Dynamic mode decomposition has emerged as a leading technique to identify
spatiotemporal coherent structures from high-dimensional data, benefiting from a strong …

We study feedback control over erasure channels with packet-dropouts. To achieve
robustness with respect to packet-dropouts, the controller transmits data packets containing …

This paper presents a teleoperation control for an exoskeleton robotic system based on the
brain-machine interface and vision feedback. Vision compressive sensing, brain-machine …

Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse
signals and images that enables sampling rates significantly below the classical Nyquist …

We present a sparse sensing framework based on dynamic mode decomposition (DMD) to
identify flow regimes and bifurcations in large-scale thermofluid systems. Motivated by real …

Abstract In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust
recovery of sparse vectors is possible from noisy, undersampled measurements via …

In this paper, we consider identifying Auto Regressive with eXternal input (ARX) models for
both Linear Time-Invariant (LTI) and Linear Time-Variant (LTV) systems. We aim at doing the …

Theoretical analysis of randomized, compressive operators often depends on a
concentration of measure inequality for the operator in question. Typically, such inequalities …

Compressive sensing (CS) enables the acquisition and recovery of sparse signals and
images at sampling rates significantly below the classical Nyquist rate. Despite significant …

In this tutorial, we review some of the recent results concerning Compressive System
Identification (CSI)(identification from few measurements) of sparse channels (and in …