Caifeng Cheng^1,2, Deshu Lin^3,*

Journal on Internet of Things, Vol.2, No.1, pp. 47-54, 2020, DOI:10.32604/jiot.2020.09117 - 06 August 2020

Abstract In this paper, the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied. The advantages and disadvantages of greedy reconstruction algorithm are analyzed. The disadvantages of signal sparsely are preset in this algorithm. The sparsely adaptive estimation algorithm is proposed. The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve. The sparse step size adaptive compressed sampling matching tracking algorithm is proposed. The improved algorithm selects the sparsely as the step size to select the support set atom, and the maximum correlation value. Half of the More >

Xingting Liu¹, Jianming Zhang^2,*, Xudong Li², Siwang Zhou¹, Siyuan Zhou², Hye-JinKim³

Journal of Cyber Security, Vol.1, No.1, pp. 29-41, 2019, DOI:10.32604/jcs.2019.06013

Abstract The theory of compressed sensing (CS) has been proposed to reduce the processing time and accelerate the scanning process. In this paper, the image recovery task is considered to outsource to the cloud server for its abundant computing and storage resources. However, the cloud server is untrusted then may pose a considerable amount of concern for potential privacy leakage. How to protect data privacy and simultaneously maintain management of the image remains challenging. Motivated by the above challenge, we propose an image encryption algorithm based on chaotic system, CS and image saliency. In our scheme, More >