Publication
Optimization-based Feedforward Path Following for Model Reference Adaptive Control of an Unmanned Helicopter
MOOCs associés (29)
Optimization: principles and algorithms - Network and discrete optimization
Introduction to network optimization and discrete optimization
Optimization: principles and algorithms - Unconstrained nonlinear optimization
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Optimization: principles and algorithms - Unconstrained nonlinear optimization
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Introduction to Discrete Choice Models
The course introduces the theoretical foundations to choice modeling and describes the steps of operational modeling.
Selected Topics on Discrete Choice
Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t
Selected Topics on Discrete Choice
Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t
Introduction to Discrete Choice Models
The course introduces the theoretical foundations to choice modeling and describes the steps of operational modeling.
Digital Signal Processing [retired]
The course provides a comprehensive overview of digital signal processing theory, covering discrete time, Fourier analysis, filter design, sampling, interpolation and quantization; it also includes a
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can
be used as a starting point or a basic refresher in elementary DSP
Digital Signal Processing II
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization