This paper explores two types of multistate Hopfield neural networks, based on commutative quaternions that are similar to Hamilton's quaternions but with commutative multiplication. In one type of the networks, t...
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This paper explores two types of multistate Hopfield neural networks, based on commutative quaternions that are similar to Hamilton's quaternions but with commutative multiplication. In one type of the networks, the state of a neuron is represented by two kinds of phases and one real number. The other type of the networks adopts the decomposed form of commutative quaternion, i.e., the state of a neuron consists of a combination of two complex values. We have investigated the stabilities of these networks, i.e., the energies monotonically decreases with respect to the changes of the network states.
This paper proposes an algorithm that solves planar homography by iterative linear optimization. We iteratively employ direct linear transformation (DLT) algorithm to robustly estimate the homography induced by a give...
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This paper proposes an algorithm that solves planar homography by iterative linear optimization. We iteratively employ direct linear transformation (DLT) algorithm to robustly estimate the homography induced by a given set of point correspondences under perspective transformation. By simple on-the-fly homogeneous coordinate adjustment we progressively minimize the difference between the algebraic error and the geometric error. When the difference is sufficiently close to zero, the geometric error is equivalently minimized and the homography is reliably solved. Backward covariance propagation is employed to do error analysis. The experiments prove that the algorithm is able to find global minimum despite erroneous initialization. It gives very precise estimate at low computational cost and greatly outperforms existing techniques.
Let H be a real Hilbert *** that T is a nonexpansive mapping on H with a fixed point, G is a L-Lipschitzian mapping on H with coefficient L > 0, and F : H → H is a k- Lipschitzian and η-strongly monotone operator...
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Let H be a real Hilbert *** that T is a nonexpansive mapping on H with a fixed point, G is a L-Lipschitzian mapping on H with coefficient L > 0, and F : H → H is a k- Lipschitzian and η-strongly monotone operator with k > 0, η > O. Let 0 2, 0 2/2)/L = τ/L. We pointed out the relationship between Yamada's method and viscosity iteration and proved that the sequence {x η } generated by the iterative method x η+1 = α n γG(x n ) + (I - μα n F)Tx n converges strongly to a fixed point x̃ ∈ F ix (T), which solves the variational inequality ((γG - μF)x̃, x-x̃) ≤ 0, for x ∈ F ix (T).
We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms fo...
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We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms for solving this class of variational inclusions. We prove the existence of solution for this kind of variational inclusions and the convergence of iterative sequences generalized by the algorithms in Hilbert spaces.
To find the corresponding target points among multi-images of the same scene is a premise of three dimensional (3D) reconstruction automatically from multi-view images. The geometrical constraints among multi-images o...
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To find the corresponding target points among multi-images of the same scene is a premise of three dimensional (3D) reconstruction automatically from multi-view images. The geometrical constraints among multi-images of the same scene are analyzed and it is concluded that the error matching cannot be removed if only using 2D information. In this paper, we present an iterative fractional step matching algorithm: (1) pre-matching the target point using constraints of epipolar geometry firstly; (2) removing the error matching points using the principle of the uniqueness of a point in 3D space; (3) bundle adjustment is added to refine the camera external parameters. After the external parameters of every image refined, repeat step (1) to step (3) until matching occurred at the same point between the adjacent iteration. Real experiments showed that error matching can be effectively removed using our algorithm, resulting in improved efficiency and accuracy in 3D reconstruction.
In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sens...
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In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sensor nodes exhibits a heavy-tail, non-Gaussian characteristic and should be fitted into a contaminated Gaussian model. This property renders conventional least square and maximum likelihood based location estimation methods ineffective. Leveraging the distributed, in-network processing nature of a WSN, a novel de-centralized robust acoustic source localization (DRASL) algorithm is proposed. With the DRASL, local sensor nodes receive sensor readings broadcast from neighboring sensors and independently compute local location estimates using a light-weight iterative Nonlinear Reweighted Least Square (INRLS) algorithm. The local location estimate then will be relayed to a fusion center where the final location estimate is obtained as a weighted average of the local estimates. The potential advantage of this algorithm is validated using extensive simulation in a real-world operation scenario. It is show that its performance is superior than existing methods while promising to be more energy efficient.
This paper proposes to extend the given reference signal in the iterative learning control context so that the resultant signal has the non-minimum phase zero structure as the given plant. This extension enables preve...
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This paper proposes to extend the given reference signal in the iterative learning control context so that the resultant signal has the non-minimum phase zero structure as the given plant. This extension enables preventing control inputs exponentially increasing. The extending signals can be synthesized by solving a linear equation. The effectiveness of the proposed method is examined by using a numerical example.
In a pursuit-evasion game, a pursuer tries to capture evader while the evader is trying to avoid capture. During the pursuit and evasion, pursuer longs for minimizing the distance from evader at any time while evader ...
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In a pursuit-evasion game, a pursuer tries to capture evader while the evader is trying to avoid capture. During the pursuit and evasion, pursuer longs for minimizing the distance from evader at any time while evader wants to the maximal distance. Although the relevant information of each side is unknown for each other, the initial information about pursuer and evader's locations and transition directions can be presented according to the prior probability. Then a Bayesian iterative process can be used to modify the probability of opponent's actions and to maximize the probability. It can make the pursuer and evader satisfy their min and max needs respectively. Simulations show that with the increase of pursuit-evasion area, capture frequency has robust convergence, and average capture time and iterative frequency increase faster.
For a class of the important problems that seek to maximize the sum rate of the multiple input multiple output relay communication system (MIMO RCS) and compute the optimal transmission distributions, we present their...
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For a class of the important problems that seek to maximize the sum rate of the multiple input multiple output relay communication system (MIMO RCS) and compute the optimal transmission distributions, we present their mathematical models, and then develop an efficient algorithm for solving this class of the problems. The fixed point theory is used to prove convergence of the proposed algorithm. The performance result of the proposed new algorithm indicates that the proposed algorithm overcomes some limitations of other algorithms. First it utilizes the machinery of parallel computation, a limitation of the previously known iterative water-filling algorithms. Second it is robust to the large number of users K, as the previously known iterative water-filling algorithms are not. Not only does the proposed algorithm sufficiently utilize the machinery of parallel computation, but it also shows a strong robustness for the number of the users K. At the same time, the proposed algorithm shows fast convergence.
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