• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.55-63
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• 2014

Up to date, numerous studies on wireless sensor networks have been performed to overcome the Energy-Constraint of the sensor system. Existing schemes for estimating the residual energy have considered only voltage of sensor system. However battery performance in the real is affected by temperature and load. In this paper we introduce more accurate scheme, for the use in wireless sensor node, based on the interpolation of lookup tables which allow for temperature and load characteristics, as well as battery voltage.

• Journal of IKEEE
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• v.18 no.2
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• pp.272-281
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• 2014

In electrical impedance tomography (EIT), modified Newton Raphson (mNR) method is widely used inverse algorithm for static image reconstruction due to its convergence speed and estimation accuracy. The unknown conductivity distribution is estimated iteratively by minimizing a cost functional such that the residual error namely the difference in measured and calculated voltages is reduced. Although, mNR method has good estimation performance, EIT inverse problem still suffers from ill-conditioned and ill-posedness nature. To mitigate the ill-posedness, generally, regularization methods are adopted. The inverse solution is highly dependent on the choice of regularization parameter. In most cases, the regularization parameter has a constant value and is chosen based on experience or trail and error approach. In situations, when the internal distribution changes or with high measurement noise, the solution does not get converged with the use of constant regularization parameter. Therefore, in this paper, in order to improve the image reconstruction performance, we propose a new scheme to determine the regularization parameter. The regularization parameter is computed based on residual error and updated every iteration. The proposed scheme is tested with numerical simulations and laboratory phantom experiments. The results show an improved reconstruction performance when using the proposed regularization scheme as compared to constant regularization scheme.