Sampling is a fundamental topic in graph signal processing, having found applications in
estimation, clustering, and video compression. In contrast to traditional signal processing …

We investigate a linear-quadratic-Gaussian (LQG) control and sensing codesign problem,
where one jointly designs sensing and control policies. We focus on the realistic case where …

This article considers the problem of selecting sensors in a large-scale system to minimize
the error in estimating its states, more specifically, the state estimation mean-square error …

Abstract Information flow among nodes in a complex network describes the overall cause-
effect relationships among the nodes and provides a better understanding of the …

This work provides performance guarantees for the greedy solution of experimental design
problems. In particular, it focuses on A-and E-optimal designs, for which typical guarantees …

This work considers the problem of selecting sensors in large scale system to minimize the
state estimation mean-square error (MSE). More specifically, it leverages the concept of …

In this paper, we present new algorithms and analysis for the linear inverse sensor
placement and scheduling problems over multiple time instances with power and …

Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an optimal
controller and estimator for linear Gaussian systems with imperfect state information …

This paper studies selecting a subset of the system's output to minimize the state estimation
mean square error (MSE). This results in the maximization problem of a set function defined …

Emerging applications of control, estimation, and machine learning, from target tracking to
decentralized model fitting, pose resource constraints that limit which of the available …