This work presents a dual-point temperature sensing system capable of high-speed based on the photonic time-stretch dispersive Fourier transformation (TS-DFT) technique. The system aims to overcome the speed limitations of traditional optical spectrum interrogators. A sensing array consisting of two cascaded fiber Bragg gratings (FBGs) with a 5-meter separation is illuminated by a 50 MHz femtosecond laser source. The reflected spectrum, encoded with temperature information, is then stretched in time by propagating through a dispersion-compensating fiber (DCF) with a total dispersion of -360 ps/nm. This process enables the direct mapping of the optical spectrum into a measurable time-domain waveform at a 50 MHz repetition rate. Experimental results demonstrate the system’s capability to perform measurements over a broad temperature range from 40 °C to 250 °C. The temperature sensitivities of both FBGs were characterized to be 0.012 nm/°C in the optical wavelength domain. Correspondingly, the obtained time-domain sensitivities are -4.28 ps/°C and -4.31 ps/°C, with a temperature resolution of approximately 5.8 °C in the time domain. This study validates the feasibility of the TS-DFT scheme for high-speed dual-point sensing in demanding applications.

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation.