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Microscale fiber-integrated vector magnetometer with on-tip field biasing using N-V ensembles in diamond microcrystals

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Physical Review Applied 2024年第3期22卷 034029页

作者： Jonas Homrighausen Frederik Hoffmann Jens Pogorzelski Peter Glösekötter Markus Gregor Department of Physics Engineering Department of Electrical Engineering and Computer Science

In quantum sensing of magnetic fields, ensembles of nitrogen-vacancy centers in diamond offer high sensitivity, high bandwidth and outstanding spatial resolution while operating in harsh environments. Moreover, the orientation of defect centers along four crystal axes forms an intrinsic coordinate system, enabling vector magnetometry within a single diamond crystal. While most vector magnetometers rely on a known bias magnetic field for full recovery of three-dimensional (3D) field information, employing external 3D Helmholtz coils or permanent magnets results in bulky, laboratory-bound setups, impeding miniaturization of the device. Here, a novel approach is presented that utilizes a fiber-integrated microscale coil at the fiber tip to generate a localized uniaxial magnetic field. The same fiber-tip coil is used in parallel for spin control by combining dc and microwave signals in a bias tee. To implement vector magnetometry using a uniaxial bias field, the orientation of the diamond crystal is preselected and then fully characterized by rotating a static magnetic field in three planes of rotation. The measurement of vector magnetic fields in the full solid angle is demonstrated with a shot-noise-limited sensitivity of 19.4nT/Hz1/2 and microscale spatial resolution while achieving a fiber sensor head cross section of less than 1mm2.

关键词： Magneto-optics Magnetometry Quantum sensing Devices Nitrogen vacancy centers in diamond Optical fibers Fluorescence Optically detected magnetic resonance

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Recent Advancements on Cyber Security for Smart-Grids: A Survey

Recent Advancements on Cyber Security for Smart-Grids: A Sur...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Samant, Indu Sekhar Panda, Subhasis Rout, Pravat Kumar Department of Computer Science Engineering Odisha India Department of Electrical Engineering Odisha India Department of Electrical and Electronics Engineering Odisha India

ISBN: (纸本)9798350339369

Smart grids are advanced power systems that have the potential to bring enormous benefits to power consumers and providers. Smart grids utilize advanced communication and information technologies to deliver power more efficiently, securely, and reliably. Cybersecurity is a critical concern in smart grid systems, as they rely heavily on networked technologies vulnerable to cyber threats. This paper provides an overview of recent advancements in cybersecurity for smart grids. The paper examines the threats and vulnerabilities of smart grids, reviews the state of art of cybersecurity for smart grids, and highlights some of the recent advancements that have been made to secure these systems. © 2023 IEEE.

关键词： Smart power grids

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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A Proactive Intrusion Detection and Mitigation System for Grid-Connected Photovoltaic Inverters

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 273-286页

作者： Mohammadi, Fazel Bok, Rasoul Saif, Mehrdad Electrical and Computer Engineering and Computer Science Department University of New Haven West HavenCT06516 United States Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

The breach of data confidentiality, integrity, and availability due to cyberattacks can adversely impact the operation of grid-connected Photovoltaic (PV) inverters. Detecting such attacks based on their signatures or behavior-based analytics and adopting corrective actions to prevent security breaches for grid-connected PV systems requires the implementation of an intelligent Intrusion Detection System (IDS). In this paper, a Proactive Intrusion Detection and Mitigation System (PIDMS) based on real-time stability boundary identification at the Point of Common Coupling (PCC) for grid-connected PV systems is presented to identify the potentially compromised grid-connected PV systems in Cyber-Physical Power and Energy Systems (CPPES). The proposed PIDMS correlates the variations in the active power and reactive power measurements to power grids voltage at the PCC in real-time and accurately identifies compromised grid-connected PV systems, and enhances the resilience of CPPES. The performance of the proposed PIDMS is validated through dynamic simulations under different operating conditions. The obtained results verify the applicability and effectiveness of the proposed PIDMS. © 2023 The Authors.

关键词： Intrusion detection

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A Multi-Modal Framework for Fake News Analysis for Detection to Deconstruction 2

A Multi-Modal Framework for Fake News Analysis for Detection...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Parveen, H. Summia School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350371055

The research discusses a multimodal framework for analyzing and detecting fake news and understanding its impact on society. This framework employs diverse strategies, including linguistic analysis, social network monitoring, and visual assessment, to capture various aspects of fabricated information and its dissemination. The first component of the framework focuses on linguistic analysis, examining the language and textual content used in fake news articles to identify misleading information, biased language, and sensationalized headlines. The second component analyzes the role of social networks in spreading fake news, tracking its propagation through platforms like social media and identifying key actors and influencers involved in its dissemination. The third component involves visual assessment, examining the images and videos used in fake news to manipulate public perceptions and emotions, including the detection of doctored images and misleading illustrations. © 2024 IEEE.

关键词： Fake News Detection Machine Learning NLP

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Phase-Field Model of Electronic Antidoping

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Physical Review Letters 2024年第25期132卷 256502-256502页

作者： Yin Shi Guo-Dong Zhao Ismaila Dabo Shriram Ramanathan Long-Qing Chen Department of Materials Science and Engineering Department of Electrical and Computer Engineering

Charge carrier doping usually reduces the resistance of a semiconductor or insulator, but was recently found to dramatically enhance the resistance in certain series of materials. This remarkable antidoping effect has been leveraged to realize synaptic memory trees in nanoscale hydrogenated perovskite nickelates, opening a new direction for neuromorphic computing. To understand these phenomena, we formulate a physical phase-field model of the antidoping effect based on its microscopic mechanism and simulate the voltage-driven resistance change in the prototypical system of hydrogenated perovskite nickelates. Remarkably, the simulations using this model, containing only one adjustable parameter whose magnitude is justified by first-principles calculations, quantitatively reproduce the experimentally observed treelike resistance states, which are shown unambiguously to arise from proton redistribution-induced local band gap enhancement and carrier blockage. Our work lays the foundation for modeling the antidoping phenomenon in strongly correlated materials at the mesoscale, which can provide guidance to the design of novel antidoping-physics-based devices.

关键词： Doping effects Neural basis of learning & memory Strongly correlated systems Phase-field modeling

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Integration of Blockchain and Fog Computing—A Review 5th

Integration of Blockchain and Fog Computing—A Review

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Kaur, Nirbeh Kumar, Shubham Kumar, Ritik Ranjana, Khushi Anand, Kumar Abhishek Gupta, Shailendra Kumar Department of Computer Science and Engineering Chandigarh University Punjab Mohali India Department of Electrical Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9789819761050

"Integration of blockchain and fog computing" involves the two different concepts, i.e. blockchain technology and fog computing which has something in common—the decentralised system. The rapid advancement of technologies all over the world has put some threats to the data and information along with its development. The fog computing concept was put forward to enhance cloud computing applications in handling the IoT data. Fog computing, on the other hand, faces a number of difficulties, including storage, privacy, and security. Integration of blockchain with fog computing is the main approach which can help in overcoming these difficulties. Several researches are being done for integrating blockchain with fog computing in the field of health care, smart home, smart cities, IoT applications, drones network, mobile devices, transportation, financial, supply chain management, and many more. This research paper is a review study of relevant papers published on this topic already and gives an insight of the applications of integration of blockchain and fog computing. Our review paper has a systematic study of existing literature on this topic, and it provides an excellent overview of all the concepts that will play a crucial role for the researchers in their future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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