M
Mohammad Abu Alsheikh
Researcher at Nanyang Technological University
Publications - 34
Citations - 2695
Mohammad Abu Alsheikh is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Wireless sensor network & Service provider. The author has an hindex of 13, co-authored 32 publications receiving 1961 citations. Previous affiliations of Mohammad Abu Alsheikh include Massachusetts Institute of Technology & University of Canberra.
Papers
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Journal ArticleDOI
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
TL;DR: An extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in WSNs is presented and a comparative guide is provided to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.
Proceedings ArticleDOI
Through-Wall Human Pose Estimation Using Radio Signals
Mingmin Zhao,Tianhong Li,Mohammad Abu Alsheikh,Yonglong Tian,Hang Zhao,Antonio Torralba,Dina Katabi +6 more
TL;DR: A deep neural network approach that parses wireless signals in the WiFi frequencies to estimate 2D poses through walls despite never trained on such scenarios, and shows that it is almost as accurate as the vision-based system used to train it.
Proceedings ArticleDOI
RF-based 3D skeletons
Mingmin Zhao,Yonglong Tian,Hang Zhao,Mohammad Abu Alsheikh,Tianhong Li,Rumen Hristov,Zachary Kabelac,Dina Katabi,Antonio Torralba +8 more
TL;DR: RF-Pose3D is the first system that infers 3D human skeletons from RF signals, and works with multiple people and across walls and occlusions, and generates dynamic skeletons that follow the people as they move, walk or sit.
Journal ArticleDOI
Mobile big data analytics using deep learning and apache spark
TL;DR: An overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark that speeds up the learning of deep models consisting of many hidden layers and millions of parameters.
Posted Content
Deep Activity Recognition Models with Triaxial Accelerometers
TL;DR: This paper shows that deep activity recognition models provide better recognition accuracy of human activities, and avoid the expensive design of handcrafted features in existing systems, and utilize the massive unlabeled acceleration samples for unsupervised feature extraction.