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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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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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Investigation of Cloud Forensic Incidents in Cloud Architecture for 6G networks

Investigation of Cloud Forensic Incidents in Cloud Architect...

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International Symposium on Advanced networks and Telecommunication Systems (ANTS)

作者： Malik Zaib Alam Asif Ali Khan Adeel Ahmad Haneef Khan Mohammad Rafeek Khan Ishan Budhiraja Nagendra Yasmani Department of Computer and Network Engineering Jazan University Saudi Arabia School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

The widespread implementation of cloud computing has brought about a significant transformation in digital operations, offering unparalleled scalability and flexibility. However, this advancement has also presented novel security obstacles and necessitated the development of efficient investigation techniques within cloud environments. The objective of this paper is to delve into the field of Cloud Forensics, which involves the intricate process of investigating security incidents, data breaches, and unauthorized access within cloud architecture. This article aims to elucidate the distinctions between cloud forensics and traditional digital forensics. It discusses the intricacies of cloud service models and their implications on deployment. Additionally, it provides a comprehensive overview of investigation methodologies, highlighting the challenges associated with evidence collection and preservation. The research emphasizes the significance of incident response, the utilization of digital evidence in legal proceedings, and the need for collaborative efforts among service providers, investigators, and legal authorities. These factors collectively contribute to the improvement of cloud security and compliance.

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The -model expansion approach for solving the (2+1)-dimensional NLSE with Kerr law nonlinearity and STD with multiplicative white noise in birefringent fibers

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Optical and Quantum Electronics 2024年第3期56卷 1-25页

作者： AlQahtani, Salman A. Shohib, Reham M. A. Alngar, Mohamed E. M. Alawwad, Abdulaziz M. New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Basic Science Department Higher Institute of Foreign Trade & Management Sciences New Cairo Academy Cario Egypt Basic Science Department Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

With incorporating of multiplicative white noise, we assess the Itô-coupled NLSE and STD in two and one dimensions with Kerr law nonlinearity and birefringent fibers. Light dynamics in birefringent fibers, including the propagation of soliton waves, the instability of modulation, and the occurrence of stochastic events, all have a connection to this issue. The study aims to facilitate in the growth of fiber lasers, benefiting fields such as in the fields of material processing, medicine, and communications. To achieve this, the $$\Phi ^{6}$$ -model approach to expansion is adopted. In addition to Jacobi-elliptic solutions, this work’s analysis also yields dark solitons, bright solitons, singular solitons, and periodic solutions. Because of such results, fiber laser systems in industries like manufacturing, medicine, and communications may be improved upon. The Jacobi-elliptic solution, as well as bright and dark solitons, may be seen in both one- and two-dimensional representations.

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DomainDemo: a dataset of domain-sharing activities among different demographic groups on Twitter

arXiv

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arXiv 2025年

作者： Yang, Kai-Cheng Goel, Pranav Quintana-Mathé, Alexi Horgan, Luke McCabe, Stefan D. Grinberg, Nir Joseph, Kenneth Lazer, David Network Science Institute Northeastern University BostonMA United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er-Sheva Israel Computer Science and Engineering Department University at Buffalo BuffaloNY United States

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains’ geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo’s approach. © 2025, CC BY.

关键词： Tweets

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Refine EEG Spectrogram Synthesized by Generative Adversarial network for Improving The Prediction of Epileptic Seizures*

Refine EEG Spectrogram Synthesized by Generative Adversarial...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Tian Yu Boyuan Cui Yaqian Xu Xilin Liu Department of Computer Science University of Toronto Toronto ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada University Health Network (UHN) Toronto ON Canada

Epilepsy is one of the most common neurological disorders and affects 1% of the world's population. Early prediction of the onset time of the seizure can enable responsive treatments and greatly improve the quality of life of epileptic patients. Although seizure detection has been extensively studied, reliable prediction of the onset of seizures remains a challenge. Despite recent advances in deep learning, most existing methods require a large amount of data to train the models, which are often not available for privacy, proprietary, or cost reasons. In this work, we show that the performance of seizure prediction can be improved by using data augmentation from only a limited amount of EEG recordings. We developed a generative adversarial network (GAN) and a refiner based on a publicly available CHB-MIT dataset. We trained a classifier using only synthesized data, but tested the classifier using real data from all 23 patients in the dataset. With a seizure prediction horizon (SPH) of 5 min and a seizure occurrence period (SOP) of 30 min, the classifier achieved an average AUC score of 61%. Improvement in the use of the refiner is 2.1% on average in all patients. The proposed method holds great promise for alleviating the problem of data scarcity and imbalance in seizure prediction and many other applications.

关键词： Neurological diseases Sociology Neural engineering Epilepsy Generative adversarial networks Electroencephalography Recording

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Optimizing Assessment Placement and Curriculum Structure through Graph-Theoretic Analysis

Optimizing Assessment Placement and Curriculum Structure thr...

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International Conference on Innovations in Information Technology

作者： Asma Wasfi Bisni Fahad Mon Mohammad Hayajneh Ahmad Slim Najah Abu Ali Department of Electrical & Communcication Engineering United Arab Emirates University AlAin United Arab Emirates Department of Computer & Network Engineering-(CIT) United Arab Emirates University AlAin United Arab Emirates Department of Electrical & Computer Engineering University of New Mexico Albuquerque New Mexico

The assurance of student learning is a crucial concern in contemporary education. Critical courses have a significant impact on how well university students do and, eventually, on graduation rates. Therefore, a key goal of decision-makers at universities should be to identify such courses. This article uses graph theory visualization and metrics to study existing curriculum structure. Gephi Software is used to visualize the courses as a network graph that shows, the courses, their prerequisites, generate course complexity and identify crucial courses of the curriculum. From the produced network, graph theory metrics is applied to calculate the courses complexity and cruciality and to identify the appropriate locations for assessment components or curriculum restructure. These techniques aid administrators and faculty to restructure the curricula and create an assessment scheme. In this work academic curriculum is treated as complex system where courses are represented by nodes and directed links between each course and their prerequisites based on course catalogue. This graph provides desirable features to analyze and visualize the course using metrics of mathematical graph theory. This work aims to explore the possibility to restructure the curriculum and provide assessment for critical courses to enhance graduation rates.

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Dynamics of cold random hyperbolic graphs with link persistence

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Physical Review E 2022年第6期106卷 064312-064312页

作者： Sofoclis Zambirinis Harrison Hartle Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Network Science Institute Northeastern University Boston Massachusetts 02115 USA

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω∈[0,1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties.

关键词： Evolving networks Random graphs

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Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

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Green Energy and Intelligent Transportation 2022年第1期1卷 121-132页

作者： Shuowei Li Caiping Zhang Jingcai Du Xinwei Cong Linjing Zhang Yan Jiang Leyi Wang National Active Distribution Network Technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China Sunwoda Electronic Co. Ltd.Shenzhen518100China Department of Electrical&Computer Engineering Wayne State UniversityDetroitMI 48202USA

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this *** geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of *** 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of *** proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal *** the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.

关键词： Electric vehicle Fault diagnosis Extended average voltage Dynamic time warping Feature clustering

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A Multi-Modal System Featuring Wireless Flexible Sensor Patches and a Depth-Sensing Imager for Home-Based Monitoring of Rehabilitation Exercises

Integrated Circuits and Systems

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Integrated Circuits and Systems 2024年第4期1卷 227-238页

作者： Runtian Yang Yuhan Hou Savanna Blade Yinfei Li Vansh Tyagi Gloria-Edith Boudreault-Morales José Zariffa Xilin Liu The Edward S. Rogers Sr. Department of Electrical and Computer Engineering (ECE) University of Toronto Toronto ON Canada Institute of Biomedical Engineering University of Toronto Toronto ON Canada KITE Research Institute Toronto Rehabilitation Institute - University Health Network (UHN) Toronto ON Canada

Monitoring rehabilitation progress at home over the long term following a spinal cord injury (SCI) is crucial for maximizing therapeutic outcomes and enhancing the quality of life of affected individuals. Comprehensive monitoring requires collecting a range of physiological data, including surface electromyography (sEMG) and exercise motion data. Currently, assessments typically take place in clinical settings, which can be both costly and inconvenient for patients. There is a lack of accessible, user-friendly systems that allow individuals with SCI to independently gather this data at home. Additionally, video recordings may be necessary to verify that patients are positioning the sensors correctly and performing the exercises accurately. To bridge this gap, we have developed a self-contained, multi-modal sensor system that captures sEMG and motion data, along with depth-sensing video to track patient exercises while ensuring privacy by minimizing identifiable details. The system includes a configurable number of wireless, multi-sensor wearable patches that are easy to attach and comfortable for extended use, along with a time-of-flight depth-sensing camera. The multi-modal data is streamed and synchronized in real-time on a Raspberry Pi, establishing an innovative platform to support SCI rehabilitation and adaptable for various clinical monitoring applications.

关键词： Wireless sensor networks Wireless communication Monitoring Cameras Biomedical monitoring Integrated circuits Muscles Injuries Hardware Video recording

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