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DC Field | Value | Language |
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dc.contributor.author | Zeng, N | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Zhang, H | - |
dc.date.accessioned | 2016-12-19T13:35:35Z | - |
dc.date.available | 2016-11-01 | - |
dc.date.available | 2016-12-19T13:35:35Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Science China Information Sciences, 59(11): pp. 1-10, (2016) | en_US |
dc.identifier.issn | 1674-733X | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/13698 | - |
dc.description.abstract | This paper is concerned with the problem of learning structure of the lateral flow immunoassay (LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize the unscented Kalman filter (UKF) algorithm to simultaneously identify not only the states but also the parameters of the improved state-space model by using short but high-dimensional experiment data in terms of images. It is shown via experiment results that the UKF approach is particularly suitable for modelling the LFIA devices. The identified model with time-delay is of great significance for the quantitative analysis of LFIA in both the accurate prediction of the dynamic process of the concentration distribution of the antigens/antibodies and the performance optimization of the LFIA devices. | en_US |
dc.description.sponsorship | This work was supported in part by National Natural Science Foundation of China (Grant No. 61403319), Fujian Natural Science Foundation (Grant No. 2015J05131), Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, and Fundamental Research Funds for the Central Universities. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Verlag | en_US |
dc.subject | Lateral flow immunoassay | en_US |
dc.subject | Biochemical reaction networks | en_US |
dc.subject | Modelling | en_US |
dc.subject | Unscented Kalman filter | en_US |
dc.title | Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s11432-016-0280-9 | - |
dc.relation.isPartOf | Science China Information Sciences | - |
pubs.issue | 11 | - |
pubs.publication-status | Published | - |
pubs.volume | 59 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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