This report presents a pressure-induced permanent metallization for MoS2 under non-hydrostatic conditions. Impedance and Raman spectra were measured to study the pressure-induced structural and electronic transformations of MoS2 at up to ∼25 GPa in diamond anvil cells under both non-hydrostatic and hydrostatic conditions. The results show evidence for isostructural hexagonal distortion from 2Hc to 2Ha and metallization at ∼17 GPa and ∼20 GPa under non-hydrostatic and hydrostatic conditions, respectively. Interestingly, the metallization is irreversible only under non-hydrostatic compression. We attribute this phenomenon to the incorporation of molecules of pressure medium between layers, which mitigate compressed stress and reduce interlayer interaction.
Based on compressive sensing and fractional-order simplest memristive chaotic system, this paper proposes an image compression and encryption scheme. First, a fractional-order simplest memristive chaotic circuit system is designed. The dynamic characteristics of the chaotic system are analyzed by the phase diagram, the Lyapunov exponents spectrum, and the bifurcation diagram to determine the parameters and pseudo-random sequences used in the encryption scheme. Secondly, an encryption scheme based on compressive sensing is designed. This scheme compresses the image twice to fully reduce the storage cost, and scrambles the pixel matrix twice through block scrambling and zigzag transformation, and then uses chaotic pseudo-random sequence and GF (17) domain diffusion image matrix to obtain the final cipher image. Finally, simulation results and performances analysis indicate that the scheme still has good reconstruction performance, even when the compression ratio is 0.25, and the security analysis shows that it can resist various attacks and has high security. INDEX TERMS Image encryption; Compressive sensing (CS); Fractional-order simplest memristive chaotic system
In agriculture, pest always causes the major damage in fields and results in significant crop yield losses. Currently, manual pest classification and counting are very time-consuming and many subjective factors can affect the population counting accuracy. In addition, the existing pest localization and recognition methods based on Convolutional Neural Network (CNN) are not satisfactory for practical pest prevention in fields because of pests' different scales and attitudes. In order to address these problems, an effective data augmentation strategy for CNN-based method is proposed in this paper. In training phase, we adopt data augmentation through rotating images by various degrees followed by cropping into different grids. In this way, we could obtain a large number of extra multi-scale examples that could be adopted to train a multi-scale pest detection model. In terms of test phase, we utilize the test time augmentation (TTA) strategy that separately inferences input images with various resolutions using the trained multi-scale model. Finally, we fuse these detection results from different image scales by non-maximum suppression (NMS) for the final result. Experimental results on wheat sawfly, wheat aphid, wheat mite and rice planthopper in our domain specific dataset, show that our proposed data augmentation strategy achieves the pest detection performance of 81.4% mean Average Precision (mAP), which improves 11.63%, 7.93% ,4.73% compared to three stateof-the-art approaches.
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