For a fringe projection profilometry, the projector calibration is the key step to ensure the measurement accuracy of the system. However, conventional methods for the projector calibration are usually involved a complex system model and a calibration process. Therefore, an approach for accurate projector calibration based on the Grey Wolf Optimization (GWO) algorithm and the multi-layer perceptron (MLP) is proposed to simplify the process and improve the generalization. In this method, in order to compensate the errors of projected image coordinates of the calibrated corners obtained by using the projective transformation matrix, the parameters of the MLP network are optimized by using the GWO to construct the GWO-MLP network. Then, it is trained to compensate the errors of the coordinates of characteristic corners and is available to calibrate the projector. The experiments verify the feasibility and effectiveness of the proposed method.
The phase unwrapping method is the important step of phase retrieval in fringe projection profilometry. Although the mask cut (MC) algorithm has been successfully applied in multiple fields, it also has inherent flaws. In order to overcome the shortcomings of MC algorithm, and synthesize the advantages of MC and quality-guided (QG) algorithm, a quality-guided mask-cutting (QG - MC) algorithm for phase unwrapping is proposed. The basic idea of QG - MC algorithm is to reduce the effects of noise on phase unwrapping at first., Then, the process of phase unwrapping is guided by the phase quality map from the point with the highest quality value to the point around that point. Take the point with the highest quality as the seed point, put its adjacent points into the queue, sort by quality value, and the new highest quality point is used as a seed point. Repeats the process until the queue is empty and the unwrapped phase will be obtained. To verify the feasibility and reliability of QG - MC algorithm, computer simulations and real experiments are carried out. The results show that the algorithm improves the efficiency of phase unwrapping.
KEYWORDS: Network on a chip, Clouds, Evolutionary algorithms, Multiplexing, Optimization (mathematics), 3D modeling, Genetic algorithms, Tantalum, Terbium, Chemical elements
In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.
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