One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.
Electrical Impedance Tomography (EIT) systems are becoming popular because they present several advantages over competing systems. However, EIT leads to images with very low resolution. Moreover, the nonuniform sampling characteristic of EIT precludes the straightforward application of traditional image super-resolution techniques. In this work, we propose a resampling based Super-Resolution method for EIT image quality improvement. Results with both synthetic and in vivo data indicate that the proposed technique can lead to substantial improvements in EIT image resolution, making it more competitive with other technologies.
In this paper we present an iterative technique based on -Monte Carlo simulations for deriving the optimal control of the infinite horizon linear regulator problem of discretetime Markovian jump linear systems for the case in which the transition probability matrix of the Markov chain is not known. It is well known that the optimal control of this problem is given in terms of the maximal solution of a set of coupled algebraic Riccati equations (CARE), which have been extensively studied over the last few years. We trace a parallel with the theory of TD(X) algorithms for Markovian decision processes to develop a TD(X) like algorithm for the optimal control associated to the maximal solution of the CARE. Some numerical examples are also presented.
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