Based on the standard atmospheric polarization model, the principle of bionic polarized light orientation is studied, and the formula of solar height angle and azimuth angle is given. Then the calculation method of po...
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Based on the standard atmospheric polarization model, the principle of bionic polarized light orientation is studied, and the formula of solar height angle and azimuth angle is given. Then the calculation method of polarization degree and polarization azimuth angle is studied. The method of calculating the heading angle when the carrier is tilted is deduced. The results show that if the horizontal angle of the carrier is known, the heading angle of the carrier can be calculated according to the polarization angle. Because the polarized light sensor can only give the heading information, the microelectromechanical system MEMS and the polarized light sensor combined navigation system are given. It is pointed out that the method of polarized light combined navigation can greatly improve the performance of the system and have a wide application field.
The boundary integral method is an efficient approach for solving time-harmonic acoustic obstacle scattering problems. The main computational task is the evaluation of an oscillatory boundary integral at each discreti...
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The boundary integral method is an efficient approach for solving time-harmonic acoustic obstacle scattering problems. The main computational task is the evaluation of an oscillatory boundary integral at each discretization point of the boundary. This paper presents a new fast algorithm for this task in two dimensions. This algorithm is built on top of directional low-rank approximations of the scattering kernel and uses oscillatory Chebyshev interpolation and local FFTs to achieve quasi-linear complexity. The algorithm is simple, fast, and kernel-independent. Numerical results are provided to demonstrate the effectiveness of the proposed algorithm.
Consider a probability distribution d on the truth assignments to a perfect binary AND-OR tree. Liu and Tanaka (2007) extends the work of Saks and Wigderson (1986), and they characterize the eigen-distribution, the di...
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ISBN:
(纸本)9789881925114
Consider a probability distribution d on the truth assignments to a perfect binary AND-OR tree. Liu and Tanaka (2007) extends the work of Saks and Wigderson (1986), and they characterize the eigen-distribution, the distribution achieving the equilibrium, as the uniform distribution on the 1-set (the set of all reluctant assignments for which the root has the value 1). We show that the uniqueness of the eigen-distribution fails provided that we restrict ourselves to directional algorithms. An alpha-beta pruning algorithm is said to be directional (Pearl, 1980) if for some linear ordering of the leaves (Boolean variables) it never selects for examination a leaf situated to the left of a previously examined leaf. We also show that the following weak version of the Liu-Tanaka result holds for the situation where only directional algorithms are considered;a distribution is eigen if and only if it is a distribution on the 1-set such that the cost does not depend on an associated deterministic algorithm.
We investigate the relationship between the directional and the undirectional complexity of read-once Boolean formulas on the randomized decision tree model. It was known that there is a read-once Boolean formula such...
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ISBN:
(纸本)9781920682903
We investigate the relationship between the directional and the undirectional complexity of read-once Boolean formulas on the randomized decision tree model. It was known that there is a read-once Boolean formula such that an optimal randomized algorithm to evaluate it is not directional. This was first pointed out by Saks and Wigderson (1986) and an explicit construction of such a formula was given by Vereshchagin (1998). We conduct a systematic search for a certain class of functions and provide an explicit construction of a read-once Boolean formula f on n variables such that the cost of the optimal directional randomized decision tree for f is Ω(nα) and the cost of the optimal randomized undirectional decision tree for f is O(nβ) with α -- β > 0.0101. This is the largest known gap so far.
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