## About Electrical & Systems Engineering

The mission of our undergraduate programs is to instill in students the knowledge and perspective — appropriate for both a professional career and the pursuit of advanced degrees — of fields that rely on key electrical engineering and systems principles and practices. Such principles and practices include rigorous quantitative reasoning and robust engineering design. This mission is accomplished by ensuring that students achieve both depth and breadth of knowledge in their studies and by maintaining a high degree of flexibility in the curriculum. Our programs also seek to provide good preparation for life, including the ability to communicate in written and oral forms and a desire to continue learning throughout life. In addition, these programs aim to provide the opportunity and training necessary for students to acquire the skills and attitudes to become leaders.

The department offers courses of study leading to degrees in both electrical engineering and systems science and engineering. Opportunities for study and research currently available in the department include semiconductor theory and devices, optoelectronics, nanophotonics, communication theory and systems, information theory, signal and image processing, tomographic imaging, linear and nonlinear dynamics and control, robotics, identification and estimation, multisensor fusion and navigation, computational mathematics, optimization, optimal control, autonomous systems, operations research and financial engineering. Students are encouraged to participate in research activities as soon as they have received training in the fundamentals appropriate for a given research area.

Electrical engineering is the profession for those intrigued with electrical phenomena and eager to contribute their skills to a society increasingly dependent on electricity and sophisticated electronic devices. It is a profession of broad scope, with many specialty careers designed for engineers who seek an endless diversity of career paths on the cutting edge of technology. The Institute of Electrical and Electronics Engineers publishes transactions on about 60 different topics, from aerospace and electronic systems to visualization and computer graphics. This is a breadth so great that no single electrical engineering department can hope to span it. Moreover, those fields themselves encompass still more fascinating specialties. We provide the basics; the future is the student's to shape.

Systems science and engineering is based on an approach that views an entire system of components as an entity rather than simply as an assembly of individual parts; each component is designed to fit properly with the other components rather than to function by itself. The engineering and mathematics of systems is a rapidly developing field. It is one of the most modern segments of applied mathematics as well as an engineering discipline. It is concerned with the identification, modeling, analysis, design and control of systems that are potentially as large and complex as the U.S. economy or as precise and vital as a space voyage. Its interests run from fundamental theoretical questions to the implementation of operational systems. It draws on the most modern and advanced areas of mathematics. A very important characteristic of the systems field is that its practitioners must, of necessity, interact within a wide interdisciplinary environment, not only with various engineers and scientists but also with economists, biologists and sociologists. Such interaction is both emphasized and practiced in the programs.

Our Department of Electrical & Systems Engineering offers a challenging basic curriculum, a broadly qualified faculty, and modern facilities so that students can receive a contemporary preparation for a career in electrical or systems engineering.

## Undergraduate Degree Programs

The Department of Electrical & Systems Engineering (ESE) offers four undergraduate degree programs: two professional degrees and two applied science degrees. The two professional degrees are the Bachelor of Science in Electrical Engineering (BSEE) and the Bachelor of Science in Systems Science & Engineering (BSSSE). These two programs are accredited by the Engineering Accreditation Commission of ABET. The two applied science degrees are the Bachelor of Science in Applied Science (Electrical Engineering) and the Bachelor of Science in Applied Science (Systems Science & Engineering). All programs have flexible curricula as well as specific requirements, and students may elect programs of study tailored to individual interests and professional goals.

In the professional BSEE curriculum, there are required courses in electrical circuits, signals and systems, digital systems and electromagnetic fields, along with laboratory and design courses, which provide students with a common core of experience. Subsequently, the student may orient the program toward breadth so that many disciplines within the profession are spanned or toward a specialty with more emphasis on depth in one or more disciplines. Areas of specialization include modern electronics, applied physics, telecommunications, control systems, and signal and image processing.

Students in the professional BSSSE degree program take required courses in engineering mathematics, signals and systems, operations research, and automatic control systems, along with laboratory and design courses. This program emphasizes the importance of real-world applications of systems theory; accordingly, students are required to take a concentration of courses in one of the traditional areas of engineering or science. There are numerous elective courses in control theory and systems, signal processing, optimization, robotics, probability and stochastic processes, and applied mathematics.

Students enrolled in any of the ESE undergraduate degree programs have a variety of opportunities to augment their educational experience at Washington University. Students may participate in the Pre-Medical Engineering program or in the Cooperative Education program. Some students pursue double majors, in which two sets of degree requirements — either within or outside the ESE department — are satisfied concurrently.

Students who seek a broad undergraduate education in electrical engineering or systems science and engineering but who plan on careers outside of engineering may pursue the applied science degrees: Bachelor of Science in Applied Science (Electrical Engineering) and Bachelor of Science in Applied Science (Systems Science & Engineering). These programs of study are appropriate for students planning to enter medical, law or business school and who desire a more technical undergraduate experience than what otherwise may be available to them.

The ESE department also offers a variety of educational opportunities for students enrolled in other departments. These include the second major in systems science and the second major in electrical engineering science, which are open to students inside as well as outside of the McKelvey School of Engineering, such as the College of Arts & Sciences and the School of Business. They also include the minor in applied physics & electrical engineering, the minor in electrical engineering, the minor in energy engineering, the minor in mechatronics, the minor in robotics, and the minor in systems science & engineering.

## BS–Master's Programs in Electrical & Systems Engineering

Students enrolled in any of the undergraduate degree programs in the McKelvey School of Engineering may choose to extend their educational experience by enrolling in a five-year BS–Master's program. The Master of Science in Electrical Engineering (MSEE), Master of Science in Systems Science and Mathematics (MSSSM), Master of Control Engineering (MCE), Master of Engineering in Robotics (MER), and Master of Science in Engineering Data Analytics and Statistics (MSDAS) degrees are participating graduate degrees, and these may be combined with any undergraduate degree that provides the appropriate background.

General requirements for the BS–Master's programs include the residency and other applicable requirements of the university and the McKelvey School of Engineering, which are found elsewhere in this *Bulletin.* In summary, students must complete all of the degree requirements for both the undergraduate and graduate degrees (at least 120 units plus 30 units for a total of 150 units), but they are not required to complete all of the undergraduate degree requirements first.

Phone: | 314-935-5565 |
---|---|

Website: | http://ese.wustl.edu |

### Chair

**Bruno Sinopoli**

Das Family Distinguished Professor

PhD, University of California, Berkley

Cyberphysical systems, analysis and design of networked embedded control systems, with applications to sensor actuators networks

### Endowed Professors

**Shantanu Chakrabartty**

Clifford W. Murphy Professor

PhD, Johns Hopkins University

New frontiers in unconventional analog computing techniques using silicon and hybrid substrates, fundamental limits of energy efficiency, sensing and resolution by exploiting computational and adaptation primitives inherent in the physics of devices

**Arye Nehorai**

Eugene and Martha Lohman Professor of Electrical Engineering

PhD, Stanford University

Statistical signal processing, machine learning, imaging, biomedicine

**Joseph A. O'Sullivan**

Samuel C. Sachs Professor of Electrical Engineering

Dean, UMSL/WashU Joint Undergraduate Engineering Program

PhD, Notre Dame University

Information theory, statistical signal processing, imaging science with applications in medicine and security, and recognition theory and systems

**Lan Yang**

Edward H. & Florence G. Skinner Professor of Engineering

PhD, California Institute of Technology

Nano/micro photonics, ultra high-quality optical microcavities, ultra-low-threshold microlasers, nano/micro fabrication, optical sensing, single nanoparticle detection, photonic molecules, photonic materials

### Professors

**Jr-Shin Li**

Professor

PhD, Harvard University

Mathematical control theory, optimization, quantum control, biomedical applications

**Neal Patwari**

Professor

PhD, University of Michigan

Intersection of statistical signal processing and wireless networking for improving wireless sensor networking and radiofrequency sensing

### Associate Professors

**ShiNung Ching**

Das Family Distinguished Career Development Assistant Professor

PhD, University of Michigan

Systems and control in neural medicine, nonlinear and constrained control, physiologic network dynamics, stochastic control

**Jung-Tsung Shen**

Das Family Distinguished Career Development Assistant Professor

PhD, Massachusetts Institute of Technology

Theoretical and numerical investigations on nanophotonics, optoelectronics, plasmonics, metamaterials

### Assistant Professors

**Ulugbek Kamilov**

PhD, École Polytechnique Fédérale de Lausanne, Switzerland

Computational imaging, signal processing, biomedical imaging

**Mark Lawrence**

PhD, University of Birmingham

Nanophotonics, nonlinear optics, metasurfaces

**Matthew D. Lew**

PhD, Stanford University

Microscopy, biophotonics, computational imaging, nano-optics

**Chuan Wang**

PhD, University of Southern California

Flexible electronics, stretchable electronics, printed electronics, nanomaterials, nanoelectronics, optoelectronics

**Yong Wang**

PhD, Washington University in St. Louis

Biomedical engineering, life science, human physiology, magnetic resonance imaging, electrocardiographic imaging

**Shen Zeng**

PhD, University of Stuttgart

Systems and control theory, data-based analysis and control of complex dynamical systems, inverse problems, biomedical applications

**Xuan "Silvia" Zhang**

PhD, Cornell University

Robotics, cyber-physical systems, hardware security, ubiquitous computing, embedded systems, computer architecture, VLSI, electronic design automation, control optimization, and biomedical devices and instrumentation

### Senior Professors

**Paul S. Min**

PhD, University of Michigan

Routing and control of telecommunication networks, fault tolerance and reliability, software systems, network management

**Robert E. Morley Jr.**

DSc, Washington University in St. Louis

Computer engineering, lower-power VLSI design, computer architecture, signal processing, microprocessors systems design

**Hiro Mukai**

PhD, University of California, Berkeley

Theory and computational methods for optimization, optimal control, systems theory, electric power system operations, differential games

**William F. Pickard**

PhD, Harvard University

Biological transport, electrobiology, energy engineering

**Daniel L. Rode**

PhD, Case Western Reserve University

Optoelectronics and fiber optics, semiconductor materials, light-emitting diodes and lasers, semiconductor processing, electronics

**Ervin Y. Rodin**

PhD, University of Texas at Austin

Optimization, differential games, artificial intelligence, mathematical modeling

**Heinz Schaettler**

PhD, Rutgers University

Optimal control, nonlinear systems, mathematical models in biomedicine

**Barbara A. Shrauner**

PhD, Harvard University (Radcliffe)

Plasma processing, semiconductor transport, symmetries of nonlinear differential equations

**Donald L. Snyder**

PhD, Massachusetts Institute of Technology

Communication theory, random process theory, signal processing, biomedical engineering, image processing, radar

**Barry E. Spielman**

PhD, Syracuse University

High-frequency/high-speed devices, radiofrequency and microwave integrated circuits, computational electromagnetics

**Tzyh Jong Tarn**

DSc, Washington University

Quantum mechanical systems, bilinear and nonlinear systems, robotics and automation, life science automation

### Professors of Practice

**Dedric Carter**

PhD, Nova Southeastern University

MBA, MIT Sloan School of Management

**Dennis Mell**

MS, University of Missouri-Rolla

Industrial automation, robotics and mechatronics, product design and development with design-for-manufacturability emphasis, prototyping, manufacturing

**Ed Richter**

MS, Washington University

Signal processing applications implemented on a variety of platforms, including ASIC, FPGA, DSP, microcontroller and desktop computers

**Jason Trobaugh**

DSc, Washington University

Ultrasound imaging, diffuse optical tomography, image-guided therapy, ultrasonic temperature imaging

### Teaching Professor

**James Feher**

PhD, Missouri University of Science and Technology

Electrical engineering, computer science, mathematics and physics

### Senior Lecturers

**Martha Hasting**

PhD, Saint Louis University

Mathematics education

**Vladimir Kurenok**

PhD, Belarus State University (Minsk, Belarus)

Probability and stochastic processes, stochastic ordinary and partial differential equations, financial mathematics

**Jinsong Zhang**

PhD, University of Miami

Modeling and performance analysis of wireless sensor networks, multi-source information fusion, ambiguous and incomplete information processing

### Lecturers

**Tsitsi Madziwa-Nussinov**

PhD, University of California, Los Angeles

**Dorothy Wang**

PhD, Virginia Tech

Fiber optic sensing and practical experience in sensor implementation and field test

### Professors Emeriti

**R. Martin Arthur**

Newton R. and Sarah Louisa Glasgow Wilson Professor of Engineering

PhD, University of Pennsylvania

Ultrasonic imaging, electrocardiography

**David L. Elliott**

PhD, University of California, Los Angeles

Mathematical theory of systems, nonlinear difference, differential equations

Please visit the following pages for more information about our majors:

- Bachelor of Science in Electrical Engineering
- Bachelor of Science in Systems Science & Engineering
- Bachelor of Science in Computer Engineering
- Bachelor of Science in Applied Science (Electrical Engineering)
- Bachelor of Science in Applied Science (Systems Science & Engineering)
- Second Major in Electrical Engineering
- Second Major in Systems Science & Engineering
- Second Major in Financial Engineering

Visit online course listings to view semester offerings for E35 ESE.

**E35 ESE 103 Introduction to Electrical Engineering**

A hands-on introduction to electrical engineering to put the fun into the electrical engineering fundamentals. Experiments are designed to be easy to conduct and understand. Some of the technologies explored are used in a variety of applications including ultrasound imaging, computed tomography, DC motors, analog to digital converters and credit card readers. Students work in groups of two in the newly renovated Urbauer 115 laboratory. Each station is equipped with modern electronic test equipment and a computer with an integrated Data Acquisition system. Using this lab equipment, students design and build solutions to the exercises. The students also learn to program in LabVIEW to control the Data Acquisition system and process the acquired signals. Also, throughout the semester, presentations are given by the ESE faculty about their research.

Credit 1 unit. EN: TU

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**E35 ESE 105 Introduction to Electrical and Systems Engineering**

This course will offer students a rigorous introduction to fundamental mathematical underpinnings of ESE and their relationship to a number of contemporary application areas. Major emphasis will be placed on linear algebra and associated numerical methods, including the use of MATLAB. Topics covered will include vector spaces, linear transformations, matrix manipulations and eigenvalue decomposition. Students will learn how this mathematical theory is enacted in ESE through the completion of four case studies spanning application areas: (i) Dynamical Systems and Control, (ii) Imaging, (iii) Signal Processing, and (iv) Circuits.

Credit 3 units. EN: TU

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**E35 ESE 205 Introduction to Engineering Design**

A hands-on course where students, in groups of two or three, will creatively develop projects and solve problems throughout the semester using tools from electrical and systems engineering. Groups will work under the supervision of an academic team consisting of faculty and higher-level students. Project objectives will be set by the academic team in collaboration with each student group. Evaluation will consider completion of these objectives, as well as the originality and innovation of the projects. Weekly 90 minute lab with academic team required, times TBD. Prerequisite Course(s): CSE131, Phy197 or equivalent.

Credit 3 units. EN: TU

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**E35 ESE 230 Introduction to Electrical and Electronic Circuits**

Electrical energy, current, voltage, and circuit elements. Resistors, Ohm's Law, power and energy, magnetic fields and DC motors. Circuit analysis and Kirchhoff's voltage and current laws. Thevenin and Norton transformations and the superposition theorem. Measuring current, voltage and power using ammeters and voltmeters. Energy and maximum electrical power transfer. Computer simulations of circuits. Reactive circuits, inductors, capacitors, mutual inductance, electrical transformers, energy storage, and energy conservation. RL, RC and RLC circuit transient responses. AC circuits, complex impedance, RMS current and voltage. Electrical signal amplifiers and basic operational amplifier circuits. Inverting, non-inverting, and difference amplifiers. Voltage gain, current gain, input impedance, and output impedance. Weekly laboratory exercises related to the lectures are an essential part of the course. Prerequisite: Phys 198. Corequisite: Math 217.

Credit 4 units. EN: TU

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**E35 ESE 232 Introduction to Electronic Circuits**

Analysis and design of linear and nonlinear electronic circuits. Detailed analysis of operational amplifier circuits, including non-ideal characteristics. Terminal characteristics of active semiconductor devices. Incremental and DC models for diodes, metal-oxide-semiconductor field effect transistors (MOSFETs), and bipolar junction transistors (BJTs). Design and analysis of single- and multi-stage amplifiers. Introduction to CMOS logic as well as static and dynamic memory circuits. Students will be required to design, analyze, build and demonstrate several of the circuits studied, including frequency response analysis and use of simulation tools. Prerequisite: ESE 230.

Credit 3 units. EN: TU

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**E35 ESE 260 Introduction to Digital Logic and Computer Design**

Introduction to design methods for digital logic and fundamentals of computer architecture. Boolean algebra and logic minimization techniques; sources of delay in combinational circuits and effect on circuit performance; survey of common combinational circuit components; sequential circuit design and analysis; timing analysis of sequential circuits; use of computer-aided design tools for digital logic design (schematic capture, hardware description languages, simulation); design of simple processors and memory subsystems; program execution in simple processors; basic techniques for enhancing processor performance; configurable logic devices. Prerequisite: CSE 131.

Same as E81 CSE 260M

Credit 3 units. EN: TU

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**E35 ESE 318 Engineering Mathematics A**

Laplace transforms; matrix algebra; vector analysis; eigenvalues and eigenvectors; vector differential calculus and vector integral calculus in three dimensions. Prerequisites: Math 233 and Math 217 or their equivalents.

Credit 3 units.

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**E35 ESE 319 Engineering Mathematics B**

Power series and Frobenius series solutions of differential equations; Legendre's equation; Bessel's equation; Fourier series and Fourier transforms; Sturm-Liouville theory; solutions of partial differential equations; wave and heat equations. Prerequisites: Math 233 and Math 217 or their equivalents.

Credit 3 units.

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**E35 ESE 326 Probability and Statistics for Engineering**

Study of probability and statistics together with engineering applications. 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 law of large numbers, the central limit theorem. Applications: reliability, quality control, acceptance sampling, linear regression, design and analysis of experiments, estimation, hypothesis testing. Examples are taken from engineering applications. Prerequisites: Math 233 or equivalent.

Credit 3 units. EN: TU

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**E35 ESE 330 Engineering Electromagnetics Principles**

Electromagnetic theory as applied to electrical engineering: vector calculus; electrostatics and magnetostatics; Maxwell's equations, including Poynting's theorem and boundary conditions; uniform plane-wave propagation; transmission lines, TEM modes, including treatment of general lossless lines, and pulse propagation; introduction to guided waves; introduction to radiation and scattering concepts. Prerequisites: Physics 198 and ESE 318 En Math A. Corequisite: ESE 319 En Math B.

Credit 3 units. EN: BME T, TU

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**E35 ESE 331 Electronics Laboratory**

Laboratory exercises provide students with a combination of hands-on experience in working with a variety of real instruments and in summarizing measurement results in written reports that clearly communicate laboratory results. A sequence of lab experiments provide hands-on experience with grounding and shielding techniques, signal analysis, realistic operational amplifier (op amp) characterization, op amp based active filter design and characterization, measurement of pulses propagating on a transmission line with various terminations, experience with FM modulation using phase locked loops and microwave techniques based on the vector network analyzer (VNA). Students will gain experience working with: sampling oscilloscopes to make measurements in the time and frequency domains, signal generators, digital multimeter and frequency measurements, microwave VNA measurements of directional coupler and antenna scattering parameters, and in creating circuits and making connections on contemporary circuit boards. The course concludes with a hands-on project to design, demonstrate and document the design of an electronic component. Prerequisite: ESE 232

Credit 3 units. EN: BME T, TU

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**E35 ESE 332 Power, Energy and Polyphase Circuits**

Fundamental concepts of power and energy; electrical measurements; physical and electrical arrangement of electrical power systems; polyphase circuit theory and calculations; principal elements of electrical systems such as transformers, rotating machines, control and protective devices, their description and characteristics; elements of industrial power system design. Prerequisite: ESE 230.

Credit 3 units. EN: BME T, TU

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**E35 ESE 351 Signals and Systems**

Introduction to concepts and methodology of linear dynamic systems in relation to discrete- and continuous-time signals. Mathematical modeling. Representation of systems and signals. Fourier, Laplace, and Z-transforms and convolution. Input-output description of linear systems: impulse response, transfer function. Time-domain and frequency-domain system analysis: transient and steady-state responses, system modes, stability, frequency spectra and frequency responses. System design: filter, modulation, sampling theorem. Continuity is emphasized from analysis to synthesis. Use of MATLAB. Prerequisites: Physics 197/198, Math 217, CSE 131, matrix addition and multiplication. Corequisite: ESE 318.

Credit 3 units. EN: BME T, TU

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**E35 ESE 362 Computer Architecture**

This course explores the interaction and design philosophy of hardware and software for digital computer systems. Topics include: Processor architecture, Instruction Set Architecture, Assembly Language, memory hierarchy design, I/O considerations, and a comparison of computer architectures. Prerequisite: CSE 260M.

Same as E81 CSE 362M

Credit 3 units. EN: BME T, TU

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**E35 ESE 400 Independent Study**

Opportunities to acquire experience outside the classroom setting and to work closely with individual members of the faculty. A final report must be submitted to the department. Not open to first-year or graduate students. Consult adviser. Hours and credit to be arranged.

Credit variable, maximum 3 units.

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**E35 ESE 401 Fundamentals of Engineering Review**

A review and preparation of the most recent NCEES Fundamentals of Engineering (FE) Exam specifications is offered in a classroom setting. Exam strategies will be illustrated using examples. The main topics for the review include engineering mathematics, statics, dynamics, thermodynamics, heat transfer, mechanical design and analysis, material science, and engineering economics. A discussion of the importance and responsibilities of professional engineering licensure along with ethics will be included.

Same as E37 MEMS 4001

Credit 1 unit.

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**E35 ESE 403 Operations Research**

Introduction to the mathematical aspects of various areas of operations research, with additional emphasis on problem formulation. This is a course of broad scope, emphasizing both the fundamental mathematical concepts involved, and also aspects of the translation of real-world problems to an appropriate mathematical model. Subjects to be covered include linear and integer programming, network problems, and dynamic programming. Prerequisites: CSE 131, Math 309, and ESE 326, or permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 404 Applied Operations Research**

Application of operations research techniques to real-world problems. Emphasis is given to integer linear programming and computational methods. Real-world examples of integer programs will be studied in areas such as network flow, facility location, partitioning, matching, and transportation. Special emphasis will be placed on techniques used to solve integer programs. Prerequisites: ESE 403 and CSE 131.

Credit 3 units. EN: BME T, TU

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**E35 ESE 405 Reliability and Quality Control**

An integrated analysis of reliability and quality control function in manufacturing. Statistical process control, acceptance sampling, process capability analysis, reliability prediction, design, testing, failure analysis and prevention, maintainability, availability, and safety are discussed and related. Qualitative and quantitative aspects of statistical quality control and reliability are introduced in the context of manufacturing. Prerequisite: ESE 326 or equivalent.

Credit 3 units. EN: BME T, TU

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**E35 ESE 415 Optimization**

This course gives a rigorous and comprehensive introduction of fundamentals of nonlinear optimization theory and computational methods. Topics include unconstrained and constrained optimization, quadratic and convex optimization, numerical optimization methods, optimality conditions, and duality theory. Algorithmic methods include Steepest Descent, Newton's method, Conjugate Gradient methods as well as exact and inexact line search procedures for unconstrained optimization. Constrained optimization methods include penalty and multiplier methods. Applications range from engineering and physics to economics. Moreover, generalized programming, interior point methods, and semi-definite programming will be discussed if time permits. Prerequisites: CSE 131, Math 309 and ESE 318 or permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 425 Random Processes and Kalman Filtering**

Probability 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; the discrete Kalman filter; linearization and the extended Kalman filter for nonlinear dynamic systems; related MATLAB exercises. Prerequisite: ESE 326 and ESE 351 or equivalent.

Credit 3 units. EN: BME T, TU

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**E35 ESE 427 Financial Mathematics**

This course is a self-contained introduction to financial mathematics at the undergraduate level. Topics to be covered include pricing of the financial instruments such as options, forwards, futures and their derivatives along with basic hedging techniques and portfolio optimization strategies. The emphasis is put on using of discrete, mostly binary models. The general, continuous case including the concepts of Brownian motion, stochastic integral, and stochastic differential equations, is explained from intuitive and practical point of view. Among major results discussed are the Arbitrage Theorem and Black-Scholes differential equations and their solutions. Prerequisites: ESE 318 and ESE 326 or the consent of the instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 429 Basic Principles of Quantum Optics and Quantum Information**

This course provides an accessible introduction to quantum optics and quantum engineering for undergraduate students. It will cover the following topics: concept of photons, quantum mechanics for quantum optics, radiative transitions in atoms, lasers, photon statistics (photon counting, sub-/super-Poissionian photon statistics, bunching, anti-bunching, theory of photodetection, shot noise), entanglement, squeezed light, atom-photon interactions, cold atoms, and atoms in cavities. The course will also provide an overview for quantum information processing, including quantum computing, quantum cryptography, and teleportation. Prerequisite: ESE 318 or equivalent.

Credit 3 units. EN: BME T, TU

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**E35 ESE 431 Introduction to Quantum Electronics**

Describing the flow of electrical current in nanodevices involves a lot more than just quantum mechanics; it requires an appreciation of some of the most advanced concepts of non-equilibrium statistical mechanics. In the past decades, electronic devices have been shrinking steadily to nanometer dimensions, and quantum transport has accordingly become increasingly important 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 to cover. The main goal of this course is to condense the essential concepts into a one-semester course that is accessible to both senior-level undergraduate and junior-level graduate students. The only background assumed for the students interested in taking this course is knowledge of simple differential equations and matrix algebra as well as familiarity with a modern scientific computing software package (e.g., MATLAB, Mathematica). This course will be accessible to students with diverse backgrounds in electrical engineering, physics, chemistry, biomedical engineering, and mathematics.

Credit 3 units. Art: NSM EN: TU

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**E35 ESE 433 Radio Frequency and Microwave Technology for Wireless Systems**

Focus is on the components and associated techniques employed 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 identification systems. A brief overview of system-level considerations is provided, including modulation and detection approaches for analog and digital systems; multiple-access techniques and wireless standards; and transceiver architectures. Focus is on RF and MW: transmission lines; filter design; active component modeling; matching and biasing networks; amplifier design; and mixer design. Prerequisite: ESE 330.

Credit 3 units. EN: BME T, TU

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**E35 ESE 434 Solid-State Power Circuits and Applications**

Study of the strategies and applications power control using solid-state semiconductor devices. Survey of generic power electronic converters. Applications to power supplies, motor drives and consumer electronics. Introduction to power diodes, thyristors and MOSFETs. Prerequisites: ESE 232, ESE 351.

Credit 3 units. EN: BME T, TU

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**E35 ESE 435 Electrical Energy Laboratory**

Experimental studies of principles important in modern electrical energy systems. Topics include: AC power measurements, electric lighting, photovoltaic cells and arrays, batteries, DC-DC and DC-AC converters, brushed and brushless DC motors and three-phase circuits. Each experiment requires analysis, simulation with MultiSim, and measurement via LabVIEW and the Elvis II platform. Prerequisites: ESE 230 and 351.

Credit 3 units. EN: BME T, TU

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**E35 ESE 436 Semiconductor Devices**

This course covers the fundamentals of semiconductor physics and operation principles of modern solid-state devices such as homo- or hetero-junction diodes, solar cells, inorganic/organic light-emitting diodes, bipolar junction transistors, and metal-oxide-semiconductor field-effect transistors. These devices form the basis for today's semiconductor and integrated circuit industry. In addition to device physics, semiconductor device fabrication processes, new materials, and novel device structures will also be briefly introduced. At the end of this course, students will be able to understand the characteristics, operation, limitations and challenges faced by state-of-the-art semiconductor devices. This course will be particularly useful for students who wish to develop careers in the semiconductor industry. Prerequisite: ESE 232.

Credit 3 units. EN: TU

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**E35 ESE 438 Applied Optics**

Topics relevant to the engineering and physics of conventional as well as experimental optical systems and applications explored. Items addressed include geometrical optics, Fourier optics such as diffraction and holography, polarization and optical birefringence such as liquid crystals, and nonlinear optical phenomena and devices. Prerequisite: ESE 330 or equivalent.

Credit 3 units. EN: BME T, TU

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**E35 ESE 439 Introduction to Quantum Communications**

This course covers the following topics: quantum optics, single-mode and two-mode quantum systems, nonlinear optics, and quantum systems theory. Specific topics include the following: Dirac notation quantum mechanics; harmonic oscillator quantization; number states, coherent states, and squeezed states; direct, homodyne, and heterodyne detection; linear propagation loss; phase insensitive and phase sensitive amplifiers; entanglement and teleportation; field quantization; quantum photodetection; phase-matched interactions; optical parametric amplifiers; generation of squeezed states, photon-twin beams, non-classical fourth-order interference, and polarization entanglement; optimum binary detection; quantum precision measurements; and quantum cryptography. Prerequisites: ESE 330 or Physics 421; Physics 217 or equivalent.

Credit 3 units. EN: TU

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**E35 ESE 441 Control Systems**

Introduction to the theory and practice of automatic control for dynamical systems. Dynamical systems as models for physical and observed phenomena. Mathematical representation of dynamical systems, such as state-space differential and difference equations, transfer functions, and block diagrams. Analysis of the time evolution of a system in response to control inputs, steady-state and transient responses, equilibrium points and their stability. Control via linear state feedback, and estimation using Leunberger observers. Relating the time response of a system to its frequency response, including Bode and Nyquist plots. Input-output stability and its relation to the stability of equilibrium points. Simple frequency-based controllers, such as PID and lead-lag compensators. Exercise involving the use of MATLAB/Simulink (or equivalent) to simulate and analyze systems. Prerequisites: CSE 131, and either ESE 351 or MEMS 431.

Credit 3 units. EN: BME T, TU

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**E35 ESE 444 Sensors and Actuators**

This course provide engineering students with basic understanding of two of the main components of any modern electrical or electromechanical system: sensors as inputs and actuators as outputs. The covered topics include transfer functions, frequency responses and feedback control; component matching and bandwidth issues; performance specification and analysis; sensors: analog and digital motion sensors, optical sensors, temperature sensors, magnetic and electromagnetic 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/A converters, and microcontrollers. This course is useful for those students interested in control engineering, robotics and systems 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 (Mechanics II); (3) prerequisite of ESE 351; or (4) permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 446 Robotics: Dynamics and Control**

Homogeneous coordinates and transformation matrices. Kinematic equations and the inverse kinematic solutions for manipulators, the manipulator Jacobian and the inverse Jacobian. General model for robot arm dynamics, complete dynamic coefficients for six-link manipulator. Synthesis of manipulation control, motion trajectories, control of single- and multiple-link manipulators, linear optimal regulator. Model reference adaptive control, feedback control law for the perturbation equations along a desired motion trajectory. Design of the control system for robotics. Prerequisites: ESE 351, knowledge of a programming language, and ESE 318; Corequisite: ESE 441.

Credit 3 units. EN: BME T, TU

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**E35 ESE 447 Robotics Laboratory**

Introduces the students to various concepts such as modeling, identification, model validation and control of robotic systems. The course focuses on the implementation of identification and control 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 of robotic systems; nonlinear model, linearized model; identification of the linearized model: input-output and state-space techniques; introduction to the identification of the nonlinear model: energy-based techniques; model validation and simulation; stabilization using linear control techniques; a closer look at the dynamics; stabilization using nonlinear control techniques. Prerequisite: ESE 351 or MEMS 431. Corequisites or Prerequisites: ESE 441 and 446.

Credit 3 units. EN: BME T, TU

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**E35 ESE 448 Systems Engineering Laboratory**

Experimental study of real and simulated systems and their control. Identification, input-output analysis, design and implementation of control systems. Noise effects. Design and implementation of control laws for specific engineering problems. Corequisite: ESE 441 and knowledge of a programming language, or permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 449 Digital Process Control Laboratory**

Applications of digital control principles to laboratory experiments supported by a networked distributed control system. Lecture material reviews background of real-time programming, data acquisition, process dynamics, and process control. Exercises in data acquisition and feedback control design using simple and advanced 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 424

Credit 3 units. EN: BME T, TU

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**E35 ESE 455 Quantitative Methods for Systems Biology**

Application of computational mathematical techniques to problems in contemporary biology. Systems of linear ordinary differential equations in reaction-diffusion systems, hidden Markov models applied to gene discovery in DNA sequence, ordinary differential equation and stochastic models applied to gene regulation networks, negative feedback in transcription and metabolic pathway regulation. Prerequisites: (1) Math 217 Differential Equations and (2) a programming course and familiarity with MATLAB.

Credit 3 units. EN: BME T, TU

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**E35 ESE 460 Switching Theory**

Advanced topics in switching theory as employed in the synthesis, analysis, and design of information processing systems. Combinational techniques: minimization, multiple output networks, state identification and fault detection, hazards, testability and design for test are examined. Sequential techniques: synchronous circuits, machine minimization, optimal state assignment, asynchronous circuits, and built-in self-test techniques. Prerequisite: CSE 260M.

Same as E81 CSE 460T

Credit 3 units. EN: BME T, TU

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**E35 ESE 461 Design Automation for Integrated Circuit Systems**

Integrated circuit systems provide the core technology that power today's most advanced devices and electronics: smart phones, wearable devices, autonomous robots, and cars, aerospace or medical electronics. These systems often consist of silicon microchips made up by billions of transistors and contain various components such as microprocessors, DSPs, hardware accelerators, memories, and I/O interfaces, therefore design automation is critical to tackle the design complexity at 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 description language (HDL) and design automation tools for very large scale integrated (VLSI) systems; 3) expose students to the design automation techniques used in the best-known academic and commercial systems. Topics covered include device and circuits for digital logic circuits, digital IC design flow, logic synthesis, physical design, circuit simulation and optimization, timing analysis, power delivery network analysis. Assignments include homework, mini-projects, term paper and group project. Prerequisites: ESE 232; ESE 260.

Credit 3 units. EN: BME T, TU

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**E35 ESE 462 Computer Systems Design**

Introduction to modern design practices, including FPGA and PCB design methodologies. Student teams use Xilinx Vivado for HDL-based FPGA design and simulation and do schematic capture, PCB layout, fabrication, and testing of the hardware portion of a selected computation system. The software portion of the project uses Microsoft Visual Studio to develop a user interface and any additional support software required to demonstrate final projects to the faculty during finals week. Prerequisites: CSE 361S and 362M from Washington University in St. Louis or permission of the instructor. Revised: 2019-02-22

Same as E81 CSE 462M

Credit 3 units. EN: TU

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**E35 ESE 465 Digital Systems Laboratory**

Hardware/software co-design; processor interfacing; procedures for reliable digital design, both combinational and sequential; understanding manufacturers' specifications; use of test equipment. Several single-period laboratory exercises, several design projects, and application of microprocessors in digital design. One lecture and one laboratory period a week. Prerequisites: ESE 260.

Credit 3 units. EN: BME T, TU

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**E35 ESE 471 Communications Theory and Systems**

Introduction to the concepts of transmission of information via communication channels. Amplitude and angle modulation for the transmission of continuous-time signals. Analog-to-digital conversion and pulse code modulation. Transmission of digital data. Introduction to random signals and noise and their effects on communication. Optimum detection systems in the presence of noise. Elementary information theory. Overview of various communication technologies such as radio, television, telephone networks, data communication, satellites, optical fiber and cellular radio. Prerequisites: ESE 351 and ESE 326.

Credit 3 units. EN: BME T, TU

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**E35 ESE 474 Introduction to Wireless Sensor Networks**

This is an introductory course on wireless sensor networks for senior undergraduate students. The course uses a combination of lecturing, reading, and discussion of research papers to help each student to understand the characteristics and operations of various wireless sensor networks. Topics covered include sensor network architecture, communication protocols on Medium Access Control and Routing, sensor network operation systems, sensor data aggregation and dissemination, localization and time synchronization, energy management, and target detection and tracking using acoustic sensor networks. Prerequisite: ESE 351 (Signals and Systems).

Credit 3 units. EN: BME T, TU

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**E35 ESE 482 Digital Signal Processing**

Introduction to analysis and synthesis of discrete-time linear time-invariant (LTI) systems. Discrete-time convolution, discrete-time Fourier transform, z-transform, rational function descriptions of discrete-time LTI systems. Sampling, analog-to-digital conversion and digital processing of analog signals. Techniques for the design of finite impulse response (FIR) and infinite impulse response (IIR) digital filters. Hardware implementation of digital filters and finite-register effects. The Discrete Fourier Transform and the Fast Fourier Transform (FFT) algorithms. Prerequisite: ESE 351.

Credit 3 units. EN: BME T, TU

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**E35 ESE 488 Signals and Communication Laboratory**

Laboratory exercises in digital signal processing, data conversion and communications using modern laboratory techniques and apparatus based on National Instruments LabVIEW and ELVIS II workstations. A laboratory course designed to complement the traditional ESE course offerings in signal processing and communication theory. Signals and systems fundamentals: continuous-time and discrete-time linear time-invariant systems, frequency response, oversampled and noise-shaped A/D conversion. Digital signal processing: FIR and IIR digital filter design, application of the Fast Fourier Transform. Communication theory: baseband, digital communication, amplitude modulation, phase modulation, bandpass digital communication. Laboratory experiments involve analog and digital electronics. Computer workstations and modern computational software used extensively for system simulation and real-time signal processing. Prerequisite: ESE 351.

Credit 3 units. EN: BME T, TU

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**E35 ESE 497 Undergraduate Research**

Undergraduate research under the supervision of a faculty member. The scope and depth of the research must be approved by the faculty member prior to enrollment. A written final report and a webpage describing the research are required.

Credit variable, maximum 3 units.

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**E35 ESE 498 Electrical Engineering Capstone Design Projects**

Capstone design project supervised by the course instructor. The project must use the theory, techniques, and concepts of the student's major: electrical engineering or systems science & engineering. The solution of a real technological or societal problem is carried through completely, starting from the stage of initial specification, proceeding with the application of engineering methods, and terminating with an actual solution. Collaboration with a client, typically either an engineer or supervisor from local industry or a professor or researcher in university laboratories, is encouraged. A proposal, an interim progress update, and a final report are required, each in the forms of a written document and oral presentation, as well as a webpage on the project. Weekly progress reports and meetings with the instructor are also required. Prerequisite: ESE senior standing and instructor's consent. Note: This course will meet at the scheduled time only during select weeks. If you cannot attend at that time, you may still register for the course.

Credit 3 units. EN: BME T, TU

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**E35 ESE 499 Systems Science and Engineering Capstone Design Project**

Capstone design project supervised by the course instructor. The project must use the theory, techniques, and concepts of the student's major: electrical engineering or systems science & engineering. The solution of a real technological or societal problem is carried through completely, starting from the stage of initial specification, proceeding with the application of engineering methods, and terminating with an actual solution. Collaboration with a client, typically either an engineer or supervisor from local industry or a professor or researcher in university laboratories, is encouraged. A proposal, an interim progress update, and a final report are required, each in the forms of a written document and oral presentation, as well as a webpage on the project. Weekly progress reports and meetings with the instructor are also required. Prerequisite: ESE senior standing and instructor's consent. Note: This course will meet at the scheduled time only during select weeks. If you cannot attend at that time, you may still register for the course.

Credit 3 units. EN: BME T, TU

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**E35 ESE 500 Independent Study**

Opportunities to acquire experience outside the classroom setting and to work closely with individual members of the faculty. A final report must be submitted to the department. Prerequisite: Students must have the ESE Research/Independent Study Registration Form (PDF) approved by the department.

Credit variable, maximum 3 units.

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**E35 ESE 501 Mathematics of Modern Engineering I**

Matrix algebra: systems of linear equations, vector spaces, linear independence and orthogonality in vector spaces, eigenvectors and eigenvalues; vector calculus: gradient, divergence, curl, line and surface integrals, theorems of Green, Stokes, and Gauss; Elements of Fourier analysis and its applications to solving some classical partial differential equations, heat, wave, and Laplace equation. Prerequisites: ESE 318 and ESE 319 or equivalent or consent of instructor. This course will not count toward the ESE doctoral program.

Credit 3 units. EN: BME T, TU

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**E35 ESE 502 Mathematics of Modern Engineering II**

Fourier series and Fourier integral transforms and their applications to solving some partial differential equations, heat and wave equations; complex analysis and its applications to solving real-valued problems: analytic functions and their role, Laurent series representation, complex-valued line integrals and their evaluation including the residual integration theory, conformal mappings and their applications. Prerequisite: ESE 318 and ESE 319 or equivalent, or consent of instructor. This course will not count toward the ESE doctoral program.

Credit 3 units. EN: BME T, TU

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**E35 ESE 513 Large-Scale Optimization for Data Science**

Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. This graduate-level course rigorously introduces optimization methods that are suitable for large-scale problems arising in these areas. Students will learn several algorithms suitable for both smooth and nonsmooth optimization, including gradient methods, proximal methods, mirror descent, Nesterov's acceleration, ADMM, quasi-Newton methods, stochastic optimization, variance reduction, and distributed optimization. Throughout the class, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Students will be required to program in Python or MATLAB. Prerequisites: CSE 247, Math 309, Math 3200 or ESE 326.

Same as E81 CSE 534A

Credit 3 units. EN: TU

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**E35 ESE 515 Nonlinear Optimization**

Nonlinear optimization problems with and without constraints and computational methods for solving them. Optimality conditions, Kuhn-Tucker conditions, Lagrange duality; gradient and Newton's methods; conjugate direction and quasi-Newton methods; primal and penalty methods; Lagrange methods. Use of MATLAB optimization techniques in numerical problems. Prerequisites: CSE 131, Math 309 and ESE 318 or permission of instructor.

Credit 3 units. EN: TU

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**E35 ESE 516 Optimization in Function Space**

Linear vector spaces, normed linear spaces, Lebesque integrals, the Lp spaces, linear operators, dual space, Hilbert spaces. Projection theorem, Hahn-Banach theorem. Hyperplanes and convex sets, Gateaux and Frächet differentials, unconstrained minima, adjoint operators, inverse function theorem. Constrained minima, equality constraints, Lagrange multipliers, calculus of variations, Euler-Lagrange equations, positive cones, inequality constraints. Kuhn-Tucker theorem, optimal control theory, Pontryagin's maximum principle, successive approximation methods, Newton's methods, steepest descent methods, primal-dual methods, penalty function methods, multiplier methods. Prerequisite: Math 4111.

Credit 3 units.

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**E35 ESE 517 Partial Differential Equations**

Linear and nonlinear first order equations. Characteristics. Classification of equations. Theory of the potential linear and nonlinear 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

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**E35 ESE 518 Optimization Methods in Control**

The course is divided in two parts: convex optimization and optimal control. In the first part we cover applications of Linear Matrix Inequalities and Semi-Definite Programming to control and estimation problems. We also cover Multiparametric Linear Programming and its application to the Model Predictive Control and Estimation of linear systems. In the second part we cover numerical methods to solve optimal control and estimation problems. We cover techniques to discretize optimal control problems, numerical methods to solve them, and their optimality conditions. We apply these results to the Model Predictive Control and Estimation of nonlinear systems. Prerequisites: ESE 551, and ESE 415 or equivalent.

Credit 3 units. EN: TU

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**E35 ESE 519 Convex Optimization**

Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. Prerequisites: Math 309 and ESE 415.

Credit 3 units.

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**E35 ESE 520 Probability and Stochastic Processes**

Review of probability theory; models for random signals and noise; calculus of random processes; noise in linear and nonlinear systems; representation of random signals by sampling and orthonormal expansions; Poisson, Gaussian, and Markov processes as models for engineering problems. Prerequisite: ESE 326.

Credit 3 units. EN: BME T, TU

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**E35 ESE 523 Information Theory**

Discrete source and channel model, definition of information rate and channel capacity, coding theorems for sources and channels, encoding and decoding of data for transmission over noisy channels. Corequisite: ESE 520.

Credit 3 units. EN: BME T, TU

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**E35 ESE 524 Detection and Estimation Theory**

Study of detection and estimation of signals in noise. Linear algebra, vector spaces, independence, projections. Data independence, factorization theorem and sufficient statistics. Neyman-Pearson and Bayes detection. Least squares, maximum-likelihood and maximum a posteriori estimation of signal parameters. Conjugate priors, recursive estimation, Wiener and Kalman filters. Prerequisite: ESE 520.

Credit 3 units. EN: BME T, TU

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**E35 ESE 526 Network Science**

This course focuses on fundamental theory, modeling, structure, and analysis methods in network science. The first part of the course includes basic network models and their mathematical principles. Topics include a review of graph theory, random graph models, scale-free network models and dynamic networks. The second part of the course includes structure and analysis methods in network science. Topics include network robustness, community structure, spreading phenomena and clique topology. Applications of the topics covered by this course include social networks, power grid, internet, communications, protein-protein interactions, epidemic control, global trade, neuroscience, etc. Prerequisites: ESE 520 (Probability and Stochastic Processes), Math 429 (Linear Algebra) or equivalent.

Credit 3 units.

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**E35 ESE 531 Nano and Micro Photonics**

This course focuses on fundamental theory, design, and applications of photonic materials and micro/nano photonic devices. It includes review and discussion of light-matter interactions in nano and micro scales, propagation of light in waveguides, nonlinear optical effect and optical properties of nano/micro structures, the device principles of waveguides, filters, photodetectors, modulators and lasers. Prerequisite: ESE 330.

Credit 3 units. EN: BME T, TU

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**E35 ESE 532 Introduction to Nano-Photonic Devices**

Introduction to photon transport in nano-photonic devices. This course focuses on the following topics: light and photons, statistical properties of photon sources, temporal and spatial correlations, light-matter interactions, optical nonlinearity, atoms and quantum dots, single- and two-photon devices, optical devices, and applications of nano-photonic devices in quantum and classical computing and communication. Prerequisites: ESE 330 and Physics 217, or permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 536 Introduction to Quantum Optics**

This course covers the following topics: quantum mechanics for quantum optics, radiative transitions in atoms, lasers, photon statistics (photon counting, sub-/super-Poissionian photon statistics, bunching, anti-bunching, theory of photodetection, shot noise), entanglement, squeezed light, atom-photon interactions, cold atoms, abd atoms in cavities. If time permits, the following topics will be selectively covered: quantum computing, quantum cryptography, and teleportation. Prerequisites: ESE 330 and Physics 217 or Physics 421

Credit 3 units. EN: BME T, TU

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**E35 ESE 538 Advanced Electromagnetic Engineering**

The course builds on undergraduate electromagnetics to systematically develop advanced concepts in electromagnetic theory for engineering applications. The following topics are covered: Maxwell's equations; fields and waves in materials; electromagnetic potentials and topics for circuits and systems; transmission-line essentials for digital electronics and for communications; guided wave principles for electronics and optoelectronics; principles of radiation and antennas; and numerical methods for computational electromagnetics.

Credit 3 units.

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**E35 ESE 543 Control Systems Design by State Space Methods**

Advanced design and analysis of control systems by state-space methods: classical control review, Laplace transforms, review of linear algebra (vector space, change of basis, diagonal and Jordan forms), linear dynamic systems (modes, stability, controllability, state feedback, observability, observers, canonical forms, 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) packages for engineering problems. Prerequisite: ESE 351 and ESE 441, or permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 544 Optimization and Optimal Control**

Constrained and unconstrained optimization theory. Continuous time as well as discrete-time optimal control theory. Time-optimal control, bang-bang controls and the structure of the reachable set for linear problems. Dynamic programming, the Pontryagin maximum principle, the Hamiltonian-Jacobi-Bellman equation and the Riccati partial differential equation. Existence of classical and viscosity solutions. Application to time optimal control, regulator problems, calculus of variations, optimal filtering and specific problems of engineering interest. Prerequisites: ESE 551, ESE 552.

Credit 3 units. EN: BME T, TU

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**E35 ESE 545 Stochastic Control**

Introduction to the theory of stochastic differential equations based on Wiener processes and Poisson counters, and an introduction to random fields. The formulation and solution of problems in nonlinear estimation theory. The Kalman-Bucy filter and nonlinear analogues. Identification theory. Adaptive systems. Applications. Prerequisites: ESE 520 and ESE 551.

Credit 3 units. EN: BME T, TU

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**E35 ESE 546 Dynamics & Control in Neuroscience & Brain Medicine**

This course provides an introduction to systems engineering approaches to modeling, analysis and control of neuronal dynamics at multiple scales. A central motivation is the manipulation of neuronal activity for both scientific and medical applications using emerging neurotechnology and pharmacology. Emphasis is placed on dynamical systems and control theory, including bifurcation and stability analysis of single neuron models and population mean-field models. Synchronization properties of neuronal networks are covered, and methods for control of neuronal activity in both oscillatory and non-oscillatory dynamical regimes are developed. Statistical models for neuronal activity are also discussed. An overview of signal processing and data analysis methods for neuronal recording modalities is provided toward the development of closed-loop neuronal control paradigms. The final evaluation is based on a project or research survey. Prerequisites: ESE 553 (or equivalent); ESE 520 (or equivalent); ESE 351 (or equivalent).

Credit 3 units. EN: BME T, TU

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**E35 ESE 547 Robust and Adaptive Control**

Graduate-level control system design methods for multi-input multi-output systems. Linear optimal-based methods in robust control, nonlinear model reference adaptive control. These design methods are currently used in most industry control system design problems. These methods are designed, analyzed and simulated using MATLAB. Linear control theory (review), robustness theory (Mu Analysis), optimal control and the robust servomechanism, H-infinity optimal control, robust output feedback controls, Kalman filter theory and design, linear quadratic gaussian with loop transfer recovery, the Loop Transfer Recovery method of Lavretsky, Mu synthesis, Lyapunov theory (review), LaSalle extensions, Barbalat's Lemma, model reference adaptive control, artificial neural networks, online parameter estimation, convergence and persistence of excitation. Prerequisite: ESE 543 or ESE 551 or equivalent.

Credit 3 units. EN: BME T, TU

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**E35 ESE 551 Linear Dynamic Systems I**

Input-output and state-space description of linear dynamic systems. Solution of the state equations and the transition matrix. Controllability, observability, realizations, pole-assignment, observers and decoupling of linear dynamic systems. Prerequisite: ESE 351.

Credit 3 units. EN: BME T, TU

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**E35 ESE 552 Linear Dynamic Systems II**

Least squares optimization problems. Riccati equation, terminal regulator and steady-state regulator. Introduction to filtering and stochastic control. Advanced theory of linear dynamic systems. Geometric approach to the structural synthesis of linear multivariable control systems. Disturbance decoupling, system invertibility and decoupling, extended decoupling and the internal model principle. Prerequisite: ESE 551.

Credit 3 units. EN: TU

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**E35 ESE 553 Nonlinear Dynamic Systems**

State space and functional analysis approaches to nonlinear systems. Questions of existence, uniqueness and stability; Lyapunov and frequency-domain criteria; w-limits and invariance, center manifold theory and applications to stability, steady-state response and singular perturbations. Poincare-Bendixson theory, the van der Pol oscillator, and the Hopf Bifurcation theorem. Prerequisite: ESE 551.

Credit 3 units. EN: TU

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**E35 ESE 554 Advanced Nonlinear Dynamic Systems**

Differentiable manifolds, vector fields, distributions on a manifold, Frobenius' theorem, Lie algebras. Controllability, observability of nonlinear systems, examined from the viewpoint of differential geometry. Transformation to normal forms. Exact linearization via feedback. Zero dynamics and related properties. Noninteracting control and disturbance decoupling. Controlled invariant distributions. Noninteracting control with internal stability. Prerequisites: ESE 553 and ESE 551.

Credit 3 units.

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**E35 ESE 557 Hybrid Dynamic Systems**

Theory and analysis of hybrid dynamic systems, which is the class of systems whose state is composed by continuous-valued and discrete-valued variables. Discrete-event systems models and language descriptions. Models for hybrid systems. Conditions for existence and uniqueness. Stability and verification of hybrid systems. Optimal control of hybrid systems. Applications to cyber-physical systems and robotics. Prerequisite: ESE 551.

Credit 3 units. EN: BME T, TU

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**E35 ESE 560 Computer Systems Architecture I**

An exploration of the central issues in computer architecture: instruction set design, addressing and register set design, control unit design, microprogramming, memory hierarchies (cache and main memories, mass storage, virtual memory), pipelining, and bus organization. The course emphasizes understanding the performance implications of design choices, using architecture modeling and evaluation using VHDL and/or instruction set simulation. Prerequisites: CSE 361S and CSE 260M.

Same as E81 CSE 560M

Credit 3 units. EN: BME T, TU

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**E35 ESE 562 Analog Integrated Circuits**

This course focuses on fundamental and advanced topics in analog and mixed-signal VLSI techniques. The first part of the course covers graduate-level materials in the area of analog circuit synthesis and analysis. The second part of the course covers applications of the fundamental techniques for designing analog signal processors and data converters. Several practical aspects of mixed-signal design, simulation and testing are covered in this course. This is a project-oriented course, and it is expected that the students apply the concepts learned in the course to design, simulate and explore different circuit topologies. Prerequisites: CSE 260 and ESE 232.

Credit 3 units.

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**E35 ESE 566A Modern System-on-Chip Design**

The System-on-Chip (SoC) technology is at the core of most electronic systems: smartphones, wearable devices, autonomous robots and cars, and aerospace and medical electronics. In these SoCs, billions of transistors can be integrated on a single silicon chip containing various components, such as microprocessors, DSPs, hardware accelerators, memories, and I/O interfaces. Topics include SoC architectures, design tools, and methods as well as system-level tradeoffs between performance, power consumption, energy efficiency, reliability, and programmability. Students will gain an insight into the early stages of the SoC design process by performing the tasks of developing functional specifications, applying partitions and map functions to hardware and/or software, and then evaluating and validating system performance. Assignments include hands-on design projects. Open to both graduate and senior undergraduate students. Prerequisite: ESE 461.

Credit 3 units. EN: BME T, TU

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**E35 ESE 567 Computer Systems Analysis**

A comprehensive course on performance analysis techniques. The topics include common mistakes, selection of techniques and metrics, summarizing measured data, comparing systems using random data, simple linear regression models, other regression models, experimental designs, 2**k experimental designs, factorial designs with replication, fractional factorial designs, one factor experiments, two factor full factorial design w/o replications, two factor full factorial designs with replications, general full factorial designs, introduction to queueing theory, analysis of single queues, queueing networks, operational laws, mean-value analysis, time series analysis, heavy tailed distributions, self-similar processes, long-range dependence, random number generation, analysis of simulation results, and art of data presentation. Prerequisites: CSE 260M.

Same as E81 CSE 567M

Credit 3 units. EN: BME T, TU

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**E35 ESE 570 Coding Theory**

Introduction to the algebra of finite fields. Linear block codes, cyclic codes, BCH and related codes for error detection and correction. Encoder and decoder circuits and algorithms. Spectral descriptions of codes and decoding algorithms. Code performances.

Credit 3 units. EN: TU

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**E35 ESE 571 Transmission Systems and Multiplexing**

Transmission and multiplexing systems are essential to providing efficient point-to-point communication over distance. This course introduces the principles underlying modern analog and digital transmission and multiplexing systems and covers a variety of system examples.

Credit 3 units. EN: TU

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**E35 ESE 572 Signaling and Control in Communication Networks**

The operation of modern communications networks is highly dependent on sophisticated control mechanisms that direct the flow of information through the network and oversee the allocation of resources to meet the communication demands of end users. This course covers the structure and operation of modern signaling systems and addresses the major design trade-offs that center on the competing demands of performance and service flexibility. Specific topics covered include protocols and algorithms for connection establishment and transformation, routing algorithms, overload and failure recovery and networking dimensioning. Case studies provide concrete examples and reveal the key design issues. Prerequisites: graduate standing and permission of instructor.

Credit 3 units. EN: BME T, TU

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**E35 ESE 575 Fiber-Optic Communications**

Introduction to optical communications via glass-fiber media. Pulse-code modulation and digital transmission methods, coding laws, receivers, bit-error rates. Types and properties of optical fibers; attenuation, dispersion, modes, numerical aperture. Light-emitting diodes and semiconductor laser sources; device structure, speed, brightness, modes, electrical properties, optical and spectral characteristics. Prerequisites: ESE 330, ESE 336.

Credit 3 units. EN: BME T, TU

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**E35 ESE 582 Fundamentals and Applications of Modern Optical Imaging**

Analysis, design, and application of modern optical imaging systems with emphasis on biological imaging. The first part of the course will focus on the physical principles underlying the operation of imaging systems and their mathematical models. Topics include ray optics (speed of light, refractive index, 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 compare modern quantitative imaging technologies, including but not limited to digital holography, computational imaging, and super-resolution microscopy. Students will evaluate and critique recent optical imaging literature. Prerequisites: ESE 318 and ESE 319 (or their equivalents); ESE 330 or PHY 421 (or equivalent).

Credit 3 units. EN: BME T, TU

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**E35 ESE 584 Statistical Signal Processing for Sensor Arrays**

Methods for signal processing and statistical inference for data acquired by an array of sensors, such as those found in radar, sonar and wireless communications systems. Multivariate statistical theory with emphasis on the complex multivariate normal distribution. Signal estimation and detection in noise with known statistics, signal estimation and detection in noise with unknown statistics, direction finding, spatial spectrum estimation, beam forming, parametric maximum-likelihood techniques. Subspace techniques, including MUSIC and ESPRIT. Performance analysis of various algorithms. Advanced topics may include structured covariance estimation, wide-band array processing, array calibration, array processing with polarization diversity, and space-time adaptive processing (STAP). Prerequisites: ESE 520, ESE 524, linear algebra, computer programming.

Credit 3 units. EN: TU

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**E35 ESE 585A Sparse Modeling for Imaging and Vision**

Sparse modeling is at the heart of modern imaging, vision, and machine learning. It is a fascinating new area of research that seeks to develop highly effective data models. The core idea in sparse modeling theory is a novel redundant transform, where the number of transform coefficients is larger compared to the original data dimension. Together with redundancy comes an opportunity of seeking the sparsest possible representation, or the one with the fewest non-zeros. This core idea leads to a series of beautiful theoretical and practical results with many applications such as regression, prediction, restoration, extrapolation, compression, detection, and recognition. In this course, we will explore sparse modeling by covering theoretical as well as algorithmic aspects with applications in computational imaging and computer vision. Prerequisites: ESE 318, Math 233, Math 309, and Math 429, or equivalents. Coding with MATLAB or Python.

Credit 3 units. EN: BME T, TU

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**E35 ESE 588 Quantitative Image Processing**

Introduction to modeling, processing, manipulation and display of images. Application of two-dimensional linear systems to image processing. 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 studies in image processing (examples: computer tomography and ultrasonic imaging). Prerequisites: ESE 326, ESE 482.

Credit 3 units. EN: BME T, TU

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**E35 ESE 589 Biological Imaging Technology**

This class develops a fundamental understanding of the physics and mathematical methods that underlie biological imaging and critically examine case studies of seminal biological imaging technology literature. The physics section examines how electromagnetic and acoustic waves interact with tissues and cells, how waves can be used to image the biological structure and function, image formation methods, and diffraction limited imaging. The math section examines image decomposition using basis functions (e.g., Fourier transforms), synthesis of measurement data, image analysis for feature extraction, reduction of multidimensional imaging datasets, multivariate regression, and statistical image analysis. Original literature on electron, confocal and two photon microscopy, ultrasound, computed tomography, functional and structural magnetic resonance imaging and other emerging imaging technology are critiqued.

Credit 3 units. EN: BME T, TU

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**E35 ESE 590 Electrical & Systems Engineering Graduate Seminar**

This satisfactory/unsatisfactory course is required for the Masters, DSc and PhD degrees in Electrical and Systems Engineering. A satisfactory grade is required for each semester of enrollment and is received by attendance at regularly scheduled ESE seminars. Masters students must attend at least 3 seminars per semester, except for first year Master's students who must attend 4. DSc and PhD students must attend at least 5 seminars per semester, except for first year PhD students who must attend 6. Part-time students are exempt except during their year of residency. Any student under continuing status is also exempt.

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**E35 ESE 591 Biomedical Optics I: Principles**

This course covers the principles of optical photon transport in biological tissue. This course covers the principles and applications 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 transfer equation, diffusion theory and applications, sensing of optical properties and spectroscopy, and photoacoustic imaging principles and applications. Prerequisite: Differential equations

Same as E62 BME 591

Credit 3 units. EN: TU

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**E35 ESE 5932 Computational Methods for Imaging Science**

Inverse problems are ubiquitous in science and engineering, and they form the basis for modern imaging methods. This course will introduce students to the mathematical formulation of inverse problems and modern computational methods employed to solve them. Specific topics covered will include regularization theory, compressive sampling, variational calculus, and a survey of relevant numerical optimization methods. The application of these methods to tomographic imaging problems will be addressed in detail. Prerequisite: ESE 5931 or permission of instructor.

Credit 3 units. EN: TU

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**E35 ESE 5933 Theoretical Imaging Science**

Imaging science encompasses the design and optimization of imaging systems to quantitatively measure information of interest. Imaging systems are important in many scientific and medical applications and may be designed for one specific application or for a range of applications. Performance is quantified for any given task through an understanding of the statistical model for the imaging data, the data processing algorithm used, and a measure of accuracy or error. Optimal processing is based on statistical decision theory and estimation theory; performance bounds include the receiver operating characteristic and Cramer-Rao bounds. Bayesian methods often lead to ideal observers. Extensions of methods from finite-dimensional spaces to function space are fundamental for many imaging applications. A variety of methods to assess image quality and resulting imaging system optimization are covered. Prerequisite: permission of instructor.

Credit 3 units. EN: TU

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**E35 ESE 596 Seminar in Imaging Science and Engineering**

This seminar course consists of a series of tutorial lectures on Imaging Science and Engineering with emphasis on applications of imaging technology. Students are exposed to a variety of imaging applications that vary depending on the semester, but may include multispectral remote sensing, astronomical imaging, microscopic imaging, ultrasound imaging and tomographic imaging. Guest lecturers come from several parts of the university. This course is required of all students in the Imaging Science and Engineering program; the only requirement is attendance. This course is graded pass/fail. Prerequisite: admission to Imaging Science and Engineering program. Same as CSE 596 (when offered) and BME 506.

Credit 1 unit.

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**E35 ESE 599 Master's Research**

Prerequisite: Students must have the ESE Research/Independent Study Registration Form (PDF) approved by the department.

Credit variable, maximum 3 units.

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