• ECTS

    5 credits

  • Component

    Faculty of Science

Description

Optimization

  • Linear optimization
  • Nonlinear optimization (gradient method, optimal-step gradient, Lagrange multipliers)
  • Optimization applied to robotics (optimal control based on quadratic programming under linear constraints)

Embedded systems

  • Architectures de Harvard & de Von Neumann
  • Knowledge and implementation of the main features of a microcontroller
  • Choice and sizing of an embedded programming solution in relation to a given need
  • C programming of a Raspberry Pi board
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  • Optimization

    • Linear optimization
    • Non-linear optimization (gradient descent, Lagrange multipliers)
    • Applying optimisation in robotics (optimal control based on quadratic programming under linear constraints)

    Embedded Systems

    • Harvard & Von Neumann Architectures
    • Knowledge and implementation of the main functions of a microcontroler
    • Choice and implementation of an embedded programming solution adapted to given design specifications
    • C Programming on a Raspberry Pi

     

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Objectives

Optimization part: by the end of the course, students will be able to formulate an optimization problem properly and propose the most appropriate tools for solving it.

Embedded systems section: at the end of the course, students will be able to choose and implement an embedded programming solution for a given need.

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Optimisation: at the end of the course, the students will know how to formulate an optimisation problem and propose the most appropriate tools for solving it.

Embedded Systems: at the end of the course, the students will know how to choose and implement an embedded programming solution, given the design specifications.

 

Contact Hours:

            Taught lectures: 15 hours

            Laboratory Practicals: 27 hours

 

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Necessary prerequisites

C programming, linear algebra, mathematical analysis.

 

Recommended prerequisites* :

Programming in Python.

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C Programming, linear Algebra, Calculus.

 

Reccommended prerequisites: Python Programming. 

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Further information

CM: 15h

Practical work: 27h

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Taught lectures: 15 hours

Laboratory Practicals: 27 hours

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