Graduate course prerequisites and topics

EEE 503 — Algorithms for Computer-Aided Design of Digital Systems

Course description: Provides the algorithmic underpinnings of CAD (Computer-Aided Design) tools for digital systems–from high-level algorithmic specifications down to an optimized network of logic cells. Covers the underlying theory and algorithms that have been incorporated into many commercial tools over the past two decades. Covers behavioral to RTL (Register-Transfer Level) synthesis, and from RTL to logic, including combinational and sequential network optimization, gate and interconnect timing models, retiming of sequential networks, clock tree design and optimization. To be successful in this course, students need an undergraduate background in combinational and sequential logic design, discrete mathematics, and a strong background (preferably at the graduate level) in fundamentals of data structures and algorithms; strong experience in programming (C or C++) and coding and documentation standards. (All code developed in class is in Python).

Prerequisites: CSE 120/230/320, MAT 243, CSE 310, basic Verilog knowledge, programming skills in C/C++ and Python

EEE 505 — Time-Frequency Signal Processing

Course description: Joint time-frequency analysis of time-varying signals and systems; linear and quadratic time-frequency representations; applications in current areas of signal processing.

Prerequisites: EEE 407

EEE — 506 Digital Spectral Analysis

Course description: Principles and applications of digital spectral analysis, least squares, random sequences, parametric, and nonparametric methods for spectral estimation.

Prerequisites: EEE 407, EEE 554

EEE 507 — Multidimensional Signal Processing

Course description: Processing and representation of multidimensional signals. Design of systems for processing multidimensional data. Introduces image and array processing issues.

Prerequisites: EEE 404, EEE 407

EEE 508 — Digital Image and Video Processing and Compression

Course description: Fundamentals of digital image perception, representation, processing, and compression. Emphasizes image coding techniques. Signals include still pictures and motion video.

Prerequisites: EEE 404, EEE 407

EEE 509 — DSP Algorithms and Software

Course description: Linear systems review, digital filter design, software aspects, DFT, FFT, random signals, programming aspects, applications projects, MATLAB and Java simulations.

Prerequisites: EEE 203, MAT 342

EEE 510 — Multimedia Signal Processing

Course description: Speech/audio coding algorithms. LPC, CELP, MPEG, Cell phone, DTV, cinema, and surround sound standards. MPEG/JPEG introduction.

Prerequisites: EEE 203, 506 or 606, MAT 342

EEE 511 — Artificial Neural Computation

Course description: Networks for computation, learning function representations from data, learning algorithms and analysis, function approximation and information representation by networks, applications in control systems and signal analysis.

Prerequisites: EEE 304

EEE 512 — System-Level Design for Heterogeneous Multiprocessor Architectures

Course description: In-depth introduction to multicore computing architectures ranging from low-power multiprocessor systems-on-chip (MpSoC) to high-performance chip-multiprocessors (CMP). Fundamental topics on modeling, analysis, and optimization of multicore systems; particular attention on low-power and communication-centric design of multicore systems.

EEE 515 — Machine Vision and Pattern Recognition

Course description: Covers the field of computer vision, whose broad goal is to create algorithms and systems for processing of visual signals (e.g., images, videos) for low-level, mid-level, and high-level perceptual tasks. Presents the broad principles and techniques for devising computer vision algorithms starting from understanding the imaging process for a pin-hole camera; understanding lenses, image-statistics such as gradients and edges, 3D structure estimation, motion estimation, illumination modeling to perceptual tasks such as shape recognition, texture modeling, face recognition, activity recognition, and scene recognition. The class is a mixture of in-class lectures and discussions, and individual and group projects.

Prerequisites: Undergraduate-level linear algebra, signal processing, and probability theory are highly recommended. Prior experience coding in Python or MATLAB is highly recommended as well.

EEE 516 — Physics-Based Computer Vision

Course description: Covers topics in physics-based computer vision and graphics. Includes work on visual computing systems including computational cameras, illumination systems, 3D scanners, rendering/animation and displays. Starting with the basics of traditional photography and the imaging pipeline, proceeds to explore new ways to capture visual information by co-designing camera hardware and software algorithms. Topics covered include plenoptic or multi-modal imaging, coded and compressive sensing, light transport and rendering, programmable illumination, and computational displays. Focuses on understanding and evaluating new imaging technology for specific applications including autonomous vehicles, entertainment and graphics, computer vision and visual recognition, and robotics. Course content delivered via lectures along with written and programming assignments as well as a final project.

EEE 517 — Sensors and Machine Learning

Course description: Integrated sensor devices and algorithms; microphone arrays, chemical sensing, mobile sensing; accelerometers and magnetometers, nanopore sensors, and flexible and patch sensors. Signal pre-processing for sensors; feature extraction; image sensing; voice processing; filters; frequency analysis; autocorrelation; principal components; k means algorithm; sensor fusion; neural nets; applications to health, security and mobile systems.

Prerequisites: Basic Circuits and Systems. Basic Signals and Systems, Basic Programming skills.

EEE 518 — Electronics Packaging for Computing and Energy Applications

Course description: Electronics packaging technologies are growing rapidly throughout the world to satisfy the demands for application systems such as consumer electronics, transport, aerospace, datacenters, IoT, AI, industry and energy conservation as evidenced by the recent Chips Acts in the U.S. and around the world, where governments and industry are investing heavily in electronics packaging technologies, research and innovation. There is an urgent need for innovation in electronics packaging and a skilled workforce to fulfill industries’ future vision of going beyond Moore’s Law. This course better prepares students for the electronics industry by teaching them the fundamental principles of electronics packaging. Students learn the key areas of package types, electrical design, thermal design, materials selection, reliability assessment and the challenges and trade-offs required for electronics packaging for different application systems.

Prerequisites: Students should have a basic understanding of electric circuits; electrical, thermal and mechanical properties of materials; theory of elasticity, fatigue; and general mechanics as taught in undergraduate engineering programs.

EEE 520 — VLSI Design for Reliability

Course description: Modeling and design solutions for very large-scale integration (VLSI) reliability. Statistical design under static and dynamic variability. Aging effects and resilient design techniques. Radiation effects in VLSI.

EEE 521 — Low-Power Bioelectronics

Course description: Begins with fundamental theory and techniques for low-power analog circuit design especially subthreshold CMOS and BJT circuits (e.g., translinear circuits), then moves to biomedical applications and bio-inspired systems focused upon neuromorphic circuits. Also touches on concepts such as wireless challenges for implants, energy harvesting and electrochemistry. Students have the opportunity to have their final projects fabricated in a commercial CMOS process.

Prerequisites: EEE 433 or equivalent and EEE 523

EEE 522 — Radio Frequency Test

Course description: Covers current production test schemes for mixed-signal and RF circuits, the economics of production test, and recent research techniques in reducing the production test cost. Prior knowledge of Matlab is necessary to be successful in this class.

Prerequisites: Prerequisite knowledge in the course topics and MATLAB.

EEE 523 — Advanced Analog Integrated Circuits

Course description: Analysis and design of analog integrated circuits: analog circuit blocks, reference circuits, operational-amplifier circuits, feedback, and nonlinear circuits.

Prerequisites: EEE 433

EEE 524 — Communication Transceiver Circuits Design

Course description: Communication transceivers and radio frequency system design; fundamentals of transceivers circuits; RF, IF, mixers, filters, frequency synthesizers, receivers, CAD tools, and lab work on IC design stations.

Prerequisites: EEE 433, EEE 455

EEE 525 — VLSI Design

Course description: Analysis and design of Very Large Scale Integrated (VLSI) circuits. Physics of small devices, fabrication, regular structures, and system timing.

Prerequisites: EEE 433, EEE 425

EEE 526 — VLSI Architectures

Course description: High throughput and low-power VLSI architectures for signal processing. Array processor systems; data path design and optimization; memory design; high-level synthesis; low-power design at system level, algorithm level, and architecture level.

Prerequisites: Students should have taken an undergraduate senior-level course in DSP and computer architectures. The computer architecture course could be completed at the same time as EEE 526, or students may study materials related to computer architecture on their own prior to the class.

EEE 527 — Analog to Digital Converters

Course description: Detailed introduction to the design of Nyquist rate, CMOS analog to digital converters. Requires knowledge of analog integrated circuits (analog circuit blocks, reference circuits, operational-amplifier circuits, feedback, and nonlinear circuits) to be successful in this course. Students must also have completed EEE 523 with a grade of C or better.

Prerequisites: EEE 523

EEE 528 — Sensors for the Internet of Things and Wearable Devicestems

Course description: Fundamentals, concepts of system analysis and design, and principles that apply to phase-locked loops (PPLs) used in frequency synthesis.

EEE 529 — Semiconductor Memory Technologies and Systems

Course description: Design of semiconductor memory technologies and systems, from the device cell structures to the array and architecture design, with emphasis on the industry trends and cutting-edge technologies including SRAM, DRAM and FLASH technologies and emerging memory technologies such as STT-MRAM, PCRAM and RRAM.

Prerequisites: Semiconductor device fundamentals and digital circuit courses, e.g. EEE 425 Digital Systems and Circuits and EEE 436 Fund Solid State Devices or an equivalent background are highly recommended.

EEE 530 — Microelectronics Manufacturing

Course description: Device scaling; integrated circuits in systems; economics of fabrication, silicon, MOS logic and memory devices; yield; physics and chemistry of unit processes (lithography, etching, ion implantation, oxidation, diffusion, CVD/PVD); interconnect; process architecture and control; advanced devices; industry limitations.

Prerequisites: Students should have taken EEE 435 or any course that includes basic semiconductor device fabrication.

EEE 531 — Semiconductor Device Theory I

Course description: Transport and recombination theory, pn and Schottky barrier diodes, bipolar and junction field-effect transistors, and MOS capacitors and transistors.

Prerequisites: EEE 436

EEE 532 — Semiconductor Device Theory II

Course description: Advanced MOSFETs, charge-coupled devices, solar cells, photodetectors, light-emitting diodes, microwave devices, and modulation-doped structures.

Prerequisites: EEE 531

EEE 533 — Semiconductor Process/Device Simulation

Course description: Device simulation concepts: conventional and advanced MOS devices, bipolar transistors, heterostructures including HEMTs and solar cells. Process simulation concepts: oxidation, ion implantation, diffusion.

Prerequisites: EEE 436 or equivalent

EEE 534 — Semiconductor Transport

Course description: Carrier transport in semiconductors. Hall effect, high electric field, Boltzmann equation, correlation functions, and carrier-carrier interactions.

Prerequisites: EEE 352, EEE 434

EEE 535 — Electron Transport in Nanostructures

Course description: Nanostructure physics and applications. 2-D electron systems, quantum wires and dots, ballistic transport, quantum interference and single-electron tunneling.

Prerequisites: EEE 434, EEE 436

EEE 536 — Semiconductor Characterization

Course description: Measurement techniques for semiconductor materials and devices. Electrical, optical, physical and chemical characterization methods.

Prerequisites: EEE 436

EEE 537 — Semiconductor Optoelectronics

Course description: Electronic states in semiconductors, quantum theory of radiation, absorption processes, radiative processes, nonradiative processes, photoluminescence, and photonic devices.

Prerequisites: EEE 434, EEE 436 (or EEE 531)

EEE 538 — Optoelectronic Devices

Course description: Provides graduate students with detailed theory and practical knowledge of semiconductors materials and optoelectronic devices such as light-emitting diodes, lasers, photodetectors and solar cells. Also discusses the applications of these devices. Offers not only classroom lectures but also demonstration of real-world experiments in research laboratories at ASU.

Prerequisites: EEE 434, 437 or 436, 531 or 537

EEE 539 — Intro Solid-State Electronics

Course description: Crystal lattices, reciprocal lattices, quantum statistics, lattice dynamics, equilibrium and nonequilibrium processes in semiconductors.

Prerequisites: EEE 352, EEE 434

EEE 540 — Fast Computational Electromagnetics

Course description: Method of moments, finite difference time-domain, finite element methods implemented using fast algorithms (wavelets, FMM, Nystrom) to gain high efficiency.

Prerequisites: EEE 445, EEE 443 (or EEE 541)

EEE 541 — Electromagnetic Fields and Guided Waves

Course description: Polarization and magnetization; dielectric, conducting, anisotropic, and semiconducting media; duality, uniqueness, and image theory; plane wave functions, waveguides, resonators, and surface guided waves.

Prerequisites: EEE 341

EEE 543 — Antenna Analysis and Design

Course description: Impedances, broadband antennas, frequency independent antennas, miniaturization, aperture antennas, horns, reflectors, lens antennas and continuous sources design techniques.

Prerequisites: EEE 443

EEE 544 — High-Resolution Radar

Course description: Fundamentals; wideband coherent design, waveforms, and processing; stepped frequency; synthetic aperture radar (SAR); inverse synthetic aperture radar (ISAR); imaging.

EEE 545 — Microwave Circuit Design

Course description: Analysis and design of microwave attenuators, in-phase and quadrature-phase power dividers, magic tees, directional couplers, phase shifters, DC blocks and equalizers.

Prerequisites: EEE 445

EEE 546 — Advanced Fiber Optics

Course description: Theory of propagation in fibers, couplers and connectors, distribution networks, modulation, noise and detection, system design, and fiber sensors.

Prerequisites: EEE 448

EEE 547 — Microwave Solid-State Circuit Design I

Course description: Applies semiconductor characteristics to practical design of microwave mixers, detectors, limiters, switches, attenuators, multipliers, phase shifters, and amplifiers.

Prerequisites: EEE 445

EEE 548 — Coherent Optics

Course description: Diffraction, lenses, optical processing, holography, electro-optics, and lasers.

Prerequisites: EEE 341

EEE 549 — Statistical Machine Learning: From Theory to Practice

Course description: Explores the design, analysis and construction of algorithms that can learn from data and make inferences or predictions about future outcomes. Covers the theory and practice of machine learning (ML) focusing on a methodical approach that highlights the role of statistical and computational methods in analysis of data. Includes a near equal dose of theory and practice with the goal of providing a thorough grounding in the fundamental methodologies and algorithms in machine learning.

EEE 550 — Transform Theory and Applications

Course description: Introduces abstract integration, function spaces, and complex analysis in the context of integral transform theory. Applications to signal analysis, communication theory, and system theory.

Prerequisites: EEE 304

EEE 551 — Information Theory

Course description: Entropy and mutual information, source and channel coding theorems, applications for communication and signal processing.

Prerequisites: EEE 554

EEE 552 — Digital and Wireless Communications

Course description: Complex signal theory, digital modulation, optimal coherent and incoherent receivers, channel codes, coded modulation, Viterbi algorithm.

Prerequisites: EEE 455, EEE 554

EEE 553 — Coding and Applications

Course description: Introduces algebra, block and convolutional codes, decoding algorithms, turbo codes, coded modulation, private and public key cryptography.

EEE 554 — Probability and Random Processes

Course description: Applies statistical techniques to the representation and analysis of electrical signals and to communications systems analysis.

Prerequisites: EEE 350

EEE 556 — Detection and Estimation Theory

Course description: Combines the classical techniques of statistical inference and the random process characterization of communication, radar and other modern data processing systems.

Prerequisites: EEE 554

EEE 557 — Broadband Networks

Course description: Physics of wireless and optical communications. Broadband multiplexing and switching methods. Blocking and queuing analysis. Network optimization, routing and economics.

Prerequisites: EEE 554

EEE 558 — Recent Advances in Communications

Course description: Cellular systems, path loss, multipath fading channels, modulation and signaling for wireless, diversity, equalization coding, spread spectrum, TDMA/FDMA/CDMA.

Prerequisites: EEE 552

EEE 559 — Wireless Networks

Course description: Design principles of cellular networks. Multiple access control protocols for wireless systems. Wireless routing and TCP/IP. Mobile management. Call admission control and resource allocation (e.g., power control and rate control). Wireless security. Future-generation wireless networks. A previous course in random signal theory is required to be successful in this class.

Prerequisites: EEE 350

EEE 560 — Mathematical Foundations of Machine Learning

Course description: Serves as a primer in statistical learning theory and as a platform for exploring emerging algorithms and theory in large-scale data analytics and learning. This study is at the intersection of information processing, statistical theory and computational sciences. Contains a healthy mix of topics from all of these disciplines.

Prerequisites: EEE 554

EEE 562 — Nuclear Reactor Theory and Design

Course description: Principles of neutron chain reacting systems. Neutron diffusion and moderation. One-, two-, and multigroup diffusion equation solution methods. Heterogeneous reactors. Nuclear fuel steady-state performance. Core thermal-hydraulics. Core thermal design.

Prerequisites: EEE 460

EEE 563 — Nuclear Reactor System Dynamics and Diagnostics

Course description: Time-dependent solution to neutron diffusion equation. Reactor kinetics and reactivity changes. Dynamics, stability and control of reactor systems. Modeling neutronic and thermal processes. System characterization in time and frequency domains. Reactor surveillance and diagnostics.

Prerequisites: EEE 562

EEE 564 — Interdisciplinary Nuclear Power Operations

Course description: Nuclear power plant systems. Studies interrelationship and propagation of effects that systems and design changes have on one another, especially in relation to nuclear power plant safety and operations. Case studies and design projects.

Prerequisites: EEE 460

EEE 565 — Solar Cells

Course description: Introduction to the generation and utilization of electricity from solar energy. Exploration of the science and engineering of direct conversion (photovoltaics), including the design, fabrication, and operation of solar cells, and the construction and performance of solar cell modules. Prior knowledge of properties of electronic materials is required to be successful in this course.

Prerequisites: Basic background in the electronic properties of materials. Familiarity with p-n junctions at the undergraduate level will be helpful.

EEE 566 — Advanced Device Modeling and Simulation

Course description: Understanding semi-classical and quantum transport theory in conjunction with device simulations at the nanoscale. Prior knowledge of semiconductor device theory, quantum mechanics and classical semiconductor device simulation is necessary to be successful in this class.

Prerequisites: EEE 434 or EEE 534

EEE 571 — Power System Transients

Course description: Simple switching transients. Transient analysis by deduction. Damping of transients. Capacitor and reactor switching. Transient recovery voltage. Travelling waves on transmission lines. Lightning. Protection of equipment against transient overvoltages. Introduces computer analysis of transients.

Prerequisites: EEE 470 or EEE 471

EEE 572 — Advanced Power Electronics

Course description: Analyzes device operation, including thyristors, gate-turn-off thyristors, and transistors. Design of rectifier and inverter circuits. Applications such as variable speed drives, HVDC, motor control and uninterruptable power supplies.

EEE 573 — Electric Power Quality

Course description: Sinusoidal waveshape maintenance; study of momentary events, power system harmonics, instrumentation, filters, power conditioners, and other power quality enhancement methods.

Prerequisites: EEE 579

EEE 574 — Computer Solution of Power Systems

Course description: Algorithms for digital computation for the Newton and fast-decoupled power flow problem, and fault analysis. Sparse matrix and vector programming methods, creation of elimination trees, network equivalencing, solution of the least squares problem, introduction to state estimation.

Prerequisites: EEE 471

EEE 575 — Power System Stability

Course description: Dynamic performance of power systems with emphasis on stability. Modeling of system components and control equipment. Analysis of the dynamic behavior of the system in response to small and large disturbances. Knowledge of EEE 470, 471 and 473 (or equivalents) is required to be successful in this course.

Prerequisites: EEE 470, EEE 471 or EEE 473

EEE 576 — Power System Dynamics

Course description: Dynamic performance of power systems with emphasis on control. Modeling of control equipment, FACTS devices, wind generators and nonlinear loads. Design of power system stabilizers. Requires prior knowledge of electric power devices, power system analysis and electrical machinery to be successful in this course.

Prerequisites: EEE 575

EEE 577 — Power Engineering Operations and Planning

Course description: Economic dispatch, unit commitment, dynamic programming, power system planning and operation, control, generation modeling, AGC and power production.

Prerequisites: EEE 470, EEE 471

EEE 579 — Power Transmission and Distribution

Course description: High-voltage transmission line electric design; conductors, corona, RI and TV noise, insulators, clearances. DC characteristic, feeders voltage drop, and capacitors.

Prerequisites: EEE 360

EEE 581 — Filtering of Stochastic Processes

Course description: Modeling, estimation, and filtering of stochastic processes, with emphasis on the Kalman filter and its applications in signal processing and control.

Prerequisites: EEE 554, EEE 582

EEE 582 — Linear System Theory

Course description: Controllability, observability and realization theory for multivariable continuous time systems. Stabilization and asymptotic state estimation. Disturbance decoupling, noninteracting control.

Prerequisites: EEE 480, EEE 481

EEE 585 — Security and Privacy in Networked Systems

Course description: Comprehensive understanding of critical cyber security and privacy threats as well as corresponding solutions in emerging wireless networks, mobile systems, social networks, Internet-of-Things, critical infrastructures, cloud computing, big data analytics, wearable and edge computing, and mobile health. Requires prior knowledge of communications systems or computer networking to be successful in this course.

Prerequisites: EEE 480 or EEE 481

EEE 586 — Nonlinear Control Systems

Course description: Stability theory, including phase-plane, describing function, Liapunov’s method, and frequency domain criteria for continuous and discrete, nonlinear, and time-varying systems.

Prerequisites: EEE 582

EEE 587 — Optimal Control

Course description: Optimal control of systems. Calculus of variations, dynamic programming, linear quadratic regulator, numerical methods, and Pontryagin’s principle.

Prerequisites: EEE 480, EEE 481

EEE 588 — Design of Multivariable Control Systems

Course description: Practical tools for designing robust MIMO controllers. State feedback and estimation, model-based compensators, MIMO design methodologies, CAD, real-world applications.

Prerequisites: EEE 582

EEE 589 — Convex Optimization

Course description: Linear algebra and convex optimization. Vector spaces, matrix algebra, linear programming, Lagrange multipliers, Karush-Kuhn-Tucker (KKT) conditions, duality theory and algorithms for convex optimization. Newton’s method, gradient and steepest descent methods. Algorithms for unconstrained, equality constrained and inequality constrained problems, which include interior point methods. Applications to approximation and data fitting and some geometric problems. Applications to signal processing, communications and control systems. Background in linear algebra necessary to be successful in this course.

Prerequisites: Solid background in Linear Algebra, Multivariate Calculus, Real Analysis, and Basic Probability.

EEE 596 — Adaptive Signal Processing

Course description: Principles and applications of adaptive signal processing, adaptive linear combiner, Wiener least-squares solution, gradient search, performance surfaces, LMS/RLS algorithms, block time/frequency domain LMS.

Prerequisites: EEE 404 or EEE 407, EEE 554

EEE 607 — Speech Compression and Recognition

Course description: Speech and audio coding algorithms for applications in wireless communications and multimedia computing.

Prerequisites: EEE 404 or EEE 407, EEE 554

EEE 625 — Advanced VLSI Design

Course description: Practical industrial techniques, circuits, and architectures appropriate to high-performance and low-power digital VLSI designs such as microprocessors.

Prerequisites: EEE 525

EEE 627 — Oversampling Sigma-Delta Data Converters

Course description: Introduces design and analysis of sigma delta oversampled data converters from an IC design perspective.

Prerequisites: EEE 523

EEE 641 — Advanced Electromagnetic Field Theory

Course description: Cylindrical wave functions, waveguides, and resonators; spherical wave functions and resonators; scattering from planar, cylindrical, and spherical surfaces; Green’s functions.

Prerequisites: EEE 541

EEE 643 — Advanced Topics in Electromagnetic Radiation

Course description: High-frequency asymptotic techniques, geometrical and physical theories of diffraction (GTD and PTD), moment method (MM), radar cross section (RCS) prediction, Fourier transforms in radiation, and synthesis methods.

Prerequisites: EEE 541 and (preferably) EEE 641

EEE 686 — Adaptive Control

Course description: Main topics covered: adaptive identification, convergence, parametric models, performance and robustness properties of adaptive controllers, persistence of excitation, and stability.

Prerequisites: EEE 582

EEE 731 — Advanced MOS Devices

Course description: Threshold voltage, subthreshold current, scaling, small geometry effects, hot electrons, and alternative structures.

Prerequisites: EEE 531