Comparison of Measurements with Finite-Element Analysis of Silicon-Diaphragm-Based Fiber-Optic Fabry-Perot Temperature Sensors

Enhanced grey wolf algorithm for automatic tuning of an ensemble neural network in predicting PCF optical properties

The structural characteristics of photonic crystal fibers (PCFs) determine their optical properties. This paper introduces an enhanced Grey Wolf Optimization algorithm termed ACD-GWO, which proposes adaptive strategies, chaotic mapping and dimension-based approaches and integrates them into the Grey Wolf Optimization framework. The aim is to achieve efficient automatic adjustment of hyperparameters and architecture for ensemble neural networks. The resulting ensemble neural network demonstrates accurate and rapid prediction of optical properties in PCFs, including effective refractive index, effective mode area, dispersion, and confinement loss, based on the PCF's structural characteristics. Compared to random forest and feedforward neural network models, the ensemble neural network achieves higher accuracy with a mean squared error of 3.78 × 10-6. Additionally, the computational time is significantly reduced, with only 2.27 minutes required for training and 0.08 seconds for prediction, which is much faster than numerical simulation software. This will provide new possibilities for optical device design and performance optimization, driving cutting-edge research and practical applications in the field of optics.

Optical Fiber Probe Microcantilever Sensor Based on Fabry–Perot Interferometer

Optical fiber Fabry–Perot sensors have long been the focus of researchers in sensing applications because of their unique advantages, including highly effective, simple light path, low cost, compact size, and easy fabrication. Microcantilever-based devices have been extensively explored in chemical and biological fields while the interrogation methods are still a challenge. The optical fiber probe microcantilever sensor is constructed with a microcantilever beam on an optical fiber, which opens the door for highly sensitive, as well as convenient readout. In this review, we summarize a wide variety of optical fiber probe microcantilever sensors based on Fabry–Perot interferometer. The operation principle of the optical fiber probe microcantilever sensor is introduced. The fabrication methods, materials, and sensing applications of an optical fiber probe microcantilever sensor with different structures are discussed in detail. The performances of different kinds of fiber probe microcantilever sensors are compared. We also prospect the possible development direction of optical fiber microcantilever sensors.

Demodulation Method of F-P Sensor Based on Wavelet Transform and Polarization Low Coherence Interferometry

Polarized low-coherence interferometry (PLCI) is widely used for the demodulation of Fabry–Perot (F-P) sensors. To avoid the influence of noise and dispersion on interference fringes, this paper proposes a data processing method in which the wavelet tools are applied to extract useful information from the extremum locations and envelope center of the fringes. Firstly, the wavelet threshold denoising (WTD) algorithm is used to remove electrical noise, and the complex Morlet wavelet is used to extract the fringe envelope. Based on this, the envelope center is used to predict the extremum locations of the specified order in its adjacent interval, the predicted locations are used as references to track the exact extremum locations, and the middle location of the peak and valley values is obtained to demodulate the F-P cavity accurately. The validity of this demodulation theory is verified by an air F-P cavity whose cavity length varies from 17 to 20 μm. With a sampling interval of 30 nm, the experimental results indicate that the repeatability accuracy is higher than 6.04 nm, and the resolution is better than 4.0 nm.