Electrical and Computer Engineering Programs at Johns Hopkins University, Lecture notes of Electromagnetism and Electromagnetic Fields Theory

Download Electrical and Computer Engineering Programs at Johns Hopkins University and more Lecture notes Electromagnetism and Electromagnetic Fields Theory in PDF only on Docsity! Electrical and Computer Engineering 1 ELECTRICAL AND COMPUTER ENGINEERING The part-time Electrical and Computer Engineering program’s strength lies in its faculty, who are drawn from the Applied Physics Laboratory, from government and local industry, and from the full-time Department of Electrical & Computer Engineering. Their active involvement in applied research and development helps to foster students’ understanding of the theory and practice of the discipline. Students study the fundamentals of electrical and computer engineering, as well as more specific aspects of current technologies based on a variety of technical groupings of courses. Courses are offered at the Applied Physics Laboratory and online. Program Committee Ashutosh Dutta, Program Chair Senior Professional Staff JHU Applied Physics Laboratory Cleon Davis, Program Vice Chair Senior Professional Staff JHU Applied Physics Laboratory Ramsey Hourani, Program Manager Senior Professional Staff JHU Applied Physics Laboratory Pedro Irazoqui Department Head, Electrical and Computer Engineering JHU Whiting School of Engineering James J. Costabile CEO Syncopated Engineering Jeffrey G. Houser Electronics Engineer US Army Research Laboratory Daniel G. Jablonski Principal Professional Staff JHU Applied Physics Laboratory Ralph Etienne-Cummings Professor, Electrical and Computer Engineering JHU Whiting School of Engineering John E. Penn Electronics Engineer US Army Research Laboratory Raymond M. Sova Principal Professional Staff JHU Applied Physics Laboratory Douglas S. Wenstrand Principal Professional Staff JHU Applied Physics Laboratory Programs • Electrical and Computer Engineering, Graduate Certificate (https://e- catalogue.jhu.edu/engineering/engineering-professionals/electrical- computer-engineering/electrical-computer-engineering-graduate- certificate/) • Electrical and Computer Engineering, Master of Science (https://e- catalogue.jhu.edu/engineering/engineering-professionals/electrical- computer-engineering/electrical-computer-engineering-master- science/) • Electrical and Computer Engineering, Post-Master’s Certificate (https://e-catalogue.jhu.edu/engineering/engineering-professionals/ electrical-computer-engineering/electrical-computer-engineering- post-masters-certificate/) Courses EN.525.201.  Circuits, Devices and Fields.  3 Credits.   This course is intended to prepare students lacking an appropriate background for graduate study in electrical and computer engineering. Fundamental mathematical concepts including calculus, differential equations, and linear algebra are reviewed. Circuit theory for linear and nonlinear devices and components is covered. An introduction to electricity and magnetism is presented along with basic wave propagation theory. Finally, Boolean algebra is studied with applications to digital circuit design and analysis. Prerequisite(s): Two or more semesters of calculus, differential equations, and at least two semesters of calculus-based physics. Course Note(s): Not for graduate credit. EN.525.202.  Signals and Systems.  3 Credits.   This course is intended to prepare students lacking an appropriate background for graduate study in electrical and computer engineering. Signal and system representations and analysis tools in both continuous time and discrete time are covered. Linear time-invariant systems are defined and analyzed. The Fourier transform, the Laplace transform, and the z-transform are treated along with the sampling theorem. Finally, fundamental concepts in probability, statistics, and random processes are considered. Prerequisite(s): Two or more semesters of calculus and differential equations. Course Note(s): Not for graduate credit. EN.525.603.  Advanced Topics in Optical Medical Imaging.  3 Credits.   The course will review the recent advances in photonics technologies for medical imaging and sensing. The course is designed for graduate students with a back ground in optics and engineering. The main topics for the course are: Light Source and Devices for Biomedical Imaging; Fluorescence, Raman, Rayleigh Scatterings; Optical Endoscopy and Virtual biopsy; Novel imaging contrast dyes, nanoparticles, and optical clearing reagents; Label-free optical technologies in clinical applications; Neurophotonics and Optogenetics. EN.525.605.  Intermediate Electromagnetics.  3 Credits.   This course provides a background in engineering electromagnetics required for more advanced courses in the field. Topics include vector calculus, Poisson’s and Laplace’s equations, Vector potentials, Green’s functions, magnetostatics, magnetic and dielectric materials, Maxwell’s equations, plane wave propagation and polarization, reflection and refraction at a plane boundary, frequency-dependent susceptibility functions, transmission lines, waveguides, and simple antennas. Practical examples are used throughout the course. 2 Electrical and Computer Engineering EN.525.606.  Electronic Materials.  3 Credits.   Materials and the interfaces between them are the key elements in determining the functioning of electronic devices and systems. This course develops the fundamental parameters of the basic solid material types and their relationships to electrical, thermal, mechanical, and optical properties. The application of these materials to the design and fabrication of electronic components is described, including integrated circuits, passive components, and electronic boards, modules, and systems. Prerequisite(s): An undergraduate degree in engineering, physics, or materials science; familiarity with materials structures and electronic devices. EN.525.607.  Intro to Electronic Packaging.  3 Credits.   Topics include fundamentals of electronic packaging engineering and basic concepts in thermal, mechanical, electrical, and environmental management of modern electronic systems. Emphasis is on high- frequency (and high-speed) package performance and its achievement through the use of advanced analytical tools, proper materials selection, and efficient computer-aided design. Packaging topics include die and lead attachment, substrates, hybrids, surface-mount technology, chip and board environmental protection, connectors, harnesses, and printed and embedded wiring boards. Prerequisite(s): An undergraduate degree in a scientific or engineering area, including familiarity with computer- aided design and engineering analysis methods for electronic circuits and systems. EN.525.608.  Next Generation Telecommunications.  3 Credits.   This course examines voice, data, and video communications through emerging technologies. Considerations include the characteristics and security requirements of the information being encoded, bandwidth requirements and limitations, and transmission standards and equipment. Topics will consider the pragmatics facing the communications system engineer including space, weight, and power. The student will review past and present network architectures and apply trade-off decisions when analyzing new system requirements. Topics include brief histories of telecommunications, speech processing, encoding, digitization, signaling, and transmission; broadband, fiber optics, and wireless network architectures; and encryption, privacy, and security issues. New and disruptive technologies are discussed each offering. Prerequisite(s): Either an undergraduate degree in electrical engineering or 525.616 Communications Systems Engineering, or consent of the instructor. EN.525.609.  Continuous Control Systems.  3 Credits.   This course examines classical methods of analysis and design of continuous control systems. Topics include system representation by linear time invariant ordinary differential equations, performance measures, sensitivity, stability, root locus, frequency domain techniques, and design methods. Several practical examples are considered. MATLAB is used as a computational tool. Prerequisite(s): Background in linear algebra and linear differential equations. EN.525.610.  Microprocessors for Robotic Systems.  3 Credits.   This course examines microprocessors as an integral part of robotic systems. Techniques required for successful incorporation of embedded microprocessor technology are studied and applied to robotic systems. Students will use hardware in a laboratory setting and will develop software that uses features of the microprocessor at a low level to accomplish the real-time performance necessary in robotic applications. Topics will include microprocessor selection, real-time constraints, sensor interfacing, actuator control, and system design considerations. Prerequisite(s): Experience with C programming and a course in digital systems or computer architecture. EN.525.612.  Computer Architecture.  3 Credits.   This course focuses on digital hardware design for all major components of a modern, reduced-instructionset computer. Topics covered include instruction set architecture; addressing modes; register-transfer notation; control circuitry; pipelining with hazard control; circuits to support interrupts and other exceptions; microprogramming; computer addition and subtraction circuits using unsigned, two’s-complement, and excess notation; circuits to support multiplication using Robertson’s and Booth’s algorithms; circuits for implementing restoring and non-restoring division; squareroot circuits; floating-point arithmetic notation and circuits; memory and cache memory systems; segmentation and paging; input/ output interfaces; interrupt processing; direct memory access; and several common peripheral devices, including analog-to-digital and digital-to-analog converters. A mini-project is required. Prerequisite(s): EN.525.642 FPGA Design using VHDL or prior knowledge of a hardware description language for FPGA design EN.525.613.  Fourier Techniques in Optics.  3 Credits.   In this course, the study of optics is presented from a perspective that uses the electrical engineer’s background in Fourier analysis and linear systems theory. Topics include scalar diffraction theory, Fourier transforming and imaging properties of lenses, spatial frequency analysis of optical systems, spatial filtering and information processing, and holography. The class discusses applications of these concepts in non- destructive evaluation of materials and structures, remote sensing, and medical imaging. Prerequisite(s): An undergraduate background in Fourier analysis and linear systems theory. EN.525.614.  Probability & Stochastic Processes for Engineers.  3 Credits.   This course provides a foundation in the theory and applications of probability and stochastic processes and an understanding of the mathematical techniques relating to random processes in the areas of signal processing, detection, estimation, and communication. Topics include the axioms of probability, random variables, and distribution functions; functions and sequences of random variables; stochastic processes; and representations of random processes. Prerequisite(s): A working knowledge of multi-variable calculus, Fourier transforms, and linear systems theory. EN.525.615.  Embedded Microprocessor Systems.  3 Credits.   This course applies microprocessors as an integral element of system design. Techniques required for successful incorporation of microprocessor technology are studied and used. Hardware and software design considerations that affect product reliability, performance, and flexibility are covered. Students use hardware to gain familiarity with machine and assembly language for software generation, interfacing to a microprocessor at the hardware level, and emulation to check out system performance. Topics include security in embedded systems, case studies in system failures, embedded processors in the space environment, communications protocols, hardware/ software system tradeoffs, and SoC/FPGA designs. The course is based on the ARM architecture, and the student will do a series of development and interfacing labs. Prerequisite(s): Some experience in designing and building digital electronic systems, some familiarity with C programming, and a course in digital systems. Electrical and Computer Engineering 5 EN.525.637.  Foundations of Reinforcement Learning.  3 Credits.   The course will provide a rigorous treatment of reinforcement learning by building on the mathematical foundations laid by optimal control, dynamic programming, and machine learning. Topics include model- based methods such as deterministic and stochastic dynamic programming, LQR and LQG control, as well as model-free methods that are broadly identified as Reinforcement Learning. In particular, we will cover on and off-policy tabular methods such as Monte Carlo, Temporal Differences, n-step bootstrapping, as well as approximate solution methods, including on- and off-policy approximation, policy gradient methods, including Deep Q-Learning. The course has a final project where students are expected to formulate and solve a problem based on the techniques learned in class. EN.525.638.  Introduction to Wireless Technology.  3 Credits.   This course introduces students to the modern technology involved with commercial wireless communications systems such as digital cellular 3G, 4G, 5G, wireless local area networks(WLAN) and other communication systems. Various multiple access methods and signal formats are considered and analyzed in detail. Hardware, software and signal processing implementations of system components are presented and analyzed using Matlab in a software based lab environment. Modulation and demodulation architectures are introduced and modeled using computer-based tools. The adaptive signal processing systems at the heart of modern digital wireless systems are a significant and unique part of this course. Prerequisite(s): An undergraduate degree in electrical engineering or the equivalent. Experience with MATLAB will be helpful but is not required. EN.525.640.  Satellite Communications Systems.  3 Credits.   This course presents the fundamentals of satellite communications link design and an in-depth treatment of practical considerations. Existing commercial, civil, and military systems are described and analyzed, including direct broadcast satellites, high throughput satellites, VSAT links, and Earth-orbiting and deep space spacecraft. Topics include satellite orbits, link analysis, antenna and payload design, interference and propagation effects, modulation techniques, coding, multiple access, and Earth station design. The impact of new technology on future systems in this dynamic field is discussed. EN.525.641.  Computer and Data Communication Networks I.  3 Credits.   This course provides a comprehensive overview of computer and data communication networks, with emphasis on analysis and modeling. Basic communications principles are reviewed as they pertain to communication networks. Networking principles covered include layered network architecture, data encoding, static and multiaccess channel allocation methods (for LAN and WAN), ARQ retransmission strategies, framing, routing strategies, transport protocols, and emerging highspeed networks. Prerequisite(s): EN.525.614 Probability and Stochastic Processes for Engineers and EN.525.616 Communication Systems Engineering, or equivalents. EN.525.642.  FPGA Design Using VHDL.  3 Credits.   This lab-oriented course covers the design of digital systems using VHSIC Hardware Description Language (VHDL) and its implementation in Field Programmable Gate Arrays (FPGAs). This technology allows cost-effective unique system realizations by enabling design reuse and simplifying custom circuit design. The design tools are first introduced and used to implement basic circuits. More advanced designs follow, focusing on integrating the FPGA with external peripherals, simple signal processing applications, utilizing soft-core processors, and using intellectual property (IP) cores. Prerequisite(s): A solid understanding of digital logic fundamentals. EN.525.643.  Real Time Computer Vision.  3 Credits.   This course introduces students to key computer vision techniques for real-time applications. Students will learn to quickly build applications that enable computers to “see,” and make decisions based on still images or video streams. Through regular assignments and in class laboratory exercises (students are advised to bring their own laptop to class), students will build real-time systems for performing tasks including object recognition and face detection and recognition. Key computer vision topics addressed in the course include human and machine vision: how does the brain recognize objects?, and what can we emulate?, camera models and camera calibration; edge, line and contour detection; optical flow and object tracking; machine learning techniques; image features and object recognition; stereo vision; 3D vision; face detection and face recognition. Students will be exposed to the mathematical tools that are most useful in the implementation of computer vision algorithms. Prerequisite(s): Python programming experience, and prior knowledge of linear algebra, geometry, and probability theory is desired. EN.525.645.  Modern Navigation Systems.  3 Credits.   This course explores the use of satellite, terrestrial, celestial, radio, magnetic, and inertial systems for the real-time determination of position, velocity, acceleration, and attitude. Particular emphasis is on the historical importance of navigation systems; avionics navigation systems for high performance aircraft; the Global Positioning System; the relationships between navigation, cartography, surveying, and astronomy; and emerging trends for integrating various navigation techniques into single, tightly coupled systems. EN.525.646.  DSP Hardware Lab.  3 Credits.   This course develops expertise and insight into the development of DSP processor solutions to practical engineering problems through hands-on experience. Structured exercises using DSP hardware are provided and used by the student to gain practical experience with basic DSP theory and operations. Course focus is on realtime, floating-point applications. This course is intended for engineers having EE or other technical backgrounds who desire to obtain practical experience and insight into the development of solutions to DSP problems requiring specialized DSP architectures. Prerequisite(s): EN.525.627 Digital Signal Processing and C programming experience. EN.525.648.  Introduction to Radar Systems.  3 Credits.   This class introduces the student to the fundamentals of radar system engineering. The radar range equation in its many forms is developed and applied to different situations. Radar transmitters, antennas, and receivers are covered. The concepts of matched filtering, pulse compression, and the radar ambiguity function are introduced, and the fundamentals of radar target detection in a noise background are discussed. Target radar cross-section models are addressed, as well as the effects of the operating environment, including propagation and clutter. MTI and pulsed Doppler processing and performance are addressed. Range, angle, and Doppler resolution/accuracy, as well as fundamental tracking concepts, will also be discussed. Prerequisite(s): EN.525.614 Probability and Stochastic Processes for Engineers, EN.525.627 Digital Signal Processing, a working knowledge of electromagnetics, and familiarity with MATLAB. 6 Electrical and Computer Engineering EN.525.651.  Introduction to Electric Power Systems.  3 Credits.   This course introduces and explains fundamentals of electrical power systems design and engineering. Phasors and their application to power systems analysis are reviewed. The concept of the per-unit system is introduced and applied to circuit calculations. Transformers and their application to electrical power transmission and distribution systems will be covered. Transmission line parameters, their calculation, and transmission line modeling are introduced. Steady-state operation of transmission lines is modeled and investigated. Power flow analysis computational techniques are covered. Short-circuit analysis and the method of symmetrical components are introduced. The concept of power system protection and the role of automatic relays will be covered. Primary and secondary distribution systems and substations are introduced. Renewable energy generation and the integration of renewable energy into the modern power grid will be introduced. Prerequisite(s): Course in electrical networks and a course in linear algebra and matrix operations. MATLAB required software. Course Note(s): Matlab is required for this course. EN.525.654.  Communications Circuits Lab.  3 Credits.   This online laboratory-based course focuses on modulation/ demodulation (MODEM) aspects of wireless communications systems. This course is designed to enhance the student’s understanding of fundamental communications waveforms and to present methods commonly used to process them. Students will be exposed to various implementations of MODEM circuits used to process waveforms such as FM, FSK, PSK, and QAM. All work is performed remotely via Internet access to the remote laboratory facility located at the Johns Hopkins University. Following an introduction to this remote laboratory implementation, students will conduct a series of laboratory exercises designed to enhance their understanding of material presented in communications engineering courses. Course modules involve the characterization of waveforms and MODEM circuits through lecture, laboratory exercises, analysis, and online discussion. Materials required for this course include a broadband Internet connection, web browser, word processing software (e.g., MS Word or equivalent), and analysis software (e.g., MATLAB or equivalent) used to process and present data collected. Prerequisite(s): EN.525.616 Communication Systems Engineering or consent of the instructor. EN.525.655.  Audio Signal Processing.  3 Credits.   This course gives a foundation in current audio and speech technologies, and covers techniques for sound processing by processing and pattern recognition, acoustics, auditory perception, speech production and synthesis, speech estimation. The course will explore applications of speech and audio processing in human computer interfaces such as speech recognition, speaker identification, coding schemes (e.g. MP3), music analysis, noise reduction. Students should have knowledge of Fourier analysis and signal processing. EN.525.656.  Antenna Design for Space Systems.  3 Credits.   This course presents an engineering approach to the design of antennas for space systems. Students will examine antennas for both large and small space based platforms in earth orbit and beyond. Antenna design is presented in the context of the space environment with particular attention to the flight design and testing cycle, thermal and mechanical considerations, space compatible materials, and high power operation. A primary focus of the course will be single, dual and shaped reflector designs including feed network topologies. Several horn antenna designs including corrugated and multimode horns will be covered as well as feed network components. A variety of other antennas including helices, patches, and arrays will be discussed for applications including: Global Navigation Satellite System (GNSS); Tracking, Telemetry and Command (TT&C); isoflux; smallsat and cubesat antennas. Course Note(s): This course is cross-listed with 675.756 Antenna Design for Space Systems. ECE students can only register for 525.656. Prerequisite(s): An undergraduate- or graduate-level introductory antenna systems course, or with approval of the instructor. EN.525.658.  Digital VLSI System Design.  3 Credits.   An introductory course in digital VLSI design in which students design digital CMOS integrated circuits and systems. The class covers transistor, behavioral, and physical level design using a variety of design tools, including circuit simulation with SPICE, logic synthesis with Verilog HDL, physical layout and automated placement and routing. The class culminates in a final project in which each student designs a more complicated digital system from architecture to final layout. Prerequisite(s): A course in digital design. EN.525.659.  Mixed-Mode VLSI Circuit Design.  3 Credits.   This course focuses on transistor-level design of mixed-signal CMOS integrated circuits. After reviewing fundamentals of MOSFET operation, the course will cover design of analog building blocks such as current- mirrors, bias references, amplifiers, and comparators, leading up to the design of digital-to-analog and analog-to-digital converters. Aspects of subthreshold operation, structured design, scalability, parallelism, low power-consumption, and robustness to process variations are discussed in the context of larger systems. The course will include use of Cadence design software to explore transistor operation and to perform functional- block designs, in the process of incrementally designing a data-converter front-end. Prerequisite(s): Familiarity with MOSFET and transistor level circuit design fundamentals. EN.525.661.  UAV Systems and Control.  3 Credits.   This hardware-supplemented course covers the guidance, navigation- and control principles common to many small fixed-wing and multirotor unmanned aerial vehicles (UAVs). Building on classical control systems and modeling theory, students will learn how to mathematically model UAV flight characteristics and sensors, develop and tune feedback control autopilot algorithms to enable stable flight control, and fuse sensor measurements using extended Kalman filter techniques to estimate the UAV position and orientation. Students will realize these concepts through both simulation and interaction with actual UAV hardware. Throughout the course, students will build a full 6-degree- of-freedom simulation of controlled UAV flight using MATLAB and Simulink. Furthermore, students will reinforce their UAV flight control knowledge by experimenting with tuning and flying actual open-source quadrotor UAVs. Prerequisite(s): Background in control systems (e.g., EN.525.609 Continuous Control Systems) and matrix theory along with a working knowledge of MATLAB. Experience using Simulink is desired. Existing familiarity with C programming language, electronics, and microcontrollers will be helpful but is not required. Electrical and Computer Engineering 7 EN.525.665.  Machine Perception.  3 Credits.   This course will cover machine perception with a focus on computer vision (i.e., feature detection, stereovision, structure from motion, deep learning object detection) as the primary use case. Additional sensor modalities will be addressed (i.e., radar, lidar) along with data fusion (i.e., Kalman filtering, target tracking) in order to provide a broad understanding of multi-modality machine perception. EN.525.666.  Linear System Theory.  3 Credits.   This course covers the structure and properties of linear dynamic systems with an emphasis on the single-input, single-output case. Topics include the notion of state-space, state variable equations, review of matrix theory, linear vector spaces, eigenvalues and eigenvectors, the state transition matrix and solution of linear differential equations, internal and external system descriptions, properties of controllability and observability and their applications to minimal realizations, state- feedback controllers, asymptotic observers, and compensator design using state-space and transfer function methods. An introduction to multi-input, multi-output systems is also included, as well as the solution and properties of timevarying systems. Prerequisite(s): Courses in matrix theory and linear differential equations. EN.525.670.  Machine Learning for Signal Processing.  3 Credits.   This course will focus on the use of machine learning theory and algorithms to model, classify, and retrieve information from different kinds of real world signals such as audio, speech, image, and video. Prerequisite(s): EN.525.627 Digital Signal Processing and EN.525.614 Probability and Stochastic Processes for Engineers EN.525.678.  Next Generation Mobile Networks and Security with 5G.  3 Credits.   The primary focus of this course is to introduce the next generation mobile networks, including both Cellular and WLAN technologies in great detail, to discuss various types of IP-based mobility protocols, namely Mobile-IP, Mobile IPv6, ProxyMIPv6, SIP-mobility, and Cellular IP, and to explore systems optimization techniques to support seamless handover during Inter RAT handover (e.g., 4G, 5G, and WLAN). Additionally, the course will briefly introduce the principles of cellular communications system and will then move on to describe the evolution of different generations of cellular systems including 2G, 3G, 4G, and 5G as being defined in 3GPP. At the same time it will discuss IEEE WLAN standards as developed by IEEE 802 working group including 802.11 (a, b, g, n) and 802.11 (ax, ay, ac). The Media Independent Handover standard IEEE 802.21 (e.g., integrating WLAN and 3G/4G cellular networks to provide session/service continuity) is also introduced. Further, the course will describe the 4G Long Term Evolution (LTE) in detail, covering its various components—namely Evolved UMTS Terrestrial Radio Access Network (E-UTRAN), EPC (Evolved Packet Network), and IMS (IP Multimedia Subsystem)—and all the associated interfaces and protocols, and the current efforts on 5G evolution and will touch upon various 5G pillars, namely SDN (Software Defined Networking), Network Function Virtualization, Cloud RAN, Network Slicing, Mobile Edge Cloud, and Edge Security. Finally, the course will highlight various standards activities within 3GPP, IEEE, IETF, NGMN, and ITU and will introduce some research problems for future study in the mobility area, presenting various deployment use cases and experimental results from the open-source testbeds. EN.525.684.  Microwave Systems & Receiver Design.  3 Credits.   This course deals with the practical aspects of RF and microwave systems and components. An overview of radar systems is followed by an introduction to communication systems. The majority of the course treats the linear and nonlinear characteristics of individual components and their relation to receiver system performance. Amplifiers, mixers, antennas, filters, and frequency sources are studied, as well as their impact on receiver performance. Top-level receiver designs for a radar system, a wide-band surveillance system, or a communication system application may be studied. Assignments reinforce the course material and may require use of design software. Prerequisite(s): An undergraduate degree in electrical engineering or equivalent. EN.525.691.  Fundamentals of Photonics.  3 Credits.   This course provides the essential background in photonics required to understand modern photonic and fiber-optic systems. Fundamental concepts established in this course are necessary for advanced coursework as well. Topics include: electromagnetic optics, polarization and crystal optics, guided-wave optics, fiber optics, photons in semiconductors, semiconductor photon sources and detectors, electro- optics and acousto-optics. Prerequisite(s): An undergraduate course in electromagnetic theory. EN.525.707.  Error Control Coding.  3 Credits.   This course presents error-control coding with a view toward applying it as part of the overall design of a data communication or storage and retrieval system. Block, trellis, and turbo codes and associated decoding techniques are covered. Topics include system models, generator and parity check matrix representation of block codes, general decoding principles, cyclic codes, an introduction to abstract algebra and Galois fields, BCH and Reed-Solomon codes, analytical and graphical representation of convolutional codes, performance bounds, examples of good codes, Viterbi decoding, BCJR algorithm, turbo codes, and turbo code decoding. Prerequisite(s): Background in linear algebra, such as EN.625.609 Matrix Theory; in probability, such as EN.525.614 Probability and Stochastic Processes for Engineers; and in digital communications, such as EN.525.616 Communication Systems Engineering. Familiarity with MATLAB or similar programming capability. EN.525.708.  Iterative Methods in Communications Systems.  3 Credits.   Generalization of the iterative decoding techniques invented for turbo codes has led to the theory of factor graphs as a general model for receiver processing. This course will develop the general theory of factor graphs and explore several of its important applications. Illustrations of the descriptive power of this theory include the development of high performance decoding algorithms for classical and modern forward error correction codes (trellis codes, parallel concatenated codes, serially concatenated codes, low-density parity check codes). Additional applications include coded modulation systems in which the error correction coding and modulation are deeply intertwined as well as a new understanding of equalization techniques from the factor graph perspective. Prerequisite(s): Background in linear algebra, such as EN.625.609 Matrix Theory; in probability, such as EN.525.614 Probability and Stochastic Processes for Engineers; and in digital communications, such as EN.525.616 Communication Systems Engineering. Familiarity with MATLAB or similar programming capability. 10 Electrical and Computer Engineering EN.525.744.  Passive Emitter Geo-Location.  3 Credits.   This course covers the algorithms used to locate a stationary RF signal source, such as a radar, radio, or cell phone. The topics covered include a review of vectors, matrices, and probability; linear estimation and Kalman filters; nonlinear estimation and extended Kalman filters; robust estimation; data association; measurement models for direction of arrival, time difference of arrival, and frequency difference of arrival; geo-location algorithms; and performance analysis. Most of the course material is developed in planar Cartesian coordinates for simplicity; however, the extension to WGS84 coordinates is provided to equip the students for practical applications. Homework consists of both analytical problems and problems that require computer simulation using software such as MATLAB. Prerequisite(s): EN.525.614 Probability and Stochastic Processes for Engineers, an undergraduate course in linear algebra/matrix theory, and familiarity with MATLAB. EN.525.745.  Applied Kalman Filtering.  3 Credits.   Theory, analysis, and practical design and implementation of Kalman filters are covered, along with example applications to real-world problems. Topics include a review of random processes and linear system theory; Kalman filter derivations; divergence analysis; numerically robust forms; suboptimal filters and error budget analysis; prediction and smoothing; cascaded, decentralized, and federated filters; linearized, extended, second-order, and adaptive filters; and case studies in GPS, inertial navigation, and ballistic missile tracking. Prerequisite(s): EN.525.614 Probability and Stochastic Processes for Engineers and EN.525.666 Linear System Theory or equivalents; knowledge of MATLAB (or equivalent software package). EN.525.746.  Image Engineering.  3 Credits.   The overall goal of the course is to provide the student with a unified view of images, concentrating on image creation, and image processing. Optical, photographic, analog, and digital image systems are highlighted. Topics include image input, output, and processing devices; visual perception; video systems; and fundamentals of image enhancement and restoration. Coding, filtering, and transform techniques are covered, with applications to remote sensing and biomedical problems. Prerequisite(s): EN.525.627 Digital Signal Processing or equivalent and knowledge of linear systems. EN.525.747.  Speech Processing.  3 Credits.   This course emphasizes processing of the human speech waveform, primarily using digital techniques. Theory of speech production and speech perception as related to signals in time and frequency-domains is covered, as well as the measurement of model parameters, short- time Fourier spectrum, and linear predictor coefficients. Speech coding, recognition, speech synthesis, and speaker identification are discussed. Application areas include telecommunications telephony, Internet VOIP, and man-machine interfaces. Considerations for embedded realization of the speech processing system will be covered as time permits. Several application-oriented software projects will be required. Prerequisite(s): EN.525.627 Digital Signal Processing and EN.525.614 Probability and Stochastic Processes for Engineers. Background in linear algebra and MATLAB is helpful. EN.525.748.  Synthetic Aperture Radar.  3 Credits.   This course covers the basics of synthetic aperture radar (SAR) from a signal processing perspective. In particular, the course will examine why there are limiting design considerations for real aperture radar and how a synthetic aperture can overcome these limitations to create high- resolution radar imaging. Various SAR geometries will be considered. Image formation algorithms, such as range Doppler, chirp scaling, omega-K, polar formatting, and backprojection, will be reviewed and, in some cases, coded by the student. Other post-processing techniques, such as motion compensation, aperture weighting (or apodization), autofocus, and multilook, will be reviewed. Advanced topics will include interferometric SAR, polarimetry, continuous wave linear FM (CWLFM) SAR, and moving objects in SAR imagery. Students will work through problems involving radar and SAR processing. Students will also develop SAR simulations, in either MATLAB or Python, based on simple point scatterers in a benign background. Prerequisite(s): EN.525.648 Introduction to Radar Systems, along with either basic MATLAB or Python skills. EN.525.751.  Software Radio for Wireless Communications.  3 Credits.   Software-defined radio (SDR) has become a common approach to rapid prototyping and deployment of communications equipment. It allows engineers to quickly move from algorithm development to functional prototype, using small form-factor commercial hardware. This course will explore modern SDR technology and implementation techniques. Students will design and implement common radio functions using field- programmable gate arrays (FPGAs) and software frameworks. During the semester, we progress from hardware considerations and basic signal processing techniques to synchronization, digital modulation, and cognitive radio. We finish with a final semester project combining multiple cognitive radio concepts. Prerequisite(s): EN.525.638 Introduction to Wireless Technology or EN.525.616 Communication Systems Engineering; EN.525.627 Digital Signal Processing; and working knowledge of MATLAB and Simulink. EN.525.752.  Digital Receiver Synchronization Techniques.  3 Credits.   This course explores synchronization techniques in modern digital receivers. Synchronization techniques, from initial detection of a signal to symbol timing recovery, is studied in this course. Students will learn practical synchronization techniques through experimentation and hands-on development. Students develop software to solve synchronization problems relevant to modern wireless communication standards. A semester project involving demodulation and synchronization is required. Prerequisite(s): EN.525.627 Digital Signal Processing EN.525.753.  Laser Systems and Applications.  3 Credits.   This course provides a comprehensive treatment of the generation of laser light, and its properties and applications. Topics include specific laser systems and pumping mechanisms, nonlinear optics, temporal and spatial coherence, guided beams, interferometric and holographic measurements, and remote sensing. Prerequisite(s): EN.525.625 Laser Fundamentals. Electrical and Computer Engineering 11 EN.525.754.  Wireless Communication Circuits.  3 Credits.   In this course, students examine modulator and demodulator circuits used in communication and radar systems. A combination of two lectures, three laboratory experiments, and a student design project address the analysis, design, fabrication, and test of common circuits. Signal formats considered include phase and frequency shift keying, as well as the linear modulations used in analog systems. The students will select a project topic of their choosing. The nature and extent of the project will be negotiated with the instructors. The project will consume about two-thirds of the semester and weighs in a similar proportion for the final grade. There are no exams in this course, it is a laboratory and project-based learning experience. Prerequisite(s): EN.525.616 Communication Systems Engineering or EN.525.624 Analog Electronic Circuit Design or EN.525.654 Communications Circuits Laboratory or permission of the instructor. EN.525.756.  Optical Propagation, Sensing, and Backgrounds.  3 Credits.   This course presents a unified perspective on optical propagation in linear media. A basic background is established using electromagnetic theory, spectroscopy, and quantum theory. Properties of the optical field and propagation media (gases, liquids, and solids) are developed, leading to basic expressions describing their interaction. The absorption line strength and shape and Rayleigh scattering are derived and applied to atmospheric transmission, optical window materials, and propagation in water-based liquids. A survey of experimental techniques and apparatus is also part of the course. Applications are presented for each type of medium, emphasizing remote sensing techniques and background noise. Computer codes such as LOWTRAN, FASCODE, and OPTIMATR are discussed. Prerequisite(s): Undergraduate courses on electromagnetic theory and elementary quantum mechanics. A course on Fourier optics is helpful. EN.525.759.  Image Compression, Packet Video, and Video Processing.  3 Credits.   This course provides an introduction to the basic concepts and techniques used for the compression of digital images and video. Video compression requirements, algorithm components, and ISO Standard video processing algorithms are studied. Image compression components that are used in video compression methods are also identified. Since image and video compression is now integrated in many commercial and experimental video processing methods, knowledge of the compression methods’ effects on image and video quality are factors driving the usability of that data in many data exploitation activities. Topics to be covered include introduction to video systems, Fourier analysis of video signals, properties of the human visual system, motion estimation, basic video compression techniques, videocommunication standards, and error control in video communications. Video processing applications that rely on compression algorithms are also studied. A mini- project is required. Prerequisite(s): EN.525.627 Digital Signal Processing. EN.525.761.  Wireless and Wireline Network Integration.  3 Credits.   This course investigates the integration of wireless and wireline networks into seamless networks. The current telecommunications environment in the United States is first discussed, including the state of technology and regulations as they apply to the wireless and wireline hybrid environment. Then each type of these hybrid networks is discussed, including its components, network services, architecture, and possible evolution, as well as important concepts that support the evolution of networks. The integration of wired network advance intelligence, wireless network mobility, and long distance capabilities are shown to provide many new combinations of wired and wireless services to users. Prerequisite(s): EN.525.608 Next-Generation Telecommunications or EN.525.616 Communication Systems Engineering, or permission of instructor. EN.525.762.  Introduction to Wavelets.  3 Credits.   This is an introductory course on wavelet analysis, with an emphasis on the fundamental mathematical principles and basic algorithms. We cover the mathematics of signal (function) spaces, orthonormal bases, frames, time-frequency localization, the windowed Fourier transform, the continuous wavelet transform, discrete wavelets, orthogonal and biorthogonal wavelets of compact support, wavelet regularity, and wavelet packets. It is designed as a broad introduction to wavelets for engineers, mathematicians, and physicists.Prerequisite: Competence with multivariable calculus, linear algebra, and a scientific programming language is required, as well as familiarity with Fourier transforms and signal processing fundamentals such as the discrete Fourier transform, convolutions, and correlations. EN.525.768.  Wireless Networks.  3 Credits.   This is a hands-on course that integrates teaching of concepts in wireless LANs as well as offering students, in an integrated lab environment, the ability to conduct laboratory experiments and design projects that cover a broad spectrum of issues in wireless LANs. The course will describe the characteristics and operation of contemporary wireless network technologies such as the IEEE 802.11 and 802.11s wireless LANs and Bluetooth wireless PANs. Laboratory experiments and design projects include MANET routing protocols, infrastructure and MANET security, deploying hotspots, and intelligent wireless LANs. The course will also introduce tools and techniques to monitor, measure, and characterize the performance of wireless LANs as well as the use of network simulation tools to model and evaluate the performance of MANETs. Prerequisite(s): EN.525.641 Computer and Data Communication Networks or EN.605.671 Principles of Data Communications Networks. 12 Electrical and Computer Engineering EN.525.770.  Intelligent Algorithms.  3 Credits.   Intelligent algorithms are, in many cases, practical alternative techniques for tackling and solving a variety of challenging engineering problems. For example, fuzzy control techniques can be used to construct nonlinear controllers via the use of heuristic information when information on the physical system is limited. Such heuristic information may come, for instance, from an operator who has acted as a “human-inthe-loop” controller for the process. This course investigates a number of concepts and techniques commonly referred to as intelligent algorithms; discusses the underlying theory of these methodologies when appropriate; and takes an engineering perspective and approach to the design, analysis, evaluation, and implementation of intelligent systems. Fuzzy systems, genetic algorithms, particle swarm and ant colony optimization techniques, and neural networks are the primary concepts discussed in this course, and several engineering applications are presented along the way. Expert (rule-based) systems are also discussed within the context of fuzzy systems. An intelligent algorithms research paper must be selected from the existing literature, implemented by the student, and presented as a final project. Prerequisite(s): Student familiarity of system-theoretic concepts is desirable. EN.525.771.  Propagation of Radio Waves in the Atmosphere.  3 Credits.   This course examines various propagation phenomena that influence transmission of radio frequency signals between two locations on earth and between satellite-earth terminals, with a focus on applications. Frequencies above 30 MHz are considered with emphasis on microwave and millimeter propagation. Topics include free space transmission, propagation, and reception; effects on waves traversing the ionosphere; and attenuation due to atmospheric gases, rain, and clouds. Brightness temperature concepts are discussed, and thermal noise introduced into the receiver system from receiver hardware and from atmospheric contributions are examined. Also described are reflection and diffraction effects by land terrain and ocean, multipath propagation, tropospheric refraction, propagation via surface and elevated ducts, scatter from fluctuations of the refractive index, and scattering due to rain. Atmospheric dynamics that contribute to the various types of propagation conditions in the troposphere are described. Prerequisite(s): An undergraduate degree in electrical engineering or equivalent. EN.525.772.  Fiber-Optic Communication Systems.  3 Credits.   This course investigates the basic aspects of fiber-optic communication systems. Topics include sources and receivers, optical fibers and their propagation characteristics, and optical fiber systems. The principles of operation and properties of optoelectronic components, as well as the signal guiding characteristics of glass fibers, are discussed. System design issues include terrestrial and submerged point-to-point optical links and fiber-optic networks. Prerequisite(s): EN.525.691 Fundamentals of Photonics. EN.525.774.  RF & Microwave Circuits I.  3 Credits.   In this course, students examine RF and microwave circuits appropriate for wireless communications and radar sensing. The course emphasizes the theoretical and experimental aspects of micro-strip design of highly integrated systems. Computer-aided design techniques are introduced and used for the analysis and design of circuits. Circuits are designed, fabricated, and tested, providing a technically stimulating environment in which to understand the foundational principles of circuit development. Couplers, modulators, mixers, and calibrated measurements techniques are also covered. Prerequisite(s): EN.525.623 Principles of Microwave Circuits or EN.525.620 Electromagnetic Transmission Systems. EN.525.775.  RF & Microwave Circuits II.  3 Credits.   This course builds upon the knowledge gained in 525.774 RF and Microwave Circuits I. Here there is a greater emphasis on designs involving active components. Linear and power amplifiers and oscillators are considered, as well as stability, gain, and their associated design circles. The course uses computer-aided design techniques and students fabricate and test circuits of their own design. Prerequisite(s): EN.525.774 RF and Microwave Circuits I. EN.525.776.  Information Theory.  3 Credits.   Information theory concerns the fundamental limits for data compressibility and the rate at which data may be reliably communicated over a noisy channel. Course topics include measures of information, entropy, mutual information, Markov chains, source coding theorem, data compression, noisy channel coding theorem, error-correcting codes, and bounds on the performance of communication systems. Classroom discussion and homework assignments will emphasize fundamental concepts, and advanced topics and practical applications (e.g., industry standards, gambling/finance, machine learning) will be explored in group and individual research projects. Prerequisite(s): EN.525.614 Probability and Stochastic Processes for Engineers or equivalent. EN.525.777.  Control System Design Methods.  3 Credits.   This course examines recent multivariable control system design methodologies and how the available techniques are synthesized to produce practical system designs. Both the underlying theories and the use of computational tools are covered. Topics include review of classical control system design and linear system theory, eigenstructure assignment, the linear quadratic regulator, the multivariable Nyquist criterion, singular value analysis, stability and performance robustness measures, loop transfer recovery, H-infinity design, and mu-synthesis. An introduction to nonlinear techniques includes sliding mode control and feedback linearization. Recent papers from the literature are discussed. Each student will be assigned a design project using PC-based design and analysis software. Prerequisite(s): EN.525.666 Linear System Theory and EN.525.609 Continuous Control Systems or the equivalent. EN.525.778.  Design for Reliability, Testability, and Quality Assurance.  3 Credits.   The design of reliable and testable systems, both analog and digital, is considered at the component, circuit, system, and network levels. Using numerous real-world examples, the trade-offs between redundancy, testability, complexity, and fault tolerance are explored. Although the emphasis is predominantly on electronics, related examples from the aerospace and software industries are included. The concepts of fault lists, collapsed fault lists, and other techniques for reducing the complexity of fault simulation are addressed. A quantitative relationship between information theory, error correction codes, and reliability is developed. Finally, the elements of a practical quality assurance system are presented. In addition to homework assignments, students will conduct an in-depth, quantitative case study of a practical system of personal interest. Prerequisite(s): EN.525.614 Probability and Stochastic Processes or equivalent.