Optical cooling in Yb-doped silica fibers using anti-Stokes fluorescence has become a subject of great interest in the fiber laser community. This paper provides an update on the development of silica fibers designed specifically to enhance their cooling properties. This growing list includes a new, nearly single-mode fiber with a borophosphosilicate core that produced –65 mK of cooling with only 260 mW of 1040-nm pump power. The silica compositions that have now been successfully cooled at atmospheric pressure by anti-Stokes fluorescence by our team include aluminosilicate, aluminofluorosilicate, borophosphosilicate, and aluminosilicate doped with one of three different alkali-earth nanoparticles (Ba, Sr, and Ca). By fitting the measured temperature dependence of the cooled fiber on pump power, two key parameters that control the degree of cooling are inferred, namely the critical quenching concentration and the absorptive loss due to impurities. The inferred values compiled for the fibers that cooled indicate that the extracted heat is highest when the Yb concentration is 2 wt.% or more (to maximize heat extraction), the Al concentration is ~0.8 wt.% or greater (to reduce quenching), and the absorptive loss is below approximately 15 dB/km, and ideally below 5 dB/km (to minimize heating due to pump absorption). Only two of the reported fibers, an LaF3-doped and an LuF3-doped nanoparticle fiber, did not cool, because their Yb and Al concentrations were not sufficiently high. This analysis shows that through careful composition control (especially the Al and Yb concentrations) and minimization of the OH contamination, a new generation of Yb-doped silica fibers is emerging with higher Yb concentrations, greater resistance to quenching, and lower residual loss than commercial Yb-doped fibers. They can be expected to have a significant impact not only on optically cooled devices but also on a much broader range of fiber lasers and amplifiers.
COVID-19 is a highly infectious disease with high morbidity and mortality, requiring tools to support rapid triage and risk stratification. In response, deep learning has demonstrated great potential to quicklyand autonomously detect COVID-19 features in lung ultrasound B-mode images. However, no previous work considers the application of these deep learning models to signal processing stages that occur prior to traditional ultrasound B-mode image formation. Considering the multiple signal processing stages required to achieve ultrasound B-mode images, our research objective is to investigate the most appropriate stage for our deep learning approach to COVID-19 B-line feature detection, starting with raw channel data received by an ultrasound transducer. Results demonstrate that for our given training and testing configuration, the maximum Dice similarity coefficient (DSC) was produced by B-mode images (DSC = 0.996) when compared with three alternative image formation stages that can serve as network inputs: (1) raw in-phase and quadrature (IQ) data before beamforming, (2) beamformed IQ data, (3) envelope detected IQ data. The best-performing simulation-trained network was tested on in vivo B-mode images of COVID-19 patients, ultimately achieving 76% accuracy to detect the same (82% of cases) or more (18% of cases) B-line features when compared to B-line feature detection by human observers interpreting B-mode images. Results are promising to proceed with future COVID-19 B-line feature detection using ultrasound B-mode images as the input to deep learning models.
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