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Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can...
We investigate the use of the particle filtering sequential Bayesian estimation technique and its hardware implementation for tracking neural activity. We propose using the multiple particle filter (MPF) approach in order to reduce the computational intensity incurred due to the large number of sensors required to observe the noninvasive magnetoencephalography (MEG) measurements needed to estimate...
We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared...
The online estimation of the instantaneous frequency (IF) of time-varying (TV) signals with highly nonlinear phase functions is a challenging problem. In this paper, we propose an IF estimation method using Bayesian techniques, which combines particle filtering and Markov Chain Monte Carlo (MCMC) methods, to sequentially estimate highly nonlinear TV frequency variations as piecewise linear functions...
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