Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/6424
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zeng, N | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Li, Y | - |
dc.contributor.author | Du, M | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2012-05-10T13:23:06Z | - |
dc.date.available | 2012-05-10T13:23:06Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE Transactions on Nanotechnology, 11(2): 321 - 327, Mar 2012 | en_US |
dc.identifier.issn | 1536-125X | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6081979 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/6424 | - |
dc.description | This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEE | en_US |
dc.description.abstract | In this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay. | en_US |
dc.description.sponsorship | This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Extended Kalman filter (EKF) | en_US |
dc.subject | Lateral flow immunoassay (LFIA) | en_US |
dc.subject | Parameter estimation | en_US |
dc.subject | Particle filter | en_US |
dc.subject | State estimation | en_US |
dc.title | Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/TNANO.2011.2171193 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Active Staff | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management | - |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 341.68 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.