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Localization of magnetic target can be estimated through a magnetic anomaly. Generally, we need to solve the high-order nonlinear equations for estimating the position and magnetic moment of the target. Therefore, optimization algorithms are applied to calculate the solution of the nonlinear equations. In this paper, we present a fast linear algorithm for locating the target based on the total magnetic field gradient. In the algorithm, we give the closed-form formula of the static magnetic target localization. According to the properties of the vector, we can obtain a cubic equation of any 1-D position of the target. Thus, we can easily solve the cubic equation and obtain the closed-form formula of the target localization. Compared with the optimization algorithms, the proposed method provides good performance using short time and can be used to locate the target in real time. The proposed method is validated by the numerical simulation and real experimental data. The position and magnetic moment of the target are calculated rapidly. And the results show that the estimated parameters of the static target using the proposed method are very close to the true values. [ABSTRACT FROM PUBLISHER]

This article describes a GPU-based high-performance computing method to tackle the protein-DNA docking problem. GPU-specific algorithmic techniques are developed to accelerate a docking algorithm that integrates Monte Carlo simulation and simulated annealing. Experiments show that such improved computation speed accelerates the conformational space sampling of the algorithm and increases the chance of finding near-native protein-DNA structures. [ABSTRACT FROM PUBLISHER]