Correlation Processing of Speckle Pattern in Multimode Polymer Optical Fiber for Deformation Monitoring in Nanometer Range

Varyshchuk, V.; Bobitski, Yaroslav

doi:10.1007/978-3-319-18543-9_23

Cited by 2 publications

(2 citation statements)

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“…Thus, the degree of pixel intensity ranges from 0 to 255, with 0 as the darkest possible spot and 255 as the brightest possible spot. To find the correlation between two specklegram images after a change in the wavelength, the correlation coefficient C(Δλ) was used, and it is expressed as [ 45 ]:

where 〈…〉 represents the mean averaging over the spatial coordinate x and y for a given wavelength-dependent specklegram.

is the spatial pixel intensity of the reference specklegram, and

is the spatial pixel intensity of a specklegram after a change in the wavelength

.…”

Section: Resultsmentioning

confidence: 99%

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A Machine Learning Specklegram Wavemeter (MaSWave) Based on a Short Section of Multimode Fiber as the Dispersive Element

Inalegwu

Gerald

Huang

2023

Sensors

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Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed using a convolutional neural network (CNN) model. The developed machine learning specklegram wavemeter (MaSWave) can accurately map specklegrams of wavelengths up to 1 pm resolution when employing a 0.1 m long MMF. Moreover, the CNN was trained with several categories of image datasets (from 10 nm to 1 pm wavelength shifts). In addition, analysis for different step-index and graded-index MMF types was carried out. The work shows how further robustness to the effects of environmental changes (mainly vibrations and temperature changes) can be achieved at the expense of decreased wavelength shift resolution, by employing a shorter length MMF section (e.g., 0.02 m long MMF). In summary, this work demonstrates how a machine learning model can be used for the analysis of specklegrams in the design of a wavemeter.

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where 〈…〉 represents the mean averaging over the spatial coordinate x and y for a given wavelength-dependent specklegram.

is the spatial pixel intensity of the reference specklegram, and

is the spatial pixel intensity of a specklegram after a change in the wavelength

.…”

Section: Resultsmentioning

confidence: 99%

Section: Specklegram Correlationmentioning

confidence: 99%

A Machine Learning Specklegram Wavemeter (MaSWave) Based on a Short Section of Multimode Fiber as the Dispersive Element

Inalegwu

Gerald

Huang

2023

Sensors

View full text Add to dashboard Cite

show abstract

Comparison of different statistical methods for specklegram-based fiber optic vibration sensor

Lujo

Bosiljevac

Šipuš

2019

2019 23rd International Conference on Applied Electromagnetics and Communications (ICECOM)

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Correlation Processing of Speckle Pattern in Multimode Polymer Optical Fiber for Deformation Monitoring in Nanometer Range

Cited by 2 publications

References 19 publications

A Machine Learning Specklegram Wavemeter (MaSWave) Based on a Short Section of Multimode Fiber as the Dispersive Element

A Machine Learning Specklegram Wavemeter (MaSWave) Based on a Short Section of Multimode Fiber as the Dispersive Element

Comparison of different statistical methods for specklegram-based fiber optic vibration sensor

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