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Sequential Monte Carlo method for parameter estimation in diffusion models of affinity-based biosensors

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3 Author(s)
Manohar Shamaiah ; Department of Electrical and Computer Engineering, University of Texas at Austin, USA ; Xiaohu Shen ; Haris Vikalo

Estimation of the amounts of target molecules in real-time affinity-based biosensors is studied. The problem is mapped to inferring the parameters of a temporally sampled diffusion process. To solve it, we rely on a sequential Monte Carlo algorithm which generates particles using transition density of the diffusion process. The transition density is not available in a closed form and is thus approximated using Hermite polynomial expansion. Simulations and experimental results demonstrate effectiveness of the proposed scheme, and show that it outperforms competing techniques.

Published in:

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

22-27 May 2011