Omar Khatib

dblp:307/5431 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2024
0000-0002-0721-9684ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 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.

Artificial intelligence
1 paper
Deep learning architectures and training · 100%
Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training
physics-informed neural network
0.612022
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions · ICLR 2022
Computational photography and imaging
phase retrieval
0.612022
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions · ICLR 2022

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

neural network · 1.1blaschke product · 1.1
YearPublicationVenuePosition
2024 Characterization of Microwave Blackbody with Monostatic Measurement
abstract
This paper describes a measurement method for characterizing the reflectivity of blackbodies used as calibration devices in spaceborne instruments. The fundamental measurement principle is based on the scattering matrix theory. A monostatic apparatus has been developed to implement the reflectivity measurements up to 220 GHz. A few blackbody emitters made of metamaterials were measured at a number of radiometric frequencies. The results demonstrated the feasibility of using metamaterial blackbodies on miniaturized satellite platforms.
Dazhen Gu, Jonah Smith, Adam Whitney, Omar Khatib, Natalie Rozman, Amanda Gregg, Willie Padilla, William R. Deal, Steven C. Reising
IGARSS4
2022 Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Juncheng Dong, Simiao Ren, Omar Khatib, Jordan M. Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh
ICLR4
2022 Planar Metamaterial Absorbers for Calibration of Microwave Radiometers for Atmospheric Remote Sensing
abstract
In this work, we present metamaterial-based microwave absorbers fabricated on organic-based printed circuit boards as promising alternatives to traditional, bulky microwave absorbers for the calibration of microwave radiometers for atmospheric remote sensing. Their use is particularly attractive for onboard calibration of sensors on CubeSats and other small satellites. Planar metamaterials can be fabricated with near-unity absorption over a very broad frequency range, and are scalable by tuning the unit cell geometry. Specifically, we describe an approach and initial measurements toward designing a broadband metamaterial emitter operating at millimeter wave sounding channels from 50 GHz 230 GHz, enabling a thin, cost-effective calibration target for millimeterwave atmospheric remote sensing.
Omar Khatib, Dazhen Gu, Jonah Smith, William R. Deal, Willie Padilla, Steven C. Reising
IGARSS1