In situ digital inline holography is a technique which can be used to acquire high‐resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real‐time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real‐time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real‐time, high‐accuracy plankton classification and it has the potential to be deployed on imaging instruments for long‐term in situ plankton monitoring.

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There has been growing concern about high risk of airborne infection during wind instrument performance as the COVID-19 pandemic evolves. In collaboration with 16 musicians from the Minnesota Orchestra, we employ multiple experimental and numerical techniques to quantify the airflow and aerosol concentration emitted from ten wind instruments under realistic performance conditions. For all instruments, the extent of the flow and aerosol influence zones are limited to 30 cm. Further away, the thermal plume generated by the human body is the dominant source of flow. Flow and aerosol concentration vary in response to changes in music amplitude, pitch, and note duration, depending on playing technique and instrument geometry. Covering the trumpet bell with speaker cloth and placing filters above the instrument outlet can substantially reduce the aerosol concentration. Our findings indicate that with appropriate risk mitigation strategies, musical instrument performance can be conducted with low risk of airborne disease transmission.

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