High-speed, large dynamic range spectral domain interrogation of fiber-optic Fabry-Perot interferometric sensors.

We report high-speed, large dynamic range spectral domain interrogation of fiber-optic Fabry-Perot (FP) interferometric sensors. An optical interrogation system employing a piezoelectric FP tunable filter and an array of fiber-Bragg gratings for wavelength referencing is developed to acquire the reflection spectrum of FP sensors at a high interrogation speed with a wide wavelength range. A 98 nm wavelength interrogation range was obtained at the resonance frequency of ∼110kHz of the FP tunable filter. At this frequency, the resolution of the FP cavity length measurement was 1.8 nm. To examine the performance of the proposed high-speed spectral domain interrogation scheme, two diaphragm-based fiber-tip FP sensors (a pressure sensor and acoustic sensor) were interrogated. The pressure measurement results show that the high-speed spectral domain interrogation method has the advantages of being robust to light intensity fluctuations and having a much larger dynamic range compared with the conventional intensity-based interrogation method. Moreover, owing to its capability of measuring the absolute FP cavity length, the proposed interrogation system mitigates the sensitivity drift that intensity-based interrogation often suffers from. The acoustic measurement results demonstrate that the high-speed spectral domain interrogation method is capable of high-frequency acoustic measurements of up to 20 kHz. This work will benefit many applications that require high-speed interrogation of fiber-optic FP interferometric sensors.

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.