This paper presents a displacement sensor designed to achieve the Optical Vernier Effect (OVE) through a simple yet robust configuration, enhancing sensitivity and precision in small displacement measurements. The sensor structure comprises a few-mode fiber (FMF) placed between two single-mode fibers (SMF) in an SMF-FMF-SMF (SFS) configuration. A series of distinct configurations of concatenated Mach–Zehnder fiber interferometers (MZFI) were examined, with the lengths of the reference FMF (FMF_Ref) and sensing FMF (FMF_Sen) adjusted to track the spectral envelope shifts. The results demonstrate that the direction of the spectral shift is governed by the ratio between the FMF_Ref and FMF_Sen lengths. The sensor achieved a sensitivity of up to 39.07 nm/mm and a magnification factor (M factor) of up to 50.09, demonstrating exceptional precision and adaptability across a range of applications. The proposed configuration also enhances the overall sensor performance, highlighting its potential for broader use in fields requiring precise displacement monitoring.

An experimental demonstration for generating harmonics in the optical Vernier effect (OVE) by cascading tapered single-mode fibres (SMF28) is presented. The tapered optical fibres conformed to a pair of in-series Mach-Zehnder fibre interferometers (MZFI), in which one MZFI was a reference while the other was a sensor. The first-order harmonic was achieved by increasing the distance between tapers of the sensor MZFI by a multiple of (i + 1) times the distance between tapers of the reference MZFI plus a mismatch factor. A series of experiments using an individual MZFI and two MZFIs with traditional and harmonic OVE as displacement sensors were performed to compare their sensitivities and magnification factors and demonstrate the improving the sensitivity. The displacement sensor with the first-order harmonic exhibited a maximum sensitivity of −33.3 nm/mm with an M-factor of 8.53 in the mm range.

Due to the increasing need for continuous use of face masks caused by COVID-19, it is essential to evaluate the filtration quality that each face mask provides. In this research, an estimation method based on thermal image processing was developed; the main objective was to evaluate the effectiveness of different face masks while being used during breathing. For the acquisition of heat distribution images, a thermographic imaging system was built; moreover, a deep learning model detected the leakage percentage of each face mask with a mAP of 0.9345, recall of 0.842 and F1-score of 0.82. The results obtained from this research revealed that the filtration effectiveness depended on heat loss through the manufacturing material; the proposed estimation method is simple, fast, and can be replicated and operated by people who are not experts in the computer field.