Small distrubance (linear) analysis techniques are presented, along with methods for assessing large disturbance (nonlinear) behavior. Students write several substantial programs dealing with concurrency and synchronization in a multi-task environment, with file systems and with memory management. Design verification: simulation, formal techniques, and post-silicon validation. Emphasis on proven field-effect and bipolar-junction transistors, also including current and speculative nanoelectronic devices. Image Processing Iterative methods of optimization and their convergence properties: transversal filters; LMS (gradient) algorithms. Non-photorealistic rendering. (3 credits) Robot Kinematics and Dynamics Current topics of interest in software systems. D-Algorithm and PODEM. Prerequisite: [(EECS 281 and (MATH 214 or 217 or 296 or 417)); (C or better, No OP/F)] or graduate standing. Prerequisite: preceded or accompanied by EECS 230 or PHYSICS 240. Estimation, Filtering, and Detection Solid-State Device Laboratory Minimun grade of “C” required for enforced prerequasites. Transmission lines. CourseProfile (ATLAS), EECS 493. Analysis and synthesis of linear arrays. Complex hardware/software system design and implementation. CourseProfile (ATLAS), EECS 551. Construction and design of lasers; gaussian beams; nonlinear optics; fiber optics; detectors; dispersion; Fourier optics; spectroscopy. Introduction to Electronic Circuits Prerequisite: EECS 230. Feel free to drop in at the EECS Undergraduate Advising Offices (3415 EECS or 2808 BBB) or email eceadvising@umich.edu. CourseProfile (ATLAS), EECS 399. Dissertation/Pre-Candidate Students develop and carry out a research plan in collaboration with a sponsoring faculty member. Antenna Theory and Design Geometrical optics and propagation in inhomogeneous media. Fundamentals of the theory of computation and complexity theory. (4 credits) (3 credits) (4 credits) Model-predictive control of hybrid systems. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Introduction to analysis and design of hybrid systems and hybrid control systems. The theory includes Hidden Markov Models and the noisy channel model, information theory, supervised and unsupervised machine learning, and probabilistic context-free and context-sensitive grammars. Logic Circuit Synthesis and Optimization Course offerings are subject to change. Prerequisite: EECS 215 and 216 or graduate standing. Continuation of nonrelativistic quantum mechanics. CEE 500. Core Courses: Computer Science: EECS 281, 370, 376. Laplace transforms, transfer functions, poles and zeros, stability. (3 credits) (4 credits) Minimum grade of “C” required for enforced prerequisite. The College of Engineering Bulletin provides a comprehensive list of courses offered at the CoE level. Prerequisite: EECS 311 or 330, or graduate standing. CourseProfile (ATLAS), EECS 499. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Introduction to nonrelativistic quantum mechanics. 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. Emphasis on both the potential breadth of application and intuitive reasonableness of various models. (3 credits) Minimum grade of “C” required for enforced prerequisite. EECS 498-007 / 598-005 Deep Learning for Computer Vision Fall 2020 Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This is a 1-credit hour seminar designed to teach students the essentials of using a computer effectively for EECS students. To be graded satisfactory/ unsatisfactory ONLY. All of these courses are geared toward different audiences, have different prerequisites, and satisfy different program requirements. (4 credits) Acceleration algorithms: culling and level-of-detail. Minimum grade requirement of C- for enforced prerequisites. We encounter it in our everyday lives in the form of E-mail, newspapers, television, the Web, and even in conversations with each other. Advanced Topics in Computer Vision Linear filtering: Wiener and Kalman filtering. Introduction to communications, control and signal processing. CourseProfile (ATLAS), EECS 523. Fundamental limits in coding and modulation. CourseProfile (ATLAS), EECS 643 (PSYCH 643). CourseProfile (ATLAS), EECS 597 (SI 760) (LING 702). Knowledge-Based Systems Instruction Mode:  Linear Feedback Control Systems 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. Credit for college-level introductory programming coursework based on a satisfactory score on an approved exam (e.g., a score of 5 on the AP Computer Science A exam) or on transfer credit for an approved introductory programming course at another college. (4 credits) System-theoretic concepts: causality, controllability, observability, realizations, canonical decomposition, stability. Special Topics in Signal Processing Neural Models and Psychological Processes Analysis of Societal Networks Fundamental concepts in Programming Languages (PL) as well as recent topics and trends in PL research. Instruction Mode: Online – Synchronous Linguistic fundamentals of natural language processing (NLP), part of speech tagging, hidden Markov models, syntax and parsing, lexical semantics, compositional semantics, word sense disambiguation, machine translation. Models: linear and nonlinear stochastic controlled systems, controlled Markov chains. Instruction Mode: In-Person – Synchronous Introduction to information visualization. CourseProfile (ATLAS), EECS 534. Theory of image formation and Fourier transformation by lenses. Lab projects on CAD software development. (4 credits)  Topics include semantics, type systems, program verification using theorem provers, software model checking, and program analysis. Minimum grade of “C”. Advisory: EECS 370. Students will learn about electrical systems operation, specifications and interactions with other modules. 300 Level Courses. While casual information consumers can simply enjoy its abundance and appreciate the existence of search engines that can help them find what they want, information professionals are responsible for building the underlying technology that search engines use. Low-power design. Error correcting codes; linear, cyclic and convolutional codes; encoding and decoding algorithms; performance evaluation of codes on a variety of channels. CourseProfile (ATLAS), EECS 598. CourseProfile (ATLAS), EECS 583. Instructor: Clayton Scott (clayscot) Classroom: GG Brown 1571 Time: MW 10:30--12:00 Office: 4433 EECS Office hours: Monday 1-4 PM or by appointment GSI: Efren Cruz (eecs545.gsi@gmail.com) GSI office hours: Tuesday 12-3, room EECS 2420, or by appointment. Introduction to Algorithms Instruction Mode: Online – Synchronous Instruction Mode: Online – Synchronous Current Topics in Databases Prerequisite: EECS 418 or graduate standing. (4 credits) Performance analysis: power, bandwidth, data rate and error probability. Prerequisite: EECS 281 and graduate standing. (3 credits) Grid Computing. The course will teach concepts and present case studies through lectures, homework, design problems, and a final project. Hybrid system modeling formalisms, specifications (automata theory, temporal logics), verification (barrier certificates, reachable sets, abstraction-based methods) and control synthesis. CourseProfile (ATLAS), EECS 376. EECS 484: Database Management Systems is a course taught by Barzan Mozafari (an Assistant Professor of Computer Science and Engineering at the University of Michigan). This course covers the fundamentals of patents for engineers. Multithreaded processors, small- and large-scale multiprocessor systems. CourseProfile (ATLAS), EECS 892. Survey-only (3 credit) and project (4 credit) versions. Specific topics vary each time the course is offered. Introduction and fundamentals to the passive, active, reflective and emissive flat panel display technologies. (4 credits) Purpose of feedback. Linear differential and difference equations. CourseProfile (ATLAS), EECS 556. CourseProfile (ATLAS), EECS 542. Essential elements of game theory, including solution concepts and equilibrium computation. CourseProfile (ATLAS), EECS 730. Basic principles of optics: light sources and propagation of light; geometrical optics, lenses and imaging; ray tracing and lens aberrations; interference of light waves, coherent and incoherent light beams; Fresnel and Fraunhofer diffraction. Instruction Mode: Online – Synchronous Traveling waves and phasors. Applications of convex geometry and convex optimization in control. (4 credits) Prerequisite: permission of instructor. EECS Course Descriptions. Advised Prerequisite: EECS 380 or concurrent election of one of SI 503 or LING 541; and Graduate Standing. CourseProfile (ATLAS), EECS 587. and one of the approved 400-level team project courses in computing (4 cr.). Minimum grade of “C” required. Fourier series, Fourier transforms, spectrum, frequency response and filtering. Highlights AES, RSA, Discrete Log, Elliptic Curves. Linear Systems Theory (4 credits) (4 credits). Advanced graduate seminar devoted to discussing current research topics in areas of solid-state electronics. CourseProfile (ATLAS), EECS 760. Topics of current interest selected by the faculty. Instruction Mode: Online – Asynchronous Problems are placed in the context of real electricity markets. (4 credits) Prerequisite: EECS 484. Prerequisite: EECS 330 and EECS 334. Teams of 3-5 students complete projects based primarily on their individual interests. Computer-aided design algorithms. For each modality the basic physics is described, leading to a systems model of the imager. Students will be introduced to nanofabrication and characterization techniques. Projects in chip design. CMOS logic circuit families, adders, multipliers, memory arrays, sequential circuits, and interconnects. Our excellence and impact comes through in the work of our two divisions. Introduction to lossy and lossless source coding for data compression. CourseProfile (ATLAS), EECS 418. This course may be taken for credit more than once. CourseProfile (ATLAS), EECS 576. CourseProfile (ATLAS), EECS 548 (SI 649). (2 credits)  And Microsystems with a specific focus on the efficiency of a search engine development, social media and... Nonlinear systems and control Prerequisite: graduate standing in CSE students will learn about electrical systems operation specifications! Economic principles they are based upon applications: filtering, denoising, deconvolution, classification others! 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