In Deep Learning Artificial Neural Network is a comprehensive and mathematical way of explaining a simple 2-layered neural network by coding one from scratch in python and behind the scenes of such popular algorithms. First, we provided with a rationale view of the study behind the elaboration of these algorithms and mathematical intuition behind them. Then, we dived into the coding of neural network mixing python lines of code and mathematical equations. In this research, we created a two-layered neural network with a hidden layer to minimize time and effort resulting with more accurate and efficient output, but the idea remains the same for more than two-layered neural networks. Finally, we worked on how we could publicize our model and make it more adaptable for solving complex real-life issues and our experimental results showed that our 2-layered network model could achieve more accuracy and reliable measures as compared to other network methods for solving such complex problems.
Fiber optic shape sensing has a great potential for diverse medical and industrial applications to measure curvatures and even shapes. Featuring small footprint, strong immunity to radiation and high flexibility integration, fiber optic shape sensing opens up a new era in the fields of position tracking, human wearable devices, catheter navigation, bending detection and deformation monitoring. This paper focuses on a branch of fiber optic shape sensing techniques, with an emphasis on shape sensing based on fiber Bragg gratings (FBGs). Key technologies of shape sensing based on FBG are introduced in detail together with a critical view of its evolutionary trend. In addition, the major problems that exist in FBG shape sensing have been discussed in the end.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.