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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Purification of Contaminated Convolutional Neural Networks via Robust Recovery: An Approach With Theoretical Guarantee in One-Hidden-Layer Case

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IEEE Journal on Selected Topics in Signal Processing 2025年第3期19卷 507-519页

作者： Lu, Hanxiao Huang, Zeyu Wang, Ren Illinois Institute of Technology Trustworthy and Intelligent Machine Learning Research Lab Department of Electrical and Computer Engineering ChicagoIL60616 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States

Convolutional neural networks (CNNs), one of the key architectures of deep learning models, have achieved superior performance on many machine learning tasks such as image classification, video recognition, and power systems. Despite their success, CNNs can be easily contaminated by natural noises and artificially injected noises such as backdoor attacks. In this paper, we propose a robust recovery method to remove the noise from the potentially contaminated CNNs and provide an exact recovery guarantee on one-hidden-layer non-overlapping CNNs with the rectified linear unit (ReLU) activation function. Our theoretical results show that both CNNs' weights and biases can be exactly recovered under the overparameterization setting with some mild assumptions. The experimental results demonstrate the correctness of the proofs and the effectiveness of the method in both the synthetic environment and the practical neural network setting. Our results also indicate that the proposed method can be extended to multiple-layer CNNs and potentially serve as a defense strategy against backdoor attacks. © 2007-2012 IEEE.

关键词： Convolutional neural networks

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Vision Transformer and Brain Connectivity Patterns for Estimating Cognitive States

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IEEE Access 2025年 13卷 74602-74612页

作者： Chakladar, Debashis Das Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå971 87 Sweden

The transformer model is excellent at handling time series signals (such as electroencephalography: EEG) because it can extract information from long-term dependencies effectively. This work combines binarization of EEG connectivity features, cognitive state classification using the vision transformer (ViT), and identifying graphical connectivity patterns for each cognitive state of the mental arithmetic task. The common spatial pattern (CSP) filter coefficient-based channel selection method selects the optimum EEG channels from the input channel set. Then, the Singular Value Decomposition (SVD) method is applied to prepare the binarized connectivity feature matrices, eliminating noisy connections between the optimum channels. The binarized functional-effective connectivity features are passed to the ViT model for cognitive state classification. The ViT model achieves the maximum classification accuracy of 94.86% with the phase-based connectivity feature. The proposed model improves classification accuracy by 6.15% compared to the state-of-the-art studies. This study also suggests a robust brain connectivity network to build a graphical connectivity pattern for each cognitive state. My findings of the EEG-based graphical patterns will bring further understanding of the scalp-level EEG channel patterns among different brain regions for other cognitive tasks. © 2013 IEEE.

关键词： Digital arithmetic

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Tight Exponential Strong Converse for Source Coding Problem with Encoded Side Information

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IEEE Transactions on Information Theory 2025年第3期71卷 1533-1545页

作者： Takeuchi, Daisuke Watanabe, Shun Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Koganei184-8588 Japan

The source coding problem with encoded side information is considered. A lower bound on the strong converse exponent has been derived by Oohama, but its tightness has not been clarified. In this paper, we derive a tight strong converse exponent. For the special case where the side-information does not exist, we demonstrate that our tight exponent of the Wyner-Ahlswede-Körner (WAK) problem reduces to the known tight expression of that special case while Oohama's lower bound is strictly loose. The converse part is proved by a judicious use of the change-of-measure argument, which was introduced by Gu and Effros and further developed by Tyagi and Watanabe. A key component of the methodology by Tyagi and Watanabe is the use of soft Markov constraint, which was originally introduced by Oohama, as a penalty term to prove the Markov constraint at the end. A technical innovation of this paper compared to Tyagi and Watanabe is recognizing that the soft Markov constraint is a part of the exponent, rather than a penalty term that should vanish at the end;this recognition enables us to derive the matching achievability bound. In fact, via numerical experiment, we provide evidence that the soft Markov constraint is strictly positive. Compared to Oohama's derivation of the lower bound, which relies on the single-letterization of a certain moment-generating function, the derivation of our tight exponent only involves manipulations of the Kullback-Leibrer divergence and Shannon entropies. The achievability part is derived by a careful analysis of the type argument;however, unlike the conventional analysis for the achievable rate region, we need to derive the soft Markov constraint in the analysis of the correct probability. Furthermore, we present an application of our derivation of the strong converse exponent to the privacy amplification. © 1963-2012 IEEE.

关键词： Markov processes

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio

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IEEE Access 2025年 13卷 80930-80940页

作者： Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara Lee, Wilaiporn Division of Electrical Engineering Department of Electrical and Computer Engineering Faculty of Engineering Bangkok10800 Thailand King Mongkut’s University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand

Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin

关键词： Sensors Reliability Barium Radio spectrum management Cooperative communication Cognitive radio Data integration Signal to noise ratio Heuristic algorithms Dynamic spectrum access

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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A Novel Proof-of-Concept AI-Driven Approach for Advanced Electromagnetic Imaging

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Progress in Electromagnetics Research C 2025年 151卷 25-31页

作者： Ghaffarpour, Ali Vasei, Tahereh Ganesh, Mahindra Amineh, Reza K. Ravan, Maryam Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States

This paper introduces an artificial intelligence (AI) methodology designed to enhance the output of two-dimensional (2D) electromagnetic imaging systems, specifically tailored for the imaging of conductive objects utilizing inductive sensors. The core of our imaging system comprises a commercial data acquisition board, alongside custom-made multilayer planar coils developed by conventional printed circuit board technology. By leveraging recent advances in AI and machine learning, our approach significantly improves the resolution and clarity of electromagnetic images. The paper uses a multi-layer perceptron (MLP) classifier to process the raw electromagnetic data captured by the imaging system. These algorithms are trained to recognize patterns and anomalies in electromagnetic field data, which are often indicative of conductive objects. The enhanced imaging capability is demonstrated through a series of experiments that compare the AI-enhanced outputs with the ground truth. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Image enhancement

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Securing Next-Generation Quantum IoT Applications using Quantum Key Distribution

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IEEE Internet of Things Magazine 2025年第1期8卷 50-56页

作者： Bera, Basudeb Das, Ashok Kumar Sikdar, Biplab The Department of Electrical and Computer Engineering National University of Singapore Singapore The International Institute of Information Technology Hyderabad India

The next generation of Quantum Internet of Things (QIoT) has the potential to revolutionize various sectors, including smart homes, healthcare, and smart cities, by enabling more sophisticated and interconnected systems. These applications incorporate advanced features, such as autonomous decision-making based on Quantum Artificial Intelligence and Machine Learning (QAI/ML) and context-aware functionality. The security of the data in these applications relies on traditional cryptographic techniques, which, however, face a growing threat due to the significant advancements in quantum computing, especially with the Shor’s algorithm. This algorithm poses a substantial risk of breaking conventional cryptographic methods within a feasible timeframe. To address these emerging security challenges in the quantum realm, we propose a Quantum Key Distribution (QKD) based on the BB84 protocol. This approach aims to provide robust authentication using certificates through a classical channel and secure quantum key exchange via a quantum channel in a public environment. The proposed QKD scheme is implemented using a client-server model in Python and Qiskit, demonstrating its practicality in real-world applications. The obtained results showcase the successful establishment of a secure session key between IoT smart (sensor) devices and gateway nodes, effectively mitigating potential threats such as eavesdropping. © 2018 IEEE.

关键词： Smart homes

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Community Detection in Multi-Aspect Functional Brain Networks: Robust Tensor Decomposition Approach

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IEEE Access 2025年 13卷 82043-82064页

作者： Al-Sharoa, Esraa Al-Wardat, Mohammad Aviyente, Selin Jordan University of Science and Technology College of Engineering Department of Electrical Engineering Irbid22110 Jordan Michigan State University College of Engineering Department of Electrical and Computer Engineering East LansingMI48824 United States

Functional organization of the brain can be characterized as a network of interconnected regions. Study of brain networks has offered new insights on human behavior and neurodegenerative diseases. Recent studies show that the functional connectivity networks are dynamic with the topology and community structure evolving with time during task performance and rest. Most of the current work on analyzing the community structure of dynamic networks either focus on single subjects or extract the group level community structure. In this paper, we present a framework for community detection in dynamic functional connectivity networks across multiple subjects and at the individual level, simultaneously. The proposed approach is based on a structured robust tensor decomposition with spectral clustering, temporal smoothness and co-clustering regularization terms to extract both the group and individual level community structures. Moreover, the temporal evolution of the community structure within the group is tracked to identify change points that may correspond to events of interest. The proposed framework is applied to dynamic functional connectivity networks constructed from task-based electroencephalogram (EEG) data. © 2013 IEEE.

关键词： Tensors Hidden Markov models Brain modeling Organizations Nonhomogeneous media Matrix decomposition Diseases Mathematical models Feature extraction Time series analysis

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