All Outputs (2)

Unsupervised feature learning and clustering of particles imaged in raw holograms using an autoencoder. (2021)
Journal Article
LIU, Z., THEVAR, T., TAKAHASHI, T., BURNS, N., YAMADA, T., SANGEKAR, M., LINDSAY, D., WATSON, J. and THORNTON, B. 2021. Unsupervised feature learning and clustering of particles imaged in raw holograms using an autoencoder. Journal of the Optical Society of America A [online], 38(10), pages 1570-1580. Available from: https://doi.org/10.1364/JOSAA.424271

Digital holography is a useful tool to image microscopic particles. Reconstructed holograms give high-resolution shape information that can be used to identify the types of particles. However, the process of reconstructing holograms is computationall... Read More about Unsupervised feature learning and clustering of particles imaged in raw holograms using an autoencoder..

Digital in-line holography for large-volume analysis of vertical motion of microscale marine plankton and other particles. (2021)
Journal Article
LIU, Z., TAKAHASHI, T., LINDSAY, D., THEVAR, T., SANGEKAR, M., WATANABE, H.K., BURNS, N., WATSON, J. and THORNTON, B. 2021. Digital in-line holography for large-volume analysis of vertical motion of microscale marine plankton and other particles. IEEE journal of oceanic engineering [online], 46(4), pages 1248-1260. Available from: https://doi.org/10.1109/JOE.2021.3066788

Measuring the distribution, characteristics and dynamics of marine microscale plankton and other particulate matter is essential to understand the vertical flux of elements in the marine environment. Digital holographic microscopy is a powerful appro... Read More about Digital in-line holography for large-volume analysis of vertical motion of microscale marine plankton and other particles..