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A dual-channel single-mode-multi-mode-single-mode (SMS) fiber optic sensor encapsulated by polydimethylsiloxane (PDMS) was proposed for the first time, for the simultaneous monitoring of the brachial and radial arteries for accurate blood pressure prediction. With the help of the machine learning algorithm Support Vector Regression (SVR), the SMS fiber sensor can continuously and accurately monitor the systolic and diastolic blood pressure. Commercial sphygmomanometers are used to calibrate the accuracy of blood pressure measurement. Compared with the single-channel system, this system can extract more pulse wave features for blood pressure prediction, such as radial artery transit time (RPTT), brachial artery transit time (BPTT), and the transit time difference between the radial artery and the brachial artery (DBRPTT). The results show that the performance of dual-channel blood pressure monitoring is more accurate than that of single-channel blood pressure monitoring in terms of the absolute value of the correlation coefficient (R) and the average value of the difference between SBP and DBP. In addition, both the single-channel and dual-channel blood pressure monitoring are in line with the Association for the Advancement of Medical Devices (AAMI), but the average deviation (DM, 0.06 mmHg) and standard deviation (SD, 1.54 mmHg) of dual-channel blood pressure monitoring are more accurate. The blood pressure monitoring system has the characteristics of low cost, high sensitivity, non-invasive and capability for remote real time monitoring, which can provide effective solution for intelligent health monitoring in the era of artificial intelligence in the future.

A high sensitivity human chorionic gonadotropin (hCG) detection was conducted by a tapered side-polished (TSP) optical fiber sensor. Experimentally, the TSP fiber sensor was made by side polishing a short section of single mode fiber to a D shape structure and tapering the D shape section to a small diameter (

Surface acoustic wave (SAW) devices are generally fabricated on rigid substrates that support the propagation of waves efficiently. Although very challenging, the realisation of SAW devices on bendable and flexible substrates can lead to new generation SAW devices for wearable technologies. In this paper, we report flexible acoustic wave devices based on ZnO thin films coated on various substrates consisting of thin layers of metal (e.g., Ni/Cu/Ni) and/or polymer (e.g., polyethylene terephthalate, PET). We comparatively characterise the fabricated SAW devices and demonstrate their sensing applications for temperature and ultraviolet (UV) light. We also investigate their acoustofluidic capabilities on different substrates. Our results show that the SAW devices fabricated on a polymer layer (e.g. ZnO/PET, ZnO/Ni/Cu/Ni/PET) show enhanced temperature responsivity, and the devices with larger wavelengths are more sensitive to UV exposure. For actuation purposes, the devices fabricated on ZnO/Ni/Cu/Ni layer have the best performance for acoustofluidics, whereas insignificant acoustofluidic effects are observed with the devices fabricated on ZnO/PET layers. We propose that the addition of a metallic layer of Ni/Cu/Ni between ZnO and polymer layers facilitates the actuation capability for the acoustofluidic applications while keeping temperature and UV sensing capabilities, thus enhancing the integration of sensing and acoustofluidic functions.

Line of Sight (LOS) deformation based on Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques cannot be used in traditional probability integration method (PIM) parameter inversion. To improve the accuracy of parameter inversion, a model based on 3D deformation was proposed. The model simulates 3D deformation using PIM directly. The inverse of the Sum of the Squared Errors (SSE) of the PIM results and the measured deformation results was used as a fitting function within the GA. Reliable PIM parameters can be obtained based on this GA model. To identify the surface movement law of the Jinfeng coal mine, 6 Global Navigation Satellite System (GNSS) monitor points were established over the 011207 and 011809 working panels. Due to the limited number of points and the large distance between the points, it is not sufficient to obtain reliable PIM parameters using GNSS only. As a complement, 83 Sentinel-1A images were analyzed with small baseline subset (SBAS) DInSAR, and the LOS direction deformation was obtained. The reliable PIM parameters were calculated with the 3D inversion model based on the combination of LOS direction deformation and GNSS-monitored deformation. Then, those parameters were used to predict the coal mine deformation of panels 011207 and 011809, which demonstrated that the prediction results coincide with the measured results. The model can be used to study the laws of mining subsidence combined with DInSAR and GNSS, which can reduce the requirements of the number of GNSS points and the impact of radar decoherence. This provides a new technical approach for studying the law of surface movement in mining subsidence research.

The fabrication and application for twist sensing of an eccentric three-core fiber were demonstrated. The fiber was made by stack-and-draw technique, in which silica rods and core canes were put in a tube and drawn on a fiber drawing tower. Three cores formed a Mach-Zehnder interferometer, where the lights transmitted in the three cores interfered with each other, resulted in the formation of envelopes on spectrum. Because two of the cores were off axis, phase differences among the cores varied with twist due to different stretches on each core, which caused shift of the spectral envelopes of the interference signal. Wide range twist measurement can be realized with relatively high sensitivity by tracking lower dips of the envelopes. Experimental results revealed that the dips shift quadratically with twist angle, which means that the sensitivity increases with twist. The compensation of temperature influence was also implemented by inscribing a Bragg grating on one of the cores with femtosecond laser. Because the fiber can be mass-produced, it is suitable for twist sensing in practical application for its low cost.

Establishing reliable correspondences between a pair of point clouds is essential for registration with partial overlaps. However, existing correspondence estimation works usually struggle to distinguish the points in overlap and non-overlap regions. This paper thus proposes an Overlap-guided Coarse-to-Fine Network, named OCFNet, which first establishes correspondences at a coarse level and then refines them at a point level. Specifically, at the coarse level, our model first aggregates two point clouds into smaller sets of super-points with associated features and overlap scores, followed by establishing coarse-level correspondences between the two sets of super-points under the guidance of overlap scores. On the fine stage, a decoder recovers the raw points while jointly learning the associated features and overlap scores. Coarse-level proposals are then expanded to patches, and point-level correspondences are sequentially refined from the corresponding patches. We conducted comprehensive experiments on 3DMatch, 3DLoMatch, and KITTI benchmarks to show the effectiveness of the proposed method. [code]