Two-parameter demodulation based on variable sensitivity matrix (VSM) for temperature and salinity measurement in seawater is proposed based on the developed refractive index (RI) expression of seawater in infrared band of 1200nm - 1650nm. The RI expression of n ( S , T , λ) is obtained from a great deal of RI data measured by microfiber Mach-Zehnder interferometer and fitted by different methods. The accuracy of the developed n ( S , T , λ) is verified experimentally, and demodulation error of about 2.5% is reached by VSM method, which is at least twice lower than traditional sensitivity matrix used. The VSM and RI expression proposed here will be useful for theoretical or experimental research on optical fiber sensors operated in infrared band and they can also be expanded to other sensors based on RI change of seawater.

In this paper, a photonic skin sensor based on a balloon-shaped optical fiber structure cascading a Fiber Bragg Grating (FBG) is proposed, which can simultaneously sense pressure and temperature. When the photonic skin feels pressure, the built-in polymer micro-cap is squeezed, causing a change in the radius of the balloon-shaped structure. At the same time, the capillary-enclosed FBG can be used for temperature compensation. The pressure sensitivity of the photon skin sensor can reach 6.556 nm/mN in the range of 0-0.5 mN and the temperature sensitivity can reach -0.192 nm/°C in the range of 30-50 °C. The demodulation of the dual parameter matrix shows that the error rates of pressure and temperature measurements are less than 0.5% and 2.01%, respectively. On this basis, the sensor is applied to robotic arm-assisted puncture experiment detection, and the five states of the puncture process are analyzed. Based on the backpropagation (BP) neural network for repeated training, the results show that its accuracy remains at 98.7%, indicating that the sensor has good robustness. The development of this sensor shows application potential in the field of robotic intelligent skin in the future.

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model.