A novel fast motion estimation method based on particle swarm optimization

Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%

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Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

Open Geosciences ◽

10.1515/geo-2019-0044 ◽

2019 ◽

Vol 11 (1) ◽

pp. 542-548

Author(s):

Wenlong Tang ◽

Hao Cha ◽

Min Wei ◽

Bin Tian ◽

Xichuang Ren

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Communication System ◽

Particle Swarm Optimization Algorithm ◽

Estimation Method ◽

Particle Swarm ◽

Automatic Identification ◽

Identification System ◽

Signal Power ◽

Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

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Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

Journal of Heat Transfer ◽

10.1115/1.4051701 ◽

2021 ◽

Author(s):

Hongmei Xu ◽

Juan Liu ◽

Kun Wang ◽

Songtao Kong ◽

Yong Shi

Keyword(s):

Particle Swarm Optimization ◽

Thermal Properties ◽

Measurement Errors ◽

Fuzzy Inference ◽

Quantum Particle ◽

Estimation Method ◽

Particle Swarm ◽

Temperature Dependent ◽

Swarm Optimization ◽

Quantum Particle Swarm Optimization

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

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