Instruction Mode: Online – Synchronous Instruction Mode: Online – Synchronous Prerequisite: EECS 281 and (MATH 214 or 217 or 296 or 417 or 419) and (EECS 367 or EECS 373). Introduction to electronic circuits. Modalities covered include radiography, x-ray computed tomography (CT), NMR imaging (MRI) and real-time ultra-sound. CourseProfile (ATLAS), EECS 574. Neural Models and Psychological Processes Students working under the supervision of a faculty member plan and execute a research project. Building a search engine involves a lot more than indexing some documents — information retrieval is the study of the interaction between users and large information environments. Techniques for routing and clock tree design. Linear differential and difference equations. To be elected by EE and EES students pursuing the master’s thesis option. CourseProfile (ATLAS), EECS 518 (AOSS 595). Instruction Mode: Online – Synchronous Instruction Mode:  In depth understanding of the device physics and working principle of some basic IC components: metal-semiconductor junctions, P-N junctions, metal-oxide-semiconductor junctions, MOSFETs and BJTs. Introduction and fundamentals of physical, optical and electrical properties of amorphous and microcrystalline semiconductor based devices: MIM structures, Schottky diodes, p-i-n junctions, heterojunctions, MIS structures, thin-film transistors, solar cells, threshold and memory switching devices and large area x-ray radiation detectors. As indicated in the CS-Eng Program Guide, the CS-Eng program includes Flexible Technical Elective courses. Current topics of interest in computer architecture. CourseProfile (ATLAS), EECS 555. Essential elements of game theory, including solution concepts and equilibrium computation. Multiple access networks: ALOHA and splitting protocols, carrier sensing, multi-access reservations. In-depth study of research issues in mobile and pervasive computing systems. Instruction Mode: In Person – Asynchronous, Hybrid – Synchronous, Online – Synchronous Topics of current interest in electrical engineering and computer science. Course goals include learning about important computational models of specific cognitive domains and evaluating the appropriateness and utility of different computational approaches to substantive problems in cognition. CourseProfile (ATLAS), EECS 700. Integrated Microsystems Laboratory The course includes a range of topics such as the quantum vibrator, resonant tunneling, quantum circuits, a quantum flip flop, quantum information, quantum vacuum, and the role of quantum behavior in nano-devices and materials. Prerequisite: permission of instructor or counselor. Prerequisite: EECS 280 and (EECS 203 or Math 465 or Math 565). Microwave Remote Sensing I: Radiometry DC and AC circuit models for diodes, bipolar junction transistors and field-effect transistors; small-signal and piecewise analysis of nonlinear circuits; analysis and design of single-stage and multi-stage transistor amplifiers: gain, biasing and frequency response; op-amp based filter design; non-ideal op-amps. Architectures of single-chip DSP processors. Time- and frequency-domain analysis of RLC circuits. Prerequisite: EECS 281 or graduate standing. RF MEMS Topics covered:  abstractions for simplifying development of distributed systems, techniques used to implement these abstractions, and case studies on the use of these techniques in real-world systems. Introduction to MEMS Natural Language Processing Select which category you would like to search and enter course numbers, title, or keywords. It provides an overview of nonlinear dynamical systems, Lyapunov methods and bifurcation analysis. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Computations, consistency semantics and failure models. Prerequisite: permission of instructor. Architectures for explicit parallelism. Minimum grade of “C”. It covers the foundations of building, using, and managing secure systems. (3 credits) Emphasizes research methods and practice, through explicit instruction, analysis of current literature, and a term project devoted to replicating published findings. Aggressive branch prediction. Prerequisite: graduate standing (3 credits) Advanced Data Mining Robust and reliable design  techniques. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Optical processes in semiconductors, spontaneous emission, absorption gain, stimulated emission. Relations between complexity classes, NP-completeness, P-completeness, and randomized computation. Dissertation/Pre-Candidate This course may be taken for credit more than once under different instructors. Primarily for graduate students. Prerequisite: EECS 216 or EECS 281 or ME 360 or CEE 212 or IOE 333 or Grad Standing. CourseProfile (ATLAS), EECS 596. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Introduction to nonrelativistic quantum mechanics. Semiconductor Lasers and LEDs Grad Course List. EECS 508. Minimum grade of “C”. CourseProfile (ATLAS), EECS 476. Emphasizes construction of systems using graphics application programming interfaces (APIs) and analysis tools. Distributed databases, advanced query optimization, query processing, transaction processing, data models and architectures. Phased arrays. Analysis and design of BJT and MOS multi-transistor amplifiers. Advanced topics and research issues in computer networks. (3 credits) Topics of current interest selected by the faculty. CourseProfile (ATLAS), EECS 564. Indicates preparedness to proceed to EECS 280. Social Computing Systems Advised Prerequisite: EECS 442 or EECS 504 or permission of instructor. (4 credits) CourseProfile (ATLAS), EECS 180. Feedback theory and application to feedback amplifiers. Prerequisite: EECS 530 and graduate standing. Instruction Mode: Online – Synchronous, In-Person – Synchronous Measures of information, such as entropy, conditional entropy, mutual and directed information and Kullback-Leibler divergence; fundamental limits to the performance of communication systems, including source coding (data compression) and channel coding (reliable transmission through noisy media); elementary source and channel coding techniques; information theoretic bounds on the performance of estimation/decision systems. Basic concepts such as speedup, load balancing, latency, system taxonomies. Special Topics in Electrical Engineering and Computer Science (4 credits) (1 credit) Instruction Mode: Online – Synchronous (3 or 4 credits) Models of dynamical devices are developed. Prerequisite: graduate standing or permission of instructor. Microwave Circuits I (4 credits) Using single and multiple inheritance and polymorphism for code reuse and extensibility; basic design idioms, patterns, and notation. Special Topics in Stochastic Systems and Control Instruction Mode: In-Person – Synchronous Stability analysis using Liapunov, input-output and asymptotic methods. Instruction Mode: Online – Synchronous, Hybrid – Synchronous Enforced Prerequisite: EECS 270 and EECS 370 and junior standing or higher. Discrete Mathematics CourseProfile (ATLAS), EECS 605. Lectures and discussion. Mobile App Development for Entrepreneurs CourseProfile (ATLAS), EECS 586. Introduction to Logic Design Principles of designing application-specific computer systems that interact with the physical world. (4 credits) Projects to design and simulate device fabrication sequence. Data Science and Machine Learning Design Laboratory   Optimization of systems described by Markov processes; dynamic programming under perfect and imperfect information, finite and infinite horizons. CourseProfile (ATLAS), EECS 569 (MFG 564). Prerequisite: EECS 470 or graduate standing or permission of instructor. Prerequisite: graduate standing. (4 credits) (4 credits) System architectures. Prerequisite: ENGR 101 or ENGR 151 or EECS 180 or EECS 183. Minimum grade of “C” required for enforced prerequisites. CourseProfile (ATLAS), EECS 517 (NERS 578). CourseProfile (ATLAS), EECS 514. Prerequisite: senior standing. The courses are divided into the 12 research areas a graduate student can major in. Prerequisite: EECS 216 or graduate standing. Theory of transmitting and receiving antennas. Propagation of laser beams: Gaussian wave optics and the ABCD law. Introduction to Distributed Systems Instruction Mode: Online – Synchronous Laplace transforms, transfer functions, poles and zeros, stability. Instruction Mode: Online – Synchronous Principles of modern medical imaging systems. EECS Grading & Repeat Policies Core Courses: Computer Science: EECS 281, 370, 376. Covers fundamental concepts, algorithms, and protocols in cryptography. Design techniques such as approximation, branch-and-bound, divide-and-conquer, dynamic programming, greed and randomization applied to polynomial and NP-hard problems. Design techniques for full-custom VLSI circuits. (3 credits) Power Semiconductor devices, inductors, capacitors. PCB design including power integrity and electromagnetic interference. 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Laboratory involves CAD-based design implemented on an FPGA including elementary interfacing. Topics covered include: propositional and predicate logic, set theory, function and relations, growth of functions and asymptotic notation, introduction to algorithms, elementary combinatorics and graph theory and discrete probability theory. Scope, procedure instantiation, recursion, abstract data types and parameter passing methods. Presents concepts and hands-on experience for designing and writing programs using one or more programming languages currently important in solving real-world problems. This course covers the fundamentals of electric power distribution systems and electric loads, including distribution grid components, topologies, and operational strategies; three-phase unbalanced power flow; electric load modeling, analysis, and control; and emerging topics such as photovoltaic and electric vehicle interconnection, distribution automation, and advanced metering infrastructure. System identification: off-line, recursive. Analysis of time and space utilization. Power System Dynamics and Control Wide-issue processors, in-order vs. out-of-order execution, instruction retirement. CourseProfile (ATLAS), EECS 559. (2 credits) Optical Waves in Crystals (4 credits) This course may be repeated for credit. Semiconductor processing techniques: oxidation, diffusion, deposition, etching, photolithography. Hands-on Robotics Instruction Mode: Online – Synchronous Students work in interdisciplinary teams. Force feedback algorithms for human computer interaction. CourseProfile (ATLAS), EECS 280. (4 credits) Correlations and spectra. Prerequisite: EECS 216 and EECS 301 or graduate standing. Introduction to lossy and lossless source coding for data compression. It covers concepts such as information need, documents and queries, indexing and searching, retrieval evaluation, multimedia and hypertext search, Web search, as well as bibliographical databases. (4 credits) Foundations of Computer Science To be elected by EECS students pursuing the Master of Engineering degree. Medical Imaging Systems Prerequisite: EECS 330, Graduate Standing. Major Design Experience Professionalism (3 credits) The course discusses in detail the theory behind important semiconductor-based experiments such as Hall effect and Hall mobility measurement; velocity-field measurement; photoluminescence; gain; pump-probe studies; pressure and strain-dependent studies. (4 credits) Design of MOS interface circuits. Purpose of feedback. Detailed analysis and design of analog integrated circuits, including power amplifiers, voltage references, voltage regulators, rectifiers, oscillators, multipliers, mixers, phase detectors and phase-locked loops. Data Structures and Algorithms Prerequisite: graduate standing, permission of instructor (to be arranged) (1-4 credits) Organic Electronic Devices and Applications The study of devices, circuits, signals & systems, electromagnetics, and programming, with additional expertise in electronics, power, control, communications, optics, and/or computers to solve problems in any field you choose. Drive, snubber circuits. (4 credits) Linear response, Kramers-Kronig relations, and pulse propagation. Properties of Transistors Minimum grade of “C” required for enforced prerequisites. Coursework comprises extensive reading, research and writing assignments, presentations, quizzes, and the replication project. CourseProfile (ATLAS), EECS 495. Computational Data Science and Machine Learning  Pattern synthesis. (4 credits) Small distrubance (linear) analysis techniques are presented, along with methods for assessing large disturbance (nonlinear) behavior. Prerequisite: permission of instructor. Environmental Systems and Processes I Advisory Prerequisite: CEE 460. Language and Information Enforced Prerequisites: SI 507 or SI 507 Waiver or SI 508 or CSE Grad Standing. Minimum grade requirement of C- for enforced prerequisites. CourseProfile (ATLAS), EECS 443. Fundamental limits in coding and modulation. Polynomial time computability and paradigms of algorithm design. Prerequisite: none. (3 credits) Topics include data and image models, multidimensional and multivariate data, design principles for visualization, hierarchical, network, textual and collaborative visualization, the visualization pipeline, data processing for visualization, visual representations, visualization system interaction design, and impact of perception. Advisory Prerequisites: A prior >= 400-level course on computer system or sensor design and analysis. Mutual impedance. Instruction Mode: In-Person – Synchronous Prerequisite: EECS 414. Practical design work is a significant part of this course. CourseProfile (ATLAS), EECS 565. (4 credits) (Credit cannot be obtained for both EECS 442 and EECS 504.) Both time- and frequency-domain methods are covered. (Students will complete an advanced project.) Non-photorealistic rendering. Instruction Mode: Online – Synchronous Students will engage in the hands-on practice of entrepreneurship by actually inventing, building and marketing their own mobile apps. (3 credits) CourseProfile (ATLAS), EECS 453. CourseProfile (ATLAS), EECS 497. Instruction Mode: Online – Synchronous Physical Processes in Plasmas CourseProfile (ATLAS), EECS 540 (APPPHYS 540). Special topics of current interest in solid-state devices, integrated circuits, microwave devices, quantum devices, noise, plasmas. Instructions executed by a processor and how to use these instructions in simple assembly-language programs. Design rule checking, logic and circuit simulation. (3 credits) Performance analysis: power, bandwidth, data rate and error probability. There is a bias toward large theories and small simulations. CourseProfile (ATLAS), EECS 498. Students submit and present a thesis to be evaluated by the sponsoring faculty member and second reader. Topics include big data systems, frequent itemsets, similarity and cluster analysis, clasification, dimensionality reduction, mining of networks, time series and data streams, and applications (e.g., social network analysis, web search). Instruction Mode: Online – Synchronous Students are introduced to the frontiers of System Science research. Directed Study Laboratory techniques for plasma ionization and diagnosis relevant to plasma processing, propulsion, vacuum electronics, and fusion. Time-varying fields: Faraday’s Law and displacement current. CourseProfile (ATLAS), EECS 481. Plasma Generation and Diagnostics Laboratory For each modality the basic physics is described, leading to a systems model of the imager. CourseProfile (ATLAS), EECS 547 (SI 652). Four aspects of starting high-tech companies are discussed: opportunity and strategy, creating new ventures, functional development, and growth and financing. CourseProfile (ATLAS), EECS 463. This course will present and critically examine contemporary algorithms for robot perception (using a variety of modalities), state estimation, mapping, and path planning. Prerequisite: EECS 592 or equivalent. Laboratory exercises using two state-of-the-art fixed-point processors:  A/D and D/A conversion, digital waveform generators, real-time FIR and IIR filters. (4 credits). Election for dissertation work by a doctoral student who has been admitted to candidate status. Instruction Mode: Online – Synchronous The development of programs for parallel computers. Prerequisite: EECS 330, graduate standing. Introduction to Embedded System Research Advised prerequisite: permission of instructor. Fundamental concepts in programming languages. CourseProfile (ATLAS), EECS 215. Correct Operation for Processors and Embedded Systems Prerequisite: EECS 203, MATH 425 (Stat 425). Electrostatics. Memory structures, including static and dynamic RAM; sequential elements; and interconnects. ing. Principles of light-emitting diodes, including transient effects, spectral and spatial radiation fields. In the modern world we depend on the efficiency of a myriad of societal networks to transact many activities. Prerequisite: EECS 501 and MATH 419. Samuel’s strategies, realistic neural networks, connectionist systems, classifier systems and related models of cognition. Interconnection networks. (3 credits) Introduction to algorithm analysis and O-notation; Fundamental data structures including lists, stacks, queues, priority queues, hash tables, binary trees, search trees, balanced trees and graphs; searching and sorting algorithms; recursive algorithms; basic graph algorithms; introduction to greedy algorithms and divide and conquer strategy. Micro electro mechanical systems (MEMS), devices and technologies. CourseProfile (ATLAS), EECS 566. (3 credits) Instruction Mode: Hybrid – Synchronous, Online – Asynchronous Design for testability. Prerequisite: EECS 215 and EECS 216. (1-4 credits) Wireless Communications Systems (4 credits) Prerequisites: EECS 592 or EECS 492. CourseProfile (ATLAS), EECS 403. (4 credits) Prerequisite: EECS 281 and graduate standing. (4 credits) Micromachining technologies such as laser machining and microdrilling, EDM, materials such as SiC and diamond. Theory of circuit partitioning, floorplanning and placement algorithms. Advanced Lasers and Optics Laboratory Instruction Mode: Online – Synchronous, In-Person – Synchronous Maxwell’s equations, constitutive relations and boundary conditions. Foundations of Artificial Intelligence Instruction Mode: Hybrid/Online – Synchronous (4 credits) Oversampling converters are also discussed. Interface programming using an object-oriented application framework. (4 credits)Geometric modeling: spline curves and surfaces, subdivision surfaces, polygonal meshes, point-based and implicit surfaces. Minimum grade of “C”. Topics include probability axioms, sigma algebras, random vectors, expectation, probability distributions and densities, Poisson and Wiener processes, stationary processes, autocorrelation, spectral density, effects of filtering, linear least-squares estimation and convergence of random sequences. Senior or graduate standing compiler for a high-level programming language is assumed practical. Advanced angular momentum theory, second quantization, non-relativistic quantum electrodynamics, advanced query optimization transaction. Cee 212 or IOE 333 or Grad standing ac machines, induction.... Recordings eecs280x F15, programming and Introductory data structures, Lecture Recordings eecs280x F15, programming and Introductory data,! Ultrashort optical pulses in linear and nonlinear minimum mean squared error estimation, filtering,,. 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