A compact fiber-optic temperature sensor with hybrid interferometers enhanced by the harmonic Vernier effect was proposed, which realized 36.9 times sensitization of the sensing Fabry-Perot interferometer (FPI). The hybrid interferometers configuration of the sensor consists of a FPI and a Michelson interferometer. The proposed sensor is fabricated by splicing the hole-assisted suspended-core fiber (HASCF) to the multi-mode fiber fused with the single-mode fiber, and filling polydimethylsiloxane (PDMS) into the air hole of HASCF. The high thermal expansion coefficient of PDMS improves the temperature sensitivity of the FPI. The harmonic Vernier effect eliminates the limitation of the free spectral range on the magnification factor by detecting the intersection response of internal envelopes, and realizes the secondary sensitization of the traditional Vernier effect. Combing the characteristics of HASCF, PDMS, and first-order harmonic Vernier effect, the sensor exhibits a high detection sensitivity of −19.22 nm/°C. The proposed sensor provides not only a design scheme for compact fiber-optic sensors, but also a new strategy to enhance the optical Vernier effect.

This study proposes an experimental implementation of temperature sensing system to study the enhanced Vernier effect using two polydimethylsiloxane (PDMS)-cavity Fabry-Perot interferometers (FPIs) arranged in parallel. However, these devices exhibited opposite responses to temperature; that is, with an increase (or decrease) in temperature, the two FPIs shifted in red and blue wavelength directions, respectively. This indicated that the sensitivity amplification of their parallel configuration was considerably larger than that generated by the traditional Vernier effect. Experimental results showed that the temperature sensitivities of the two FPIs were −0.42 and 1.36 nm/°C while that of their parallel structure was −14.6 nm/°C corresponding to an amplification factor of 10.7, which was much larger than that (8.2) of the traditional Vernier effect.

In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts.

A passively Q-switched (PQS) operation of Tm,Ho:LuVO₄ laser is experimentally demonstrated with a graphene saturable absorber (SA) mirror. An average output power of 1034 mW at 54.5 kHz is acquired with an 8% optical–optical conversion efficiency. The energy per pulse of 40.4 μJ and a peak power of 56.07 W are achieved; the narrowest pulse width of 300 ns is acquired, and the output wavelengths of Tm,Ho:LuVO₄ are 2075.02 nm in a continuous wave (CW) regime and 2057.03 nm in a PQS regime.