CourseProfile (ATLAS), EECS 589. Instruction Mode: Online – Synchronous Operating system design and implementation: multi-tasking; concurrency and synchronization; inter-process communication; deadlock; scheduling; resource allocation; memory and storage management; input-output; file systems; protection and security. CourseProfile (ATLAS), EECS 552 (APPPHYS 552). (3 credits) This course may be repeated for credit. Purpose of feedback. Software Development for Accessibility Prerequisite: View a historical document of past teaching assignments for EECS classes. Descriptions of undergradaute liberal arts courses in the college of literature, science, and the arts at the university of Michigan. Advisory Prerequisite: EECS 505 or 551 or graduate equivalent. Minimun grade of “C” required for enforced prerequasites. NA 568/EECS 568/ROB 530. Topics include semantics, type systems, program verification using theorem provers, software model checking, and program analysis. Mobile App Development for Entrepreneurs High-Tech Entrepreneurship Introduction to Autonomous Robotics Topics include search, logic, knowledge representation, reasoning, planning, decision making under uncertainty, and machine learning. Image Processing Optical, electronic, magnetic, thermal and biomedical applications of plasmonics will be discussed. Topics covered will include the following: lexical scanning, parsing (top-down and bottom-up), abstract syntax trees, semantic analysis, code generation and optimization. Instruction Mode: Online – Synchronous CourseProfile (ATLAS), EECS 591. Current topics of interest in software systems. Projects to design and simulate device fabrication sequence. Computer-aided design for MEMS layout, fabrication and analysis. Computer-aided design algorithms. Prerequisite: permission of instructor or graduate standing. If you would like to have a course evaluated for potential EECS course credit (for example you took CS 367 at the University of Wisconsin and you want to know if it will count as an EECS course here at UM-Ann Arbor) you should make an appointment in the EECS Undergraduate Advising Office to … (4 credits) CourseProfile (ATLAS), EECS 559. Topics include control-flow and data-flow analysis, optimization, instruction scheduling, register allocation. Prerequisite: EECS 281 or graduate standing in CSE. Students write several substantial programs implementing protocols at different layers of the network stack. Horns, reflector and lens antennas. 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. Foundations of Computer Vision Advanced topics in microwave measurements: power spectrum and noise measurement, introduction to state-of-the-art microwave test equipment, methods for measuring the dielectric constant of materials, polarimetric radar cross section measurements, near field antenna pattern measurements, electromagnetic emission measurement (EM compatibility). (4 credits). (4 credits) Substantial student-defined team design project. Advised Prerequisite: (EECS 562) or (EECS 560 and permission of instructor.) Basic interdisciplinary concepts needed to implement a microprocessor based control system. Linear Feedback Control Systems Prerequisite: EECS 281 or equivalent. Acceleration algorithms: culling and level-of-detail. (3 credits) General properties and design of linear and nonlinear solid state microwave circuits including: amplifier gain blocks, low-noise, broadband and power amplifiers, oscillators, mixer and multiplier circuits, packaging, system implementation for wireless communication. Students working under the supervision of a faculty member plan and execute a research project. Computer Vision Tutorial Lecture Series in System Science CourseProfile (ATLAS), EECS 513. Instruction Mode: Online – Synchronous The development of programs for parallel computers. CourseProfile (ATLAS), EECS 250 (NAVSCI 202). Prerequisite: EECS 281 or EECS 478 or graduate standing. Prerequisite: EECS 301 or MATH 425 or STATS 250 or STATS 412 or STATS 426 or IOE 265 or graduate standing. Prerequisite: MATH 215, PHYS 240 (or 260), EECS 215. (3 credits) Microarchitecture Theoretical topics include subspaces, eigenvalue and singular value decomposition, projection theorem, constrained, regularized and unconstrained least squares techniques and iterative algorithms. Digital System Testing Prerequisite: EECS 501, EECS 554. Instruction Mode: Online – Synchronous Prerequisite: EECS 434. Groups design an advanced (e.g. To be graded on a satisfactory/unsatisfactory basis ONLY. Sensors and actuators. Prerequisite: EECS 537. High performance and low power VLSI systems. Special topics of current interest in solid-state devices, integrated circuits, microwave devices, quantum devices, noise, plasmas. Students design and model complete converters. Combinational circuit testing. Interactive Computer Graphics Instruction Mode: Hybrid – Synchronous, Online – Synchronous In the modern world we depend on the efficiency of a myriad of societal networks to transact many activities. Interferometry and theory of coherence. (4 credits) Minimum grade of “C” required for enforced prerequisites. Review of single variable systems and extensions to multivariable systems. Fluency in a standard object-oriented programming language is assumed. Instruction Mode: Hybrid – Synchronous, Online – Synchronous Introduction to nonrelativistic quantum mechanics. Sensor and actuator analysis and design through CAD. EECS Course Descriptions. Introduction to the mathematical foundations of computer science. Current Topics in Databases Prerequisite: permission of instructor (to be arranged) Advised Prerequisite: ENGR 100 or ENGR 101 or ENGR 151 or EECS 180 or EECS 280. Lecture, seminar, or laboratory. As a general rule, courses that meets one of the following criteria can meet the cognate requirement: Any course outside EECS department; Any course on the ECE Course List that does not have a M or E in the entire row of the course. Reciprocity. Instruction Mode: Hybrid – Synchronous, Online – Asynchronous CourseProfile (ATLAS), EECS 445. Multiple team projects, culminating in a major design experience (MDE) project. Propagation, optical amplification and nonlinearities in fibers are discussed, and examples include transmission systems and lasers. Instruction Mode: Online – Synchronous CourseProfile (ATLAS), EECS 555. (1-4 credits) Topics of current interest selected by the faculty. 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 … Our excellence and impact comes through in the work of our two divisions. This course covers the basics of fibers and applications in fields as diverse as highpower and broadband lasers, bio-medical diagnostics and therapeutics, telecommunications and internet communications. Real world projects, usually in partnership with hospitals for specific disabled clients. (4 credits). Scattering by half plane (Wiener-Hopf method) and wedge (Maliuzhinets method); edge diffraction. 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, Online – Synchronous Fundamental concepts in programming languages. Advanced very large scale integrated (VLSI) circuit design. Problems are placed in the context of real electricity markets. Programs and automata that “learn” by adapting to their environment; programs that utilize genetic algorithms for learning. Samuel’s strategies, realistic neural networks, connectionist systems, classifier systems and related models of cognition. Other topics covered include litigation, ethics and licensing. (3-4 credits) May be taken more than once up to a total of 6 credit hours. Theory of circuit partitioning, floorplanning and placement algorithms. (4 credits) Instruction Mode: Online – Synchronous Artificial intelligence systems, such as NETL and SOAR, are examined for their impact upon machine learning and cognitive science. Design of stabilizing controllers using a variety of methods: linearization, absolute stability theory, vibrational control, sliding modes and feedback linearization. Not intended for Engineering students (who should take ENGR 101), nor for CS majors in LSA who qualify to enter EECS 280. Systems will incorporate state-of-the-art technology dealing with disability, illness, and accessibility. (1 credit) CEE 500. Instruction Mode: Online – Synchronous Pragmatic aspects of the production of software systems, dealing with structuring principles, design methodologies and informal analysis. Applied Quantum Mechanics II Quadrature decoding. Control design concepts for linear multivariable systems. Prerequisite: graduate standing or permission of instructor. Students submit and present a thesis to be evaluated by the sponsoring faculty member and second reader. Instruction Mode: Advanced topics in data mining. CourseProfile (ATLAS), EECS 398. Prerequisite: permission of instructor. CourseProfile (ATLAS), EECS 492. Both time- and frequency-domain methods are covered. CourseProfile (ATLAS), EECS 536. Interconnection networks. (4 credits)Geometric modeling: spline curves and surfaces, subdivision surfaces, polygonal meshes, point-based and implicit surfaces. Semester-long, open-ended design based on a societally-relevant challenge. The course presents both the theoretical and practical design, analysis, construction, and measurement of circuits and components in different types of power converters. Software Engineering Instruction Mode: Hybrid – Synchronous, Online – Synchronous Instruction Mode: Online – Synchronous System Design of a Search Engine Students who have previously enrolled in 453 or 505 cannot get credit for 551. Quantum Theory of Light Lasers Ultrafast Optoelectronics. (4 credits) Topics include small-signal models; digital and analog control; switched, sampled-data, and averaged models; large signal considerations; distributed power; and tools for computer modeling and simulation. Central to this course is a team project in real-time DSP design (including software and hardware). CourseProfile (ATLAS), EECS 373. Introduction to Antennas and Wireless Systems Modeling and identification. Advanced Data Mining Prerequisite: EECS 215 and PHYSICS 240 or 260. Prerequisite: (EECS 423 or EECS 425) and EECS 311 and EECS 320. CourseProfile (ATLAS), EECS 429. CourseProfile (ATLAS), EECS 481. Theoretical foundations. CourseProfile (ATLAS), EECS 537 (APPPHYS 537). 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. CourseProfile (ATLAS), EECS 670. Design of algorithms for nonnumeric problems involving sorting, searching, scheduling, graph theory and geometry. CourseProfile (ATLAS), EECS 399. CourseProfile (ATLAS), EECS 990. Students are expected to work in project teams. Major design project to implement a RISC processor. See lists alphabetically and numerically. The mathematical tools required to describe spatial motion of a rigid body will be presented in full. (A 4-credit option is available with addition of a substantial design and simulation component to the project.) Emphasis is placed on performance trade-offs in protocol and architecture designs. Instruction Mode: Online – Synchronous Instruction Mode: Online – Synchronous CourseProfile (ATLAS), EECS 631. Mobile Robotics: Methods and Algorithms (3 credits) Prerequisite: EECS 311 or EECS 312 or EECS 414 or graduate standing. Instruction Mode: In Person – Synchronous, Online – Synchronous, In Person – Asynchronous Advanced issues in VLSI design addressing the areas of high performance, low power and reliability. Instruction Mode: In Person – Synchronous, Hybrid – Synchronous, Online – Synchronous Applications of Laplace transform theory to RLC circuit analysis. The specific topics vary each time the course is offered. Design techniques such as approximation, branch-and-bound, divide-and-conquer, dynamic programming, greed and randomization applied to polynomial and NP-hard problems. Fundamental concepts and skills of programming in a high-level language. Numerical techniques for antennas and scattering; integral representation: solutions of integral equations: method of moments, Galerkin’s technique, conjugate gradient FFT; finite element methods for 2-D and 3-D simulations; hybrid finite element/boundary integral methods; applications: wire, patch and planar arrays; scattering composite structures. Magnetosphere and Solar Wind Analog Circuits Instruction Mode: Online – Synchronous Design principles for multidisciplinary team projects, team strategies, entrepreneurial skills, ethics, social and environmental awareness, and life long learning. Instruction Mode: Online – Synchronous Principles and practice of distributed system design. Data structures: strings, arrays, records, lists, tables. Prerequisite: preceded or accompanied by EECS 230 or PHYSICS 240. (4 credits) Techniques: scalar and vector quantization; transform and differential coding; variable-length, Lempel-Ziv and arithmetic lossless coding. Advanced MEMS Devices and Technologies Message delay: Markov processes, queuing, delays in statistical multiplexing, multiple users with reservations, limited service, priorities. • Talk to the EECS Undergraduate Advising Office staff. Analysis of Electric Power Distribution Systems and Loads Emphasizes the application of AI techniques. Discussion of major programming approaches used in the design and development of knowledge-based systems. Signal acquisition, processing analysis and display are performed. are discussed. Boolean algebra, digital design techniques, logic gates, logic and state minimization, standard combinational circuits, latches and flip-flops, sequential circuits, synthesis of synchronous sequential circuits, state machines, FPGAs, memories, arithmetic circuits, and computer-aided design. Semiconductor material and device fabrication and evaluation: diodes, bipolar and field-effect transistors, passive components. Instruction Mode: Online – Synchronous Theory and application of matrix methods to signal processing, data analysis and machine learning. Following a brief overview, the course will examine: (1) Biological principles governing brain computation (e.g., population coding, computation maps, adaptive plasticity, self-organization and modularization, etc. Laboratory experience with power electronic circuits. Interface programming using an object-oriented application framework. Probability and Statistics: STATS 250, 280, 412, 426, STATS 265/IOE 265, ECON 451 (F17), or TO 301 (F17). Theory of image formation and Fourier transformation by lenses. (3 credits) Emphasis is given to development of large, complex software systems. Prerequisite: EECS 281 and EECS 376 Minimum grade of “C” required for enforced prerequisites. EECS Course List (links to Michigan Engineering Bulletin) Special Topics Courses for the Current Term. Advanced topics on stochastic systems such as stochastic calculus, nonlinear filtering, stochastic adaptive control, decentralized control and queuing networks. Prerequisite: EECS 460 or AEROSP 348 or MECHENG 461 and AEROSP 550 (EECS 560). Design project(s). CourseProfile (ATLAS), EECS 597 (SI 760) (LING 702). Minimum grade of “C” required for enforced prerequisites. (4 credits) (4 credits). Advanced Embedded Systems Linear differential and difference equations. Enforced Prerequisite: EECS 215 and Math 216. Prerequisite: (EECS 270 and EECS 312) or graduate standing. Instruction Mode: Online – Synchronous (4 credits) CourseProfile (ATLAS), EECS 547 (SI 652). 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. Optimization Methods in Signal Processing and Machine Learning CMOS circuit delay and power analysis. Semiconductor processing techniques: oxidation, diffusion, deposition, etching, photolithography. CourseProfile (ATLAS), EECS 521. Prerequisite: permission of instructor. CourseProfile (ATLAS), EECS 584. Prerequisite: permission of instructor. Apply electrical engineering concepts in circuits, computing, control, sensors, optics, power, signal processing, and wireless communications to a system such as a robot, and adapt the system to achieve competition objectives within defined engineering constraints. Discrete Event Systems Computability, undecidability, and logic. (4 credits) Introduction to Algorithms Neural Models and Psychological Processes CourseProfile (ATLAS), EECS 410 (ENGR 410) Patent Fundamentals for Engineers Programming and Introductory Data Structures Minimum grade of “C” required for enforced prerequisites. CourseProfile (ATLAS), EECS 592. Solid State Devices (3 credits) 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. Chirped-pulse amplification. Software methods and implementation for robot perception, world mapping, and control, using physical robots. (4 credits) CourseProfile (ATLAS), EECS 496. Principles of Real-Time Computing DSP methods and applications. Geometric and algebraic approaches to the analysis and design of nonlinear control systems. Prerequisite: none. 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