The purpose of this paper is to give an overview of the skew Toeplitz approach to H∞ control of a class of infinite dimensional systems. Numerical steps involved in the computations of optimal and suboptimal controllers are demonstrated with different examples, including flexible beam models and systems with time delays.

This paper is dedicated to the stability analysis of a class of uncertain distributed delay systems, the kernel of which can be modeled as a polynomial function of the delay. The results are constructed by rewriting the system as an uncertain interconnected model. Appropriate robust control tools, i.e. quadratic separation, are then used to address the stability issue. To this end, some relatio...

A semi-empirical model for surf zone wave height decay is adapted to the parabolic equation method in order to include the effect of depth-limited wave breaking in combined refraction/diffraction calculations. Several examples for plane beaches are presented in order to show correspondence between the empirical model, its numerical formulation, and previous laboratory data. The model is then ap...

We propose some new tools for visualizing functional data and for identifying functional outliers. The proposed tools make use of robust principal component analysis, data depth and highest density regions. We compare the proposed outlier detection methods with the existing “functional depth” method, and show that our methods have better performance on identifying outliers in French male age-sp...

A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive the innuence functions and the corresponding asymptotic variances for these robust estimators of eigenvalues and eigenvectors. The behavior of several of these estimators is investigated by a simulation...

We explore the application of Principal Component Analysis for extracting melody and vocals from a piece of music. In order to solve this problem, we explore Robust Principal Component Analysis as a technique for making PCA robust to large sparse noise, and we investigate multiple techniques for solving the RPCA problem. We find that by using Augmented Lagrangians and the ADMIP methods, we are ...

We propose an approach for performing adaptive principal component extraction. By this approach, the Least Mean Squared Error Reconstruction (LMSER) Principle is implemented in a successive way such that the reconstruction error is fedback as inputs for training the network's weights. Simulations results have shown that this type of LMSER implementation can perform Robust Principal Component An...

Randomized algorithms reduce the complexity of low-rank recovery methods only w.r.t dimension p of a big dataset Y ∈ <p×n. However, the case of large n is cumbersome to tackle without sacrificing the recovery. The recently introduced Fast Robust PCA on Graphs (FRPCAG) approximates a recovery method for matrices which are low-rank on graphs constructed between their rows and columns. In this pap...