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Eyad Almaita
Researcher at Tafila Technical University
Publications - 19
Citations - 1027
Eyad Almaita is an academic researcher from Tafila Technical University. The author has contributed to research in topics: Artificial neural network & Harmonics. The author has an hindex of 5, co-authored 15 publications receiving 677 citations. Previous affiliations of Eyad Almaita include Western Michigan University.
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Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
Hazim Shakhatreh,Ahmad Sawalmeh,Ahmad Sawalmeh,Ala Al-Fuqaha,Ala Al-Fuqaha,Zuochao Dou,Eyad Almaita,Issa Khalil,Noor Shamsiah Othman,Abdallah Khreishah,Mohsen Guizani +10 more
TL;DR: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection.
Proceedings ArticleDOI
An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services
TL;DR: This paper presents the approach of fingerprint preparation and setup and how it utilized machine learning techniques using Long Short-Term Memory (LSTM) Neural Networks for location estimation and shows that the localization approach outperforms well-known existing approaches like the KNN and localization techniques.
Journal ArticleDOI
A platform for power management based on indoor localization in smart buildings using long short-term neural networks
Journal ArticleDOI
Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms
Eyad Almaita,Johnson Asumadu +1 more
TL;DR: Two radial basis function neural networks are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory and the small size and the robustness of the resulting network models reflect the effectiveness of the algorithm.
Proceedings ArticleDOI
On-line harmonic estimation in power system based on sequential training radial basis function neural network
Eyad Almaita,Johnson Asumadu +1 more
TL;DR: A radial basis function neural network is used to dynamically identify and estimate the fundamental, fifth harmonic, and seventh harmonic components in converter waveforms and the fast training algorithm and the small size of the resulted networks prove effectiveness of the proposed method.