Negar Naghashzadeh

dblp:309/0019 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › visual analytics › visual analytics system
simulation-based visual analytics
0.712023
Visual Analysis and Detection of Contrails in Aircraft Engine Simulations · IEEE Trans. Vis. Comput. Graph. 2023
High-performance computing
scientific computing systems
0.212023
Visual Analysis and Detection of Contrails in Aircraft Engine Simulations · IEEE Trans. Vis. Comput. Graph. 2023

Methods — techniques the papers use, named apart from their topics

visual computing · 1.3clustering · 1.3
YearPublicationVenuePosition
2023 Visual Analysis and Detection of Contrails in Aircraft Engine Simulations
abstract
Contrails are condensation trails generated from emitted particles by aircraft engines, which perturb Earth's radiation budget. Simulation modeling is used to interpret the formation and development of contrails. These simulations are computationally intensive and rely on high-performance computing solutions, and the contrail structures are not well defined. We propose a visual computing system to assist in defining contrails and their characteristics, as well as in the analysis of parameters for computer-generated aircraft engine simulations. The back-end of our system leverages a contrail-formation criterion and clustering methods to detect contrails' shape and evolution and identify similar simulation runs. The front-end system helps analyze contrails and their parameters across multiple simulation runs. The evaluation with domain experts shows this approach successfully aids in contrail data investigation.
Nafiul Nipu, Carla Floricel, Negar Naghashzadeh, Roberto Paoli, G. Elisabeta Marai
IEEE Trans. Vis. Comput. Graph.3