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Omar Hatem

dblp:164/8453 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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
Robot manipulation · 50% Motion planning and robot control · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › motion control
closed-loop motion control
0.212015
Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control · ICRA 2015
Robotics › Robot manipulation
micro/nano manipulation
0.212015
Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control · ICRA 2015
Robotics › Robot manipulation › micromanipulation
micro-particle manipulation
0.212015
Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control · ICRA 2015
Robotics › Motion planning and robot control
robot control
0.212015
Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control · ICRA 2015
Medical and health informatics › precision medicine
targeted therapy
0.112015
Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control · ICRA 2015

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

magnetic torque control · 0.4oscillating magnetic fields · 0.2oscillating magnetic field · 0.2
YearPublicationVenuePosition
2024 Brain-Brake: A Hybrid Brain-Operated Computer Vision-Enabled System for Collision Avoidance
abstract
A major challenge in daily automotive use is the high incidence of car accidents, primarily attributed to human errors. This paper introduces Brain-Brake, an innovative brain-operated advanced driver-assistance system (ADAS) designed for collision avoidance. Utilizing electroencephalography (EEG) signals, Brain-Brake detects a driver's braking intention faster than traditional methods, addressing critical reaction time delays. It integrates a computer vision module that measures the proximity of objects to the vehicle, complementing the EEG- based intention detection for enhanced reliability. Implemented on embedded chips using the AUTOSAR framework, preliminary results indicate a significant reduction in reaction times during emergency braking scenarios, highlighting the system's potential to enhance road safety significantly.
Omar Hatem, Omar Sheta, Abdallah Abdelaziz, Ali Ashraf, Hashem El Maleeh, Mariam Gadallah, Muhammad Habash, Ahmed Moro, Seif Eldawlatly
BSN1
2015 Paramagnetic microparticles sliding on a surface: Characterization and closed-loop motion control
abstract
In targeted therapy, clusters of drug carriers (nanoparticles and microparticles) could be in contact with a surface such as the lumen of blood vessels and the interior of the gastrointestinal tract. We study the motion characteristics of clusters of microparticles when they slide on a surface under the influence of weak oscillating magnetic fields (less than 11 mT). The oscillating magnetic fields exert a magnetic torque on the microparticles and allow them to oscillate, and hence overcome the static friction and slide on a surface. We characterize the frequency response of clusters of microparticles by applying oscillating magnetic fields with a frequency range of 0 Hz to 55 Hz, in the presence of a constant magnetic field gradient (0.9 T/m). Clusters of 3 to 4 and 5 to 9 microparticles achieve maximum sliding speeds of 1100 µm/s and 1150 µm/s, at oscillating magnetic fields of 30 Hz. In addition, we experimentally demonstrate closed-loop motion control of the clusters with maximum position error of 20 µm. Furthermore, we show that the magnetic field gradient required to drive a cluster of microparticles (with 3 to 4 microparticles) decreases by 75% in the presence of oscillating magnetic fields from 5 Hz to 50 Hz.
Kareem Youakim, Mohamed Ehab, Omar Hatem, Sarthak Misra, Islam S. M. Khalil
ICRA3