The geometries of azobenzene compounds are optimized with B3LYP/6-311G* method, and analyzed with nature bond orbital, then their visible absorption maxima are calculated with TD-DFT method and ZINDO/S method respectively. The results agree well with the observed values. It was found that for the calculation of visible absorption using ZINDO/S method could rapidly yield better results by adjusting OWF(pi-pi) (the relationship between pi-pi overlap weighting factor) value than by the TD-DFT method. The method of regression showing the linear relationship between OWF(pi-pi) and BL(N-N) (nitrogen-nitrogen bond lengths) as OWF(pi-pi)=-8.1537+6.5638BL(N-N), can be explained in terms of quantum theory, and also be used for prediction of visible absorption maxima of other azobenzne dyes in the same series. This study on molecules' orbital geometry indicates that their visible absorption maxima correspond to the electron transition from HOMO (the highest occupied molecular orbital) to LUMO (the lowest unoccupied molecular orbital).
With the advantages of high positioning accuracy and low cost, visible light positioning (VLP) is becoming a promising solution for practical indoor positioning system. However, most of the VLP systems require at least two VLP LED lamps for accurate position calculation. Therefore, the application of VLP in practical scenarios may be restricted due to this limitation. In this paper, we propose a fast and high-accuracy single-LED based VLP system. Firstly, an unbalanced single-LED VLP algorithm is proposed to increase the positioning accuracy and reduce the computational complexity. Secondly, a fast beacon searching algorithm is proposed to further reduce the processing time for each captured image. Finally, since the proposed algorithms have the advantages of high accuracy and low complexity, the proposed system can also be implemented on a low-end hardware platform. Experimental results show that the average positioning error of the proposed system is decreased to 2.26 cm at the height of 3 m, and the average positioning time is reduced to 6.3 ms on a laptop and 60ms on a low-end embedded platform.
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