Deep Learning for Spectral Processing in Microbottle Resonator Humidity Sensors

Huda Adnan Zain; Khurram Karim Qureshi

doi:10.1109/LSENS.2026.3670903

Deep Learning for Spectral Processing in Microbottle Resonator Humidity Sensors

Huda Adnan Zain^*
, Khurram Karim Qureshi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

This work demonstrates a deep-learning-assisted microbottle resonator humidity sensor coated with agarose and coupled to a tapered fiber. The convolutional neural network (CNN) processes barcoded spectral data, enabling joint analysis of wavelength shifts, intensity variations, and linewidth changes. The sensor exhibits a CNN classification accuracy of 95%. The results confirm the suitability of deep learning for multidimensional spectral processing in whispering gallery mode humidity sensing.

Original language	English
Article number	3502004
Journal	IEEE Sensors Letters
Volume	10
Issue number	4
DOIs	https://doi.org/10.1109/LSENS.2026.3670903
State	Published - 1 Apr 2026

Bibliographical note

Publisher Copyright:
© 2026 IEEE.

Keywords

Electromagnetic wave sensors
convolutional neural network (CNN)
microbottle resonator (MBR)
optical fiber sensors
relative humidity (RH) sensor
whispering gallery mode (WGM)

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

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10.1109/LSENS.2026.3670903

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title = "Deep Learning for Spectral Processing in Microbottle Resonator Humidity Sensors",

abstract = "This work demonstrates a deep-learning-assisted microbottle resonator humidity sensor coated with agarose and coupled to a tapered fiber. The convolutional neural network (CNN) processes barcoded spectral data, enabling joint analysis of wavelength shifts, intensity variations, and linewidth changes. The sensor exhibits a CNN classification accuracy of 95\%. The results confirm the suitability of deep learning for multidimensional spectral processing in whispering gallery mode humidity sensing.",

keywords = "Electromagnetic wave sensors, convolutional neural network (CNN), microbottle resonator (MBR), optical fiber sensors, relative humidity (RH) sensor, whispering gallery mode (WGM)",

author = "Zain, \{Huda Adnan\} and Qureshi, \{Khurram Karim\}",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.",

year = "2026",

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doi = "10.1109/LSENS.2026.3670903",

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AU - Qureshi, Khurram Karim

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N2 - This work demonstrates a deep-learning-assisted microbottle resonator humidity sensor coated with agarose and coupled to a tapered fiber. The convolutional neural network (CNN) processes barcoded spectral data, enabling joint analysis of wavelength shifts, intensity variations, and linewidth changes. The sensor exhibits a CNN classification accuracy of 95%. The results confirm the suitability of deep learning for multidimensional spectral processing in whispering gallery mode humidity sensing.

AB - This work demonstrates a deep-learning-assisted microbottle resonator humidity sensor coated with agarose and coupled to a tapered fiber. The convolutional neural network (CNN) processes barcoded spectral data, enabling joint analysis of wavelength shifts, intensity variations, and linewidth changes. The sensor exhibits a CNN classification accuracy of 95%. The results confirm the suitability of deep learning for multidimensional spectral processing in whispering gallery mode humidity sensing.

KW - Electromagnetic wave sensors

KW - convolutional neural network (CNN)

KW - microbottle resonator (MBR)

KW - optical fiber sensors

KW - relative humidity (RH) sensor

KW - whispering gallery mode (WGM)

UR - https://www.scopus.com/pages/publications/105031805454

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JO - IEEE Sensors Letters

JF - IEEE Sensors Letters

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M1 - 3502004

ER -

Deep Learning for Spectral Processing in Microbottle Resonator Humidity Sensors

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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