Specialty fibers have introduced new functionalities and opportunities in distributed fiber sensing applications. Particularly, Brillouin optical time domain analysis (BOTDA) systems have leveraged the unique features of specialty fibers to achieve performance enhancement in various sensing applications. This paper provides an overview of recent developments of the specialty fibers based BOTDA technologies and their sensing applications. The specialty fibers based BOTDA systems are categorized and reviewed based on the new features or performance enhancements. The prospects of using specialty fibers for BOTDA systems are discussed.

This paper presents the fabrication of a fiber Bragg grating (FBG)-based Fabry–Perot (FP) structure (7 mm total length) in an adiabatic fiber taper, investigates its strain and temperature characteristics, and compares the sensing characteristics with a standard polyimide coated FBG sensor. Firstly, a simulation of the said structure is presented, followed by the fabrication of an adiabatic fiber taper having the outer diameter reduced to 70 $\mathsf{\mu }$ m (core diameter to 4.7 $\mathsf{\mu }$ m). Next, the sensing structure, composed of two identical uniform FBG spaced apart by a small gap, is directly inscribed point-by-point using infrared femtosecond laser (fs-laser) micromachining. Lastly, the strain and temperature behavior for a range up to 3400 $\mathsf{\mu }\mathsf{\epsilon }$ and 225 ${}^{°}$ C, respectively, are investigated for the fabricated sensor and the FBG, and compared. The fabricated sensor attains a higher strain sensitivity (2.32 pm/ $\mathsf{\mu }\mathsf{\epsilon }$ ) than the FBG (0.73 pm/ $\mathsf{\mu }\mathsf{\epsilon }$ ), while both the sensors experience similar sensitivity to temperature (8.85 pm/ ${}^{°}$ C). The potential applications of such sensors include continuous health monitoring where precise strain detection is required.

Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 $\mathsf{\mu }\mathsf{\epsilon }$), pressure (0.14 bar), and refractive index (6 × 10⁻⁵ RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/$\mathsf{\mu }\mathsf{\epsilon }$, −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.

The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured $\phi$-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.