About Mohammed Hussain AlSharif Mohammed Hussain AlSharif Postdoctoral Research Fellow, California Institute of Technology (Caltech) Hand Gestures Recognition Ultrasonic-based Localization indoor localization systems underwater acoustic localization communication signal design machine learning Dr. Mohammed AlSharif is a Postdoctoral Fellow at the California Institute of Technology (Caltech) and a recipient of the KAUST Ibn Rushd Fellowship. His research bridges Riemannian geometry, statistical estimation, and learning for autonomous systems, with applications to high-accuracy pose estimation and robotics. Articles Related News July 2019 ISL Students place 4th at Microsoft Indoor Localization Competition 2017 1 min read · Mon, Jul 29 2019 News Accurate indoor positioning has the potential to transform the way people navigate indoors similar to the way the GPS transformed outdoor navigation. Over the last 20 years, many indoor positioning technologies have been proposed and experimented by both academia and industry. We developed an accurate acoustic 3D positioning system, which consists of one mobile device (MD) and four base stations (BSs) with known locations. The MD transmits a series of ultrasound and RF sequences, named Zadoff-Chu (ZC) sequences, at the same time. The propagation time of the RF signal is negligible compared to
ISL Students place 4th at Microsoft Indoor Localization Competition 2017 1 min read · Mon, Jul 29 2019 News Accurate indoor positioning has the potential to transform the way people navigate indoors similar to the way the GPS transformed outdoor navigation. Over the last 20 years, many indoor positioning technologies have been proposed and experimented by both academia and industry. We developed an accurate acoustic 3D positioning system, which consists of one mobile device (MD) and four base stations (BSs) with known locations. The MD transmits a series of ultrasound and RF sequences, named Zadoff-Chu (ZC) sequences, at the same time. The propagation time of the RF signal is negligible compared to
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