This paper develops a high-precision miniature three-dimensional Fiber Bragg Grating (FBG) force sensor designed for detecting and locating hard masses within tissues during Minimally Invasive Surgery (MIS) and robotic-assisted MIS palpation processes. The prototype consists of three FBG-etched optical fibers and an integrated elastomer. The fibers are suspended and parallel to each other within the elastomer, allowing each FBG to be directly compressed or stretched under stress. A novel model, combining segment-by-segment sensitivity and differential decoupling, is adopted for decoupling three-dimensional forces. This approach significantly reduces the crosstalk error caused by spatial forces, avoids the complexity of assembling multiple fibers in previous designs, and reduces the sensor's size, facilitating its integration into MIS procedures. Calibration experiments are conducted to determine the sensor's force sensitivity coefficients, achieving resolutions of 4.35 mN, 3.99 mN, and 3.30 mN in the X, Y, and Z directions, respectively. Through discrete and drag palpation tests on silicone with different types of hard inclusions, the results demonstrate that the proposed prototype has the ability to effectively identify the size, hardness, and depth of hard objects from the contact force information. Additionally, ex vivo porcine kidney palpation experiments are conducted to further verify the feasibility of locating hard masses during MIS palpation.

An exceptionally high-performance, high-birefringent photonic crystal fiber (PCF) is meticulously designed. The core design features a unique arrangement in which the central row of circular air holes is substituted by three rows of smaller circular holes. Subsequently, the middle row is adjusted to achieve various rectangular mode field configurations. With the optimized structure parameters, the birefringence of the PCF can reach 3.57 × 10${}^{-2}$ at a wavelength of 1.55 $\mathsf{\mu }$m. The confinement loss is as low as 8.4 × 10${}^{-6}$ dB/m. The nonlinear coefficient is up to 41 W${}^{-1}$·km${}^{-1}$. The dispersion is relatively flat within the range from 1.3 $\mathsf{\mu }$m to 1.9 $\mathsf{\mu }$m. These remarkable characteristics render the proposed PCF a strong candidate for applications in polarization preservation, dispersion compensation, wideband supercontinuum generation, and other related fields.

This paper establishes a widely applicable method for designing hollow-core anti-resonant fiber (HC-ARF) model based on geometric principles, which is feasible under practical manufacturing conditions and is defined by concise equations. We define this problem as a combinatorial optimization issue under the constraints of mixed integer nonlinearity. We iteratively refine the solution space through multiple steps, culminating in an integrated algorithm for automated modeling computation and data extraction. Through exhaustive calculation of models under limited conditions, we obtained multiple rigid HC-ARFs with low confinement loss (CL), low bend loss, and relatively high birefringence. The minimum CL is $\text{8.8}\times \text{10}^{-3}$ dB/m. At bending radius of 5 cm, the lowest bend loss is $\text{2.7}\times \text{10}^{-2}$ dB/m, and the highest birefringence is $\text{2.2}\times \text{10}^{-6}$ . After statistical analysis, two key factors influencing single anti-resonance layer HC-ARF have been determined: the number and radius of the rings hold the primary importance, followed by the rings arrangement. Further study demonstrates that the proposed model design method can be applied to various optical fiber designs.

To address the optimization problem of expensive multi-modal multi-objective black-box functions, this paper proposes a systematic optimization method based on a back propagation neural network as a proxy model, combined with clustering algorithm to cluster and differentiate multiple modes within the model. The method utilizes normalization and weighted summation of numerical experimental data to optimize the hyperparameters of the optimization algorithm's decision-making process. This approach resolves the multiple constraints of traditional optimization methods. Finally, applied to the hollow-core anti-resonant fiber with gap angle constraints to ensure its topological properties remain unchanged, the model optimizes three categories totaling six modes, optimizing the objectives of birefringence and loss. The top five optimal models achieve a minimum loss of $\text {3.54} \times \text {10}^\text{-3}$ dB/m, maximum birefringence of $\text {1.25} \times \text {10}^\text{-4}$, higher order modulation enhanced receiver of 50, and bandwidth of 1.425 $\mu$m.

This paper proposes a novel optimization method for hollow-core, anti-resonant fiber based on a gradient descent algorithm assisted via a radial basis-function surrogate model. This approach significantly reduces the number of optimization iterations, achieving a stable improvement in birefringence performance by an order of magnitude across the operating wavelength band. Furthermore, various optimization algorithms were compared, and the indicators of their Pareto sets were analyzed to demonstrate the effectiveness of the proposed method in multi-objective optimization.

This paper employs five different general-purpose optimization methods to perform parameter optimization on single-layer hollow antiresonant fibers. It provides guidance on the establishment of hyperparameters for various optimization methods, with the aim of further defining and standardizing the necessary conditions and convergence criteria for applying optimization algorithms to specialty optical fibers. Through numerical experiments, the study ultimately obtains the converged optimal performance and the range of optimized parameter guidance for single-layer, double-layer, and triple-layer antiresonant fibers with different topological structures.