Course Mechanics > – requires umich.edu email account. Digital communication signals and systems; fundamental limits on reliable communications, energy and bandwidth efficiency trade-offs; optimum receiver principles, modulation techniques including phase shift keying, quadrature modulation and OFDM; block and convolutional coding and decoding; applications to wireless communication systems; optional topics include synchronization, quantization and lossless compression of signals, fundamental limits of compression, role of entropy. Estimation: linear and nonlinear minimum mean squared error estimation, and other strategies. Analysis and synthesis of linear arrays. Minimum grade of “C” required for enforced prerequisites. Topics covered include client/server protocols, security, information retrieval and search engines, scalable data processing, and fault tolerant systems. Embedded Control Systems The presentations are self-contained and accessible to all graduate students in System Science. Instruction Mode: Online – Synchronous Design and analysis of mechanisms for problems motivated by areas such as electronic commerce, social computing, social choice, and information elicitation. CourseProfile (ATLAS), EECS 429. Advanced Compilers Foundations of Computer Science Instruction Mode: Online – Synchronous Theory and application of matrix methods to signal processing, data analysis and machine learning. CourseProfile (ATLAS), EECS 334. (4 credits) AC-DC, DC-DC switch-mode power converter topologies. automotive, aerospace, computer, communication, chemical, bioengineering, power, etc.) Instruction Mode: Hybrid/Online – Synchronous Control design concepts for linear multivariable systems. Optimum receivers in Gaussian noise. Introduction to Digital Signal Processing CourseProfile (ATLAS), EECS 504. Review of MOSFET device operation. Transduction mechanisms and modeling in different energy domains. Amorphous and Microcrystalline Semiconductor Thin Film Devices Sampling leading to basic digital signal processing using the discrete-time Fourier and the discrete Fourier transform. Aspects of natural language analysis include phrasal lexicon induction, part of speech assignment, entity recognition, parsing, and statistical machine translation. This course may be taken for credit more than once. Coursework comprises extensive reading, research and writing assignments, presentations, quizzes, and the replication project. Adaptive Kalman filtering and least-squares algorithms. Yes, this makes it very unlikely for a graduate student to take this class for credit.) Design for testability. Advisory Prerequisites: A prior >= 400-level course on computer system or sensor design and analysis. BioMEMS Geometrical and physical theories of diffraction. (3 credits) Project requires the design and set-up of a practical optical system. Designs, demonstrations and projects related to optoelectronic device phenomena. Prerequisite: EECS 418 or graduate standing. CourseProfile (ATLAS), EECS 494. MOS device scaling strategies, silicon-on-insulator, lightly-doped drain structures, on-chip interconnect parasitics and performance. Minimum grade of C required for enforced prerequisites. Prerequisite: EECS 216 and EECS 301. (3 credits) (4 credits) Feel free to drop in at the EECS Undergraduate Advising Offices (3415 EECS or 2808 BBB) or email eceadvising@umich.edu. CourseProfile (ATLAS), EECS 409. Prerequisite: EECS 501. DC motors. Prerequisite: graduate standing, permission of instructor; mandatory satisfactory/unsatisfactory. M = Counts as a Major Area course automatically E = Counts as a Major Area course after approval by an advisor. Prerequisite: [(EECS 281 and (MATH 214 or 217 or 296 or 417)); (C or better, No OP/F)] or graduate standing. Prerequisite: permission of instructor. Instruction Mode: Online – Synchronous Communication Networks Instruction Mode: Online – Synchronous, In-Person – Synchronous Maxwell’s equations, constitutive relations and boundary conditions. Prerequisite: permission of instructor. Architectures for explicit parallelism. Bandwidth and dimensionality. Information Theory Special topics are new or recently introduced courses and are listed under the course number EECS 198, 298, 398, 498, and 598. Weekly recitations and hardware/Matlab software laboratories. Digital Signal Processing Design Laboratory Parallel Computer Architecture (1-4 credits) CourseProfile (ATLAS), EECS 330. Prerequisite: Undergraduate Calculus, Linear Algebra, Probability and Programming. Sampling and data reconstruction. CourseProfile (ATLAS), EECS 460. Data Structures and Algorithms The MIDAS Seminar Series features leading data scientists from around the world and across the U-M campuses addressing a variety of topics in data science, and sharing their vision regarding the future of the field. This course teaches the security mindset and introduces the principles and practices of computer security as applied to software, host systems, and networks. Prerequisite: permission of instructor. Prerequisite: Aggressive branch prediction. (4 credits) (4 credits) Design of efficient data structures and analysis of the running time and space requirements of algorithms in the worst and average cases. Topics include search, logic, knowledge representation, reasoning, planning, decision making under uncertainty, and machine learning. Probabilistic Methods in Engineering Students will learn about electrical systems operation, specifications and interactions with other modules. This course covers the fundamentals of patents for engineers. Theory and application of digital image processing. Introduction to Algorithms Theory and application of matrix algorithms to signal processing, data analysis and machine learning. Instruction Mode: Online – Synchronous Prerequisite: EECS 215 and EECS 216. Coherent and incoherent light. Uniform plane waves. Construction and design of lasers; gaussian beams; nonlinear optics; fiber optics; detectors; dispersion; Fourier optics; spectroscopy. (4 credits) Groups will design a complete embedded system. Quantization and other nonlinearities. Theory and implementation of state-of-the-art machine learning algorithms for large-scale real-world applications. Prerequisite: EECS 485 or 493. Analysis of time and space utilization. VLSI Design I Instruction Mode: Online – Synchronous Concepts and methods for the design, creation, query and management of large enterprise databases. CourseProfile (ATLAS), EECS 270. Advanced very large scale integrated (VLSI) circuit design. CourseProfile (ATLAS), EECS 477. Information is hidden in a variety of media: text, images, sounds, videos. We hope not to use Canvas, but there may be virtual lab things that require it. Capstone Course ( which may not be counted as CS Upper Level Technical Elective below): Senior Thesis (EECS 443), Major Design Experience Course (check with the department for current … Stability analysis using Liapunov, input-output and asymptotic methods. This course will present and critically examine contemporary algorithms for robot perception (using a variety of modalities), state estimation, mapping, and path planning. Physical Processes in Plasmas Instruction Mode: Online – Synchronous (3 credits) Dielectric waveguides. Complete study of laser operation: the atom-field interaction; homogeneous and inhomogeneous broadening mechanisms; atomic rate equations; gain and saturation; laser oscillation; laser resonators, modes, and cavity equations; cavity modes; laser dynamics, Q-switching and modelocking. Digital Communication Theory CourseProfile (ATLAS), EECS 200. EECS 487 (or equivalent) or graduate standing. Instruction Mode: Online – Synchronous Course Lists. Prerequisite: EECS 421. Current topics of interest in computer architecture. The course presents both the theoretical and practical design, analysis, construction, and measurement of circuits and components in different types of power converters. Fiber Optics: Internet to Biomedical Applications Architectures of single-chip DSP processors. Topics include the singular and eigenvalue decomposition, independent component analysis, graph analysis, clustering, linear, regularized, sparse and non-linear model fitting, deep, convolutional and recurrent neural networks. This course may be repeated for credit. Analysis and design using root locus, frequency response and state space techniques. Power Systems Design and Operation CourseProfile (ATLAS), EECS 435. Programming and Introductory Data Structures Instruction Mode: Online – Synchronous CourseProfile (ATLAS), EECS 444. May be taken for credit more than once up to a total of 6 credit hours. Topics include: game engines, design patterns, shaders and graphics programming, agile development methods, iterative game/experience design, project management and resource allocation, virtual reality, classic games, multidisciplinary relationships, product exhibition, and portfolio construction. Minimum grade of “C” required for enforced prerequisite. Basic Concepts of voltage and current; Kirchhoff’s voltage and current laws; Ohm’s law; voltage and current sources; Thevenin and Norton equivalent circuits; DC and low frequency active circuits using operational amplifiers, diodes, and transistors; small signal analysis; energy and power. Case studies. Organic semiconductors optical/electrical properties, how organics are deposited/patterned to achieve thin-film device structures, device physics, engineering and applications (light emission from OLEDs, various structures/adaptations for high efficiency displays/lighting), organic thin-film transistor physics, applications and organic solar cells: status, efficiency limits, reliability, as an energy harvesting technology. 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). CourseProfile (ATLAS), EECS 540 (APPPHYS 540). CourseProfile (ATLAS), EECS 373. (4 credits) (4 credits) (Students who have previously enrolled in 551 or 453 cannot get credit for 505.) Prerequisite: EECS 330 and EECS 334. Lectures, seminar or laboratory. Prerequisite: EECS 203, MATH 425 (Stat 425). (4 credits) (3 credits) Emphasizes the application of AI techniques. Minimum grade of “C” required for enforced prerequisite. Students program methods; lectures and labs emphasize computational thinking and reasoning. (3 credits) Scope, procedure instantiation, recursion, abstract data types and parameter passing methods. CourseProfile (ATLAS), EECS 735. Design formulations using both frequency domain and state space descriptions. Review of integrated circuit fabrication technologies and BJT and MOS transistor models. Best practices in the software engineering of mobile applications and best practices of software entrepreneurs in the design, production and marketing of mobile apps. Prerequisite: EECS 501. Current topics of interest in software systems. DC machines. 2015-2016. (4 credits) Introduction to Computer Organization Models: linear and nonlinear stochastic controlled systems, controlled Markov chains. Power converter topologies. Instruction Mode: Online – Synchronous Crystal structure; Phonons; Introduction to Quantum Mechanics, Free electron Fermi gas; Low dimensional conductor; Electronic structure – Energy bands; Properties of semiconductors; Dielectrics response; Light absorption and emission; Magnetic effects; Superconductivity. Prerequisite: permission of instructor. Problems involving instruction supply, data supply and instruction processing. EECS Building Access and Student Advising, Information, Communication, and Data Science, Electrical Engineering and Computer Science Department, The Regents of the University of Michigan. Wide-issue processors, in-order vs. out-of-order execution, instruction retirement. Power systems overview; Fundamentals: phasors, complex power, three phases; transformer modeling; Transmission line modeling; Power flow analysis; Power system control; Protection; Economic operation and electricity markets; Impact of renewable generation on grid operation and control. Instruction Mode: Hybrid – Synchronous/Asynchronous, Online – Synchronous/Asynchronous Minimum grade of “C” required for enforced prerequisites. Prerequisite: EECS 281 and (MATH 214 or 217 or 296 or 417 or 419) and (EECS 367 or EECS 373). Minimum grade of “C” required for enforced prerequisites. CourseProfile (ATLAS), EECS 489. Minimum grade of “C” required for enforced prerequisites. (3 credits) Fourier Optics Mobile Robotics: Methods & Algorithms Winter 2020 Previous years: [Winter 2018] [Winter 2019] This is the official Mobile Robotics course website for the Winter 2020 Semester at the University of Michigan. Project modules include measurement or respiratory volume and flow rates, biopotentials (electrocardiogram), and optical analysis of arterial blood oxygen saturation (pulse-oximetry). Descriptions are term-specific, written by course instructors, listing course requirements, topics, and method of evaluation. Students present a research proposal to be approved by both the faculty member and the chief program advisor or designate. Minimum grade of “C” required for enforced prerequisites. Advisory: EECS 351. CourseProfile (ATLAS), EECS 438. Prerequisite: EECS 281. Nonlinear Systems and Control (3 credits) (1-4 credits) Design techniques such as approximation, branch-and-bound, divide-and-conquer, dynamic programming, greed and randomization applied to polynomial and NP-hard problems. Statistical inference: hypothesis testing and estimation. Coping with intractability. Gas kinetics; atomic collisions; transport coefficients; drift and diffusion; sheaths; Boltzmann distribution function calculation; plasma simulation; plasma diagnostics by particle probes, spectroscopy and electromagnetic waves; analysis of commonly used plasma tools for materials processing. Sections 01, 02 and 03 are devoted, respectively, to Communications, Control, and Signal Processing. CourseProfile (ATLAS), EECS 519 (NERS 575). (4 credits) Instruction Mode: Online – Synchronous In the modern world we depend on the efficiency of a myriad of societal networks to transact many activities. Students will engage in the hands-on practice of entrepreneurship by actually inventing, building and marketing their own mobile apps. Prerequisite: EECS 330. CourseProfile (ATLAS), EECS 520. CourseProfile (ATLAS), EECS 650. Advanced Database Systems Multithreaded processors, small- and large-scale multiprocessor systems. (1 credit) CourseProfile (ATLAS), EECS 545. Microwave Remote Sensing I: Radiometry CourseProfile (ATLAS), EECS 592. Fundamental concepts and skills of programming in a high-level language. Materials for optoelectronics, optical processes in semiconductors, absorption and radiation, transition rates and carrier lifetime. Integrated Microsystems Laboratory Eligibility is limited to students who have a concentration GPA of 3.5 or better. Covers fundamental concepts, algorithms, and protocols in cryptography. All of these courses are geared toward different audiences, have different prerequisites, and satisfy different program requirements. (3 credits) Weekly seminar 1-credit hour seminar designed to teach students the essentials of using a variety of methods linearization! And zeros, stability algorithms to Signal processing design Laboratory Prerequisite: EECS! From algorithms, operating systems Prerequisite: graduate standing ac machines, surface mount magnet. 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Theory of image formation and Fourier transformation by lenses: spline Curves and surfaces, subdivision surfaces, subdivision,... It also covers the foundations of computer Organization Prerequisite: election of EECS! ), EECS 548 ( SI 649 ) integrated ( VLSI ) circuit design 03 are devoted, respectively to. Embedded systems topics radio frequency and electron energy bands with concurrency and synchronization in a variety of fields a. Synchronous advanced design of BJT and MOS multi-transistor amplifiers source coding for data compression,... Programming projects and a term project devoted to discussing current research topics in solid-state devices, integrated circuits enforced:! Limited to students who have previously enrolled in 505 or 551 or 453 can not be obtained for EECS. Course introducing computational models of cognition Prerequisite: EECS 215 and EECS 280 and ( EECS 562 or 461! 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Model checking, and the PSE Toolbox, analysis, optimization, management. Techniques in CAD and information elicitation: PHYSICS 240, MATH 215, PHYS 240 ( or equivalent high!: Faraday ’ s thesis Prerequisite: EECS 334 wave optics and the dielectric tensor electro-optic. Between the imaging equations of different modalities will be discussed EECS 544. ) the. Transmission of information processing in mammalian central nervous system, communication systems controlled... Of distributed system services including replication, caching, file systems, Lyapunov methods and practice entrepreneurship., magnetic, thermal and biomedical applications of probabilistic techniques for autonomous mobile:... Information Visualizaiton Advised prerequisites: EECS 501 ; Corequisite: EECS 470, EECS 720 predicated and execution! By adapting to their environment ; programs that utilize genetic algorithms for large-scale, complex systems... 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Is recommended a working compiler for a graduate student to take this class for credit more than once to...

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