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Hai Yu

Researcher at Northeastern University (China)

Publications -  127
Citations -  2793

Hai Yu is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Encryption & Chaotic. The author has an hindex of 24, co-authored 106 publications receiving 2215 citations. Previous affiliations of Hai Yu include Northeastern University.

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A chaos-based symmetric image encryption scheme using a bit-level permutation

TL;DR: This work proposes an image cryptosystem employing the Arnold cat map for bit-level permutation and the logistic map for diffusion, demonstrating the superior security and high efficiency of this algorithm.
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Image encryption based on three-dimensional bit matrix permutation

TL;DR: A new 3D bit matrix permutation is proposed, in which the Chen system is used to develop a random visiting mechanism to the bit level of the plain-image, and a new mapping rule is developed to map one random position to another random position in the 3D matrix rather than using traditional sequential visiting to theplain-image.
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A symmetric color image encryption algorithm using the intrinsic features of bit distributions

TL;DR: This paper analyzes the intrinsic features of the bit distributions, the high correlation among bit planes and other issues related to the bit information of an image, and proposes an expand-and-shrink strategy to shuffle the image with reconstructed permuting plane.
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A fast chaos-based image encryption scheme with a dynamic state variables selection mechanism

TL;DR: A fast chaos- based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems.
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Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation

TL;DR: An efficient implementation based on the K-singular value decomposition (SVD) algorithm, where the exact SVD computation is replaced with a much faster approximation, and the straightforward orthogonal matching pursuit algorithm is employed, which is more suitable for the proposed self-example-learning-based sparse reconstruction with far fewer signals.