Cédric Join

Bio: Cédric Join is an academic researcher from University of Lorraine. The author has contributed to research in topics: Nonlinear system & Fault detection and isolation. The author has an hindex of 32, co-authored 178 publications receiving 4562 citations. Previous affiliations of Cédric Join include Nancy-Université & Concordia University Wisconsin.

Model-free control for unknown delayed systems

Abstract: The use of model-free control (MFC) spreads now more and more in industry. Nevertheless, control unknown delayed systems with this method remains an open problem. In this contribution, we present the use of model-free control in this context and we propose a solution to improve the effectiveness of this approach using a parameter estimation.

Vers une commande multivariable sans mod\`ele

Abstract: A control strategy without any precise mathematical model is derived for linear or nonlinear systems which are assumed to be finite-dimensional. Two convincing numerical simulations are provided.

Freeway ramp metering control made easy and efficient

Abstract: ''Model-free'' control and the related ''intelligent'' proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow speed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key role. Several excellent computer simulations are provided and analyzed.

Estimation des dérivées d'un signal multidimensionnel avec applications aux images et aux vidéos

Abstract: Recent techniques for estimating derivatives of noisy transient signals are extended to the multidimensional case, i.e., to image and video processing. Numerical simulations are provided for noise removal and edge detection.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge: