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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Source-likelihood Mapping Using POMDP in Autonomous Hydrothermal Vent Prospecting 32nd

Source-likelihood Mapping Using POMDP in Autonomous Hydrothe...

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32nd International Ocean and Polar engineering Conference, ISOPE 2022

作者： Li, Jinlong Pang, Shuo Jiu, Haifeng Jiang, Yin Marine Design & Research Institute of China Shanghai China Department of Electrical Engineering and Computer science Embry-Riddle Aeronautical University FL United States Department of Mechanics Harbin University of Commerce Harbin China China Eastern Technology Application Research and Development Center Co. LTD Shanghai China

ISBN: (纸本)9781880653814

Chemical signals contained in hydrothermal fluid provide the clues to infer the location of hydrothermal vent by autonomous underwater vehicle (AUV). A partially observable Markov decision process (POMDP) is introduced to model the searching area and observation behavior in non-buoyant plume height. A POMDP-based belief state estimation method is introduced to maintain source-likelihood mapping. A real-time simulation platform is built to verify the source-likelihood mapping approach which provides a novel solution to effectively infer the vent location. The influences of memory length of flow sensor information on mapping is studied, and the length is not recommended to be very long. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Mapping

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Improved Dictionary Learning for FMRI data Analysis Capturing Common and Individual Activation Maps

Improved Dictionary Learning for FMRI Data Analysis Capturin...

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Asilomar Conference on Signals, Systems & computers

作者： Shuai Xu Rui Jin Seung-Jun Kim Tülay Adali Vince D. Calhoun Department of Computer Science and Electrical Engineering University of Maryland Baltimore County USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology and Emory University USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

A dictionary learning (DL)-based method for mul-tisubject functional magnetic resonance imaging (fMRI) data analysis is proposed. The method can incorporate group attributes to find the neural activation maps that are common across groups as well as those that are explanatory of the group differences. The key contribution is to significantly improve the map estimation performance by mitigating undesirable local optima obtained from a supervised DL formulation for fMRI data analysis. We introduce two novel ideas, namely, developing a variant of Fisher's discriminant cost, which is imposed on the non-discriminative features, and optimally permuting the dictionary and the sparse factor such that the solution is not trapped to unwanted local optima. Preliminary tests on synthetic and real data sets verify the effectiveness of the proposed approach.

关键词： computers data analysis Dictionaries Costs Estimation Machine learning Functional magnetic resonance imaging

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Authentication using Biometric data from Mobile Cloud Computing in Smart Cities

Authentication using Biometric Data from Mobile Cloud Comput...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Artificial Intelligence Engineering Dept AI and Robotics Institute Near Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITAM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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PseudoSC: A Binary Approximation to Stochastic Computing within Latent Operation-Space for Ultra-Lightweight on-Edge DNNs 23

PseudoSC: A Binary Approximation to Stochastic Computing wit...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jihe Wang Zhaoqing Wang Danghui Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350323481

Recently, stochastic computing (SC) is increasingly popular in constructing MAC for on-edge DNNs benefiting from its outstanding energy-efficiency, including its adequate precision and gatelevel operation. However, current SC-DNN systems always include a lot of costly SNGs/APCs for inevitably switches between binary and stochastic domains, which mortgages incongruous resources to pay the "bill" of the domain-switches and impedes highly-concurrent deployments. In this work, PseudoSC, a binary approximation to low-discrepancy SC, is proposed to totally remove the domain-switch for SNG/APC-free SC-DNNs. Its basic idea is to virtually re-arrange a couple of stochastic operands into a 2-D latent op-space, in which, original Monte Carlo sampling can be partitioned into three sub-ops, i.e., two fixed binary-ops and a fractal recursion. In theory, the recursion forms an isomorphic partition of the sampling repeated in smaller scales until the binary base-case achieved, as a result, a SC-op is well approximated only with binary-ops. Based on above theory, a multi-lane micro-architecture is designed to unroll the recursion within a few cycles and its advantages on hardware saving is verified under popular DNNs. The evaluation shows that the DNN-models with our schemes achieve 98.7% accuracy of the fixed-point implementations, which significantly outperform other SOTA methods. In addition, its reduced structure improves the power efficiency by 3.67 times on average.

关键词： stochastic computing

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CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition

arXiv

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arXiv 2024年

作者： Wang, Yifan Gui, Jie Tang, Yuan Yan Kwok, James Tin-Yau School of Cyber Science and Engineering Southeast University Nanjing210000 China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Ministry of Education Purple Mountain Laboratories Nanjing210000 China The Department of Computer and Information Science University of Macao Macao999078 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong999077 Hong Kong

Finger vein recognition technology has become one of the primary solutions for high-security identification systems. However, it still has information leakage problems, which seriously jeopardizes user’s privacy and anonymity and cause great security risks. In addition, there is no work to consider a fully integrated secure finger vein recognition system. So, different from the previous systems, we integrate preprocessing and template protection into an integrated deep learning model. We propose an end-to-end cancelable finger vein network (CFVNet), which can be used to design an secure finger vein recognition system. It includes a plug-and-play BWR-ROIAlign unit, which consists of three sub-modules: Localization, Compression and Transformation. The localization module achieves automated localization of stable and unique finger vein ROI. The compression module losslessly removes spatial and channel redundancies. The transformation module uses the proposed BWR method to introduce unlinkability, irreversibility and revocability to the system. BWR-ROIAlign can directly plug into the model to introduce the above features for DCNN-based finger vein recognition systems. We perform extensive experiments on four public datasets to study the performance and cancelable biometric attributes of the CFVNet-based recognition system. The average accuracy, EERs and D↔sys on the four datasets are 99.82%, 0.01% and 0.025, respectively, and achieves competitive performance compared with the state-of-the-arts. © 2024, CC BY-NC-SA.

关键词： Boiling water reactors

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of computer-aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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An Overview of Calibration Technology of Industrial Robots

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 23-36页

作者： Zhibin Li Shuai Li Xin Luo the School of Computer Science and Technology Dongguan University of TechnologyDongguanGuangdong 523808 the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China the Department of Electronics and Electrical Engineering Swansea UniversitySwansea SA18EN the Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the Department of Electronics and Electrical Engineering Swansea UniversitySwansea SA18ENUK

With the continuous improvement of automation,industrial robots have become an indispensable part of automated production *** widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these ***,the robotic technology is developing towards high precision,high *** calibration technology has a great significance to improve the accuracy of ***,it has much work to be done in the identification of robot *** parameter identification work of existing serial and parallel robots is *** the one hand,it summarizes the methods for parameter calibration and discusses their advantages and *** the other hand,the application of parameter identification is *** overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for ***,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.

关键词： Absolute pose accuracy identification method industrial robots parameter identification robot calibration technology robot parameters

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Cooperative Visual Inertial Odometry for Heterogeneous Swarm of Drones Navigating in Noisy Environments

Cooperative Visual Inertial Odometry for Heterogeneous Swarm...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kamil Canberk Atik Enes Erdogan Ahmet Yusuf Yahsi Feyzullah Kara Baris Yalcin Gü rkan Cetin Nazim Kemal Ure Faculty of Aeronautics and Astronautics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Havelsan Ankara Turkey

This paper presents a methodology for enhancing the localization performance of drone swarms by collaborative propagation of the estimates of drones equipped with higher cost sensors to the drones with lower-cost sensors. Although recent advances in visual-inertial odometry (VIO) enabled good performance in GPS denied environments, the localization performance is still highly dependent on the accuracy of inertial sensors. Moreover, environmental effects that corrupt visual inputs also contribute to the error rate of VIO estimates thus localization in noisy environments with low-grade sensors is still widely an open problem. We show that this problem can be alleviated when drones are navigating in the form of a swarm, and the relative distance measurements between drones are available through ultra-wideband (UWB) signals. In particular, we develop a collaborative Kalman filtering and optimization framework, where the drones with lower grade sensors receive updates from drones that have access to higher-quality measurements. We validate our approach in a simulation environment with realistic visual representations and show that the proposed methodology can significantly improve the localization performance, especially for the scenarios where the camera measurements are corrupted with high noise. We further present results in simplistic flight tests to demonstrate the applicability of our approach to real hardware.

关键词： Location awareness Visualization Costs Navigation Surveillance Teamwork Trajectory

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Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI

arXiv

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arXiv 2024年

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwanju Institute of Science and Technology Gwangju Korea Republic of

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © 2024, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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