ORCID Profile
0000-0003-0074-9786
Current Organisations
Western Sydney University
,
Victorian Institute of Technology
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Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 11-2019
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 12-2018
Publisher: Royal Society of Chemistry (RSC)
Date: 2010
DOI: 10.1039/C0CC02917C
Abstract: A library of triazole-based telomeric quadruplex-selective ligands has been developed that mimic an established family of tri-substituted acridine-based ligands, using crystal structure data as a starting-point for computer-based design. Binding affinities, estimated by electrospray mass spectrometry, are in accord with the design concept.
Publisher: MDPI AG
Date: 31-10-2020
DOI: 10.3390/S20216238
Abstract: Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be ided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that ides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.
Location: Portugal
No related grants have been discovered for Payal Mahida.