224 research outputs found

    Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

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    In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, the number of fringes, fringe direction, and optics wavelength verify the effectiveness of the proposed method.Comment: 26 page

    Effect of growth temperature on the morphology and phonon properties of InAs nanowires on Si substrates

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    Catalyst-free, vertical array of InAs nanowires (NWs) are grown on Si (111) substrate using MOCVD technique. The as-grown InAs NWs show a zinc-blende crystal structure along a < 111 > direction. It is found that both the density and length of InAs NWs decrease with increasing growth temperatures, while the diameter increases with increasing growth temperature, suggesting that the catalyst-free growth of InAs NWs is governed by the nucleation kinetics. The longitudinal optical and transverse optical (TO) mode of InAs NWs present a phonon frequency slightly lower than those of InAs bulk materials, which are speculated to be caused by the defects in the NWs. A surface optical mode is also observed for the InAs NWs, which shifts to lower wave-numbers when the diameter of NWs is decreased, in agreement with the theory prediction. The carrier concentration is extracted to be 2.25 × 1017 cm-3 from the Raman line shape analysis. A splitting of TO modes is also observed

    An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability

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    Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability
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