activity
20132024
most citedOn the ISAR Image Analysis and Recovery with Unavailable or Heavily Corrupted Data

43 citations · 162 across the 29 of their papers we have counts for

collaborators
Showing 2019Show all

10 papers · 1 filter

eess.SP2019

Portfolio Cuts: A Graph-Theoretic Framework to Diversification

Bruno Scalzo Dees, Ljubisa Stankovic, Anthony G. Constantinides +1

Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate…

cs.IT2019★ 11 cited

Graph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +3

The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to ad…

eess.SP2019★ 5 cited

Unitary Shift Operators on a Graph

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A unitary shift operator (GSO) for signals on a graph is introduced, which exhibits the desired property of energy preservation over both backward and forward graph shifts. For rig…

cs.IT2019★ 10 cited

Graph Signal Processing -- Part I: Graphs, Graph Spectra, and Spectral Clustering

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +3

The area of Data Analytics on graphs promises a paradigm shift as we approach information processing of classes of data, which are typically acquired on irregular but structured do…

eess.SP2019★ 1 cited

A Class of Doubly Stochastic Shift Operators for Random Graph Signals and their Boundedness

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper -boundedness for locally stationary random graph signals…

eess.SP2019★ 2 cited

Vertex-Frequency Graph Signal Processing: A review

Ljubisa Stankovic, Danilo P. Mandic, Milos Dakovic +4

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices…

cs.IT2019

Quantization in Compressive Sensing: A Signal Processing Approach

Isidora Stankovic, Milos Brajovic, Milos Dakovic +2

Influence of the finite-length registers and quantization effects on the reconstruction of sparse and approximately sparse signals is analyzed in this paper. For the nonquantized m…

cs.IT2019★ 2 cited

On the Decomposition of Multivariate Nonstationary Multicomponent Signals

Ljubisa Stankovic, Milos Brajovic, Milos Dakovic +1

With their ability to handle an increased amount of information, multivariate and multichannel signals can be used to solve problems normally not solvable with signals obtained fro…

cs.IT2019★ 2 cited

An Intuitive Derivation of the Coherence Index Relation in Compressive Sensing

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +1

The existence and uniqueness conditions are a prerequisite for reliable reconstruction of sparse signals from reduced sets of measurements within the Compressive Sensing (CS) parad…

eess.SP2019★ 2 cited

An Example-Driven Introduction to Data Analytics on Graphs

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +3

Graphs are irregular structures which naturally account for data integrity, however, traditional approaches have been established outside Signal Processing, and largely focus on an…