Asset Details
Do you wish to reserve the book?
Deep learning-based approach for high spatial resolution fibre shape sensing
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Deep learning-based approach for high spatial resolution fibre shape sensing
Do you wish to request the book?
Deep learning-based approach for high spatial resolution fibre shape sensing
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Deep learning-based approach for high spatial resolution fibre shape sensing
2024
Overview
Fiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor’s shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a data-driven technique to overcome this limitation by removing strain measurement, curvature estimation, and shape reconstruction steps. We designed an end-to-end convolutional neural network that is trained to directly predict the sensor’s shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (
e.g
., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the preliminary steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.
High-resolution fiber shape sensors face limited application due to high costs. Manavi et al. proposed a solution employing deep learning for shape prediction directly from the fiber sensor’s spectrum. This approach eliminates the need for expensive measurements and complex post-processing, providing a cost-effective yet accurate method for detecting complex multi-bend deformations.
