601, 602 SPECIAL PROBLEMS (BA, BA)**

Prerequisite: Permission of instructor.

Investigation in selected areas of electrical engineering.

**16:332:618 SEMINAR IN SYSTEMS ENGINEERING (1)**

Presentation involving current research given by advanced students and invited speakers. Term papers

required.

**16:332:638 SEMINAR IN DIGITAL SIGNAL PROCESSING (1)**

Presentation involving current research given by advanced students and invited speakers. Term papers

required.

**16:332:658 SEMINAR IN COMMUNICATIONS ENGINEERING (1)**

Presentation involving current research given by advanced students and invited speakers. Term papers

required.

**16:332:678 SEMINAR IN COMPUTER ENGINEERING (1)**

Presentation involving current research given by advanced students and invited speakers. Term papers

required.

**16:332:698 SEMINAR IN SOLID-STATE ELECTRONICS (1)**

Presentation involving current research given by advanced students and invited speakers. Term papers

required.

**16:332:699 COLLOQUIUM IN ELECTRICAL & COMPUTER ENGINEERING (0)**

Research presentations by distinguished lecturers.

**16:332:701,702 RESEARCH IN ELECTRICAL ENGINEERING (BA, BA)**

Research supervised by faculty in the Department of Electrical and Computer Engineering.

Typically 1 to 3 credits per semester.