ing at the effective embedding and extraction of multi-watermarking information, an adaptive multi-watermarking algorithm based on peak signal-noise ratio-normalized correlation coefficient function (PSNR-NC) optimization and undecimated dual tree complex wavelet transform is proposed. First, the algorithm uses the PSNR-NC function to determine the best embedding position of the watermark. Then, multiple independent watermarks are embedded into the color host image by the unsampled double tree complex wavelet transform-singular value decomposition (UDTCWT-SVD) algorithm. Finally, the watermark extraction algorithm is used to extract multiple watermarks in the watermarked image, which effectively realizes the embedding and extraction of multiple copyright information. The experimental results show that the embedded watermark image has good invisibility, and the proposed algorithm is robust to common image processing attacks, especially in resisting JPEG compression, noise attacks and filtering attacks.
.ing at the identification of the characteristics of the discrete three-dimensional fluorescence spectra for the planktonic mixed algae community, the spiecies identification accuracy and concentration measurement accuracy of mixed data of five common phylum species of algae (Microcystis aeruginosa, Scenedesmus obliquus, Nitzschia sp., Peridinium umbonatum var.inaequale and Cryptomonas obovata.) are compared and analyzed by the plain convolutional neural network (PlainCNN) model and the text convolutional neural network (TextCNN) model. The results show that in the algae independent identification and concentration regression analysis, the average identification accuracy of the test set and the average mean square error of the results of the concentration output of the PlainCNN model are 90% and 0.052 respectively, which are better than that of TextCNN model. In order to realize species identification and concentration analysis of mixed algae at the same time, a multi-task convolutional neural network, i.e., PlainCNN-MT model, is proposed based on the PlainCNN model. The average accuracy of the model for the species identification of mixed algae is increased to 95%, and the average mean square error of the results of the concentration output is reduced to 0.039, indicating that the multi-task convolutional neural network has more advantages in the identification and quantitative analysis of planktonic algae community.
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