Machine Learning For A Vernier-effect-based Optical Fiber Sensor

In recent years, the optical Vernier effect has been demonstrated as an effective tool to improve the sensitivity of optical fiber interferometer-based sensors, potentially facilitating a new generation of highly sensitive fiber sensing systems. Previous work has mainly focused on the physical implementation of Vernier-effect-based sensors using different combinations of interferometers, while the signal demodulation aspect has been neglected. However, accurate and reliable extraction of useful information from the sensing signal is critically important and determines the overall performance of the sensing system. In this Letter, we, for the first time, propose and demonstrate that machine learning (ML) can be employed for the demodulation of optical Vernier-effect-based fiber sensors. ML analysis enables direct, fast, and reliable readout of the measurand from the optical spectrum, avoiding the complicated and cumbersome data processing required in the conventional demodulation approach. This work opens new avenues for the development of Vernier-effect-based high-sensitivity optical fiber sensing systems

Fs-Laser Fabricated Miniature Fabry–Perot Interferometer in a No-Core Fiber for High-Temperature Applications †

This Paper Reports a Fiber In-Line Fabry–Perot Interferometer (FPI) Fabricated in a No-Core Fiber using the Direct Femtosecond Laser Writing Technique for High-Temperature Sensing Applications. Two In-Line Reflectors Are Directly Inscribed in a No-Core Fiber to Construct a Low-Finesse FPI. Fringe Visibility Greater Than 10 DB is Obtained from the Reflection Spectra of the Fabricated No-Core Fiber FPIs. Temperature Responses of a Prototype No-Core Fiber FPI Are Characterized Up to 1000 °C. the Proposed Configuration is Compact and Easy to Fabricate, Making It Attractive for Sensing Applications in High-Temperature Harsh Environments

From Fiber Bragg Gratings To Coaxial Cable Bragg Gratings: One-dimensional Microwave Quasi-periodic Photonic Crystals

Coaxial cables and optical fibers are two types of cylindrical waveguides used in telecommunications. Fiber Bragg gratings (FBGs) have found successful applications in various fields, such as optical communications, fiber lasers, and fiber-optic sensing. In this paper, we propose and numerically investigate the implementations of various fiber Bragg configurations, including uniform, chirped, apodized, and phase-shifted configurations, on coaxial cables to generate the corresponding special types of coaxial cable Bragg gratings (CCBGs). The simulation results of different CCBGs match well with the well-known FBG theories. It is demonstrated that the reflection spectrum of a CCBG can be flexibly tailored by introducing various quasi-periodic perturbations in the permittivity of the dielectric layer along the coaxial cable. The proposed special types of CCBGs with unique characteristics could find potential applications in radio frequency signal processing, communication, and sensing fields

Simultaneous And Multiplexed Measurement Of Curvature And Strain Based On Optical Fiber Fabry-Perot Interferometric Sensors

Optical fiber sensors that have a compact size and the capability for multi-parameter sensing are desired in various applications. This article reports a miniaturized optical fiber Fabry-Perot interferometric sensor with a length of hundreds of µm that is able to simultaneously measure variations of curvature, temperature, and strain. The sensor is easy to fabricate, requiring only the fusion splicing of a short section of the silica capillary tube between two single-mode fibers (SMFs). The combined mechanism of the Fabry-Perot interference occurred in the two interfaces between the capillary and the SMFs, and the Anti resonant guidance induced by the capillary tube makes the device capable of realizing multi-parameter sensing. A simplified coefficient matrix approach is developed to decouple the contributions from different parameters. In addition, the capability of the device for multiplexing is investigated, where four such prototypes with different air cavity lengths are multiplexed in a system in parallel. The spectral behavior of an individual device for measuring curvature and strain is reconstructed and investigated, showing reliable responses and little crosstalk between different devices. The proposed device is easy to fabricate, cost-effective, robust, and could find potential applications in the field of structural health monitoring and medical and human–machine interactive sensing

Multi-Point Optical Fiber Fabry-Perot Curvature Sensor Based On Microwave Photonics

This article reports a multi-point curvature sensor system based on multiplexed optical fiber Fabry-Perot interferometric (FPI) sensor devices and a microwave photonics interrogation technique. The FPI sensor is fabricated with the assistance of a capillary tube, where a short section of the capillary is sandwiched between two single-mode fibers, forming the airgap Fabry-Perot cavity. Bending of the FPI device leads to changes in the fringe contrast of its reflection spectrum. Based on the microwave photonics filtering technique, variations of the fringe contrast are encoded into the changes in the peak magnitude of the passband in the frequency response of the FPI device. By multiplexing such FPI devices with different cavity lengths, multi-point measurements of curvature can be realized by tracking changes in corresponding passbands in the frequency response of the system. The FPI curvature sensor is easy-to-manufacture and cost-effective, and the microwave photonics-based system provides an alternative and robust solution to interrogating the multiplexed FPI sensors for multi-point curvature sensing that could be desired in structural health monitoring, human-machine interface sensing, and other related fields

Graphene oxide integrated silicon photonics for detection of vapour phase volatile organic compounds

From Springer Nature via Jisc Publications RouterHistory: received 2020-01-07, accepted 2020-05-17, registration 2020-05-20, pub-electronic 2020-06-12, online 2020-06-12, collection 2020-12Publication status: PublishedAbstract: The optical response of a graphene oxide integrated silicon micro-ring resonator (GOMRR) to a range of vapour phase Volatile Organic Compounds (VOCs) is reported. The response of the GOMRR to all but one (hexane) of the VOCs tested is significantly higher than that of the uncoated (control) silicon MRR, for the same vapour flow rate. An iterative Finite Difference Eigenmode (FDE) simulation reveals that the sensitivity of the GO integrated device (in terms of RIU/nm) is enhanced by a factor of ~2, which is coupled with a lower limit of detection. Critically, the simulations reveal that the strength of the optical response is determined by molecular specific changes in the local refractive index probed by the evanescent field of the guided optical mode in the device. Analytical modelling of the experimental data, based on Hill-Langmuir adsorption characteristics, suggests that these changes in the local refractive index are determined by the degree of molecular cooperativity, which is enhanced for molecules with a polarity that is high, relative to their kinetic diameter. We believe this reflects a molecular dependent capillary condensation within the graphene oxide interlayers, which, when combined with highly sensitive optical detection, provides a potential route for discriminating between different vapour phase VOCs

Graphene Twistronics: Tuning the Absorption Spectrum and Achieving Metamaterial Properties

Graphene twistronics using multilayer graphene is presented in such a way that it provides a metamaterial effect. This manuscript also analyzes the prediction of behavior using machine learning. The metamaterial effect is achieved by twisting the graphene layers. Graphene twistronics is a new concept for changing the electrical and optical properties of bilayer graphene by applying a small angle twist between the layers. The angle twists of 5°, 10°, and 15° are analyzed for the proposed graphene twistronics design. Tuning in the absorption spectrum is achieved by applying small twists to the angles of the bilayer graphene. Results in the form of absorption, conductivity, permeability, permittivity, and impedance are presented for different twist angles. The twisted graphene layers also demonstrate negative permittivity and negative permeability, similar to metamaterials. These negative refraction properties of graphene twistronics provide flexibility and transparency, which can be applied in photovoltaic applications. Machine-learning-based regression models are used to reduce the simulation time and resources. The results show that a regression model can reliably estimate intermediate wavelength absorption values with an R2 of 0.9999

Graphene Twistronics: Tuning the Absorption Spectrum and Achieving Metamaterial Properties

Graphene twistronics using multilayer graphene is presented in such a way that it provides a metamaterial effect. This manuscript also analyzes the prediction of behavior using machine learning. The metamaterial effect is achieved by twisting the graphene layers. Graphene twistronics is a new concept for changing the electrical and optical properties of bilayer graphene by applying a small angle twist between the layers. The angle twists of 5o, 10o, and 15o are analyzed for the proposed graphene twistronics design. Tuning in the absorption spectrum is achieved by applying small twists to the angles of the bilayer graphene. Results in the form of absorption, conductivity, permeability, permittivity, and impedance are presented for different twist angles. The twisted graphene layers also demonstrate negative permittivity and negative permeability, similar to metamaterials. These negative refraction properties of graphene twistronics provide flexibility and transparency, which can be applied in photovoltaic applications. Machine-learning-based regression models are used to reduce the simulation time and resources. The results show that a regression model can reliably estimate intermediate wavelength absorption values with an R2 of 0.9999

A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration

With urban and rural infrastructure development, the power system is being forced to operate at or near its full capacity. This paper proposes four new methodologies to find the solution to the optimal reactive power dispatch (ORPD) problem, considering the capabilities of modern DFIG-based WTs and VSI-based solar PV. The proposed formulation considers the techno-economic objective functions, specifically the minimization of the active and reactive power cost and the maximization of reactive power reserve. This leads to an effective solution to the probabilistic multi-objective ORPD (PMO-ORPD) problem, especially in the context of modern wind farms (WFs) and solar PV. The proposed formulations are necessary for effectively managing power systems with renewable energy sources and contribute to developing efficient and sustainable power systems. Additionally, this study employs probabilistic mathematical modeling that incorporates Weibull, lognormal, and normal probability distribution functions (PDFs) to represent uncertainties in the wind, solar, and load demand. Monte-Carlo simulation (MCS) is employed to generate probabilistic scenarios, allowing for a comprehensive analysis of the PMO-ORPD problem. A new two-phase (ToP) multi-objective evolutionary algorithm is proposed, which incorporates the superiority of feasibility constraints to effectively solve the probabilistic multi-objective optimal reactive power dispatch (PMO-ORPD) problem. From the analysis and comparison of simulation results, it has been observed that the proposed algorithm effectively solves the deterministic and PMO-ORPD problems