2020
DOI: 10.3390/s20133682
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Ultrasonic Sensor Fusion Inverse Algorithm for Visually Impaired Aiding Applications

Abstract: Depth mapping can be carried out by ultrasound measuring devices using the time of flight method. Ultrasound measurements are favorable in such environments, where the light or radio frequency measurements can not be applied due to the noise level, calculation complexity, reaction time, size and price of the device, accuracy or electromagnetic compatibility. It is also usual to apply and fusion ultrasound sensors with other types of sensors to increase the precision and reliability. In the case of visu… Show more

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Cited by 7 publications
(7 citation statements)
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“…Depth mapping with light or radio frequency for reliable indoor space positioning and multiple obstacle distance information have limitations due to the noise level, calculation complexity, reaction time and many others. To address these, the system in [ 74 ] consisting of a device using a single ultrasound source and two to three receivers attached to a headset employes ultrasonic sensor fusion to find obstacles. The results are then transmitted to the user via audio feedback.…”
Section: Resultsmentioning
confidence: 99%
“…Depth mapping with light or radio frequency for reliable indoor space positioning and multiple obstacle distance information have limitations due to the noise level, calculation complexity, reaction time and many others. To address these, the system in [ 74 ] consisting of a device using a single ultrasound source and two to three receivers attached to a headset employes ultrasonic sensor fusion to find obstacles. The results are then transmitted to the user via audio feedback.…”
Section: Resultsmentioning
confidence: 99%
“…The traditional algorithm will cause too much loss of image information during denoising, and the effect is not good, and it cannot automatically adjust parameters, resulting in low work efficiency. In recent years, deep learning has rapidly developed into a research hotspot in medical image analysis, which can automatically localize implicit disease diagnosis features from medical image big data [ 22 ]. In this work, convolutional downsampling was introduced into the UNet network model to replace the original max pooling downsampling, and a residual structure and a Recon module were added to it.…”
Section: Discussionmentioning
confidence: 99%
“…The study used SDT to improve fall detection accuracy by more than 20% compared with a stand-alone sensor on continuous data acquisition. Kovacs and Nagy [34] investigated the use of an ultrasonic echolocation-based aid for the visually impaired using a mathematical model that allowed the fusion of as many sensors as possible, notwithstanding their positions or formations. Huang et al [35] proposed the fusion of images from a depth sensor and a hyperspectral camera to improve high-throughput phenotyping.…”
Section: Healthcare Applicationsmentioning
confidence: 99%