Experimental Demonstration of Multimode Microresonator Sensing by Machine Learning

Lu, Jin; Niu, Rui; Wan, Shanhong; Dong, Chun‐Hua; Le, Zichun; Qin, Yali; Hu, Yingtian; Hu, Weisheng; Zou, Chang‐Ling; Ren, Hongliang

doi:10.1109/jsen.2020.3049015

Cited by 18 publications

(8 citation statements)

References 44 publications

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“…For example, in the field of biomedicine, AI can be applied to medical image processing, disease diagnosis, precision medicine, medical management, and many other areas [ 54 – 57 ]. During this period, the interdisciplinary fusion of photonics and artificial intelligence has also made great progress [ 58 – 62 ]. The outstanding modeling ability and powerful data processing capability of AI can free researchers from tedious and repetitive data processing tasks.…”

Section: Multimode Sensing Applicationsmentioning

confidence: 99%

“…With a wider dynamic range and lower uncertainty, it provides an important platform for fine measurement [ 27 ]. For example, Lu et al combined an artificial neural network model to achieve voltage detection by extracting multimode transmission depth, with a detection limit 6.7% lower than that achieved by using single-mode detection [ 58 ].…”

Section: Multimode Sensing Applicationsmentioning

confidence: 99%

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Multimode sensing based on optical microcavities

Wu,

Duan,

et al. 2023

Front. Optoelectron.

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Optical microcavities have the ability to confine photons in small mode volumes for long periods of time, greatly enhancing light-matter interactions, and have become one of the research hotspots in international academia. In recent years, sensing applications in complex environments have inspired the development of multimode optical microcavity sensors. These multimode sensors can be used not only for multi-parameter detection but also to improve measurement precision. In this review, we introduce multimode sensing methods based on optical microcavities and present an overview of the multimode single/multi-parameter optical microcavities sensors. Expected further research activities are also put forward. Graphical abstract

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Section: Multimode Sensing Applicationsmentioning

confidence: 99%

Section: Multimode Sensing Applicationsmentioning

confidence: 99%

Multimode sensing based on optical microcavities

Wu,

Duan,

et al. 2023

Front. Optoelectron.

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“…Although FBT technology can be developed and applied, we can currently essentially fully satisfy the functional and basic needs of businesses as a result of the expansion of the couple market [15]. However, Chinese businesses will need to rely on further advancements in technology and research if they are to maintain their absolute core competitiveness and absolute market advantages in the future, when industrial competition is expected to become more harsh and complex [16]. The technological bar has been raised and significant advances in research have been accomplished.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multimode Fibre Optic Communication through Deep Learning-based Mode Identification

Wang,

Liu

2023

J. Comb. Math. Comb. Comput.

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Multimode fibre optic communication systems, employing mode/mode group multiplexing, present challenges in accurately identifying numerous modes and mode groups for improved performance. In this study, we propose an intelligent identification model utilizing a fully convolutional neural network (CNN) to precisely identify multimode fibre modes and their clusters. The model is simulated and experimentally validated, considering noise influences on linear polarisation modes. Using a platform with OM2 multimode fibre and a multiplane optical conversion mode multiplexer, we capture optical field information for 10 modes and their corresponding mode groups. Extensive data are employed for training and validation, achieving a 100% recognition rate for all modes and mode groups in experiments. Notably, when employing a 44-photodetector array, an impressive 98.3% recognition efficiency is attained, showcasing the potential of deep learning in advancing multimode fibre optic communication systems.

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“…In addition to the aforementioned solution on imaging of the signal from multiple microresonators at the fixed frequency, [21] the other machine learning (ML)-based application for WGM sensors utilizes the complete spectrally resolved information of a single microresonator. [25][26][27][28] Further examples of optical sensors supported by the ML techniques include surface plasmon resonance method [29] and attenuated total reflectance approach. [30] The reported intelligent WGM sensor [21] has been proven highly efficient for parameters prediction under the equilibrium condition when a steady state of the sensed medium around the optical sensor is reached.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the aforementioned solution on imaging of the signal from multiple microresonators at the fixed frequency, [ 21 ] the other machine learning (ML)‐based application for WGM sensors utilizes the complete spectrally resolved information of a single microresonator. [ 25–28 ] Further examples of optical sensors supported by the ML techniques include surface plasmon resonance method [ 29 ] and attenuated total reflectance approach. [ 30 ]…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optical Microresonator Imaging Sensor for Early Stage Classification of Dynamical Variations

Saetchnikov

Tcherniavskaia

Saetchnikov

et al. 2021

Advanced Photonics Research

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Although machine learning (ML) solutions are conquering science and technology with a growing number of the affected branches, only a few examples have been demonstrated so far to boost the highly sensitive and label‐free optical detectors. Nevertheless, ML methods can indeed provide novel strategies for emerging optical sensing technologies to overcome the challenges of real‐world implementation. On this way, the sensing techniques become intelligent self‐learning systems capable for predictions of the probed parameters in an automated and self‐adjusting manner. Herein, a report on the upgraded ML engine of an affordable multiresonator imaging sensor operating at a fixed frequency is provided. It is demonstrated for the first time that such a sensor supplemented by a long short‐term memory (LSTM) network processing engine enables accurate prediction of the dynamical sensor responses already within the early stages of the observed variations. On example of the experimental data of solutions mixing dynamics, the possibility for accurate (>95%) differentiation of the dynamical variations at the refractive index level of 5.5 × 10−4 within a six times shorter period than the whole time series is shown. The impact of the parameters and architecture of the LSTM‐based processing engine on the speed and prediction accuracy is analyzed.

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Experimental Demonstration of Multimode Microresonator Sensing by Machine Learning

Cited by 18 publications

References 44 publications

Multimode sensing based on optical microcavities

Multimode sensing based on optical microcavities

Enhancing Multimode Fibre Optic Communication through Deep Learning-based Mode Identification

Intelligent Optical Microresonator Imaging Sensor for Early Stage Classification of Dynamical Variations

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