Ms. Rabeeya Hamid Profile

Rabeeya Hamid

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 March 2022 Presentation + Paper

Use of machine learning in a speckle-based optical fiber sensor for temperature detection

Rabeeya Hamid, M. Imran Cheema

Proceedings Volume 12019, 120190J (2022) https://doi.org/10.1117/12.2608492

KEYWORDS: Speckle pattern, Optical fibers, Temperature metrology, Machine learning, Data modeling, Gold, Temperature sensors, Fiber optics sensors, Speckle, Copper

Read Abstract +

The speckle is a granular pattern observed at the end of a multimode fiber due to the interference of propagating modes. The speckle pattern will change in case of any perturbations at any point in the fiber and thus forms the basis of highly sensitive fiber specklegram sensors. Early researchers developed speckle interferometry and imaging tools to extract information from the speckle. However, general drawbacks in these approaches are difficulty in extracting information from non-linear speckle patterns and associated high computational costs. For this reason, we propose a machine learning-based solution to analyze speckle patterns in gold-coated multimode fibers for temperature sensing. We build a temperature-controlled environment on a 4 cm length of a copper rod using a heater with a proportional-integral-derivative control. We coat around a 10 nm gold layer on a 4 cm stripped region of a 50/125 μm multimode optical fiber using the DC sputtering process. The gold-coated fiber is then attached to the copper rod. We find that a thin layer of gold on the fiber surface enhances the overall temperature sensing sensitivity. We record speckle pattern images through a CCD camera over a range of 25.9-28.9° C with roughly 150 images per temperature setting. Using time-efficient and straightforward machine learning models such as k-nearest neighbors and ridge regression, we achieve temperature sensing accuracy as high as 96% and mean squared error as low as 0.000844° C. We anticipate that the current work can pave the way for efficient sensing applications in photo-acoustics, strain, and pressure measurements.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

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.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years