Bul le t in 2021-22Elect r ica l & Systems Engineer ing (10 /13 /21)

Electrical & SystemsEngineeringPhone: 314-935-5565

Website: https://ese.wustl.edu/academics/undergraduate-programs/index.html

CoursesVisit online course listings to view semester offerings forE35 ESE (https://courses.wustl.edu/CourseInfo.aspx?sch=E&dept=E35&crslvl=1:5).

E35 ESE 105 Introduction to Electrical and SystemsEngineeringThis course will offer students a rigorous introduction tofundamental mathematical underpinnings of ESE and theirrelationship to a number of contemporary application areas.Major emphasis will be placed on linear algebra and associatednumerical methods, including the use of MATLAB. Topicscovered will include vector spaces, linear transformations, matrixmanipulations and eigenvalue decomposition. Students willlearn how this mathematical theory is enacted in ESE throughthe completion of four case studies spanning application areas:(i) Dynamical Systems and Control, (ii) Imaging, (iii) SignalProcessing, and (iv) Circuits.Credit 3 units. EN: TU

E35 ESE 205 Introduction to Engineering DesignThis is a hands-on course in which students, in groups of twoor three, will creatively develop projects and solve problemsthroughout the semester using tools from electrical andsystems engineering. Groups will work under the supervisionof an academic team consisting of faculty and higher-levelstudents. Project objectives will be set by the academic team incollaboration with each student group. Evaluation will considercompletion of these objectives as well as the originality andinnovation of the projects. A weekly 90-minute lab with theacademic team is required. Prerequisites: CSE 131, Physics197, or equivalent.Credit 3 units. EN: TU

E35 ESE 230 Introduction to Electrical and ElectronicCircuitsElectrical energy, current, voltage, and circuit elements.Resistors, Ohm's Law, power and energy, magnetic fieldsand DC motors. Circuit analysis and Kirchhoff's voltage andcurrent laws. Thevenin and Norton transformations and thesuperposition theorem. Measuring current, voltage and powerusing ammeters and voltmeters. Energy and maximum electricalpower transfer. Computer simulations of circuits. Reactivecircuits, inductors, capacitors, mutual inductance, electricaltransformers, energy storage, and energy conservation. RL,RC and RLC circuit transient responses. AC circuits, compleximpedance, RMS current and voltage. Electrical signal amplifiersand basic operational amplifier circuits. Inverting, non-inverting,and difference amplifiers. Voltage gain, current gain, inputimpedance, and output impedance. Weekly laboratory exercisesrelated to the lectures are an essential part of the course.Prerequisite: Phys 198. Corequisite: Math 217.

Credit 4 units. EN: BME T, TU

E35 ESE 232 Introduction to Electronic CircuitsAnalysis and design of linear and nonlinear electronic circuits.Detailed analysis of operational amplifier circuits, includingnon-ideal characteristics. Terminal characteristics of activesemiconductor devices. Incremental and DC models for diodes,metal-oxide-semiconductor field effect transistors (MOSFETs),and bipolar junction transistors (BJTs). Design and analysis ofsingle- and multi-stage amplifiers. Introduction to CMOS logicas well as static and dynamic memory circuits. Students will berequired to design, analyze, build and demonstrate several of thecircuits studied, including frequency response analysis and useof simulation tools. Prerequisite: ESE 230.Credit 3 units. EN: TU

E35 ESE 260 Introduction to Digital Logic and ComputerDesignIntroduction to design methods for digital logic and fundamentalsof computer architecture. Boolean algebra and logic minimizationtechniques; sources of delay in combinational circuits andeffect on circuit performance; survey of common combinationalcircuit components; sequential circuit design and analysis;timing analysis of sequential circuits; use of computer-aideddesign tools for digital logic design (schematic capture,hardware description languages, simulation); design of simpleprocessors and memory subsystems; program execution insimple processors; basic techniques for enhancing processorperformance; configurable logic devices. Prerequisite: CSE 131.Same as E81 CSE 260MCredit 3 units. EN: TU

E35 ESE 318 Engineering Mathematics ALaplace transforms; matrix algebra; vector analysis; eigenvaluesand eigenvectors; vector differential calculus and vector integralcalculus in three dimensions. Prerequisites: Math 233 and Math217 or their equivalents.Credit 3 units.

E35 ESE 319 Engineering Mathematics BPower series and Frobenius series solutions of differentialequations; Legendre's equation; Bessel's equation; Fourierseries and Fourier transforms; Sturm-Liouville theory; solutionsof partial differential equations; wave and heat equations.Prerequisites: Math 233 and Math 217 or their equivalents.Credit 3 units.

E35 ESE 326 Probability and Statistics for EngineeringStudy of probability and statistics together with engineeringapplications. Probability and statistics: random variables,distribution functions, density functions, expectations, means,variances, combinatorial probability, geometric probability,normal random variables, joint distribution, independence,correlation, conditional probability, Bayes theorem, the lawof large numbers, the central limit theorem. Applications:reliability, quality control, acceptance sampling, linear regression,design and analysis of experiments, estimation, hypothesistesting. Examples are taken from engineering applications.Prerequisites: Math 233 or equivalent.Credit 3 units. EN: TU

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Bul le t in 2021-22Elect r ica l & Systems Engineer ing (10 /13 /21)

E35 ESE 330 Engineering Electromagnetics PrinciplesElectromagnetic theory as applied to electrical engineering:vector calculus; electrostatics and magnetostatics; Maxwell'sequations, including Poynting's theorem and boundaryconditions; uniform plane-wave propagation; transmission lines,TEM modes, including treatment of general lossless lines, andpulse propagation; introduction to guided waves; introduction toradiation and scattering concepts. Prerequisites: Physics 198and ESE 318 En Math A. Corequisite: ESE 319 En Math B.Credit 3 units. EN: BME T, TU

E35 ESE 331 Electronics LaboratoryLaboratory exercises provide students with a combination ofhands-on experience in working with a variety of real instrumentsand in summarizing measurement results in written reportsthat clearly communicate laboratory results. A sequence oflab experiments provide hands-on experience with groundingand shielding techniques, signal analysis, realistic operationalamplifier (op amp) characterization, op amp based activefilter design and characterization, measurement of pulsespropagating on a transmission line with various terminations,experience with FM modulation using phase locked loops andmicrowave techniques based on the vector network analyzer(VNA). Students will gain experience working with: samplingoscilloscopes to make measurements in the time and frequencydomains, signal generators, digital multimeter and frequencymeasurements, microwave VNA measurements of directionalcoupler and antenna scattering parameters, and in creatingcircuits and making connections on contemporary circuitboards. The course concludes with a hands-on project todesign, demonstrate and document the design of an electroniccomponent. Prerequisite: ESE 232Credit 3 units. EN: BME T, TU

E35 ESE 332 Power, Energy and Polyphase CircuitsFundamental concepts of power and energy; electricalmeasurements; physical and electrical arrangement of electricalpower systems; polyphase circuit theory and calculations;principal elements of electrical systems such as transformers,rotating machines, control and protective devices, theirdescription and characteristics; elements of industrial powersystem design. Prerequisite: ESE 230.Credit 3 units. EN: BME T, TU

E35 ESE 351 Signals and SystemsThis course presents an introduction to concepts andmethodology of linear dynamic systems in relation to discrete-and continuous-time signals. Topics include mathematicalmodeling; representation of systems and signals; Fourier,Laplace, and Z-transforms and convolution; input-outputdescription of linear systems, including impulse response andtransfer function; time-domain and frequency-domain systemanalysis, including transient and steady-state responses, systemmodes, stability, frequency spectra, and frequency responses;and system design, including filter, modulation, and samplingtheorem. Continuity is emphasized from analysis to synthesis.MATLAB will be used. Prerequisites: Physics 197/198, Math 217,CSE 131, and matrix addition and multiplication. Corequisite:ESE 318.Credit 3 units. EN: BME T, TU

E35 ESE 359 Signals, Data and EquityThis course introduces the design of classification and estimationsystems for equity -- that is, with the goal of reducing theinequities of racism, sexism, xenophobia, ableism, and othersystems of oppression. Systems that change the allocationof resources among people can increase inequity due to theirinputs, the systems themselves, or how the systems interact inthe context in which they are deployed. This course presentsbackground in power and oppression to help predict hownew technological and societal systems might interact andwhen they might confront or reinforce existing power systems.Measurement theory -- the study of the mismatch between asystem's intended measure and the data it actually uses -- iscovered. Multiple examples of sensing and classification systemsthat operate on people (e.g., optical, audio, and text sensors) arecovered by implementing algorithms and quantifying inequitableoutputs. Prerequisite: ESE 105 or ESE 217A or CSE 417T.Background readings will be available.Credit 3 units. EN: BME T, TU

E35 ESE 362 Computer ArchitectureThis course explores the interaction and design philosophyof hardware and software for digital computer systems.Topics include: Processor architecture, Instruction SetArchitecture, Assembly Language, memory hierarchy design, I/O considerations, and a comparison of computer architectures.Prerequisite: CSE 260M.Same as E81 CSE 362MCredit 3 units. EN: BME T, TU

E35 ESE 400 Independent StudyOpportunities to acquire experience outside the classroomsetting and to work closely with individual members of thefaculty. A final report must be submitted to the department. Notopen to first-year or graduate students. Consult adviser. Hoursand credit to be arranged.Credit variable, maximum 3 units.

E35 ESE 401 Fundamentals of Engineering ReviewA review and preparation of the most recent NCEESFundamentals of Engineering (FE) Exam specifications is offeredin a classroom setting. Exam strategies will be illustrated usingexamples. The main topics for the review include engineeringmathematics, statics, dynamics, thermodynamics, heattransfer, mechanical design and analysis, material science, andengineering economics. A discussion of the importance andresponsibilities of professional engineering licensure along withethics will be included.Same as E37 MEMS 4001Credit 1 unit.

E35 ESE 403 Operations ResearchIntroduction to the mathematical aspects of various areas ofoperations research, with additional emphasis on problemformulation. This is a course of broad scope, emphasizingboth the fundamental mathematical concepts involved, andalso aspects of the translation of real-world problems to anappropriate mathematical model. Subjects to be covered includelinear and integer programming, network problems, and dynamicprogramming. Prerequisites: CSE 131, Math 309, and ESE 326,or permission of instructor.Credit 3 units. EN: BME T, TU

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Bul le t in 2021-22Elect r ica l & Systems Engineer ing (10 /13 /21)

E35 ESE 404 Applied Operations ResearchApplication of operations research techniques to real-worldproblems. Emphasis is given to integer linear programmingand computational methods. Real-world examples of integerprograms will be studied in areas such as network flow, facilitylocation, partitioning, matching, and transportation. Specialemphasis will be placed on techniques used to solve integerprograms. Prerequisites: ESE 403 and CSE 131.Credit 3 units. EN: BME T, TU

E35 ESE 405 Reliability and Quality ControlAn integrated analysis of reliability and quality control function inmanufacturing. Statistical process control, acceptance sampling,process capability analysis, reliability prediction, design, testing,failure analysis and prevention, maintainability, availability, andsafety are discussed and related. Qualitative and quantitativeaspects of statistical quality control and reliability are introducedin the context of manufacturing. Prerequisite: ESE 326 orequivalent.Credit 3 units. EN: BME T, TU

E35 ESE 415 OptimizationThis course gives a rigorous and comprehensive introduction offundamentals of nonlinear optimization theory and computationalmethods. Topics include unconstrained and constrainedoptimization, quadratic and convex optimization, numericaloptimization methods, optimality conditions, and duality theory.Algorithmic methods include Steepest Descent, Newton'smethod, Conjugate Gradient methods as well as exact andinexact line search procedures for unconstrained optimization.Constrained optimization methods include penalty and multipliermethods. Applications range from engineering and physics toeconomics. Moreover, generalized programming, interior pointmethods, and semi-definite programming will be discussed iftime permits. Prerequisites: CSE 131, Math 309 and ESE 318 orpermission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 417 Introduction to Machine Learning and PatternClassificationThis course provides a broad introduction to machine learningand statistical pattern classification. Students will studytheoretical foundations of learning and several importantsupervised and unsupervised machine learning methodsand algorithms, including linear model of regression andclassification, logistic regression, Bayesian learning methods,neural networks, nearest neighbor method, support vectormachines methods, clustering methods and principal componentanalysis. Students will also learn to use Python programminglanguage to implement learned models and methods to solvepattern classification problems. Prerequisites: ESE 326, Math233, and Python programming experience.Credit 3 units. EN: BME T

E35 ESE 425 Random Processes and Kalman FilteringProbability and random variables; random processes,autocorrelation, power spectral density; transient and steady-state analysis of linear dynamic systems and random inputs,filters, state-space, discretization; optimal estimation; thediscrete Kalman filter; linearization and the extended Kalmanfilter for nonlinear dynamic systems; related MATLAB exercises.Prerequisite: ESE 326 and ESE 351 or equivalent.Credit 3 units. EN: BME T, TU

E35 ESE 427 Financial MathematicsThis course is a self-contained introduction to financialmathematics at the undergraduate level. Topics to be coveredinclude pricing of the financial instruments such as options,forwards, futures and their derivatives along with basic hedgingtechniques and portfolio optimization strategies. The emphasisis put on using of discrete, mostly binary models. The general,continuous case including the concepts of Brownian motion,stochastic integral, and stochastic differential equations, isexplained from intuitive and practical point of view. Among majorresults discussed are the Arbitrage Theorem and Black-Scholesdifferential equations and their solutions. Prerequisites: ESE 318and ESE 326 or the consent of the instructor.Credit 3 units. EN: BME T, TU

E35 ESE 429 Basic Principles of Quantum Optics andQuantum InformationThis course provides an accessible introduction to quantumoptics and quantum engineering for undergraduate students.It will cover the following topics: concept of photons, quantummechanics for quantum optics, radiative transitions in atoms,lasers, photon statistics (photon counting, sub-/super-Poissionian photon statistics, bunching, anti-bunching, theoryof photodetection, shot noise), entanglement, squeezedlight, atom-photon interactions, cold atoms, and atoms incavities. The course will also provide an overview for quantuminformation processing, including quantum computing, quantumcryptography, and teleportation. Prerequisite: ESE 318 orequivalent.Credit 3 units. EN: BME T, TU

E35 ESE 4301 Quantum Mechanics for EngineersThis course provides an accessible introduction to quantummechanics and quantum engineering for undergraduatestudents. Examples are drawn from practical areas ofapplications of quantum engineering. This course covers thefollowing topics and examples: quantum mechanics and nano-technology, Schrodinger's equation, electron transport in variouspotential profiles, quantum dots and defects, harmonic oscillator,nano-mechanical oscillator and quantum LC circuit, Stark effectin semiconductors, Bloch theorem, crystal and band structures,Kronig-Penney and tight-binding models, semiclassical andquantum descriptions of light-atom interactions, spontaneousand stimulated emissions, quantum flip-flops, approximatemethods in quantum mechanics, spin, quantum gyroscope, spintransistor, and many-particle quantum mechanics for bosons andfermions. Prerequisites: Simple differential equations and matrixalgebra at the level of ESE 318/319 Engineering Mathematics A/B or equivalent and familiarity with a modern scientific computingsoftware package (e.g., MATLAB, Mathematica).Credit 3 units. EN: BME T, TU

E35 ESE 431 Introduction to Quantum ElectronicsDescribing the flow of electrical current in nanodevicesinvolves a lot more than just quantum mechanics; it requires anappreciation of some of the most advanced concepts of non-equilibrium statistical mechanics. In the past decades, electronicdevices have been shrinking steadily to nanometer dimensions,and quantum transport has accordingly become increasinglyimportant not only to physicists but also to electrical engineers.Traditionally, these topics are spread out over many physics/chemistry/engineering courses that take many semesters tocover. The main goal of this course is to condense the essential

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concepts into a one-semester course that is accessible to bothsenior-level undergraduate and junior-level graduate students.The only background assumed for the students interested intaking this course is knowledge of simple differential equationsand matrix algebra as well as familiarity with a modern scientificcomputing software package (e.g., MATLAB, Mathematica). Thiscourse will be accessible to students with diverse backgroundsin electrical engineering, physics, chemistry, biomedicalengineering, and mathematics.Credit 3 units. Art: NSM EN: TU

E35 ESE 433 Radio Frequency and Microwave Technologyfor Wireless SystemsFocus is on the components and associated techniquesemployed to implement analog and digital radio frequency (RF)and microwave (MW) transceivers for wireless applications,including: cell phones; pagers; wireless local area networks;global positioning satellite-based devices; and RF identificationsystems. A brief overview of system-level considerations isprovided, including modulation and detection approaches foranalog and digital systems; multiple-access techniques andwireless standards; and transceiver architectures. Focus is onRF and MW: transmission lines; filter design; active componentmodeling; matching and biasing networks; amplifier design; andmixer design. Prerequisite: ESE 330.Credit 3 units. EN: BME T, TU

E35 ESE 434 Solid-State Power Circuits and ApplicationsStudy of the strategies and applications power control usingsolid-state semiconductor devices. Survey of generic powerelectronic converters. Applications to power supplies, motordrives and consumer electronics. Introduction to power diodes,thyristors and MOSFETs. Prerequisites: ESE 232, ESE 351.Credit 3 units. EN: BME T, TU

E35 ESE 435 Electrical Energy LaboratoryExperimental studies of principles important in modern electricalenergy systems. Topics include: AC power measurements,electric lighting, photovoltaic cells and arrays, batteries, DC-DC and DC-AC converters, brushed and brushless DC motorsand three-phase circuits. Each experiment requires analysis,simulation with MultiSim, and measurement via LabVIEW andthe Elvis II platform. Prerequisites: ESE 230 and 351.Credit 3 units. EN: BME T, TU

E35 ESE 436 Semiconductor DevicesThis course covers the fundamentals of semiconductor physicsand operation principles of modern solid-state devices suchas homo- or hetero-junction diodes, solar cells, inorganic/organic light-emitting diodes, bipolar junction transistors, andmetal-oxide-semiconductor field-effect transistors. Thesedevices form the basis for today's semiconductor and integratedcircuit industry. In addition to device physics, semiconductordevice fabrication processes, new materials, and novel devicestructures will also be briefly introduced. At the end of thiscourse, students will be able to understand the characteristics,operation, limitations and challenges faced by state-of-the-artsemiconductor devices. This course will be particularly usefulfor students who wish to develop careers in the semiconductorindustry. Prerequisite: ESE 232.Credit 3 units. EN: BME T, TU

E35 ESE 438 Applied OpticsTopics relevant to the engineering and physics of conventionalas well as experimental optical systems and applicationsexplored. Items addressed include geometrical optics, Fourieroptics such as diffraction and holography, polarization andoptical birefringence such as liquid crystals, and nonlinear opticalphenomena and devices. Prerequisite: ESE 330 or equivalent.Credit 3 units. EN: BME T, TU

E35 ESE 439 Introduction to Quantum CommunicationsThis course covers the following topics: quantum optics,single-mode and two-mode quantum systems, nonlinearoptics, and quantum systems theory. Specific topics includethe following: Dirac notation quantum mechanics; harmonicoscillator quantization; number states, coherent states,and squeezed states; direct, homodyne, and heterodynedetection; linear propagation loss; phase insensitive andphase sensitive amplifiers; entanglement and teleportation;field quantization; quantum photodetection; phase-matchedinteractions; optical parametric amplifiers; generation ofsqueezed states, photon-twin beams, non-classical fourth-orderinterference, and polarization entanglement; optimum binarydetection; quantum precision measurements; and quantumcryptography. Prerequisites: ESE 330 or Physics 421; Physics217 or equivalent.Credit 3 units. EN: TU

E35 ESE 441 Control SystemsIntroduction to the theory and practice of automatic controlfor dynamical systems. Dynamical systems as models forphysical and observed phenomena. Mathematical representationof dynamical systems, such as state-space differential anddifference equations, transfer functions, and block diagrams.Analysis of the time evolution of a system in response to controlinputs, steady-state and transient responses, equilibriumpoints and their stability. Control via linear state feedback,and estimation using Leunberger observers. Relating the timeresponse of a system to its frequency response, includingBode and Nyquist plots. Input-output stability and its relationto the stability of equilibrium points. Simple frequency-basedcontrollers, such as PID and lead-lag compensators. Exerciseinvolving the use of MATLAB/Simulink (or equivalent) to simulateand analyze systems. Prerequisites: CSE 131, and either ESE351 or MEMS 431.Credit 3 units. EN: BME T, TU

E35 ESE 444 Sensors and ActuatorsThis course provide engineering students with basicunderstanding of two of the main components of any modernelectrical or electromechanical system: sensors as inputsand actuators as outputs. The covered topics include transferfunctions, frequency responses and feedback control;component matching and bandwidth issues; performancespecification and analysis; sensors: analog and digital motionsensors, optical sensors, temperature sensors, magnetic andelectromagnetic sensors, acoustic sensors, chemical sensors,radiation sensors, torque, force and tactile sensors; actuators:stepper motors, DC and AC motors, hydraulic actuators,magnet and electromagnetic actuators, acoustic actuators;introduction to interfacing methods: bridge circuits, A/D and D/Aconverters, and microcontrollers. This course is useful for thosestudents interested in control engineering, robotics and systems

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Bul le t in 2021-22Elect r ica l & Systems Engineer ing (10 /13 /21)

engineering. Prerequisites: one of the following four conditions:(1) prerequisite of ESE 230 and corequisite of ESE 351; (2)prerequisites of ESE 230, ESE 318 and MEMS 255 (MechanicsII); (3) prerequisite of ESE 351; or (4) permission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 446 Robotics: Dynamics and ControlHomogeneous coordinates and transformation matrices.Kinematic equations and the inverse kinematic solutionsfor manipulators, the manipulator Jacobian and the inverseJacobian. General model for robot arm dynamics, completedynamic coefficients for six-link manipulator. Synthesis ofmanipulation control, motion trajectories, control of single-and multiple-link manipulators, linear optimal regulator.Model reference adaptive control, feedback control law forthe perturbation equations along a desired motion trajectory.Design of the control system for robotics. Prerequisites: ESE351, knowledge of a programming language, and ESE 318;Corequisite: ESE 441.Credit 3 units. EN: BME T, TU

E35 ESE 447 Robotics LaboratoryIntroduces the students to various concepts such as modeling,identification, model validation and control of robotic systems.The course focuses on the implementation of identification andcontrol algorithms on a two-link robotic manipulator (the so-called pendubot) that will be used as an experimental testbed.Topics include: introduction to the mathematical modeling ofrobotic systems; nonlinear model, linearized model; identificationof the linearized model: input-output and state-space techniques;introduction to the identification of the nonlinear model: energy-based techniques; model validation and simulation; stabilizationusing linear control techniques; a closer look at the dynamics;stabilization using nonlinear control techniques. Prerequisite:ESE 351 or MEMS 431. Corequisites or Prerequisites: ESE 441and 446.Credit 3 units. EN: BME T, TU

E35 ESE 448 Systems Engineering LaboratoryThis course involves the experimental study of real andsimulated systems and their control. Topics include identification,input-output analysis, and design and implementation of controlsystems; noise effects; and the design and implementation ofcontrol laws for specific engineering problems. Corequisite: ESE441 and knowledge of a programming language, or permissionof instructor.Credit 3 units. EN: BME T, TU

E35 ESE 4480 Control Systems Design LaboratoryThis course involves the experimental study of real andsimulated systems and their control. Topics covered will includemodeling; identification; model validation and control of systems,including noise effects, using a two-link robotic manipulator as anexperimental testbed; mathematical modeling of robotic systems;nonlinear and linearized models; input-output and state-spacetechniques; model validation and simulation; and stabilizationusing linear and nonlinear control techniques. Prerequisite: ESE351 or MEMS 431. Corequisite or prerequisite: ESE 441.Credit 3 units. EN: BME T, TU

E35 ESE 4481 Autonomous Aerial Vehicle ControlLaboratoryThis course covers the integration of dynamical systems andcontrol engineering principles toward the manipulation of aquadrotor unmanned aerial vehicle (UAV), sometimes referredto as a drone. Students will analytically transform a nonlineardescription of the UAV system used for dynamic simulation intoa conventional, linear state space system. Students will use keycontrol engineering concepts -- including system identification,state estimation and control synthesis -- to command theirUAVs to hover, climb, and orbit. In addition to principles ofestimation and identification, students will learn about thetheory of guidance and navigation, with projects such as flightplanning and execution, collision avoidance, and competitive orcooperative tasks (e.g., formation flight). The overall objectiveis to expose students to the fusion of control, estimation, andidentification techniques that are fundamental to systems theory.Prerequisites: ESE 441 and knowledge of a programminglanguage, or permission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 449 Digital Process Control LaboratoryApplications of digital control principles to laboratory experimentssupported by a networked distributed control system. Lecturematerial reviews background of real-time programming, dataacquisition, process dynamics, and process control. Exercises indata acquisition and feedback control design using simple andadvanced control strategies. Experiments in flow, liquid level,temperature, and pressure control. Term project. Prerequisite:ESE 441 or EECE 401 or equivalent.Same as E44 EECE 424Credit 3 units. EN: BME T, TU

E35 ESE 455 Quantitative Methods for Systems BiologyApplication of computational mathematical techniques toproblems in contemporary biology. Systems of linear ordinarydifferential equations in reaction-diffusion systems, hiddenMarkov models applied to gene discovery in DNA sequence,ordinary differential equation and stochastic models applied togene regulation networks, negative feedback in transcriptionand metabolic pathway regulation. Prerequisites: (1) Math217 Differential Equations and (2) a programming course andfamiliarity with MATLAB.Credit 3 units. EN: BME T, TU

E35 ESE 460 Switching TheoryAdvanced topics in switching theory as employed in thesynthesis, analysis, and design of information processingsystems. Combinational techniques: minimization, multipleoutput networks, state identification and fault detection,hazards, testability and design for test are examined. Sequentialtechniques: synchronous circuits, machine minimization, optimalstate assignment, asynchronous circuits, and built-in self-testtechniques. Prerequisite: CSE 260M.Same as E81 CSE 460TCredit 3 units. EN: BME T, TU

E35 ESE 461 Design Automation for Integrated CircuitSystemsIntegrated circuit systems provide the core technology thatpower today's most advanced devices and electronics: smartphones, wearable devices, autonomous robots, and cars,aerospace or medical electronics. These systems often consist

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of silicon microchips made up by billions of transistors andcontain various components such as microprocessors, DSPs,hardware accelerators, memories, and I/O interfaces, thereforedesign automation is critical to tackle the design complexityat the system level. The objectives of this course is to 1)introduce transistor-level analysis of basic digital logic circuits;2) provide a general understanding of hardware descriptionlanguage (HDL) and design automation tools for very largescale integrated (VLSI) systems; 3) expose students to thedesign automation techniques used in the best-known academicand commercial systems. Topics covered include device andcircuits for digital logic circuits, digital IC design flow, logicsynthesis, physical design, circuit simulation and optimization,timing analysis, power delivery network analysis. Assignmentsinclude homework, mini-projects, term paper and group project.Prerequisites: ESE 232; ESE 260.Credit 3 units. EN: BME T, TU

E35 ESE 462 Computer Systems DesignIntroduction to modern design practices, including FPGA andPCB design methodologies. Student teams use Xilinx Vivadofor HDL-based FPGA design and simulation and do schematiccapture, PCB layout, fabrication, and testing of the hardwareportion of a selected computation system. The software portionof the project uses Microsoft Visual Studio to develop a userinterface and any additional support software required todemonstrate final projects to the faculty during finals week.Prerequisites: CSE 361S and 362M from Washington Universityin St. Louis or permission of the instructor. Revised: 2019-02-22Same as E81 CSE 462MCredit 3 units. EN: TU

E35 ESE 463 Digital Integrated Circuit Design andArchitectureThis is a project-oriented course on digital VLSI design. Thecourse material focuses on bottom-up design of digital integratedcircuits, starting from CMOS transistors, CMOS inverters,combinational circuits and sequential logic designs. Importantdesign aspects of digital integrated circuits such as propagationdelay, noise margins and power dissipation are covered in theclass, and design challenges in sub-micron technology areaddressed. The students design combinational and sequentialcircuits at various levels of abstraction using a state-of-the-artCAD environment provided by Cadence Design Systems. Thegoal of the course is to design a microprocessor in 0.5 microntechnology that will be fabricated by a semiconductor foundry.Prerequisites: CSE 260M and ESE 232.Same as E81 CSE 463MCredit 3 units. EN: BME T, TU

E35 ESE 465 Digital Systems LaboratoryHardware/software co-design; processor interfacing; proceduresfor reliable digital design, both combinational and sequential;understanding manufacturers' specifications; use of testequipment. Several single-period laboratory exercises,several design projects, and application of microprocessors indigital design. One lecture and one laboratory period a week.Prerequisites: ESE 260.Credit 3 units. EN: BME T, TU

E35 ESE 469 Fundamentals of Machine Learning HardwareThsi course provides an overview of machine learning algorithmsand hardware; inference engines; training engines; emerginghardware architectures; performance analysis; and testingof machine learning accelerators. Prerequisites: ESE 417 orequivalent, ESE 260 or equivalent, and working knowledge ofMATLAB.Credit 3 units. EN: BME T, TU

E35 ESE 471 Communications Theory and SystemsIntroduction to the concepts of transmission of information viacommunication channels. Amplitude and angle modulation forthe transmission of continuous-time signals. Analog-to-digitalconversion and pulse code modulation. Transmission of digitaldata. Introduction to random signals and noise and their effectson communication. Optimum detection systems in the presenceof noise. Elementary information theory. Overview of variouscommunication technologies such as radio, television, telephonenetworks, data communication, satellites, optical fiber andcellular radio. Prerequisites: ESE 351 and ESE 326.Credit 3 units. EN: BME T, TU

E35 ESE 474 Introduction to Wireless Sensor NetworksThis is an introductory course on wireless sensor networks forsenior undergraduate students. The course uses a combinationof lecturing, reading, and discussion of research papers to helpeach student to understand the characteristics and operations ofvarious wireless sensor networks. Topics covered include sensornetwork architecture, communication protocols on MediumAccess Control and Routing, sensor network operation systems,sensor data aggregation and dissemination, localization and timesynchronization, energy management, and target detection andtracking using acoustic sensor networks. Prerequisite: ESE 351(Signals and Systems).Credit 3 units. EN: BME T, TU

E35 ESE 482 Digital Signal ProcessingIntroduction to analysis and synthesis of discrete-time lineartime-invariant (LTI) systems. Discrete-time convolution, discrete-time Fourier transform, z-transform, rational function descriptionsof discrete-time LTI systems. Sampling, analog-to-digitalconversion and digital processing of analog signals. Techniquesfor the design of finite impulse response (FIR) and infiniteimpulse response (IIR) digital filters. Hardware implementationof digital filters and finite-register effects. The Discrete FourierTransform and the Fast Fourier Transform (FFT) algorithms.Prerequisite: ESE 351.Credit 3 units. EN: BME T, TU

E35 ESE 488 Signals and Communication LaboratoryLaboratory exercises in digital signal processing, dataconversion and communications using modern laboratorytechniques and apparatus based on National InstrumentsLabVIEW and ELVIS II workstations. A laboratory coursedesigned to complement the traditional ESE course offeringsin signal processing and communication theory. Signals andsystems fundamentals: continuous-time and discrete-time lineartime-invariant systems, frequency response, oversampled andnoise-shaped A/D conversion. Digital signal processing: FIR andIIR digital filter design, application of the Fast Fourier Transform.Communication theory: baseband, digital communication,amplitude modulation, phase modulation, bandpass digital

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communication. Laboratory experiments involve analogand digital electronics. Computer workstations and moderncomputational software used extensively for system simulationand real-time signal processing. Prerequisite: ESE 351.Credit 3 units. EN: BME T, TU

E35 ESE 497 Undergraduate ResearchUndergraduate research under the supervision of a facultymember. The scope and depth of the research must beapproved by the faculty member prior to enrollment. A writtenfinal report and a webpage describing the research are required.Credit variable, maximum 3 units.

E35 ESE 498 Electrical Engineering Capstone DesignProjectsCapstone design project supervised by the course instructor.The project must use the theory, techniques, and conceptsof the student's major: electrical engineering or systemsscience & engineering. The solution of a real technological orsocietal problem is carried through completely, starting from thestage of initial specification, proceeding with the application ofengineering methods, and terminating with an actual solution.Collaboration with a client, typically either an engineer orsupervisor from local industry or a professor or researcher inuniversity laboratories, is encouraged. A proposal, an interimprogress update, and a final report are required, each in theforms of a written document and oral presentation, as well as awebpage on the project. Weekly progress reports and meetingswith the instructor are also required. Prerequisite: ESE seniorstanding and instructor's consent. Note: This course will meetat the scheduled time only during select weeks. If you cannotattend at that time, you may still register for the course.Credit 3 units. EN: BME T, TU

E35 ESE 499 Systems Science and Engineering CapstoneDesign ProjectCapstone design project supervised by the course instructor.The project must use the theory, techniques, and conceptsof the student's major: electrical engineering or systemsscience & engineering. The solution of a real technological orsocietal problem is carried through completely, starting from thestage of initial specification, proceeding with the application ofengineering methods, and terminating with an actual solution.Collaboration with a client, typically either an engineer orsupervisor from local industry or a professor or researcher inuniversity laboratories, is encouraged. A proposal, an interimprogress update, and a final report are required, each in theforms of a written document and oral presentation, as well as awebpage on the project. Weekly progress reports and meetingswith the instructor are also required. Prerequisite: ESE seniorstanding and instructor's consent. Note: This course will meetat the scheduled time only during select weeks. If you cannotattend at that time, you may still register for the course.Credit 3 units. EN: BME T, TU

E35 ESE 500 Independent StudyOpportunities to acquire experience outside the classroomsetting and to work closely with individual members of thefaculty. A final report must be submitted to the department.Prerequisite: Students must have the ESE Research/Independent Study Registration Form approved by thedepartment.Credit variable, maximum 3 units.

E35 ESE 501 Mathematics of Modern Engineering IMatrix algebra: systems of linear equations, vector spaces, linearindependence and orthogonality in vector spaces, eigenvectorsand eigenvalues; vector calculus: gradient, divergence, curl, lineand surface integrals, theorems of Green, Stokes, and Gauss;Elements of Fourier analysis and its applications to solving someclassical partial differential equations, heat, wave, and Laplaceequation. Prerequisites: ESE 318 and ESE 319 or equivalent orconsent of instructor. This course will not count toward the ESEdoctoral program.Credit 3 units. EN: BME T, TU

E35 ESE 502 Mathematics of Modern Engineering IIThis course covers Fourier series and Fourier integral transformsand their applications to solving some partial differentialequations and heat and wave equations. It also presentscomplex analysis and its applications to solving real-valuedproblems, including analytic functions and their role, Laurentseries representation, complex-valued line integrals and theirevaluation (including the residual integration theory), andconformal mappings and their applications. Prerequisites: ESE318 and ESE 319 or equivalent, or permission of instructor. Thiscourse will not count toward the ESE doctoral program.Credit 3 units. EN: BME T, TU

E35 ESE 513 Large-Scale Optimization for Data ScienceLarge-scale optimization is an essential component of moderndata science, artificial intelligence, and machine learning.This graduate-level course rigorously introduces optimizationmethods that are suitable for large-scale problems arising inthese areas. Students will learn several algorithms suitable forboth smooth and nonsmooth optimization, including gradientmethods, proximal methods, mirror descent, Nesterov'sacceleration, ADMM, quasi-Newton methods, stochasticoptimization, variance reduction, and distributed optimization.Throughout the course, we will discuss the efficacy of thesemethods in concrete data science problems, under appropriatestatistical models. Students will be required to program in Pythonor MATLAB. Prerequisites: CSE 247, Math 309, Math 3200, orESE 326.Credit 3 units. EN: TU

E35 ESE 515 Nonlinear OptimizationNonlinear optimization problems with and without constraintsand computational methods for solving them. Optimalityconditions, Kuhn-Tucker conditions, Lagrange duality; gradientand Newton's methods; conjugate direction and quasi-Newtonmethods; primal and penalty methods; Lagrange methods. Useof MATLAB optimization techniques in numerical problems.Prerequisites: CSE 131, Math 309 and ESE 318 or permission ofinstructor.Credit 3 units. EN: TU

E35 ESE 516 Optimization in Function SpaceLinear vector spaces, normed linear spaces, Lebesque integrals,the Lp spaces, linear operators, dual space, Hilbert spaces.Projection theorem, Hahn-Banach theorem. Hyperplanes andconvex sets, Gateaux and Frächet differentials, unconstrainedminima, adjoint operators, inverse function theorem. Constrainedminima, equality constraints, Lagrange multipliers, calculus ofvariations, Euler-Lagrange equations, positive cones, inequalityconstraints. Kuhn-Tucker theorem, optimal control theory,

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Pontryagin's maximum principle, successive approximationmethods, Newton's methods, steepest descent methods, primal-dual methods, penalty function methods, multiplier methods.Prerequisite: Math 4111.Credit 3 units.

E35 ESE 517 Partial Differential EquationsLinear and nonlinear first order equations. Characteristics.Classification of equations. Theory of the potential linear andnonlinear diffusion theory. Linear and nonlinear wave equations.Initial and boundary value problems. Transform methods.Integral equations in boundary value problems. Prerequisites:ESE 318 and 319 or equivalent or consent of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 518 Optimization Methods in ControlThe course is divided in two parts: convex optimization andoptimal control. In the first part we cover applications of LinearMatrix Inequalities and Semi-Definite Programming to controland estimation problems. We also cover Multiparametric LinearProgramming and its application to the Model Predictive Controland Estimation of linear systems. In the second part we covernumerical methods to solve optimal control and estimationproblems. We cover techniques to discretize optimal controlproblems, numerical methods to solve them, and their optimalityconditions. We apply these results to the Model PredictiveControl and Estimation of nonlinear systems. Prerequisites: ESE551, and ESE 415 or equivalent.Credit 3 units. EN: TU

E35 ESE 519 Convex OptimizationConcentrates on recognizing and solving convex optimizationproblems that arise in applications. Convex sets, functions,and optimization problems. Basics of convex analysis.Least-squares, linear and quadratic programs, semidefiniteprogramming, minimax, extremal volume, and other problems.Optimality conditions, duality theory, theorems of alternative,and applications. Interior-point methods. Applications tosignal processing, statistics and machine learning, control andmechanical engineering, digital and analog circuit design, andfinance. Prerequisites: Math 309 and ESE 415.Credit 3 units.

E35 ESE 520 Probability and Stochastic ProcessesThis course covers a review of probability theory; models forrandom signals and noise; calculus of random processes; noisein linear and nonlinear systems; representation of randomsignals by sampling and orthonormal expansions; and Poisson,Gaussian, and Markov processes as models for engineeringproblems. Prerequisite: ESE 326.Credit 3 units. EN: BME T, TU

E35 ESE 523 Information TheoryDiscrete source and channel model, definition of informationrate and channel capacity, coding theorems for sources andchannels, encoding and decoding of data for transmission overnoisy channels. Corequisite: ESE 520.Credit 3 units. EN: BME T, TU

E35 ESE 524 Detection and Estimation TheoryStudy of detection and estimation of signals in noise. Linearalgebra, vector spaces, independence, projections. Dataindependence, factorization theorem and sufficient statistics.Neyman-Pearson and Bayes detection. Least squares,maximum-likelihood and maximum a posteriori estimation ofsignal parameters. Conjugate priors, recursive estimation,Wiener and Kalman filters. Prerequisite: ESE 520.Credit 3 units. EN: BME T, TU

E35 ESE 526 Network ScienceThis course focuses on fundamental theory, modeling, structure,and analysis methods in network science. The first part of thecourse includes basic network models and their mathematicalprinciples. Topics include a review of graph theory, randomgraph models, scale-free network models and dynamic networks.The second part of the course includes structure and analysismethods in network science. Topics include network robustness,community structure, spreading phenomena and clique topology.Applications of the topics covered by this course include socialnetworks, power grid, internet, communications, protein-proteininteractions, epidemic control, global trade, neuroscience, etc.Prerequisites: ESE 520 (Probability and Stochastic Processes),Math 429 (Linear Algebra) or equivalent.Credit 3 units.

E35 ESE 527 Practicum in Data Analytics & StatisticsIn this course, students will learn through hands-on experiencethe application of analytics to support data-driven decisions.Through lectures and the execution of a project (to be definedat the beginning of the semester), students will learn to usedescriptive, predictive, and prescriptive analytics. Lectures willfocus on presenting analytic topics relevant to the executionof the project, including analytic model development, dataquality and data models, review of machine learning algorithms(unsupervised, supervised, and semi-supervised approaches),model validation, insights generation and results communication,and code review and code repository. Students are expectedto demonstrate the application of these concepts through theexecution of a one-semester project. Students can propose theirown projects or choose from a list of projects made availableby the lecturer. Projects should reflect real-world problemswith a clear value proposition. Progress will be evaluated andgraded periodically during the semester, and the course willinclude a final presentation open to the academic community.Prerequisites: ESE 520 (or Math 493 and 494), ESE 417 or CSE417T, ESE 415, and declaration of the MS in DAS.Credit 3 units. EN: BME T, TU

E35 ESE 531 Nano and Micro PhotonicsThis course focuses on fundamental theory, design, andapplications of photonic materials and micro/nano photonicdevices. It includes review and discussion of light-matterinteractions in nano and micro scales, propagation of light inwaveguides, nonlinear optical effect and optical properties ofnano/micro structures, the device principles of waveguides,filters, photodetectors, modulators and lasers. Prerequisite: ESE330.Credit 3 units. EN: BME T, TU

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E35 ESE 532 Introduction to Nano-Photonic DevicesIntroduction to photon transport in nano-photonic devices.This course focuses on the following topics: light and photons,statistical properties of photon sources, temporal and spatialcorrelations, light-matter interactions, optical nonlinearity, atomsand quantum dots, single- and two-photon devices, opticaldevices, and applications of nano-photonic devices in quantumand classical computing and communication. Prerequisites: ESE330 and Physics 217, or permission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 536 Introduction to Quantum OpticsThis course covers the following topics: quantum mechanics forquantum optics, radiative transitions in atoms, lasers, photonstatistics (photon counting, sub-/super-Poissionian photonstatistics, bunching, anti-bunching, theory of photodetection, shotnoise), entanglement, squeezed light, atom-photon interactions,cold atoms, abd atoms in cavities. If time permits, the followingtopics will be selectively covered: quantum computing, quantumcryptography, and teleportation. Prerequisites: ESE 330 andPhysics 217 or Physics 421Credit 3 units. EN: BME T, TU

E35 ESE 538 Advanced Electromagnetic EngineeringThe course builds on undergraduate electromagnetics tosystematically develop advanced concepts in electromagnetictheory for engineering applications. The following topics arecovered: Maxwell's equations; fields and waves in materials;electromagnetic potentials and topics for circuits and systems;transmission-line essentials for digital electronics and forcommunications; guided wave principles for electronics andoptoelectronics; principles of radiation and antennas; andnumerical methods for computational electromagnetics.Credit 3 units.

E35 ESE 543 Control Systems Design by State SpaceMethodsAdvanced design and analysis of control systems by state-space methods: classical control review, Laplace transforms,review of linear algebra (vector space, change of basis, diagonaland Jordan forms), linear dynamic systems (modes, stability,controllability, state feedback, observability, observers, canonicalforms, output feedback, separation principle and decoupling),nonlinear dynamic systems (stability, Lyapunov methods).Frequency domain analysis of multivariable control systems.State space control system design methods: state feedback,observer feedback, pole placement, linear optimal control.Design exercises with CAD (computer-aided design) packagesfor engineering problems. Prerequisite: ESE 351 and ESE 441,or permission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 544 Optimization and Optimal ControlConstrained and unconstrained optimization theory. Continuoustime as well as discrete-time optimal control theory. Time-optimalcontrol, bang-bang controls and the structure of the reachableset for linear problems. Dynamic programming, the Pontryaginmaximum principle, the Hamiltonian-Jacobi-Bellman equationand the Riccati partial differential equation. Existence of classicaland viscosity solutions. Application to time optimal control,regulator problems, calculus of variations, optimal filtering andspecific problems of engineering interest. Prerequisites: ESE551, ESE 552.

Credit 3 units. EN: BME T, TU

E35 ESE 545 Stochastic ControlIntroduction to the theory of stochastic differential equationsbased on Wiener processes and Poisson counters, and anintroduction to random fields. The formulation and solution ofproblems in nonlinear estimation theory. The Kalman-Bucyfilter and nonlinear analogues. Identification theory. Adaptivesystems. Applications. Prerequisites: ESE 520 and ESE 551.Credit 3 units. EN: BME T, TU

E35 ESE 546 Dynamics & Control in Neuroscience & BrainMedicineThis course provides an introduction to systems engineeringapproaches to modeling, analysis and control of neuronaldynamics at multiple scales. A central motivation is themanipulation of neuronal activity for both scientific andmedical applications using emerging neurotechnology andpharmacology. Emphasis is placed on dynamical systemsand control theory, including bifurcation and stability analysisof single neuron models and population mean-field models.Synchronization properties of neuronal networks are covered,and methods for control of neuronal activity in both oscillatoryand non-oscillatory dynamical regimes are developed. Statisticalmodels for neuronal activity are also discussed. An overviewof signal processing and data analysis methods for neuronalrecording modalities is provided toward the development ofclosed-loop neuronal control paradigms. The final evaluationis based on a project or research survey. Prerequisites: ESE553 (or equivalent); ESE 520 (or equivalent); ESE 351 (orequivalent).Credit 3 units. EN: BME T, TU

E35 ESE 547 Robust and Adaptive ControlGraduate-level control system design methods for multi-input multi-output systems. Linear optimal-based methods inrobust control, nonlinear model reference adaptive control.These design methods are currently used in most industrycontrol system design problems. These methods are designed,analyzed and simulated using MATLAB. Linear control theory(review), robustness theory (Mu Analysis), optimal control andthe robust servomechanism, H-infinity optimal control, robustoutput feedback controls, Kalman filter theory and design,linear quadratic gaussian with loop transfer recovery, the LoopTransfer Recovery method of Lavretsky, Mu synthesis, Lyapunovtheory (review), LaSalle extensions, Barbalat's Lemma,model reference adaptive control, artificial neural networks,online parameter estimation, convergence and persistence ofexcitation. Prerequisite: ESE 543 or ESE 551 or equivalent.Credit 3 units. EN: BME T, TU

E35 ESE 551 Linear Dynamic Systems IInput-output and state-space description of linear dynamicsystems. Solution of the state equations and the transitionmatrix. Controllability, observability, realizations, pole-assignment, observers and decoupling of linear dynamicsystems. Prerequisite: ESE 351.Credit 3 units. EN: BME T, TU

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E35 ESE 552 Linear Dynamic Systems IILeast squares optimization problems. Riccati equation, terminalregulator and steady-state regulator. Introduction to filteringand stochastic control. Advanced theory of linear dynamicsystems. Geometric approach to the structural synthesis of linearmultivariable control systems. Disturbance decoupling, systeminvertibility and decoupling, extended decoupling and the internalmodel principle. Prerequisite: ESE 551.Credit 3 units. EN: TU

E35 ESE 553 Nonlinear Dynamic SystemsState space and functional analysis approaches to nonlinearsystems. Questions of existence, uniqueness and stability;Lyapunov and frequency-domain criteria; w-limits and invariance,center manifold theory and applications to stability, steady-stateresponse and singular perturbations. Poincare-Bendixson theory,the van der Pol oscillator, and the Hopf Bifurcation theorem.Prerequisite: ESE 551.Credit 3 units. EN: BME T, TU

E35 ESE 554 Advanced Nonlinear Dynamic SystemsDifferentiable manifolds, vector fields, distributions on amanifold, Frobenius' theorem, Lie algebras. Controllability,observability of nonlinear systems, examined from the viewpointof differential geometry. Transformation to normal forms. Exactlinearization via feedback. Zero dynamics and related properties.Noninteracting control and disturbance decoupling. Controlledinvariant distributions. Noninteracting control with internalstability. Prerequisites: ESE 553 and ESE 551.Credit 3 units.

E35 ESE 557 Hybrid Dynamic SystemsTheory and analysis of hybrid dynamic systems, which is theclass of systems whose state is composed by continuous-valued and discrete-valued variables. Discrete-event systemsmodels and language descriptions. Models for hybrid systems.Conditions for existence and uniqueness. Stability andverification of hybrid systems. Optimal control of hybridsystems. Applications to cyber-physical systems and robotics.Prerequisite: ESE 551.Credit 3 units. EN: BME T, TU

E35 ESE 559 Special Topics in Systems and ControlThis course provides a rigorous introduction to recentdevelopments in systems and controls. Focus is on thediscussion of interdisciplinary applications of complex systemsthat motivate emerging topics in dynamics and control aswell as state-of-the-art methods for addressing the controland computation problems involving these large-scalesystems. Topics to be covered include the control of ensemblesystems, pseudospectral approximation and high-dimensionaloptimization, the mathematics of networks, dynamic learningand topological data analysis, and applications to biology,neuroscience, brain medicine, quantum physics, and complexnetworks. Both model-based and data-driven approaches areintroduced. Students learn about state-of-the-art research in thefield, and they ultimately apply their knowledge to conduct a finalproject. Prerequisites: Math 429 or equivalent, ESE 415, ESE551, ESE 553, and ESE 520.Credit 3 units. EN: TU

E35 ESE 5591 Special Topics in Engineering andNeuroscienceCredit 2 units. EN: TU

E35 ESE 560 Computer Systems Architecture IAn exploration of the central issues in computer architecture:instruction set design, addressing and register set design, controlunit design, microprogramming, memory hierarchies (cache andmain memories, mass storage, virtual memory), pipelining, andbus organization. The course emphasizes understanding theperformance implications of design choices, using architecturemodeling and evaluation using VHDL and/or instruction setsimulation. Prerequisites: CSE 361S and CSE 260M.Same as E81 CSE 560MCredit 3 units. EN: BME T, TU

E35 ESE 562 Analog Integrated CircuitsThis course focuses on fundamental and advanced topics inanalog and mixed-signal VLSI techniques. The first part of thecourse covers graduate-level materials in the area of analogcircuit synthesis and analysis. The second part of the coursecovers applications of the fundamental techniques for designinganalog signal processors and data converters. Several practicalaspects of mixed-signal design, simulation and testing arecovered in this course. This is a project-oriented course, andit is expected that the students apply the concepts learnedin the course to design, simulate and explore different circuittopologies. Prerequisites: CSE 260 and ESE 232.Credit 3 units.

E35 ESE 566A Modern System-on-Chip DesignThe System-on-Chip (SoC) technology is at the core ofmost electronic systems: smartphones, wearable devices,autonomous robots and cars, and aerospace and medicalelectronics. In these SoCs, billions of transistors canbe integrated on a single silicon chip containing variouscomponents, such as microprocessors, DSPs, hardwareaccelerators, memories, and I/O interfaces. Topics include SoCarchitectures, design tools, and methods as well as system-level trade-offs between performance, power consumption,energy efficiency, reliability, and programmability. Studentswill gain an insight into the early stages of the SoC designprocess by performing the tasks of developing functionalspecifications, applying partitions and map functions to hardwareand/or software, and then evaluating and validating systemperformance. Assignments include hands-on design projects.This course is open to both graduate and senior undergraduatestudents. Prerequisite: ESE 461.Credit 3 units. EN: BME T, TU

E35 ESE 567 Computer Systems AnalysisA comprehensive course on performance analysis techniques.The topics include common mistakes, selection of techniquesand metrics, summarizing measured data, comparing systemsusing random data, simple linear regression models, otherregression models, experimental designs, 2**k experimentaldesigns, factorial designs with replication, fractional factorialdesigns, one factor experiments, two factor full factorial designw/o replications, two factor full factorial designs with replications,general full factorial designs, introduction to queueing theory,analysis of single queues, queueing networks, operational

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laws, mean-value analysis, time series analysis, heavy taileddistributions, self-similar processes, long-range dependence,random number generation, analysis of simulation results, andart of data presentation. Prerequisites: CSE 260M.Same as E81 CSE 567MCredit 3 units. EN: BME T, TU

E35 ESE 570 Coding TheoryIntroduction to the algebra of finite fields. Linear block codes,cyclic codes, BCH and related codes for error detection andcorrection. Encoder and decoder circuits and algorithms.Spectral descriptions of codes and decoding algorithms. Codeperformances.Credit 3 units. EN: TU

E35 ESE 571 Transmission Systems and MultiplexingTransmission and multiplexing systems are essential to providingefficient point-to-point communication over distance. This courseintroduces the principles underlying modern analog and digitaltransmission and multiplexing systems and covers a variety ofsystem examples.Credit 3 units. EN: TU

E35 ESE 572 Signaling and Control in CommunicationNetworksThe operation of modern communications networks is highlydependent on sophisticated control mechanisms that directthe flow of information through the network and oversee theallocation of resources to meet the communication demandsof end users. This course covers the structure and operationof modern signaling systems and addresses the major designtrade-offs that center on the competing demands of performanceand service flexibility. Specific topics covered include protocolsand algorithms for connection establishment and transformation,routing algorithms, overload and failure recovery and networkingdimensioning. Case studies provide concrete examples andreveal the key design issues. Prerequisites: graduate standingand permission of instructor.Credit 3 units. EN: BME T, TU

E35 ESE 575 Fiber-Optic CommunicationsIntroduction to optical communications via glass-fiber media.Pulse-code modulation and digital transmission methods, codinglaws, receivers, bit-error rates. Types and properties of opticalfibers; attenuation, dispersion, modes, numerical aperture.Light-emitting diodes and semiconductor laser sources; devicestructure, speed, brightness, modes, electrical properties, opticaland spectral characteristics. Prerequisites: ESE 330, ESE 336.Credit 3 units. EN: BME T, TU

E35 ESE 582 Fundamentals and Applications of ModernOptical ImagingAnalysis, design, and application of modern optical imagingsystems with emphasis on biological imaging. The first partof the course will focus on the physical principles underlyingthe operation of imaging systems and their mathematicalmodels. Topics include ray optics (speed of light, refractiveindex, laws of reflection and refraction, plane surfaces, mirrors,lenses, aberrations), wave optics (amplitude and intensity,frequency and wavelength, superposition and interference,interferometry), Fourier optics (space-invariant linear systems,Huygens-Fresnel principle, angular spectrum, Fresnel diffraction,Fraunhofer diffraction, frequency analysis of imaging systems),

and light-matter interaction (absorption, scattering, dispersion,fluorescence). The second part of the course will comparemodern quantitative imaging technologies, including but notlimited to digital holography, computational imaging, and super-resolution microscopy. Students will evaluate and critique recentoptical imaging literature. Prerequisites: ESE 318 and ESE 319(or their equivalents); ESE 330 or PHY 421 (or equivalent).Credit 3 units. EN: TU

E35 ESE 584 Statistical Signal Processing for Sensor ArraysMethods for signal processing and statistical inference for dataacquired by an array of sensors, such as those found in radar,sonar and wireless communications systems. Multivariatestatistical theory with emphasis on the complex multivariatenormal distribution. Signal estimation and detection in noisewith known statistics, signal estimation and detection in noisewith unknown statistics, direction finding, spatial spectrumestimation, beam forming, parametric maximum-likelihoodtechniques. Subspace techniques, including MUSIC andESPRIT. Performance analysis of various algorithms. Advancedtopics may include structured covariance estimation, wide-band array processing, array calibration, array processing withpolarization diversity, and space-time adaptive processing(STAP). Prerequisites: ESE 520, ESE 524, linear algebra,computer programming.Credit 3 units. EN: TU

E35 ESE 585A Sparse Modeling for Imaging and VisionSparse modeling is at the heart of modern imaging, vision, andmachine learning. It is a fascinating new area of research thatseeks to develop highly effective data models. The core idea insparse modeling theory is a novel redundant transform, wherethe number of transform coefficients is larger compared to theoriginal data dimension. Together with redundancy comes anopportunity for seeking the sparsest possible representationor the one with the fewest non-zeros. This core idea leadsto a series of beautiful theoretical and practical results withmany applications, such as regression, prediction, restoration,extrapolation, compression, detection, and recognition. In thiscourse, we will explore sparse modeling by covering theoreticalas well as algorithmic aspects with applications in computationalimaging and computer vision. Prerequisites: ESE 318, Math233, Math 309, and Math 429 (or equivalents), as well as codingexperience with MATLAB or Python.Credit 3 units. EN: BME T, TU

E35 ESE 588 Quantitative Image ProcessingIntroduction to modeling, processing, manipulation and display ofimages. Application of two-dimensional linear systems to imageprocessing. Two-dimensional sampling and transform methods.The eye and perception. Image restoration and reconstruction.Multiresolution processing, noise reduction and compression.Boundary detection and image segmentation. Case studiesin image processing (examples: computer tomography andultrasonic imaging). Prerequisites: ESE 326, ESE 482.Credit 3 units. EN: BME T, TU

E35 ESE 589 Biological Imaging TechnologyThis class develops a fundamental understanding of the physicsand mathematical methods that underlie biological imaging andcritically examine case studies of seminal biological imagingtechnology literature. The physics section examines howelectromagnetic and acoustic waves interact with tissues andcells, how waves can be used to image the biological structure

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and function, image formation methods, and diffraction limitedimaging. The math section examines image decompositionusing basis functions (e.g., Fourier transforms), synthesisof measurement data, image analysis for feature extraction,reduction of multidimensional imaging datasets, multivariateregression, and statistical image analysis. Original literatureon electron, confocal and two photon microscopy, ultrasound,computed tomography, functional and structural magneticresonance imaging and other emerging imaging technology arecritiqued.Credit 3 units. EN: BME T, TU

E35 ESE 590 Electrical & Systems Engineering GraduateSeminarThis satisfactory/unsatisfactory course is required for themaster's, DSc, and PhD degrees in Electrical & SystemsEngineering. A satisfactory grade is required for each semesterof enrollment, and this is achieved by student attendance atregularly scheduled seminars. Master's students must attend atleast three seminars per semester, except for first-year master'sstudents, who must attend four. DSc and PhD students mustattend at least five seminars per semester, except for first-year PhD students who must attend six. Part-time students areexempt except during their year of residency. Any student undercontinuing status is also exempt.

E35 ESE 591 Biomedical Optics I: PrinciplesThis course covers the principles of optical photon transportin biological tissue. This course covers the principles andapplications of optical photon transport in biological tissue.Topics include a brief introduction to biomedical optics, single-scatterer theories, Monte Carlo modeling of photon transport,convolution for broad-beam responses, radiative transferequation, diffusion theory and applications, sensing of opticalproperties and spectroscopy, and photoacoustic imagingprinciples and applications. Prerequisite: Differential equationsSame as E62 BME 591Credit 3 units. EN: TU

E35 ESE 5931 Mathematics of Imaging ScienceThis course will expose students to a unified treatment of themathematical properties of images and imaging. This will includean introduction to linear vector space theory, operator theory onHilbert spaces, and concepts from applied functional analysis.Concepts from generalized functions, Fourier analysis, andradon transform will also be discussed. These tools will beapplied to conduct deterministic analyses of imaging systemsthat are described as continuous-to-continuous, continuous-to-discrete, and discrete-to-discrete mappings from objectproperties to image data. In addition, imaging systems will beanalyzed in a statistical framework in which stochastic modelsfor objects and images will be introduced. Prerequisite: Seniorstanding or permission of instructor.Same as E62 BME 570Credit 3 units.

E35 ESE 5932 Computational Methods for Imaging ScienceInverse problems are ubiquitous in science and engineering, andthey form the basis for modern imaging methods. This coursewill introduce students to the mathematical formulation of inverseproblems and modern computational methods employed tosolve them. Specific topics covered will include regularizationtheory, compressive sampling, variational calculus, and a survey

of relevant numerical optimization methods. The applicationof these methods to tomographic imaging problems will beaddressed in detail. Prerequisite: ESE 5931 or permission ofinstructor.Credit 3 units. EN: TU

E35 ESE 5933 Theoretical Imaging ScienceImaging science encompasses the design and optimizationof imaging systems to quantitatively measure information ofinterest. Imaging systems are important in many scientific andmedical applications and may be designed for one specificapplication or for a range of applications. Performance isquantified for any given task through an understanding of thestatistical model for the imaging data, the data processingalgorithm used, and a measure of accuracy or error. Optimalprocessing is based on statistical decision theory and estimationtheory; performance bounds include the receiver operatingcharacteristic and Cramer-Rao bounds. Bayesian methodsoften lead to ideal observers. Extensions of methods from finite-dimensional spaces to function space are fundamental for manyimaging applications. A variety of methods to assess imagequality and resulting imaging system optimization are covered.Prerequisite: permission of instructor.Credit 3 units. EN: TU

E35 ESE 5934 Practicum in Imaging ScienceStudents develop research results in computational imaging andwrite a conference paper on the results. This course involvesthe process of research project design and implementationin imaging science, participation in research teams, thedevelopment of milestones for a project, and the processof meeting expectations. The role of machine learning,computational methods, theoretical methods, datasets,and experiments in imaging science research are covered.Prerequisite: Permission of instructor.Credit 3 units.

E35 ESE 596 Seminar in Imaging Science and EngineeringThis seminar course consists of a series of tutorial lectures onImaging Science and Engineering with emphasis on applicationsof imaging technology. Students are exposed to a variety ofimaging applications that vary depending on the semester, butmay include multispectral remote sensing, astronomical imaging,microscopic imaging, ultrasound imaging and tomographicimaging. Guest lecturers come from several parts of theuniversity. This course is required of all students in the ImagingScience and Engineering program; the only requirement isattendance. This course is graded pass/fail. Prerequisite:admission to Imaging Science and Engineering program. Sameas CSE 596 (when offered) and BME 506.Credit 1 unit.

E35 ESE 599 Master's ResearchPrerequisite: Students must have the ESE Research/Independent Study Registration Form (PDF) (https://ese.wustl.edu/research/areas/Documents/Independent%20Study%20Form_1.pdf) approved by the department.Credit variable, maximum 3 units.

E35 ESE 600 Doctoral ResearchCredit variable, maximum 9 units.

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E35 ESE 601 Research Rotation for ESE Doctoral StudentsDoctoral students in Electrical and Systems Engineering arerequired to complete two rotations during their first year andmay complete three rotations, with research mentors acceptableto the separtment. The rotations must be mutually agreeableto both the student and the faculty member. The grade will beassigned based on a written report from one of the rotations. Therotations allow students to sample different research projectsand laboratory working environments and to enable the matchingof doctoral students with the research mentors with whom theywill carry out PhD dissertation research.Credit 3 units.

E35 ESE 883 Master's Continuing Student Status

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