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ALCencryption:A Secure and Efficient Algorithm for Medical Image Encryption

Computer Modeling in Engineering & Sciences 2020年第12期125卷 1083-1100页

作者： Jiao Ge College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage and transmission,so it needs to be *** order to solve the security problem of medical images on mobile devices,a safe and efficient medical image encryption algorithm called ALCencryption is *** algorithm first analyzes the medical image and distinguishes the color image from the gray *** gray images,the improved Arnold map is used to scramble them according to the optimal number of iterations,and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion *** color image is encrypted by cross-diffusion algorithm of double chaotic *** and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels,large key space,strong key sensitivity,high safety and short encryption *** is suitable for medical image encryption of mobile devices with high real-time requirements.

关键词： Patient privacy DICOM medical image encryption scrambling degree cross-diffusion

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2-D shape completion with shape priors

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Chinese Science Bulletin 2013年第27期58卷 3430-3436页

作者： GUO Ge JIANG TingTing WANG YiZhou GAO Wen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences National Engineering Laboratory for Video Technology Key Laboratory of Machine Perception School of EECS Peking University

We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.

关键词：形状结构计算机视觉贝叶斯公式电子相关最佳估计最小曲率先验知识仿射变换

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More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

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Neurocomputing 2025年 645卷

作者： Fangyan Wang Ge Jiao Guowen Yue College of Computer Science and Technology Hengyang Normal University Hengyang 421002 Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421002 Hunan China Hunan Engineering Research Center of Cyberspace Security Technology and Applications Hengyang Normal University Hengyang 421002 Hunan China

Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods.

关键词： Object detection

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Multi-stage hybrid multiobjective evolutionary algorithm for multi-type vehicle routing problem with simultaneous pickup and delivery and time windows

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Computers & Industrial Engineering 2025年

作者： Wenqiang Zhang Shun Li Miaolei Deng Yashuang Mu Peng Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology China School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China Institute for Complexity Science Henan University of Technology China

The vehicle routing problem (VRP) is a fundamental and extensively studied problem in logistics and transportation. Despite its significance, real-world applications often require more complex variants to address specific operational constraints and objectives. To address these limitations, this paper introduces the multi-type vehicle routing problem with simultaneous pickup and delivery and time windows (MTVRPSPDTW). Although hybrid multiobjective algorithms have been successful in combination optimization problems in recent years, it is still challenging to improve the performance of the algorithms by combining them with different timing. We propose a multi-stage hybrid evolutionary multi-objective optimization with a multi-region sampling strategy (MS-HEMO-MRSS) to optimize both vehicle number and wait time of MTVRPSPDTW. The algorithm integrates a three-stage hybrid approach, combining a global search using the multi-region sampling strategy (MRSS) with a local search based on routing sequence differential evolution (RSDE). The initial stage employs MRSS to quickly position the population near the Pareto front from various directions. The second stage utilizes RSDE to accelerate convergence towards central and edge areas of the Pareto front. In the final stage, individuals on Pareto front are selected and RSDE is used again to guide them towards the edge regions to enhance distribution performance. Specialized encoding, decoding techniques, and genetic operators tailored for two vectors are introduced to optimize MTVRPSPDTW. Comparative experiments with traditional multiobjective evolutionary algorithms demonstrate significant convergence and notable distribution performances, highlighting the benefits of employing different optimization strategies at different evolutionary stages.

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Degradation of AlGaN/GaN High Electron Mobility Transistors with Different AlGaN Layer Thicknesses under Strong Electric Field

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Chinese Physics Letters 2010年第2期27卷 243-245页

作者：杨凌马晶晶朱诚郝跃马晓华 Key Laboratory of Wide Band Gap Semiconductor Material and Device Institute of Microelectronics Xidian University Xi'an 710071 Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) Institute of Intelligent Information Processing Xidian University Xi'an 710071 National Key Laboratory of Science and Technology on Antennas and Microwaves Xidian University Xi'an 710071

The degradation of AlGaN/GaN high electron mobility transistors （HEMTs） has a close relationship with a model of traps in AlGaN barriers as a result of high electric field. We mainly discuss the impacts of strong electrical field on the AlGaN barrier thickness of AlGaN/GaN HEMTs. It is found that the device with a thin AlGaN barrier layer is more easily degraded. We study the degradation of four parameters, i.e. the gate series resistance RGate, channel resistance R channel, gate current IG,off at VGS=-5 and VDS=0.1 V, and drain current ID,max at VGS=2 and VDS=5 V. In addition, the degradation mechanisms of the device electrical parameters are also investigated in detail.

关键词： Chinese climate network complex systems small world community

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Star extraction by star sensors for daytime images affected by atmospheric turbulence

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Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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SCENERY:a lightweight block cipher based on Feistel structure

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Frontiers of Computer Science 2022年第3期16卷 193-202页

作者： Jingya FENG Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

In this paper,we propose a new lightweight block cipher called *** main purpose of SCENERY design applies to hardware and software *** is a 64-bit block cipher supporting 80-bit keys,and its data processing consists of 28 *** round function of SCENERY consists of 84×4 S-boxes in parallel and a 32× 32 binary matrix,and we can implement SCENERY with some basic logic *** hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology,and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles 0118-bit microcontrollers and 364 clock cycles on 64-bit *** with other encryption algorithms,the performance of SCENERY is well balanced for both hardware and *** the security analyses,SCENERY can achieve enough security margin against known attacks,such as differential cryptanalysis,linear cryptanalysis,impossible differential cryptanalysis and related-key attacks.

关键词： lightweight block cipher feistel structure cryptanalysis internet of things

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Side-channel analysis attacks based on deep learning network

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Frontiers of Computer Science 2022年第2期16卷 35-45页

作者： Yu OU Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

There has been a growing interest in the sidechannel analysis(SCA)field based on deep learning(DL)*** DL network or model has been developed to improve the efficiency of ***,few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate *** on the convolutional neural network and the autoencoder,this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three *** TAPDC model detects the periodicity of power trace,relating power to the intermediate values and mining the deeper features by the multi-layer convolutional *** implement the TAPDC model and compare it with two classical models in a fair *** evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption ***,Using the classifier layer,this model links power information to the probability of intermediate *** completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

关键词： side-channel analysis template attack machine learning deep learning

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Unified convergence analysis of a second-order method of multipliers for nonlinear conic programming

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Science China Mathematics 2022年第11期65卷 2397-2422页

作者： Liang Chen Junyuan Zhu Xinyuan Zhao School of Mathematics Hunan UniversityChangsha 410082China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Changsha 410082China School of Mathematics Beijing University of TechnologyBeijing 100124China

In this paper,we accomplish the unified convergence analysis of a second-order method of multipliers(i.e.,a second-order augmented Lagrangian method)for solving the conventional nonlinear conic optimization ***,the algorithm that we investigate incorporates a specially designed nonsmooth(generalized)Newton step to furnish a second-order update rule for the *** first show in a unified fashion that under a few abstract assumptions,the proposed method is locally convergent and possesses a(nonasymptotic)superlinear convergence rate,even though the penalty parameter is fixed and/or the strict complementarity ***,we demonstrate that for the three typical scenarios,i.e.,the classic nonlinear programming,the nonlinear second-order cone programming and the nonlinear semidefinite programming,these abstract assumptions are nothing but exactly the implications of the iconic sufficient conditions that are assumed for establishing the Q-linear convergence rates of the method of multipliers without assuming the strict complementarity.

关键词： second-order method of multipliers augmented Lagrangian method convergence rate generalized Newton method second-order cone programming semidefinite programming

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A Delay-Aware Congestion Control Protocol for Wireless Sensor Networks

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Chinese Journal of Electronics 2017年第3期26卷 591-599页

作者： PEI Tingrui LEI Fangqing LI Zhetao ZHU Gengming PENG Xin Youngjune CHOI Hiroo SEKIYA The College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University Graduate School of Engineering Chiba University

In wireless sensor networks,congestion leads to buffer overflowing,and increases *** traditional solutions use rate adjustment to mitigate congestion,thus increasing the delay.A Delay-aware congestion control protocol(DACC)was presented to mitigate congestion and decrease *** order to improve the accuracy of the existing congestion detection model which is based on the buffer occupancy of a single node,DACC presents a new model considering both the real-time buffer occupancy and the average transmission time of *** uses the untapped bits in the IEEE 802.11 Distributed coordination function(DCF)frames header to carry congestion *** the congestion alleviation period,DACC presents a channel occupancy mechanism which is based on the real-time buffer occupancy for the purpose of decreasing delay and preventing packet *** results indicate that in terms of delay,packet delivery ratio,collision and buffer load,DACC has comparative advantages than those of 802.11 DCF,Priority-based congestion control protocol(PCCP)and Decoupling congestion control and fairness(DCCF).

关键词： Wireless sensor network Congestion control Delay Medium access control(MAC) protocol

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