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High-sensitivity fiber-optic interferometric refractive index sensors (FIRSs) have been of interest to researchers due to their potential to fabricate specific physical, chemical, and biological sensors. Fabricating interferometers working near the dispersion turning point (DTP) is an effective approach to improve the sensitivity of FIRSs. However, the group effective refractive index (RI) difference approaching 0 and the ratio of the variation of the effective RI difference to the external RI change being −1 cannot be simultaneously realized in low RI sensing, which restricts the further improvement of sensing sensitivity. Here, dual-path Mach-Zehnder interferometers (MZIs) with unequal geometrical path length are proposed for ultrasensitive RI sensing. The dual-path MZIs contain fiber path and sample path of different geometrical lengths to form the optical path difference. The dual-path MZIs can not only show a turning point where the sensing sensitivity tends to be infinite, which is similar to the previously reported DTP, but also get the constant value −1 for the RI response factor, leading to the result that the dips within an over-500-nm band width around the turning point can achieve high sensitivity reaching 105 nm/RIU level or higher. Ultrahigh sensitivity of −1.26 × 106 nm/RIU has been experimentally demonstrated at the RI around 1.35022. The dual-path MZIs proposed here may enlighten new ideas for developing high-sensitivity FIRSs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perform poorly in extreme cases, where the crowd density in different regions of an image is either too low or too high, leading to crowd underestimation or overestimation. The proposed solution is based on the observation that detecting and handling such extreme cases in a specialized way leads to better crowd estimation. Additionally, existing methods find it hard to differentiate between the actual crowd and the cluttered background regions, resulting in further count overestimation. To address these issues, we propose a simple yet effective modular approach, where an input image is first subdivided into fixed-size patches and then fed to a four-way classification module labeling each image patch as low, medium, high-dense or no-crowd. This module also provides a count for each label, which is then analyzed via a specifically devised novel decision module to decide whether the image belongs to any of the two extreme cases (very low or very high density) or a normal case. Images, specified as high- or low-density extreme or a normal case, pass through dedicated zooming or normal patch-making blocks respectively before routing to the regressor in the form of fixed-size patches for crowd estimate. Extensive experimental evaluations demonstrate that the proposed approach outperforms the state-of-the-art methods on four benchmarks under most of the evaluation criteria. [ABSTRACT FROM AUTHOR]