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In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.

In this work, we proposed a robust and blind watermarking approach to adequately secure medical images exchanged in telemedicine. This approach ensures the traceability and integrity of the medical and essential image for data security in the field of telemedicine. In this paper, a blind watermarking method is proposed to adequately secure the electronic patient records. The integration of the watermark will be carefully performed by combining the parity of the successive values. This innovative approach will be typically implemented in the three insertion domains: spatial, frequency and multi-resolution. For the spatial domain, the watermark will be integrated into the colorimetric values of the image. In the frequency domain, the watermark bits will be substituted to the DCT coefficient’s least significant bit. For the multi-resolution domain insertion, after calculating a DWT, the obtained LL sub-band coefficients will be used for the integration process. After comparing our approaches to the various recent works in the three domains, the obtained results demonstrate that our proposed approach offers a good imperceptibility for the frequency and spatial domains insertion.

A numerical approach is presented to calculate the dynamic response of a group of rigid surface foundations. The formulation is unconditionally stable and has the computational simplicity with only the algebraic calculations involved. It imposes no limit to the foundation shape, foundation separations, thickness of the layered medium and magnitude of frequency. In the analysis, the foundation–ground interface is discretized into a number of sub square-regions. The Green’s function, which is obtained by the Fourier–Bessel transform and precise integration method, is employed to calculate the dynamic response of each sub-region. Finally, a system of linear algebraic equation in terms of the contact forces within each sub-region is observed, which leads to the desired dynamic impedance functions of the foundations. Comparison is carried out between the proposed method and the solutions available in the literature. Parametric studies on the dynamic interaction between adjacent foundations are also described. Addressed in this study are the effects of the distance and direction of foundation alignment. Several conclusions are drawn the significance of each factor. Illustrative results for a case of several closely-spaced foundations are also presented. Although the dynamic interaction analysis of foundations is concerned here, further applications of this approach can be extended to the interaction analysis of structures.

Structured illumination microscopy (SIM) achieves super-resolution (SR) by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction. The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain, it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary, besides, the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts. Here, we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets, and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets (the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function (OTF)). Experiments on reconstructing raw datasets including nonbiological, biological, and simulated samples demonstrate that our method has SR capability, high reconstruction speed, and high robustness to aberration and noise.

Due to the variety of information contained and the complexity of spatial structure for remote sensing images, graph representation learning (GRL) has aroused a wide concern because of the ability to learn more representative data features. As one of the most representative works, graph neural networks (GNNs) can take full advantage of the high-order correlation information of data with arbitrary structures to improve its learning performance when only a few labeled data are available. In recent years, an increasing number of GNN models including the spatial-domain-based GNN and spectral-domain-based GNN have been proposed and achieved excellent performance in many areas. The more typical model graph convolutional networks (GCNs) first generalizes the convolution operation of convolutional neural networks (CNNs) on Euclidean space to graphs. GCN fuses the original feature information and the complex and high-order data correlation relationships between each node by spectral graph Laplacian convolution with the one-order approximation to learn richer data features. However, how to better preserve and acquire the locality and similarity relationships of graph structured data has always been a challenging task. In this chapter, we summarize many applications of the existing GNN. Under the framework of the combination of GCN and manifold assumptions, many GCN variants have been proposed to exactly acquire the spatial structure information of graph structured data when exploring and exploiting the local geometry structure of data manifold. Specially, we present its theory analysis in detail as well as some applications of the GCN variants, such as two-order GCN, Hypergraph p-Laplacian GCN and manifold regularized dynamic GCN.