E E 201 Computer Hardware Skills (2) RSN
Lab-based course focused on a wide range of basic hands-on skills for electrical and computer engineers. Provides an overview of topic areas and career paths in electrical and computer engineering. Topics include introduction to physical circuit building, microcontroller programming, proportional-integral-derivative design, soldering, circuit simulation, 3D design and printing, printed circuit board control, and sensors. Prerequisite: CSE 122, CSE 123, CSE 142, or CSE 143, any of which may be taken concurrently Offered: AWSp.
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E E 205 Introduction to Signal Conditioning (4) RSN
Introduces analog circuits interfacing sensors to digital systems. /includes connection, attenuation, amplification, sampling, filtering, termination, controls, Kirchhoff's Laws, sources, resistors, op amps, capacitors, inductors, PSice, and MATLAB. Intended for non-EE majors. Prerequisite: either MATH 126 or MATH 136; and either PHYS 122 or PHYS 142. Offered: W.
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E E 215 Fundamentals of Electrical Engineering (4) NSc
Introduction to electrical engineering. Basic circuit and systems concepts. Mathematical models of components. Kirchhoff's laws. Resistors, sources, capacitors, inductors, and operational amplifiers. Solution of first and second order linear differential equations associated with basic circuit forms. Course overlaps with: TCES 215. Prerequisite: either MATH 136, or MATH 126 and either MATH 207, MATH 307, or AMATH 351, any of which may be taken concurrently; and either PHYS 122 or PHYS 142.
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E E 242 Signals, Systems, and Data I (5)
Introduction to signal processing, including both continuous- and discrete-time signals and systems. Basic signals including impulses, unit steps, periodic signals and complex exponentials. Convolution of signals. Fourier series and transforms. Linear, time-invariant filters. Computer laboratory. Course overlaps with: E E 235; B EE 235; B EE 341; and TCES 310. Prerequisite: either MATH 135, MATH 207, or AMATH 351, any of which may be taken concurrently; and either E E 241 or CSE 163. Offered: AWSp.
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E E 271 Digital Circuits and Systems (5)
Overview of digital computer systems. Covers logic, Boolean algebra, combinational and sequential circuits and logic design; programmable logic devices; and the design and operation of digital computers, including ALU, memory, and I/O. Weekly laboratories. Course overlaps with: CSE 369 and B EE 271. Prerequisite: either CSE 121, CSE 122, CSE 123, CSE 142, or CSE 143.
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E E 280 Exploring Devices (4)
Overview of modern electronic and photonic devices underlying modern electronic products including smartphones, traffic lights, lasers, solar cells, personal computers, and chargers. Introduction to modeling and principles of physics relevant to the analysis of electrical and optical/photonic devices. Prerequisite: E E 215, which may be taken concurrently; and either PHYS 122 or PHYS 142; recommended: either Python programming or Matlab; and Linux. Offered: AWSp.
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E E 345 Introduction to Foundations of Machine Learning (4)
Introduction to the mathematical foundations of machine learning models and algorithms, with core ideas motivated through examples from statistics, decision-making and control, communication and signal processing, and data science. Topics include linear algebra for data science, supervised learning methods such as linear regression and classification, and unsupervised learning methods such as clustering. Prerequisite: either MATH 136, MATH 208, or AMATH 352; and either E E 241 or CSE 163.
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E E 347 Introduction to Robotics and Control Systems (5)
Introduces fundamentals of robotics and control systems, focusing on the modeling, design, and control of robotic systems. Topics include forward and inverse kinematics, control theory, robot sensing, navigation, and path planning. Incorporates simulations, and hands-on labs for program and control of robotics. Prerequisite: E E 241 or CSE 163; and either MATH 136, MATH 208, or AMATH 352.
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E E 351 Energy Systems (5)
Develops understanding of modern energy systems through theory and analysis of the system and its components. Discussions of generation, transmission, and utilization are complemented by environmental and energy resources topics as well as electromechanical conversion, power electronics, electric safety, renewable energy, and electricity blackouts. Course overlaps with: B EE 457. Prerequisite: a minimum grade of 1.0 in E E 215.
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E E 371 Design of Digital Circuits and Systems (5)
Provides a theoretical background in, and practical experience with, tools, and techniques for modeling complex digital systems with the Verilog hardware description language, maintaining signal integrity, managing power consumption, and ensuring robust intra- and inter-system communication. Prerequisite: either E E 205 or E E 215; either E E 271 or CSE 369. Offered: jointly with CSE 371.
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E E 398 Introduction to Professional Issues (1)
Covers topics of interest to students planning their educational and professional path, including salaries, the value of advanced degrees, societal expectations of engineering professionals, the corporate enterprise, ethical dilemmas, patents and trade secrets, outsourcing, and the global market.
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E E 417 Modern Wireless Communications (4)
Introduction to wireless networks as an application of basic communication theorems. Examines modulation techniques for digital communications, signal space, optimum receiver design, error performance, error control coding for high reliability, mulitpath fading and its effects, RF link budget analysis, WiFi and Wimax systems. Course overlaps with: B EE 417. Prerequisite: E E 416
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E E 419 Introduction to Computer-Communication Networks (4)
Computer network architectures and protocols. OSI Layers and performance analysis. Transmission media, switching, multiple access arbitration. Network routing, congestion control, flow control. Transport protocols, real-time, multicast, network security. Course overlaps with: TCES 425. Prerequisite: either CSE 122, CSE 123, CSE 142, or CSE 143; and either IND E 315, MATH 394/STAT 394, STAT 390, or STAT 391.
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E E 421 Quantum Mechanics for Engineers (3)
Covers the basic theory of quantum mechanics in the context of modern examples of technological importance involving 1D, 2D, and 3D nanomaterials. Develops a qualitative and quantitative understanding of the principles of quantization, band structure, density of states, and Fermi's golden rule (optical absorption, electron-impurity/phonon scattering). Prerequisite: either MATH 135, MATH 207, MATH 307, or AMATH 351; recommended: Calculus through differential equations.
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E E 423 Introduction to Synthetic Biology (3)
Studies mathematical modeling of transcription, translation, regulation, and metabolism in cell; computer aided design methods for synthetic biology; implementation of information processing, Boolean logic and feedback control laws with genetic regulatory networks; modularity, impedance matching and isolation in biochemical circuits; and parameter estimation methods. Prerequisite: either MATH 136, MATH 207, MATH 307, AMATH 351, or CSE 311; and either MATH 208, MATH 308, or AMATH 352. Offered: jointly with BIOEN 423/CHEM E 476/CSE 486.
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E E 425 Laboratory Methods in Synthetic Biology (4)
Designs and builds transgenic bacterial using promoters and genes taken from a variety of organisms. Uses construction techniques including recombination, gene synthesis, and gene extraction. Evaluates designs using sequencing, fluorescence assays, enzyme activity assays, and single cell studies using time-lapse microscopy. Prerequisite: E E 423/BIOEN 423/CHEM E 476/CSE 486; and either CHEM 142, CHEM 143, or CHEM 145. Offered: jointly with BIOEN 425/CHEM E 478/CSE 488.
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E E 442 Digital Signals and Filtering (3)
Methods and techniques for digital signal processing. Review of sampling theorems, A/D and D/A converters. Demodulation by quadrature sampling. Z-transform methods, system functions, linear shift-invariant systems, difference equations. Signal flow graphs for digital networks, canonical forms. Design of digital filters, practical considerations, IIR and FIR filters. Digital Fourier transforms and FFT techniques. Prerequisite: a minimum grade of 1.0 in either E E 341 or E E 342.
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E E 443 Machine Learning for Signal Processing Applications (4)
Application of machine learning and deep learning algorithms to real-world signal, image, and video processing problems using cloud computing with central, graphics, and tensor processing units (CPU/GPU/TPU). Characteristics of multi-dimensional signals and systems. Unsupervised and supervised learning. Deep learning convolutional neural networks. Generative adversarial learning. Open long-tailed recognition. Object detection and segmentation. Course overlaps with: TEE 461. Prerequisite: a minimum grade of 1.0 in E E 242; either MATH 136, both MATH 126 and MATH 208, or both MATH 126 and AMATH 352; and either E E 391, IND E 315, MATH 394/STAT 394, or STAT 390.
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E E 445 Fundamentals of Optimization and Machine Learning (4)
Introduction to optimization and machine learning models motivated by their application in areas including statistics, decision-making and control, and communication and signal processing. Topics include convex sets and functions, convex optimization problems and properties, convex modeling, duality, linear and quadratic programming, with emphasis on usage in machine learning problems including regularized linear regression and classification. Prerequisite: E E 345.
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E E 446 Tiny Machine Learning for Ultra Low-Power Edge Computing (4)
Studies the design and deployment of machine learning (ML) models on ultra low-power edge devices such as microcontrollers and embedded sensors. Covers development and build of responsive, private, and reliable ML applications at the edge with compact and accurate models under tight memory, compute, and energy budgets. Prerequisite: E E 242; either AMATH 352, MATH 136, or MATH 208; and either IND E 315, MATH 394/STAT 394, STAT 390, or E E 391; recommended: E E 344; and familiarity with Python programming and basic C/C++programming. Offered: Sp.
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E E 460 Neural Engineering (3)
Introduces the field of Neural Engineering: overview of neurobiology, recording and stimulating the nervous system, signal processing, machine learning, powering and communicating with neural devices, invasive and non-invasive brain-machine interfaces, spinal interfaces, smart prostheses, deep-brain stimulators, cochlear implants and neuroethics. Heavy emphasis on primary literature. Prerequisite: either BIOL 130, BIOL 162, or BIOL 220; and either MATH 208, AMATH 301, or AMATH 352. Offered: jointly with BIOEN 460; A.
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E E 464 Antennas: Analysis and Design (4)
Fundamentals of antennas, analysis, synthesis, and computer-aided design, and applications in communications, remote sensing, and radars. Radiation pattern, directivity, impedance, wire antennas, arrays, numerical methods for analysis, horn antennas, microstrip antennas, and reflector antennas. Prerequisite: 1.0 in E E 361.
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E E 467 Machine Learning for Cybersecurity (4)
Application of machine learning algorithms to cybersecurity. Anomaly detection, spam detection and IP blacklisting, use of natural language processing to improve performance in architecture identification. Optimization methods for determining adversarial inputs to bias the detection and classification outputs of deep neural networks (DNN). Backdoor trigger embedding in DNN. Generative Adversarial Networks. Hands-on, practical course. Prerequisite: either CSE 163 or E E 241; either AMATH 352, MATH 208, MATH 308, or MATH 136; and either IND E 315 , MATH 394/STAT 394, or STAT 390; recommended: E E 445.
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E E 475 Embedded Systems Capstone (5)
Capstone design experience. Prototype a substantial project mixing hardware, software, and communications. Focuses on embedded processors, programmable logic devices, and emerging platforms for the development of digital systems. Provides a comprehensive experience in specification, design, and management of contemporary embedded systems. Course overlaps with: TME 441. Prerequisite: E E 271 or CSE 369; and E E 472 or CSE 474/E E 474. Offered: jointly with CSE 475.
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E E 476 Digital VLSI: Transistors to Gates (5)
Breadth-first introduction to digital VLSI design. Integrated CMOS logic design. CMOS logic delay and power analysis. Introduction to IC- mask-layout, gate-sizing, VLSI building blocks (adders, multipliers, counters, shifters etc.), design for testability, and memory. Projects involve some layout design, and mostly transistor and gate-level schematic design. Prerequisite: E E 215; and either E E 271 or CSE 369; recommended: basic circuit theory and basic digital design experience. Offered: AWSp.
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E E 482 Semiconductor Devices (4)
Fundamentals of state-of-the-art semiconductor devices and emerging semiconductor technologies including diodes, LEDs, solar cells, photodetectors, MOS field-effect transistors, power transistors, and nanoscale devices. In-depth analysis of devices using carrier diffusion, drift, effective mass, and density of states. Prerequisite: either E E 280, E E 331, MSE 351, or PHYS 324; and either MATH 135, MATH 207, or AMATH 351. Offered: A.
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E E 486 Fundamentals of Integrated Circuit Technology (3)
Processing physics, chemistry, and technology, including evaporation, sputtering, epitaxial growth, diffusion, ion implantation, laser annealing, oxidation, chemical vapor deposition, photoresists. Design considerations for bipolar and MOS devices, materials and process characterization. Future trends. Prerequisite: EE 331 or MSE 351. Offered: jointly with MSE 486; AW.
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E E 487 Introduction to Photonics (4)
Introduction to optical principles and phenomena. Topics include electromagnetic theory of light, optical interference, diffraction, polarization, optical waveguides, and optical fibers. Course overlaps with: E E 485. Prerequisite: either E E 361, PHYS 123, or PHYS 143.
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E E 496 Engineering Entrepreneurial Systems and Design (2)
Fundamentals of systems engineering methods, system life cycle, project management and scheduling, trade studies, risk mitigation, configuration management, budgeting, procurement, prototyping, technical reviews, and associated tools; startup life cycle, intellectual property, trade secrets, patents, startup financing, incorporation, business plan, market research, roles of officers.
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E E 503 Modeling of MEMS (4)
Microelectro mechanical systems (MEMS) including lumped modeling, conjugate power variables, electrostatic and magnetic actuators, linear transducers, linear system dynamics, design optimization, and thermal analysis. Numerical modeling topics include electro (quasi) static, mechanical, electro mechanical, magneto (quasi) static, and fluidic phenomena; parametric analysis, visualization of multi-dimensional solutions; and verification of results. Offered: jointly with MSE 505.
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E E 505 Probability and Random Processes (4)
Foundations for the engineering analysis of random processes: set theoretic fundamentals, basic axioms of probability models, conditional probabilities and independence, discrete and continuous random variables, multiple random variables, sequences of random variables, limit theorems, models of stochastic processes, noise, stationarity and ergodicity, Gaussian processes, power spectral densities. Prerequisite: graduate standing and understanding of probability at the level of E E 416.
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E E 508 Stochastic Processes in Engineering (3)
Non-measure theoretic introduction to stochastic processes. Topics include Poisson processes, renewal processes, Markov and semi-Markov processes, Brownian motion, and martingales, with applications to problems in queuing, supply chain management, signal processing, control, and communications. Prerequisite: E E 505. Offered: jointly with IND E 508.
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E E 511 Introduction to Statistical Learning (4)
Covers classification and estimation of vector observations, including both parametric and nonparametric approaches. Includes classification with likelihood functions and general discriminant functions, density estimation, supervised and unsupervised learning, feature reduction, model selection, and performance estimation. Prerequisite: either E E 505 or CSE 515.
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E E 512 Graphical Models in Pattern Recognition (4)
Bayesian networks, Markov random fields, factor graphs, Markov properties, standard models as graphical models, graph theory (e.g., moralization and triangulation), probabilistic inference (including pearl's belief propagation, Hugin, and Shafer-Shenoy), junction threes, dynamic Bayesian networks (including hidden Markov models), learning new models, models in practice. Prerequisite: E E 508; E E 511.
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E E 513 Introduction to Deep Learning Applications in Engineering (5)
Covers neural networks and their applications in engineering. Introduces fundamental deep learning concepts, practical skills, and common applications of neural networks in the areas of data analysis and data generation. Examines the building blocks of neural networks, their training, and basic architectures. Includes a survey of advanced topics and architectures such as generative networks, transformers, and reinforcement learning. Recommended: coursework in Python programming (E E 241); linear algebra (MATH 208); probability and statistics (STAT 390 or E E 416); and machine learning (E E 511).
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E E 514 Information Theory I (4)
Includes entropy, mutual information, Shannon's source coding theorem, data compression to entropy limit, method of types, Huffman coding, Kraft inequality, arithmetic coding, Kolmogorov complexity, communication at channel capacity (channel coding), coding theory, introduction to modern statistical coding techniques, differential entropy, and Gaussian channels. Prerequisite: E E 505.
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E E 515 Information Theory II (4)
Includes advanced modern statistical coding techniques (statistical coding), advanced codes and graphs, source coding with errors (rate distortion), alternating minimization principles, channel coding with errors, network information theory, multiple description coding, and information theory in other areas including pattern recognition, bio-informatics, natural language processing, and computer science. Prerequisite: E E 514.
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E E 517 Introduction to Large Language Models (3)
Introduces the principles, theoretical foundations, and applications of large language models (LLMs) with a focus on electrical and computer engineering contexts. Topics include architecture design, training and fine-tuning methods, evaluation metrics, information-theoretic and scaling law analyses, ethical considerations, and applications to engineering domains. Prerequisite: either E E 344, E E 345, or equivalent; and either E E 241, CSE 163, or equivalent.
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E E 519 Stochastic Analysis of Data From Physical Systems (4)
Computer systems for acquisition and processing of stochastic signals. Calculation of typical descriptors of such random processes as correlation functions, spectral densities, probability densities. Interpretation of statistical measurements made on a variety of physical systems (e.g., electrical, mechanical, acoustic, nuclear). Lecture plus laboratory. Prerequisite: E E 505.
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E E 520 Spectral Analysis of Time Series (4)
Estimation of spectral densities for single and multiple time series. Nonparametric estimation of spectral density, cross-spectral density, and coherency for stationary time series, real and complex spectrum techniques. Bispectrum. Digital filtering techniques. Aliasing, prewhitening. Choice of lag windows and data windows. Use of the fast Fourier transform. Prerequisite: either STAT 342, STAT 390, STAT 509/CS&SS 509/ECON 580, or IND E 315. Offered: jointly with STAT 520.
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E E 521 Quantum Mechanics for Engineers (4)
Covers the basic theory of quantum mechanics in the context of modern examples of technological importance involving 1D, 2D, and 3D nanomaterials. Develops a qualitative and quantitative understanding of the principles of quantization, band structure, density of states, and Fermi's golden rule (optical absorption, electron-impurity/phonon scattering). Prerequisite: MATH 207 or AMATH 351.
View course details in MyPlan: E E 521
E E 522 Quantum Information Practicum (4)
Team-based experience solving quantum engineering problems. Student teams design, implement, and test solutions to real-world problems. Includes project planning, project management, and technical communication components. Prerequisite: either PHYS 521, CHEM 561/MSE 561, or permission of instructor; recommended: practical experience with cloud-based quantum processors. Offered: Sp.
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E E 523 Introduction to Synthetic Biology (3)
Studies mathematical modeling of transcription, translation, regulation, and metabolism in cell; computer aided design methods for synthetic biology; implementation of information processing, Boolean logic and feedback control laws with genetic regulatory networks; modularity, impedance matching and isolation in biochemical circuits; and parameter estimation methods. Prerequisite: either MATH 136, MATH 207, MATH 307, AMATH 351, or CSE 311; and either MATH 208, MATH 308, or AMATH 352. Offered: jointly with BIOEN 523/CHEM E 576/CSE 586/MOLENG 525.
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E E 524 Advanced Systems and Synthetic Biology (3)
Covers advanced concepts in system and synthetic biology. Includes kinetics, modeling, stoichiometry, control theory, metabolic systems, signaling, and motifs. All topics are set against problems in synthetic biology. Prerequisite: E E 523/BIOEN 523/CHEM E 576/CSE 586/MOLENG 525. Offered: jointly with BIOEN 524/CHEM E 577/CSE 587.
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E E 527 Microfabrication (4)
Principles and techniques for the fabrication of microelectronics devices and integrated circuits. Includes clean room laboratory practices and chemical safety, photolithography, wet and dry etching, oxidation and diffusion, metallization and dielectric deposition, compressed gas systems, vacuum systems, thermal processing systems, plasma systems, and metrology. Extensive laboratory with limited enrollment. Course overlaps with: EE P 527. Recommended: Cannot be taken for credit if credit received for EE P 527.
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E E 528 Quantum Optics for Quantum Information Applications (4)
Topics include mathematical methods for quantum optics, quantization of the electromagnetic field, quantum states of optical systems, open quantum systems, quantum distribution theory, quantum correlation functions, atom classical field interactions, atom-quantum field (photon) interactions, collective effects in multi-atom systems. Prerequisite: MATH 207; MATH 208; and E E 521.
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E E 535 Applied Nanophotonics (4)
Concepts of optics at wave-length, scale-structured medium. Topics include photonic crystal, dielectric and metallic optical resonators, and meta-photonic devices. Introduction to cavity quantum electrodynamics. Students learn about nanoscale photonic devices, via literature survey, problem solving and numerical simulations. Prerequisite: either E E 361, PHYS 321, or equivalent course or experience with nanophotonics.
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E E 540 Wearable Robotics (3)
Introduces the field of wearable robotics, with emphasis on lower-limb exoskeletons and prostheses. Students learn modern approaches for design, control, and evaluation of wearable robots for human locomotion. Topics include neural control of movement, musculoskeletal modeling and simulation, robotic control architectures, applications of machine learning, neural interfacing, and tools for real-world physiological evaluation of wearable robots. Prerequisite: experience with MATLAB, or similar programming experience.
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E E 545 High-Performance Computer Architectures (4)
Algorithm design, software techniques, computer organizations for high-performance computing systems. Selected topics from: VLSI complexity for parallel algorithms, compiling techniques for parallel and vector machines, large MIMD machines, interconnection networks, reconfigurable systems, memory hierarchies in multiprocessors, algorithmically specialized processors, data flow architectures. Course overlaps with: TECE 510. Prerequisite: CSE 548/E E 544. Offered: jointly with CSE 549.
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E E 547 Linear Systems Theory (4)
Linearity, linearization, finite dimensionality, time-varying vs. time-invariant linear systems, interconnection of linear systems, functional/structural descriptions of linear systems, system zeros and invertibility, linear system stability, system norms, state transition, matrix exponentials, controllability and observability, realization theory. Course overlaps with: EE P 547; M E 547; TECE 551; and TECE 555. Prerequisite: E E 510/A A 510/CHEM E 510/M E 510. Offered: jointly with A A 547.
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E E 548 Linear Multivariable Control (3)
Introduction to MIMO systems, successive single loop design comparison, Lyapunov stability theorem, full state feedback controller design, observer design, LQR problem statement, design, stability analysis, and tracking design. LQG design, separation principle, stability robustness. Course overlaps with: A E 513. Prerequisite: A A 547/E E 547/M E 547. Offered: jointly with A A 548/M E 548.
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E E 550 Nonlinear Optimal Control (3)
Calculus of variations for dynamical systems, definition of the dynamic optimization problem, constraints and Lagrange multipliers, the Pontryagin Maximum Principle, necessary conditions for optimality, the Hamilton-Jacobi-Bellman equation, singular arc problems, computational techniques for solution of the necessary conditions. Offered: jointly with A A 550/M E 550.
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E E 560 Neural Engineering (3)
Introduces the field of Neural Engineering: overview of neurobiology, recording and stimulating the nervous system, signal processing, machine learning, powering and communicating with neural devices, invasive and non-invasive brain-machine interfaces, spinal interfaces, smart prostheses, deep-brain stimulators, cochlear implants and neuroethics. Heavy emphasis on primary literature. Offered: jointly with BIOEN 560; A.
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E E 563 Submodular Functions, Optimization, and Applications (4)
Submodularity and supermodularity. Definitions, properties, operations that preserve submodularity, variants, certain special submodular functions, computational properties, matroids and lattices, polyhedral properties, semidifferentials, convex/concave extensions, constrained and unconstrained minimization and maximization, and generalizations of submodularity and uses in machine learning. Prerequisite: E E 510/A A 510/CHEM E 510/M E 510. Offered: even years.
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E E 575 Radar Remote Sensing (4)
Introduces radar remote sensing. Covers the fundamentals of radar systems, monostatic and bistatic topologies, radar equation, range-time diagram; ambiguity function, pulse compression, elementary estimation and detection theory, spectrum estimation for underspread and overspread targets; interferometry, source imaging; and Time Difference of Arrival, Aperture Synthesis (SAR and ISAR).
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E E 578 Convex Optimization (4)
Basics of convex analysis: Convex sets, functions, and optimization problems. Optimization theory: Least-squares, linear, quadratic, geometric and semidefinite programming. Convex modeling. Duality theory. Optimality and KKT conditions. Applications in signal processing, statistics, machine learning, control communications, and design of engineering systems. Prerequisite: A A 510, CHEM E 510, E E 510, or M E 510. Offered: jointly with A A 578/CSE 578/M E 578.
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E E 582 Semiconductor Devices (4)
Fundamentals of state-of-the-art semiconductor devices and emerging semiconductor technologies including diodes, LEDs, solar cells, photodetectors, MOS field-effect transistors, power transistors, and nanoscale devices. In-depth analysis of devices using carrier diffusion, drift, effective mass, and density of states. Course overlaps with: EE P 530. Prerequisite: either E E 280, E E 331, MSE 351, or PHYS 324; and either MATH 135, MATH 207, or AMATH 351. Offered: A.
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E E 587 Introduction to Photonics (4)
Introduction to optical principles and phenomena. Topics include electromagnetic theory of light, optical interference, diffraction, polarization, optical waveguides, and optical fibers. Prerequisite: either EE 361, PHYS 123, or PHYS 143; recommended: basic principles of electromagnetism; complex numbers and functions; introductory differential and integral calculus; linear differential equations.
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E E 594 Robust Control (3)
Basic foundations of linear analysis and control theory, model realization and reduction, balanced realization and truncation, stabilization problem, coprime factorizations, Youla parameterization, matrix inequalities, H-infinity and H2 control, KYP lemma, uncertain systems, robust H2, integral quadratic constraints, linear parameter varying synthesis, applications of robust control. Prerequisite: A A 547/E E 547/M E 547. Offered: jointly with A A 594/M E 594; Sp, odd years.
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E E 595 Advanced Topics in Communication Theory (1-5, max. 16)
Extension of E E 507, E E 508, E E 518, E E 519, E E 520. Material differs each year, covering such topics as: detection theory, decision theory, game theory, adaptive communication systems, nonlinear random processes.
View course details in MyPlan: E E 595
E E 597 Networked Dynamics Systems (3)
Provides an overview of graph-theoretic techniques that are instrumental for studying dynamic systems that coordinate their states over a signal-exchange network. Topics include network models, network properties, dynamics over networks, formation control, biological networks, observability, controllability, and performance measures over networks. Prerequisite: A A 547/E E 547/M E 547. Offered: jointly with A A 597/M E 597.
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