Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/186066
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dc.titleFLUORESCENCE SPECTROSCOPY ANALYSIS THROUGH CONVOLUTIONAL NEURAL NETWORKS
dc.contributor.authorTANG WAI HOH
dc.date.accessioned2021-02-01T18:01:41Z
dc.date.available2021-02-01T18:01:41Z
dc.date.issued2021-01-09
dc.identifier.citationTANG WAI HOH (2021-01-09). FLUORESCENCE SPECTROSCOPY ANALYSIS THROUGH CONVOLUTIONAL NEURAL NETWORKS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/186066
dc.description.abstractAn important analysis in fluorescence spectroscopy is molecular dynamics, in particular, quantification of diffusion coefficient. In this research, we apply a convolutional neural network (CNN) to determine the diffusion coefficient of freely diffusing molecules. We construct the CNN based on our concurrent study of model identification of autoregression moving average (ARMA) time series through CNN, which illustrates the application of CNN to temporal data. We train the network with purely simulated data under a quadratic loss function. The performance of our network is validated using simulated data and data from lipid bilayer experiments. For simulated validation data, we find that our network performs better at 2,500 frames against the conventional non-linear least squares curve fitting approach in Imaging FCS method, in terms of mean squared error. We demonstrate using scatter plots that our network predictions for lipid bilayer data are comparable to Imaging FCS.
dc.language.isoen
dc.subjectFluorescence spectroscopy analysis, Imaging FCS, convolutional neural network, autoregression moving average, ARMA model identification, 2D diffusion
dc.typeThesis
dc.contributor.departmentSTATISTICS AND DATA SCIENCE
dc.contributor.supervisorRoellin, Adrian
dc.contributor.supervisorWohland, Thorsten
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (FOS)
dc.identifier.orcid0000-0002-6717-9426
Appears in Collections:Ph.D Theses (Open)

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