Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

This paper discussed the image reconstructions using the improved compressive sensing algorithm based on the wavelet transform. First, by using the wavelet transform, the image is decomposed into low-frequency wavelet coefficients and high-frequency wavelet coefficients; Second, keep the low-frequency wavelet coefficients unchanged, use the measurement matrix to measure the high-frequency wavelet coefficients, and then combine with the unchanged low-frequency coefficients to reconstruct the image. Compared with the traditional compressive sensing algorithm, the improved compressive sensing algorithm can effectively improve the peak signal-to-noise ratio (PSNR) of the reconstructed images. Moreover, in the improved compressive sensing algorithm, we use different matrices such as Gaussian random matrix, Bernoulli matrix, Toeplitz matrix, and Hadamard matrix to reconstruct the images. Comparisons show that the reconstructed images can achieve high PSNR when using Hadamard matrix.

Internet of Medical Things-based healthcare system (IoMTHS) is a kind of industrial information system that integrates life monitoring, pathological inference and drug therapy. However, the sensitive nature and high value of its data make it a prime target for cyberattacks. Although many multiserver authentication protocols have been studied in recent years to ensure that only authorized users can access medical services, new vulnerabilities are always identified and covertly utilized by the smarter adversary due to lack of continuous monitoring and dynamic authentication, reducing the trustworthiness of IoMTHS. To address above challenges, in this article, we propose a multiserver authentication scheme with integrated monitoring (MAIM) for IoMTHS, which achieves user locked access control by strictly and continuously binding system access permissions and user behavior. MAIM consists of a three-factor-based static authentication (TFSA) and a deep learning-based continuous authentication (DLCA). TFSA utilizes double-anonymity strategy to protect users’ privacy and track their malicious behaviors, and uses physical unclonable function (PUF) to protect the security of privacy information in users’ devices and servers, which achieves lightweight and three-factor secrecy. The DLCA trains a deep neural network to recognize the legitimacy of users based on the user behavior transmitted by their sensing devices. TFSA is provably secure under the random oracle model, whereas DLCA exhibits high feasibility with experimental accuracy reaching 100%.

To improve the generation quality of image style transfer, this paper proposes a light progressive attention adaptive instance normalization (LPAdaIN) model that combines the adaptive instance normalization (AdaIN) layer and the convolutional block attention module (CBAM). In the construction of the model structure, first, a lightweight autoencoder is built to reduce the information loss in the encoding process by reducing the number of network layers and to alleviate the distortion of the stylized image structure. Second, each AdaIN layer is progressively applied after the three relu layers in the encoder to obtain the fine-grained stylized feature maps. Third, the CBAM is added between the last AdaIN layer and the decoder, ensuring that the main objects in the stylized image are clearly visible. In the model optimization, a reconstruction loss is designed to improve the decoder’s ability to decode stylized images with more precise constraints and refine the structure of the stylized images. Compared with five classical style transfer models, the LPAdaIN is visually shown to more finely apply the texture of the style image to the content image, in order to obtain a stylized image, in which the main objects are clearly visible and the structure can be maintained. In terms of quantitative metrics, the LPAdaIN achieved good results in running speed and structural similarity.