16 citations · 17 across the 3 of their papers we have counts for
3 papers
eess.SP2024
Efficient Recovery of Sparse Graph Signals from Graph Filter Outputs
Gal Morgenstern, Tirza Routtenberg
This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the s…
eess.SP2023★ 1 cited
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Gal Morgenstern, Jip Kim, James Anderson +2
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-ba…
eess.SP2020★ 16 cited
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems
Gal Morgenstern, Tirza Routtenberg
Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial l…