Optimization & Embedded Systems

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

• Harvard & Von Neumann architectures
• Knowledge and implementation of the main features of a microcontroller
• Choosing and sizing an embedded programming solution for a given need
• Programming a Raspberry Pi board in C
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• Optimization

  • Linear optimization
  • Non-linear optimization (gradient descent, Lagrange multipliers)
  • Applying optimization 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 microcontroller
  • Selection and implementation of an embedded programming solution adapted to specific design specifications
  • C Programming on a Raspberry Pi

   

Optimization section: by the end of the course, students will be able to properly formulate an optimization problem and suggest the most appropriate tools to solve it.

Embedded systems section: by 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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Optimization: at the end of the course, students will know how to formulate an optimization problem and propose the most appropriate tools for solving it.

Embedded Systems: at the end of the course, 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

C programming, linear algebra, mathematical analysis.

Recommended prerequisites:

Programming in Python.

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C Programming, Linear Algebra, Calculus.

Recommended prerequisites: Python Programming. 

CM: 3 p.m.

Practical work: 27 hours

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Taught lectures: 15 hours

Laboratory Practicals: 27 hours