Hangyu Zhou

Xiangtan University

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Liang Xiao

Xiamen University

Xuncai Zhang

Zhengzhou University of Light Industry

Ying Niu

Zhengzhou University of Light Industry

Yan Ma

Beijing University of Posts and Telecommunications

Yukun Lou

Zhejiang University

Jinbiao Zhao

Purple Mountain Observatory

Na Chen

Beijing University of Chemical Technology

Cangju Xing

Beijing University of Chemical Technology

Xiaojing Zhang

Chinese PLA General Hospital

Hongjin Ren

Nanjing University of Information Science and Technology

Cooperative Institutions

Beijing University of Chemical Technology

Zhengzhou University of Light Industry

Beijing University of Posts and Telecommunications

Zhengzhou University

Xi'an Jiaotong University

Zhejiang University

Hebei Medical University

University of Electronic Science and Technology of China

Huazhong University of Science and Technology

China University of Mining and Technology

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A Dual Attention KPConv Network Combined with Attention Gates for Semantic Segmentation of ALS Point Clouds

IEEE Transactions on Geoscience and Remote Sensing (2024)

Jinbiao Zhao Hangyu Zhou Feifei Pan

10.1109/tgrs.2024.3422829

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Citations (1)

Architecture of Multiple Convolutional Neural Networks to Construct a Subject-Specific Knee Model for Estimating Local Specific Absorption Rate

Applied Magnetic Resonance (2020)

Liang Xiao Hangyu Zhou Na Chen Yan Ma Cangju Xing

Specific absorption rate

10.1007/s00723-020-01301-2

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Citations (3)

Cross-layer Features Fusion Network with Attention MLP for ALS Point Cloud Segmentation

Jinbiao Zhao Hangyu Zhou

10.1109/cvidl62147.2024.10604068

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Citations (1)

Image encryption scheme based on improved four-dimensional chaotic system and evolutionary operators

Scientific Reports (2024)

Ying Niu Hangyu Zhou Xuncai Zhang

Abstract To enhance the security of image data transmission, and address the weaknesses of existing image encryption schemes based on chaotic systems, particularly concerning resistance to differential attacks and the unstable performance of chaotic systems, this article introduces an improved four-dimensional chaotic system and integrates evolutionary operators to propose an image encryption scheme. Firstly, a method for generating pseudo-random sequences associated with the plaintext is designed. The change rate of the ciphertext pixel value exceeds 0.9967 after a slight modification of the plaintext pixel value, significantly improving the plaintext sensitivity and the scheme's ability to resist selected plaintext attacks. Secondly, an individual rearrangement operation is introduced to achieve bit-level scrambling, and pixel-level scrambling is achieved by selection strategy. Subsequently, crossover and mutation operations are incorporated into image encryption. To reflect the randomness of the pairing, we adopt the pseudo-random sequence generated by the chaotic system to control the crossover and mutation operators, and a diffusion operation is performed on selected pixel pairs. Finally, ciphertext feedback is applied. Experimental results and performance analysis demonstrate that the proposed scheme not only enhances the security of encrypted images but also effectively resists noise and cropping attacks. This method effectively meets the high-security requirements of images in network transmission and provides new ideas for further research in the field of image encryption.

Scrambling

Plain text

Plaintext-aware encryption

10.1038/s41598-024-57756-x

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Citations (16)

Knee Model Construction Using Deep Neural Networks with Boundary Information for Local SAR Estimation

Applied Magnetic Resonance (2024)

Liang Xiao Hongjin Ren Hangyu Zhou Cangju Xing

10.1007/s00723-024-01662-y

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Citations (0)

Segmentation of the knee tissues using U-Net neural network based on T1- and T2-weighted MR images

Na Chen Hangyu Zhou Yan Ma Yukun Lou Liang Xiao

Objective: To automatically and precisely segment knee tissues for aiding diagnosis or constructing model, a segmentation method using a convolutional neural network which was based on the data set of T1- and T2-weighted MR (magnetic resonance) images was proposed in this paper. Materials and methods: The data set consisted of training set, validation set, and test set, and each of them had image sets of 14, 4, and 12 volunteers, respectively. Each knee image set integrated T1- and T2- weighted MR images in the sagittal plane from a low-field scanner. A fully convolutional network, U-Net, was used to segmented knee tissues, which was composed of cartilage, meniscus, effusion, bone, muscle, and fat. For U-net, convolution layer number was 13, kernel size was 7×7, and cross entropy function was used as the loss function. To eliminate isolated pixels, output images were processed using morphological filtering. This method was compared with those methods that only used T1- or T2-weighted images by several quantitative measures. The manual delineation results were used as the ground truth. Results: Good segmentation performance was demonstrated on the test set. The quantitative measures of most tissues of the proposed method were found to be superior to those of other methods mentioned above. Conclusions: The proposed method adopted an advanced neural network and implemented a comprehensive use of information contained in T1- and T2-weighted images. Therefore, it exhibited promising potential for automatic and precise segmentation of knee tissues.

Convolution (computer science)

Kernel (algebra)

Ground truth

10.1109/icmipe47306.2019.9098230

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Citations (0)

An Image Encryption Algorithm Based on Hyper-chaotic System and Genetic Algorithm

Communications in computer and information science (2018)

Xuncai Zhang Hangyu Zhou Zheng Zhou Lingfei Wang Chao Li

Bitwise operation

Plain text

10.1007/978-981-13-2829-9_43

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Citations (7)

Hybrid Encryption Algorithm Based on Gray Curve and Josephus Permutation

Computational Intelligence and Neuroscience (2022)

Ying Niu Hangyu Zhou Xuncai Zhang Limin Qin

This paper proposes a new image encryption algorithm based on chaos map systems, hash algorithm, and Josephus permutation. The algorithm consists of chaos initialization, pixel position permutation, and pixel information diffusion. The algorithm's initialization is generated by the original image, which has a high sensitivity to the initial value. The permutation step length is composed of Josephus permutation and gray curve permutation, which completely disturbs the pixel distribution. The diffusion process is composed of cross operation and ciphertext feedback, which breaks the strong correlation between pixels. The simulation results of the encryption algorithm are used to analyze its information entropy, the correlation between elements, and other indicators. The ciphertext image is attacked in several ways, and we analyzed its defense ability. Simulation results show that the algorithm can effectively encrypt image information and has a good defense against various attacks.

Plain text

Initialization

10.1155/2022/7076416

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Citations (1)

Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design

Journal of Nanoelectronics and Optoelectronics (2020)

Ying Niu Hangyu Zhou Wang Shida Kai Zhao Xiaoxiao Wang

The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.

Sequence (biology)

Local optimum

10.1166/jno.2020.2882

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Citations (3)

A Rapid Method for Constructing an Approximate Knee Model for Local SAR Estimation Based on a Convolutional Neural Network

Hangyu Zhou Yan Ma Yukun Lou Liang Xiao

Purpose: The local specific absorption rate (SAR) of tissues is a key consideration in high-field MRI of knee joint. To automatically and rapidly construct individual's knee model for SAR simulation and estimation, a segmentation method for MR images using U-Net network and tissue simplification is proposed in this paper. Materials and methods: Based on a data set of low-field MR Tl-weighted images in the axial plane, the knee tissues were simplified and classified as muscle, fat, and bone for labeling and segmentation. Single U-net was adopted to segmented images, the number of convolutional layers was 13, the convolution kernel size was 7x7, and the mini batch was 16. The data set consisted of 30 volunteers' knee joint images (in the axial plane): 14, 4, and 12 for the training, validation, and test set respectively. To overcome the limitation of imaging slice number and make the SAR simulation result more accurate, the constructed knee model was extended in the axial direction using slice extrapolating. Based on the test set, electromagnetic simulation in a 3T coil was carried out and local SAR was evaluated using the models constructed with the proposed method and manual delineation. Result and discussions: The two constructed models had similar SARlOg distribution, the locations of hot spots were basically unchanged, and the mean and standard deviation of the errors between their maximum SARlOg values were relatively small. Conclusions: The method by using single U-Net networks, tissue simplification and model extending shows promising potential for constructing knee model on which approximate local SAR could be estimated.

Convolution (computer science)

Kernel (algebra)

Specific absorption rate

Data set

10.1109/icmipe47306.2019.9098231

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Citations (1)