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    Item type:Publication,
    Digital holography with deep learning for algae identification and classification
    (2024-01-01)
    Ruttanasirawit, Chinnaphat
    ;
    Plaipichit, Suwan
    ;
    Thongsuwan, Setthanun
    ;
    Thonglim, Pachara
    ;
    Phunpruch, Saranya
    Recently, the characterization of marine objects, populations and biophysical interactions have become crucial within the research community. In this study, we leverage digital holographic imaging systems and deep learning networks to classify three distinct types of micro-algae: Chlamydomonas, Scenedesmus armatus, and Scenedesmus_sp-L. We employed reconstructed digital holographic images and deep learning to identify the results from both approaches. The integration of holographic imaging holds promises in replacing expensive characterization systems like AFM, x-ray diffraction, and Raman spectroscopy, offering a more costeffective solution. In our system, we utilize in-line microscopic digital holographic imaging to record and reconstruct images of the algae specimens. An essential advantage of holographic techniques is that they do not require intact samples of the specimens for effective object identification. To further enhance the process, we combined deep learning algorithms with holographic imaging, capitalizing on the advanced computers. This combination enables highly effective characterizing and classification of different types of algae. These innovative approaches pave the way for exciting advancement in marine research and monitoring.
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    Simple stochastic model for random waste absorption of an algae cell: Analytic approach
    (2013-11-04)
    Nokkaew, Artorn
    ;
    Amornsamankul, Somkid
    ;
    Pimpunchat, Busayamas
    ;
    Saengpayab, Yaowapa
    ;
    Triampo, Wannapong
    Over the past few decades, research on water quality using biological treatment has been considerably done. The problem of how the algae dynamically absorb waste is of great important both environmental and biological science particularly concerning a problem of waste water treatment. With this regards, we have applied a model in which a Brownian agent interacts with a spin or clock in 1D to describe and predict the system of waste absorption by algae. We assumed that the waste particles executing Brownian like motion and occasionally absorbed by algae. Analytic results are presented and discussed in connection with the waste absorption by algae. It was found that the absorption nature is dominated by the exponential like nature. How this model can be improved to better understanding or match with the real system is also elaborated.