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Camden Newark New Brunswick/Piscataway
Catalogs
  Graduate School-New Brunswick 2017 Programs, Faculty, and Courses Electrical and Computer Engineering 332 Graduate Courses  
Graduate School-New Brunswick
16:332:501 (F) System Analysis (3) Fundamental system concepts, solution of linear differential and difference equations. Transform methods involving Fourier and Laplace transforms, double-sided Laplace transforms, Z-transforms, Hilbert transforms, convolution in time and frequency domain. Complex variables and application of Residue Theorem for transform inversion. Review of matrix algebra involving similarity transformations. Cayley-Hamilton theorem; state space concepts, controllability, observability, and minimal realization.
16:332:502 Technology Entrepreneurship (3) Structure and framework of entrepreneurial endeavors. Phases of a startup, business organization, intellectual property, financing, financial modeling, business plan writing. Mammone
16:332:503 Programming Methodologies for Numerical Computing and Computational Finance (3) Fundamentals of object-oriented programming and C++ with an emphasis on numerical computing and computational finance. Design oriented. Topics include C++ basics, object-oriented concepts, data structures, algorithm analysis, and applications.
Parashar, Silver
16:332:504 (F) Sensor-Based Systems and Applications (3) 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. Corequisite: 16:332:543.
16:332:505 (S) Control Theory (3) 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. Prerequisite: 16:332:501.
16:332:506 (F) Applied Controls (3) Review of linear system stability, controllability, and observability. Introduction to linear stochastic systems. Linear system driven by white noise. Kalman filtering. Linear quadratic optimal controllers. Basic properties of Lyapunov and Riccati equations. Introduction to robust control and H-2 and H-infinity optimization. Introduction to nonlinear systems. Basic techniques for controlling nonlinear systems. Introduction to game theory for linear dynamic systems (zero-sum games, Pareto optimization, Nash and Stackelberg optimal strategies). Prerequisite: 16:332:505.
16:332:507 (F) Security Engineering (3) 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) Review of linear discrete-time systems and the Z-transform. Sampling of continuous-time linear 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. Prerequisite: 16:332:505.
16:332:509 (S) Convex Optimization for Engineering Applications (3) Theory, algorithms, and tools to formulate and solve convex optimization problems that seek to minimize cost function subject to constraints; engineering applications. Dana
16:332:510 (S) Optimal Control Systems (3) 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. Prerequisites: 16:332:505, 506.
16:332:512 (S) Nonlinear and Adaptive Control Theory (3) Nonlinear servo systems; general nonlinearities; describing function and other linearization methods; phase-plane analysis and Poincare theorems. Lyapunov'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. Prerequisite: 16:332:505.
16:332:514 (S) Stochastic Control Systems (3) 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 the presence of measurement noise. Prerequisite: 16:332:505.
16:332:519 Advanced Topics in Systems Engineering (3) Advanced study of various aspects of automatic control systems. Possible topics include identification, filtering, optimal and adaptive control, learning systems, digital and sampled data implementations, singular perturbation theory, large-scale systems, game theory, geometric control theory, and control of large flexible structures. Topics vary from year to year. Prerequisite: Permission of instructor.
16:332:521 (F) Digital Signals and Filters (3) Sampling and quantization of analog signals; Z-transforms; digital filter structures and hardware realizations; digital filter design methods; DFT and FFT methods and their application to fast convolution and spectrum estimation; introduction to discrete-time random signals. Corequisite: 16:332:501.
16:332:525 (F) Optimum Signal Processing (3) 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. Prerequisite: 16:332:521 or permission of instructor.
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) 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. Prerequisite: 16:332:521.
16:332:529 (S) Image Coding and Processing (3) Visual information, image restoration, coding for compression and error control, motion compensation, advanced television. Prerequisites: 16:332:521, 16:642:550. Recommended: 16:332:535.
16:332:533 (S) Computational Methods for Signal Recovery (3) Computational methods for estimating signals in noise, for forecasting trends in noisy data, for clustering data, and 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, and face recognition. Prerequisites: 16:332:521, 541.
16:332:535 (F) Multiresolution Signal Processing Algorithms (3) Algebraic models and algorithms, sampling lattices, multiresolution transforms, filters, rate conversion, deconvolution, and projection. Prerequisite: 16:332:521 or permission of instructor. Corequisite: 16:642:550.
16:332:539 Advanced Topics in Digital Signal Processing (3) Emphasis on current research areas. Advanced treatment of such topics as digital filter design, digital filtering of random signals, discrete spectral analysis methods, and digital signal processor architectures. Prerequisite: Permission of instructor.
16:332:541 (F) Stochastic Signals and Systems (3) 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. Corequisite: 16:332:501.
16:332:542 (S) Information Theory and Coding (3)   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. Prerequisite: 16:332:541.
16:332:543 (F) Communication Networks I (3) 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. Queuing network analysis.
16:332:544 (S) Communication Networks II (3) 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. Prerequisite: 16:332:543.
16:332:545 (S) Digital Communication Systems (3) Orthonormal expansions, effect of additive noise in electrical communications, vector channels, waveform channels, matched filters, bandwidth, and dimensionality. Optimum receiver structures, probability of error, bit and block signaling, introduction to coding techniques. Prerequisite: 16:332:541.
16:332:546 (S) Wireless Communications Technologies (3) 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, and mobility models. Prerequisite: 16:332:545.
16:332:548 (S) Error Control Coding (3) Application of information-theoretic principles to communication system analysis and design. Source and channel-coding considerations, rudiments of rate-distortion theory, and 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. Corequisite: 16:332:542.
16:332:549 (S) Detection and Estimation Theory (3) 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, and applications. Linear estimation, Wiener filtering, Kalman filtering. Prerequisite: 16:332:541.
16:332:553 (S) Wireless Access to Information Networks (3) 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) Microwave subsystems, including front-end and transmitter components, antennas, radar, terrestrial communications, and satellites. Prerequisite: 16:332:580 or equivalent.
16:332:559 Advanced Topics in Communications Engineering (3) Topics such as source and channel coding, modern modulation techniques, telecommunication networks, and information processing. Prerequisite: Permission of instructor.
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) Image processing and pattern recognition. Principles of image understanding. Image formation, boundary detection, region growing, texture, and characterization of shape. Shape from monocular cues, stereo, and motion. Representation and recognition of 3-D structure. Prerequisite: 16:332:501.
16:332:562 (S) Visualization and Advanced Computer Graphics (3) Advanced visualization techniques, including volume representation, volume rendering, ray tracing, composition, surface representation, and advanced data structures. User interface design, parallel and object-oriented graphic techniques, and advanced modeling techniques. Prerequisite: 16:332:560.
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) Advanced hardware and software issues in mainstream computer architecture design and evaluation. 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. Prerequisite: 16:332:563.
16:332:565 (F) Neurocomputer System Design (3) 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. Prerequisite: 16:332:563.
16:332:566 (S) Introduction to Parallel and Distributed Computing (3) 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. Programming assignments and a final project. Prerequisites: 16:332:563 and 564.
16:332:567 (F) Software Engineering (3) Overview of software development process. Formal techniques for requirements 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 of Web Applications (3) Program-development and software-design methodologies. Abstract data types, information hiding, and program documentation. Program testing and reusability. Axiomatic and functional models. Case studies. Prerequisite: 16:332:567.
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) A toolbox of advanced methods for computer vision using robust estimation, clustering, probabilistic techniques, and invariance. Applications include feature extraction, image segmentation, object recognition, and 3-D recovery. Prerequisite: 16:332:561.
16:332:571 (S) Virtual Reality Technology (3) Introduction to virtual reality; input/output tools; computing architecture; modeling; virtual reality programming; human factors, applications; and future systems. Prerequisite: 16:332:560.
16:332:572 (S) Parallel and Distributed Computing (3) 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. Prerequisites: 16:332:563, 564, and 566.
16:332:573 (S) Data Structures and Algorithms (3) Programming in C and C++. Data structures and algorithms commonly used in engineering software applications. Stacks, linked lists, queues, sorting, trees, search trees, hashing, heaps, graphs, and graph algorithms. Computation models and complexity.
16:332:574 (F) Computer-Aided Digital VLSI Design (3) Advanced computer-aided digital VLSI chip design, CMOS technology, domino logic, precharged busses, case studies of chips, floor planning, layout synthesis, routing, compaction circuit extraction, multilevel circuit simulation, circuit modeling, fabrication processes, and other computer-aided design tools.
16:332:575 (S) VLSI Array Processors (3) VLSI technology and algorithms; systolic and wavefront-array architecture; bit-serial pipelined architecture; DSP architecture; transputer; interconnection networks; wafer-scale integration; neural networks. Prerequisite: 16:332:574.
16:332:576 (S) Testing of Ultra Large-Scale Circuits (3) Algorithms for test-pattern generation for combinational, sequential, and CMOS circuits. Design of circuits for easy testability. Design of built-in self-testing circuits. Prerequisite: 16:332:563.
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. Prerequisite: 16:332:574.
16:332:578 (S) Deep Submicron VLSI Design (3) 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; emerging nanotechnology devices. Prerequisite: 16:332:574 CAD VLSI Design.
16:332:579 Advanced Topics in Computer Engineering (3) In-depth study of topics pertaining to computer engineering, such as microprocessor system design; fault-tolerant computing; real-time system design. Subject areas vary from year to year. Prerequisite: Permission of instructor.
16:332:580 (F) Electric Waves and Radiation (3) 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, and antenna types. Prerequisite: Elementary electromagnetics.
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) Charge transport; diffusion and drift current; injection, lifetime, recombination, and generation processes; p-n junction devices; transient behavior; FETs, 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) Review of microwave devices, O- and M-type devices, microwave diodes, Gunn, IMPATT, TRAPATT, scattering parameters and microwave amplifiers, heterostructures, and III-V compound-based BJTs and FETs. Prerequisite: 16:332:583.
16:332:585 (S) Sustainable Energy (3) Assessment of different technologies in terms of cost, efficiency, and impact; considers all major nonrenewable and renewable energy sources.
16:332:586 Biosensing and Bioelectronics (3) This course covers state-of-the-art and emerging biosensors, biochips, microfluidics, which will be studied in the context of molecular diagnostics. Topics will include microfluidics and mass transfer limits; electrode-electrolyte interfaces; electrochemical noise processes; biosensor system level characterization; determination of performance parameters such as throughput, detection limit, and cost; integration of sensor with microfluidics; and electronic readout circuitry architectures. Novel nanobiosensors such as nanopores, nanowire FETS, surface plasmon resonance, surface-enhanced Raman scattering, fluorescence, and single molecule detection will also be covered.
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) Design of digital integrated circuits based on NMOS, CMOS, bipolar, BiCMOS, and GaAs FETs; fabrication and modeling; analysis of saturating and nonsaturating digital circuits, sequential logic circuits, semiconductor memories, gate arrays, PLA, and GaAs LSI circuits. Prerequisite: 16:332:587.
16:332:589 (S) RF Integrated Circuit Design (3) 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) 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. Prerequisites: 16:332:580 and 581 or 583.
16:332:592 (S) Optoelectronics II (3) 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: Ferim's golden rule for laser transition, noise, quantum well lasers, and quantum cascade lasers. Nonlinear optics: parametric amplification, stimulated Raman/Brillouin scattering, Q-switching, and mode-locked lasers. Prerequisite: 16:332:583.
16:332:594 (F) Solar Cells (3) Photovoltaic material and devices, efficiency criteria, Schottky barrier, p-n diode, heterojunction and MOS devices, processing technology, concentrator systems, power system designs, and storage. Prerequisite: 16:332:583 or equivalent.
16:332:597 (S) Material Aspects of Semiconductors (3) 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. Prerequisite: 16:332:581.
16:332:599 Advanced Topics in Solid-State Electronics (3) Topics vary and include semiconductor materials, surfaces, and devices; optoelectronic devices; sensors; photovoltaics; fiber optics; and analog/digital circuit design. Prerequisite: Permission of instructor.
16:332:601,602 Special Problems (BA,BA) Investigation in selected areas of electrical engineering. Prerequisite: Permission of instructor.
16:332:618 Seminar in Systems Engineering (1) Presentation involving current research given by advanced students and invited speakers. Semester papers required.
16:332:638 Seminar in Digital Signal Processing (1) Presentation involving current research given by advanced students and invited speakers. Semester papers required.
16:332:658 Seminar in Communications Engineering (1) Presentation involving current research given by advanced students and invited speakers. Semester papers required.
16:332:678 Seminar in Computer Engineering (1) Presentation involving current research given by advanced students and invited speakers. Semester papers required.
16:332:698 Seminar in Solid-State Electronics (1) Presentation involving current research given by advanced students and invited speakers. Semester papers required.
16:332:699 Colloquium in Electrical and Computer Engineering (0) Eminent figures in electrical and computer engineering invited as guest lecturers on current research topics and major trends. Each full-time M.S. and Ph.D. student must take the colloquium, and each must have 80 percent attendance records. M.S. students must take the colloquium for two semesters but get 0 credits. Ph.D. students must take the colloquium for four semesters but get 0 credits.
16:332:701,702 Research in Electrical Engineering (3,3)
 
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