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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic systems

IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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Coexistence of Distributed Sensing and Ultra-simple Coherent RoF based Front-haul for Radio Access Network

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Journal of Lightwave technology 2025年第12期43卷 5669-5675页

作者： Liu, Maoqi Wang, Jingchuan Liu, Chen Yu, Changyuan Lu, Chao Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Wuhan430074 China The Hong Kong Polytechnic University Photonics Research Institute Department of Electrical and Electronic Engineering Hong Kong

The radio access network requires high-speed transmission and multifunctionality to support the increasing demand for advanced communication services. Radio-over-fiber (RoF) technology fulfills these requirements by enabling the delivery of millimeter-wave (MMW) band wireless signals via optical fiber front-haul. This paper presents an ultra-simple RoF system that achieves the coexistence of distributed sensing and telecommunication. A commercially available coherent modulator achieves signal modulation and remote up-conversion by the beat between the residual carrier and the modulated radio signal. The linear-frequency modulated carrier enables distributed sensing while simultaneously serving as the beat tone of RoF communication, showcasing the potential for integrated sensing and communication (ISAC) applications in next-generation radio access networks. We demonstrate the sensing and transmission performance over a 10-km fiber link and a 1-m wireless MMW channel. The system achieves a distributed sensing detection sensitivity of 28 pΕ/√ Hz and supports 1.6 GBaud 16QAM radio transmission in the Ka-band of 28 GHz. © 1983-2012 IEEE.

关键词： Radio access networks

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An Ambiguous Edge Detection Method for Computed Tomography Scans of Coronavirus Disease 2019 Cases

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第1期54卷 352-364页

作者： Singh, Pritpal Huang, Yo-Ping Central University of Rajasthan Quantum Optimization Research Lab Department of Data Science and Analytics Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Magong88046 Taiwan The Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan The Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan The Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c

关键词： Coronavirus

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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Reinforcement Learning Based Truck-and-Drone Coordinated Delivery

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 754-763页

作者： Wu, Guohua Fan, Mingfeng Shi, Jianmai Feng, Yanghe Central South University School of Traffic Transportation Engineering Changsha410073 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory College of System Engineering Changsha410073 China National University of Defense Technology College of Systems Engineering Changsha410073 China

Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities and medical items for home-quarantined people. Considering the unmanned operation, agility, and use of clean energy of drones, we propose a novel truck-and-drone coordinated delivery system that does not require direct human contact during the delivery process. All final deliveries are completed by the drone while the truck acts as a movable charging station and a carrier, so that the contagion risks are reduced. Moreover, we spilt the whole delivery problem into the customer-clustering problem and the routing problems of both the truck and the drone. An encoder-decoder framework combined with reinforcement learning is created to solve the routing problems without handcrafted designed heuristics. We design the different problem contexts specific to the truck routing problem and the drone routing problem. The experimental results show that the proposed approach has good generality and can consequently be applied to problems of different scales with high time efficiency. © 2020 IEEE.

关键词： Reinforcement learning

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A Novelty Framework in Image-Captioning with Visual Attention-Based Refined Visual Features

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Computers, Materials & Continua 2025年第3期82卷 3943-3964页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Mohammed ELAffendi Sajid Shah School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information ScienceDepartment University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic ***,traditional models still rely on static visual features that do not evolve with the changing linguistic context,which can hinder the ability to form meaningful connections between the image and the generated *** limitation often leads to captions that are less accurate or *** this paper,we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic *** model strengthens the alignment between visual and linguistic elements,resulting in more coherent and contextually appropriate ***,we introduce two innovative modules:the Visual Weighting Module(VWM)and the Enhanced Features Attention Module(EFAM).The VWM adjusts visual features using partial attention,enabling dynamic reweighting of the visual inputs,while the EFAM further refines these features to improve their relevance to the generated *** continuously adjusting visual features in response to the linguistic context,our model bridges the gap between static visual features and dynamic language *** demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset,where our method outperforms state-of-the-art techniques in terms of caption quality and contextual *** results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.

关键词： Image-captioning visual attention deep learning visual features

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Fusion Strategy for Improving Medical Image Segmentation

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Computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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