16:332:501 SYSTEMS ANALYSIS (3) Fundamentals of linear system concepts via solution of linear differential and difference equations. State space approach for multi-input multi-output (MIMO) linear systems. Introduction to concepts of linear system stability, 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, and business plan writing.
16:332:503 PROGRAMMING METHODOLOGY FOR NUMERICAL COMPUTING AND COMPUTATIONAL FINANCE (3)
Fundamentals of object-oriented programming and 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 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 CONTROL SYSTEM 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.
16:332:506 APPLIED CONTROLS (3)
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 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 DIGITAL CONTROL SYSTEMS (3)
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 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 OPTIMUM 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.
16:332:512 NONLINEAR AND ADAPTIVE CONTROL THEORY (3)
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 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.
16:332:515 REINFORCEMENT LEARNING FOR ENGINEERS (3)
16:332:516 CLOUD COMPUTING AND BIG DATA (3) This course will introduce students to fundamentals of Cloud Computing Concepts. It will emphasize both in the distributed communication and coordination aspects of clouds and the new parallel and distributed technologies, concepts, techniques, and algorithms that make-up the Cloud Infrastructure as we have built it up or … envision it today. We will investigate more or less in depth how each individual component works and contributes to the Cloud and also how it works in symphony with all the remaining components of the Cloud Framework. We will ask ourselves at the beginning of the class… What is a Cloud? We will attempt to describe. We will ask ourselves again at the end of the course: What is a Cloud after all? And the goal is that everyone now will color this answer with his/her own personal experience on working, touching, approaching, altering some parts of the Cloud!
16:332:518 MOBILE EMBEDDED SYSTEMS AND ON-DEVICE AI (3) This course introduces computing principles in mobile embedded systems and artificial intelligence (AI) technologies on mobile devices. It focuses on emerging computing paradigms in the areas of context-aware pervasive systems, spatiotemporal access control with distributed software agents, mobile sensing, and trust and privacy in mobile environments. It also introduces techniques for implementing AI and developing deep learning models on resource-constrained mobile devices.
16:332:519 ADVANCED TOPICS IN SYSTEMS ENGINEERING (3)
16:332:521 DIGITAL SIGNALS AND FILTERS (3) Prerequisite: 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 OPTIMUM SIGNAL PROCESSING: SIGNAL PROCESSING MACHINE LEARNING FOR ENGINEERS
16:332:526 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 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 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, and advanced television.
16:32:530 – INTRODUCTION TO DEEP LEARNING (3) This course offers an introduction to deep learning, the critical technology behind modern artificial intelligence. Students will learn the fundamentals of deep learning, including the construction, training, testing, and application of deep neural networks. Additionally, the course will cover approaches for deploying deep neural networks efficiently and address the vulnerabilities and robustness of these networks.
16:332:531 - PROBABILISTIC METHODS FOR LARGE SCALE SIGNAL PROCESSING AND LEARNING (3) This course provides a “mathematical toolkit” for analyzing large-scale signal processing and machine learning algorithms. Topics in this course include: concentration of measure, high-dimensional geometry, packings and coverings, random matrices, random processes, and application
16:332:532 - MULTIMODAL MACHINE LEARNING FOR SENSING SYSTEMS (3) This graduate-level course teaches multimodal machine learning and sensor data analysis through signal processing, control, and machine learning techniques. Students will gain hands-on experience in filters, time series analysis, and deep learning models for sensor fusion and inference.
16:332:533 - MACHINE LEARNING FOR INVERSE PROBLEMS (3) Solving inverse problems is at the core of a wide range of applications, and machine learning has had a significant impact in this area. In this course, we will review different classes of inverse problems and for each class of inverse problems review both classic and machine learning-based solutions.
16:332:535 MULTIDIMENSIONAL 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 (3)
16:332:541 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.
16:332:542 INFORMATION THEORY AND CODING (3)
Prerequisite: 16:332:541
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 COMMUNICATION NETWORKS I (3)
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 COMMUNICATION NETWORKS II (3) Prerequisite: 16:332:541 - 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 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 WIRELESS COMMUNICATIONS TECHNOLOGIES (3)
Syllabus 16:332:546 syllabus
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 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. 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 DETECTION AND ESTIMATION THEORY (3)
Prerequisite: 16:332:541
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 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 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.
16:332:557 QUANTUM COMPUTING AND COMMUNICATION ALGORITHMS (3)
16:332:558 QUANTUM COMPUTING AND INFORMATION SYSTEMS (3)
16:332:59 ADVANCED TOPICS IN COMM ENGINEERING (3)
16:332:560 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 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 clues, stereo and motion. Representation and recognition of 3-D structures.
16:332:562 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 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 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 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 INTRODUCTION TO PARALLEL AND DISTRIBUTED COMPUTING (3)
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 SOFTWARE ENGINEERING I (3)
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 SOFTWARE ENGINEERING WEB APPLICATIONS (3)
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 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 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 VIRTUAL REALITY TECHNOLOGY (3)
Introduction to Virtual Reality. Input/Output tools. Computing architectures. Modeling. Virtual Reality programming. Human factors. Applications and future systems.
16:332:572 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 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 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 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 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 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 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. 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. Topics covered in Fall 2023 include High Performance and Distributed Computing, Machine Learning for IOT and Distributed Deep Learning.
16:332:580 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 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 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 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 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.
16:332:586 BIOSENSING & BIOELECTRONICS (3)
16:332:587 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 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 non-saturating digital circuits, sequential logic circuits, semiconductor memories, gate arrays, PLA and GaAs LSI circuits.
16:332:589 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.
16:332:590 SOCIALLY COGNIZANT ROBOTICS (3)
Students of the “Socially Cognizant Robotics” course will learn basic principles and state-of-the-art developments of robotics so as to learn the expected trajectory of this technology and its impact on individuals and society. The course is designed for both STEM students as well as computationally-oriented cognitive and social science students. The interdisciplinary curriculum has seven underlying disciplines spanning STEM fields to social and behavioral sciences. It includes traditionally technical disciplines, such as robot embodiment and control, and extends to areas which support human interaction, such as visual learning and language processing, to cognitive modeling, which enables more high level human-robot cooperation, and finally to areas such as behavioral research and public policy. The course will utilize open-source software libraries in robotics, computer vision, and deep learning. Recent innovations at the intersection of deep reinforcement learning and human behavior modeling will be explored in the context of optimizing collaborative robot action.
16:332:591 OPTOELECTRONICS I (3)
Prerequisites: 16:332:580
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 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: Ferim’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 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.
16:332:595 DESIGN METHODS FOR SOCIALLY COGNIZANT ROBOTICS (3) Prerequisites: 16:332:590 – Socially Cognizant Robotics - Students of the “Design Methods in Socially Cognizant Robotics” course will be exposed to basic principles and state-of-the-art developments of robotics through a hands-on, experiential process. The objective is to learn the expected trajectory of this technology, which will impact individuals and society, and gain the experience of putting together robotics systems that are socially-aware. Learning goals include Develop and utilize socially cognizant design principles, learning to develop and control robotic systems which interact with humans, iplement methods of robot control in the context of human-robot collaboration that emphasizes pro-social performance metrics, developing coding skills in python to integrate vision libraries (opencv), robotics libraries (ROS), or machine learning libraries (pytorch), and demonstrating use of cognitive modeling of human behavior in order to design better collaborative robotic systems that are tuned to human desires and that can be used to learn human intent.
16:332:597 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.
16:332:598 BIOMEDICAL TECHNOLOGIES: DESIGN AND DEVELOPMENT - This is an interdisciplinary course that introduces students to the field of biomedical technologies and provides a detailed background on the engineering principles used for biosensor development. Students learn fundamental concepts in the areas of bioelectrical engineering, point-of-care sensors, fabrication, micro/ nano technologies, microfluidics, data processing, and global healthcare applications.
16:332:599 ADVANCED TOPICS IN SOLID STATE ELECTRONICS
In Spring 2023, topics covered included Microelectronics Processing and Fundamental Elements of DESI. In Fall 2023, topics covered include Semiconductors for AI.
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.
16:332:640 ROBOTICS AND SOCIETY - Robotics and Society is an interdisciplinary course, drawing on instructors, theory, and empirical work from the social sciences, policy, engineering, and natural sciences. The course will introduce those with a robotics background to social science theory and methods and, for those with a social science and/or policy or planning background, a greater understanding of the technology world through course work with students from those disciplines and projects that deepen their technical knowledge. Students will critically examine recent technological advances in robotics with respect to whether and how they meet social needs, and to learn about the social processes that shape technology artifacts and systems. They will focus on applications in which humans and robots closely interact. The module on research methods will provide students a critical understanding the strengths and weaknesses of different methods and provide them the tools to be discerning consumers of research.
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.
16:332:601, 602 SPECIAL PROBLEMS (BA, BA)
Prerequisite: Permission of instructor.
Investigation in selected areas of electrical engineering.
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.