Dr Zonghua Liu's Outputs (6)

Species‐specific diatom dynamics shape their vertical distribution and export during bloom decline. (2025)
Journal Article
GIERING, S.L.C., CARVALHO, F., IVERSEN, M.H., LIU, Z., MERCHIERS, O., MOORE, C.M., OCKWELL, M., PASSOW, U., POULTON, A.J., ROMANELLI, E., THEVAR, T. and SANDERS, R. [2025]. Species-specific diatom dynamics shape their vertical distribution and export during bloom decline. Limnology and oceanography [online], Early View. Available from: https://doi.org/10.1002/lno.12818

The Southern Ocean is a critical region for global biogeochemical cycles, particularly for carbon dioxide uptake and organic carbon export, partly driven by extensive phytoplankton blooms. In naturally iron‐fertilized regions of this otherwise iron‐l... Read More about Species‐specific diatom dynamics shape their vertical distribution and export during bloom decline..

Ship detection and identification for maritime security and safety based on IMO numbers using deep learning. (2024)
Presentation / Conference Contribution
KHAN, M.M.H., LIU, Z., PRABHU, R., ZHENG, H. and JAVIED, A. 2024. Ship detection and identification for maritime security and safety based on IMO numbers using deep learning. In Bouma, H., Prabhu, R., Yitzhahy, Y. and Kuijf, H.J. (eds.) Advanced materials, biomaterials, and manufacturing technologies for security and defence II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK. Proceedings of SPIE, 13206. Bellingham, WA: SPIE [online], paper 1320608. Available from: https://doi.org/10.1117/12.3031425

In marine safety and security, the ability to rapidly, autonomously, and accurately detect and identify ships is the highest priority. This study presents a novel approach using deep learning to accurately identify ships based on their International... Read More about Ship detection and identification for maritime security and safety based on IMO numbers using deep learning..

Machine-learning-based size estimation of marine particles in holograms recorded by a submersible digital holographic camera. (2023)
Presentation / Conference Contribution
LIU, Z., GIERING, S., THEVAR, T., BURNS, N., OCKWELL, M. and WATSON, J. 2023. Machine-learning-based size estimation of marine particles in holograms recorded by a submersible digital holographic camera. In Proceedings of OCEANS 2023 - Limerick, 5-8 June 2023, Limerick, Ireland. Piscataway: IEEE [online], article number 10244456. Available from: https://doi.org/10.1109/OCEANSLimerick52467.2023.10244456

Particle size estimation is key to understanding carbon fluxes and storage in the marine ecosystem. Images of particles provide much information about their size. A subsea digital holographic camera was used to image particles in vertical trajectory... Read More about Machine-learning-based size estimation of marine particles in holograms recorded by a submersible digital holographic camera..

Multimodal image and spectral feature learning for efficient analysis of water-suspended particles. (2023)
Journal Article
TAKAHASHI, T., LIU, Z., THEVAR, T., BURNS, N., LINDSAY, D., WATSON, J., MAHAJAN, S., YUKIOKA, S., TANAKA, S., NAGAI, Y. and THORNSTON, B. 2023. Multimodal image and spectral feature learning for efficient analysis of water-suspended particles. Optics express [online], 31(5), pages 7492-7504. Available from: https://doi.org/10.1364/OE.470878

We have developed a method to combine morphological and chemical information for the accurate identification of different particle types using optical measurement techniques that require no sample preparation. A combined holographic imaging and Raman... Read More about Multimodal image and spectral feature learning for efficient analysis of water-suspended particles..

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..