Influence of Modeling Errors on the Initial Estimate for Nonlinear Myocardial Activation Times Imaging Calculated With Fastest Route Algorithm
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Noninvasive reconstruction of cardiac electrical activity has a great potential to support clinical decision making, planning, and treatment. Recently, significant progress has been made in the estimation of the cardiac activation from body surface potential maps (BSPMs) using boundary element method (BEM) with the equivalent double layer (EDL) as a source model. In this formulation, noninvasive assessment of activation times results in a nonlinear optimization problem with an initial estimate calculated with the fastest route algorithm (FRA). Each FRA-simulated activation sequence is converted into the ECG. The best initialization is determined by the sequence providing the highest correlation between predicted and measured potentials. We quantitatively assess the effects of the forward modeling errors on the FRA-based initialization. We present three simulation setups to investigate the effects of volume conductor model simplifications, neglecting the cardiac anisotropy and geometrical errors on the localization of ectopic beats starting on the ventricular surface. For the analysis, 12-lead ECG and 99 electrodes BSPM system were used. The areas in the heart exposing the largest localization errors were volume conductor model and electrode configuration specific with an average error <;10 mm. The results show the robustness of the FRA-based initialization with respect to the considered modeling errors.Keywords:
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There is some literature on the application of linear boundary element method (BEM) for real-time simulation of biological organs. However, literature is scant when it comes to the application of nonlinear BEM, although there is a possibility that the use of nonlinear BEM would result in better simulations. Hence the present paper explores the possibility of using nonlinear BEM for real-time simulation of biological organs. This paper begins with a general discussion about using the nonlinear BEM for real-time simulation of biological organs. Literature on nonlinear BEM is reviewed and the literature that deal with nonlinear formulations and coding are noted down next. In the later sections, some results obtained from nonlinear analyses are compared with the corresponding results from linear analyses. The last section concludes with remarks that indicate that it might be possible to obtain better simulations in the future by using nonlinear BEM.
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Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
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Simultaneous pose and correspondence estimation problem is used to determine the pose of a 3D object from a single 2D image when corresponding relation is unknown between 3D object points and 2D image points. The problem arises in many areas of computer vision and some algorithms have been presented. However, all the state-of-art algorithms rely on appropriate initialization and the correct solution may not be reached to in many times with the traditional initialization method which starts randomly. We derive a novel method which estimates the initial value based on genetic algorithm, considering the influences of different initial guesses comprehensively. Using this initialization method, the proper initial guess could be calculated and the simultaneous pose and correspondence problem could be easily solved. Simulation results and experiments on real images prove the effectiveness and robustness of our proposed initialization method.
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Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
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In this paper, we have further discussed the initialization issue of Caputo derivative. Specially, based on an initialization theory established by Lorenzo and Hartley, we give a new result about the initialization function of Caputo derivative under the assumption of terminal initialization.
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Initialization is one of the fundamental tasks to set up an ad hoc network, which involves assigning each of the n MSs a distinct ID number from 1 to n, distributedly. In this paper, an algorithm for initializing an ad hoc network with carrier sense capability is described. A novel acknowledgement scheme is first proposed to notify a transmitting MS whether its transmission is successful during the initialization. A distributed initialization algorithm is then developed and analyzed under the assumptions of a known number of users in the network. The algorithm is obtained based on the optimized key parameter to minimize the total time required to complete the initialization. Theoretical analysis and simulation indicates that the proposed initialization algorithm outperforms the randomized initialization algorithm.
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There is some literature on the application of linear boundary element method (BEM) for real-time simulation of biological organs. However, literature is scant when it comes to the application of nonlinear BEM, although there is a possibility that the use of nonlinear BEM would result in better simulations. Hence the present paper explores the possibility of using nonlinear BEM for real-time simulation of biological organs. This paper begins with a general discussion about using the nonlinear BEM for real-time simulation of biological organs. Literature on nonlinear BEM is reviewed and the literature that deal with nonlinear formulations and coding are noted down next. In the later sections, some results obtained from nonlinear analyses are compared with the corresponding results from linear analyses. The last section concludes with remarks that indicate that it might be possible to obtain better simulations in the future by using nonlinear BEM.
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