Topological Approach to Measure the Recoverability of Optical Networks

Optical networks are vulnerable to failures due to targeted attacks or large-scale disasters. The recoverability of optical networks refers to the ability of an optical network to return to a desired performance level after suffering topological perturbations such as link failures. This paper proposes a general topological approach and...

Reachability-based Robustness of Controllability in Sparse Communication Networks

In this paper, we propose closed-form analytic approximations for the number of controllable nodes in sparse communication networks from the aspect of network controllability, considering link-based random attack, targeted attack, as well as random attack under the protection of critical links. We compare our approximations with simulation...

Using Machine Learning to Quantify the Robustness of Network Controllability

This paper presents machine learning based approximations for the minimum number of driver nodes needed for structural controllability of networks under link-based random and targeted attacks. We compare our approximations with existing analytical approximations and show that our machine learning based approximations significantly outperform...

The Recoverability of Network Controllability

Network recoverability refers to the ability of a network to recover to a desired performance level after suffering topological perturbations such as link failures. The minimum number of driver nodes is a typical metric to denote the network controllability. In this paper, we propose closed-form analytic approximations for the minimum number of...

Quantifying the Robustness of Network Controllability

In this paper, we propose closed-form analytic approximations for the minimum number of driver nodes needed to fully control networks, where links are removed according to both random and targeted attacks. Our approximations rely on the concept of critical links. A link is called critical if its removal increases the required number of driver...

3-D Linear Reconstruction of the Experimental Fresnel Data by the GMMV Inversion Method

In this paper, the generalized multiple measurement vectors (GMMV) linear inversion method is applied to the reconstruction of the 3-D Fresnel data, provided by the Institue Fresnel (Marseille, France). The results show that the GMMV-based method can obtain good resolution along the x- and y- axes, while poor resolution along the z-axis,...

A Linear Method for Shape Reconstruction based on the Generalized Multiple Measurement Vectors Model

In this paper, a novel linear method for shape reconstruction is proposed based on the generalized multiple measurement vectors (GMMV) model. Finite difference frequency domain (FDFD) is applied to discretized Maxwell’s equations, and the contrast sources are solved iteratively by exploiting the joint sparsity as a regularized...

Extension of the GMMV-based Linear Method to Quantitative Inverse Scattering

The linear shape reconstruction method based on the generalized multiple measurement vectors model is a newly proposed approach which is able to effectively retrieve the morphological information of dielectric/metallic scatterers with competitive imaging resolution. In this letter, we have extended this approach to quantitative inversion, and...

A Linear Model for Microwave Imaging of Highly Conductive Scatterers

In this paper, a linear model based on multiple measurement vectors' model is proposed to formulate the inverse scattering problem of highly conductive objects at one single frequency. Considering the induced currents that are mostly distributed on the boundaries of the scatterers, joint sparse structure is enforced by a sum-of-norm...

Inversion of Multifrequency Data With the Cross-Correlated Contrast Source Inversion Method

Cross-correlated contrast source inversion (CC-CSI) is a nonlinear iterative inversion method that is proposed recently for solving the inverse scattering problems. In CC-CSI, a cross-correlated error is constructed and introduced to the cost functional, which improves the inversion ability when compared to the classical design of the cost...

Cross-Correlated Contrast Source Inversion

In this paper, we improved the performance of the contrast source inversion (CSI) method by incorporating a so-called cross-correlated cost functional, which interrelates the state error and the data error in the measurement domain. The proposed method is referred to as the cross-correlated CSI. It enables better robustness and higher inversion...