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  • 16 299: Introduction to Feedback Control Systems

    12.00 Credits
    Carnegie Mellon University

    This course is designed as a first course in feedback control systems for computer science majors. Course topics include classical linear control theory (differential equations, Laplace transforms, feedback control), linear state-space methods (controllability/observability, pole placement, LQR), nonlinear systems theory, and an introduction to control using computer learning techniques. Laboratory work includes implementation of controllers robotic devices. Priorities will be given to computer science majors with robotics minor.
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  • 16 311: Introduction to Robotics

    12.00 Credits
    Carnegie Mellon University

    This course presents an overview of robotics in practice and research with topics including vision, motion planning, mobile mechanisms, kinematics, inverse kinematics, and sensors. In course projects, students construct robots which are driven by a microcontroller, with each project reinforcing the basic principles developed in lectures. Students nominally work in teams of three: an electrical engineer, a mechanical engineer, and a computer scientist. This course will also expose students to some of the contemporary happenings in robotics, which includes current robot lab research, applications, robot contests and robots in the news.
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  • 16 362: Mobile Robot Programming Laboratory

    12.00 Credits
    Carnegie Mellon University

    This course is a comprehensive hands-on introduction to the concepts and basic algorithms needed to make a mobile robot function reliably and effectively. We will work in groups with Nomad Scout robots and interface to them using laptops programmed in the Java programming language in a modern code development environment. This is a lab course with emphasis is on hands-on learning. You will get experience in this course in addition to some theory. Lectures are focussed on the content of the next lab. There is a lab every week and they build on each other so that a complete robot software system results. The course will culminate with a class-wide competition that tests the performance of all of your code implemented in the semester. Typically, your code is at least 5000 lines of Java written jointly with 2 other people. Students must have a 2nd year science/engineering level background in mathematics (matrices, vectors, coordinate systems, basic kinematics) to succeed in the course. Students must have mastered (1 programming course experience) computer programming in a procedure language like C or Java to succeed in the course. The following experience, while not required, will be an asset: a) familiarity with basic computer science data structures and algorithms (equivalent to taking 15-121), b) experience with Eclipse and Subversion or equivalent software development tools, c) experience collaboratively designing and implementing a software system >= 5,000 lines of code.
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  • 16 597: Undergraduate Reading and Research

    0.00 - 48.00 Credits
    Carnegie Mellon University

    No course description available.
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  • 17 413: Software Engineering Reflection

    6.00 Credits
    Carnegie Mellon University

    No course description available.
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  • 18 050: Study Abroad

    0.00 Credits
    Carnegie Mellon University

    No course description available.
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  • 18 051: Study Abroad

    0.00 Credits
    Carnegie Mellon University

    Students are encouraged to various international collaborative programs offered through the department of Electrical and Computer Engineering.
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  • 18 100: Introduction to Electrical and Computer Engineering

    12.00 Credits
    Carnegie Mellon University

    The goals of this freshman engineering course are: * To introduce basic concepts in electrical and computer engineering in an integrated manner; * To motivate basic concepts in the context of real applications; * To illustrate a logical way of thinking about problems and their solutions, and; * To convey the excitement of the profession. These goals are attained through analysis, construction and testing of an electromechanical system (e.g., a robot) that incorporates concepts from a broad range of areas within Electrical and Computer Engineering. Some of the specific topics that will be covered include system decomposition, ideal and real sources, Kirchhoff's Current and Voltage Laws, Ohm's Law, piecewise linear modeling of nonlinear circuit elements, Ideal Op-Amp characteristics, combinational logic circuits, Karnaugh Maps, Flip-Flops, sequential logic circuits, and finite state machines. 3 hrs. lec., 1 hr. rec., 3 hr. lab.
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  • 18 200: Emerging Trends in Electrical and Computer Engineering

    1.00 Credits
    Carnegie Mellon University

    This class consists of a series of individual lectures given by different faculty members and distinguished alumni. The lectures are designed to serve the following purposes: 1) provide students a good understanding of our curriculum structure and the courses in each of our five principle subject areas; 2) introduce to students the emerging trends in electrical and computer engineering and the relevance of our courses; 3) present to students our faculty's research fields; 4) discuss basic learning and working ethics; 5) prepare students career-making skills; 6) introduce new undergraduate courses and research opportunities. The class will contain 12 lectures from faculty members, 2 lectures on learning and working ethics, and 2 lectures from our alumni. Students are required to attend each lecture. An award for the best lecturer, selected by students, will be given at the end of the semester. 1 credit, 1 hr per week, pass/fail, required to graduate. Sophomore standing required.
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  • 18 202: Mathematical Foundations of Electrical Engineering

    12.00 Credits
    Carnegie Mellon University

    This course covers topics from engineering mathematics that serve as foundations for descriptions of electrical engineering devices and systems. It is the corequisite mathematics course for 18-220, Fundamentals of Electrical Engineering. The topics include: (1) MATLAB as a robust computational tool, used to reinforce, enrich and integrate ideas throughout the course, including software exercises and projects in combination with homework assignments, (2) Complex Analysis, including rectangular and polar representations in the complex plane with associated forms of complex arithmetic, powers, roots and complex logarithms, complex differentiation, analytic functions and Cauchy-Riemann equations, complex Taylor series, complex exponential, sinusoidal and hyperbolic functions, and Euler's formula, (3) Fourier Analysis, including orthogonality of sinusoids, trigonometric and exponential forms of Fourier series, Fourier integrals and Fourier transforms, (4) Linear, Constant-Coefficient Differential Equations, including complex exponential solutions to homogeneous equations and particular solutions with polynomial and sinusoidal driving functions described by phasors, (5) Difference Equations, with emphasis upon their relationship to differential equations, and (6) Linear Algebra and Matrices, including matrix arithmetic, linear systems of equations and Gaussian elimination, vector spaces and rank of matrices, matrix inverses and determinants, eigenvalue problems and their relationship to systems of homogeneous differential equations. 4 hrs. lec., 1hr. rec.
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