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The Low-Attenuation Endfire Leaky-Wave State on an Optically Transparent Lossy Film

engineering 2024年第12期43卷 72-80页

作者： Ziheng Zhou Yongjian Zhang Yilin Zheng Ke Chen Yueming Gao Yuehe Ge Yue Li Yijun Feng Zhizhang(David)Chen College of Physics and Information Engineering Fuzhou UniversityFuzhou 350108China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Soochow UniversitySoochow 215006China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering Dalhousie UniversityHalifaxNS B3H 4R2Canada

The development of high-performance optically transparent radio frequency(RF)radiators is limited by the intrinsic loss issue of transparent conductive films(TCFs).Instead of pursuing expensive endeavors to improve the TCFs'electrical properties,this study introduces an innovative approach that leverages leaky-wave mode manipulation to mitigate the TCFs'attenuating effect and maximize the RF *** finding reveals that the precise control of the mode confinement on glass-coated TCFs can create a low-attenuation window for leaky-wave propagation,where the total attenuation caused by TCF dissipation and wave leakage is effectively *** observed low-attenuation leaky-wave state on lossy TCFs originates from the delicate balance between wave leakage and TCF dissipation,attained at a particular glass cladding *** leveraging the substantially extended radiation aperture achieved under suppressed wave attenuation,this study develops an optically transparent antenna with an enhanced endfire realized gain exceeding 15 dBi and a radiation efficiency of 66%,which is validated to offer competitive transmission performance for advancing ubiquitous wireless communication and sensing applications.

关键词： Antennas Endfire radiation Low attenuation Material losses Transparent conductive films

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Investigating How Data Poising Attacks Can Impact An EEG-Based Federated Learning Model 2

Investigating How Data Poising Attacks Can Impact An EEG-Bas...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alshebli, Shamma Alshehhi, Muna Yeun, Chan Yeob Khalifa University Department Of Electrical Engineering And Computer Science Abu Dhabi United Arab Emirates Khalifa University C2PS Department Of Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350394962

Detecting potential security threats from individuals within an organization can be achieved using an Electroencephalogram (EEG), which captures the brain's electrical activity. The concept is based on the premise that certain brainwave patterns might be associated with malicious intentions or deceptive behaviors. Recent research on insider threat detection has utilized traditional machine learning classifiers to recognize patterns in brainwave data that correlate with malicious intent. However, these methods pose privacy and data security concerns because they require access to all user data. A recently introduced framework, Federated Learning (FL), offers a solution to this problem. FL aims to develop a global model classifier without the need to access users' local data, thus safeguarding their privacy and sensitive information. Thus, we developed an FL-based insider threat detection model trained on a dataset that contains the EEG signals of 17 participants captured from five electrodes across five power bands using the Emotiv Insight. The model's accuracy within our framework attained a rate of (94.71%) for MLP. However, this method faces potential security threats and attacks, as clients could act maliciously, or external malicious actors might disrupt the network. Therefore, we additionally explore the data poisoning attacks, emphasizing label-flipping scenarios within our federated learning system for EEG-based insider threat detection and illustrating how factors such as the number of poisoned clients and the percentage of poisoning affect an FL-based system. Based on our findings, a higher number of poisoned clients is much more damaging to FL-based systems and should thus be a focal point of consideration in the security design process of these systems. © 2024 IEEE.

关键词： Electroencephalography

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Examining the Security Landscape of Mobile Payment Systems 2

Examining the Security Landscape of Mobile Payment Systems

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alshebli, Shamma Yeun, Chan Yeob Khalifa University Department Of Electrical Engineering And Computer Science Abu Dhabi United Arab Emirates Khalifa University C2PS Department Of Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350394962

The widespread use of mobile payment systems such as Apple Pay, Google Pay, and Samsung Pay has led to them becoming a prime target for malicious attacks. Consequently, concern regarding their safety has risen, and demands for securing and protecting them against such attacks have increased. Thus, this paper surveys recent literature and research published on this topic in the last five years. A thorough analysis and comparison of the selected papers is carried out, with the secure electronic transaction (SET) protocol acting as a benchmark for the proposed attacks and solutions. Then, we offer recommendations and suggestions for future work based on the observations and findings of our analysis and conclude the survey. The purpose of this survey is to illustrate the current state of this research field, serve as a starting point for future research, and steer efforts toward any noticed gaps. © 2024 IEEE.

关键词： Near field communication

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VLSI-Implementation-Based Fast Symbol Detector for PQSM MIMO Systems

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Journal of Signal Processing Systems 2024年第8期96卷 415-429页

作者： Lu, Hoang-Yang Yen, Mao-Hsu Li, Yi-Jang Li, Guang-Yan Department of Electrical Engineering National Taiwan Ocean University Taiwan R.O.C. No.2 Beining Rd Keelung202301 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan R.O.C. No.2 Beining Rd Keelung202301 Taiwan

Parallel quadrature spatial modulation (PQSM) multiple-input multiple-output (MIMO) technology is a variant of spatial modulation (SM) technology and has shown significant performance gain. However, few low-complexity detection algorithms with optimal or near-optimal performance have been proposed. In this paper, a near-optimal fast detection scheme is presented along with a corresponding hardware architecture. The proposed algorithm first uses the features of PQSM MIMO systems to further merge the channel matrices of transmit antenna combinations (TACs) into corresponding smaller matrices. Then, the remaining steps of the proposed algorithm include conducting Givens rotations (GRs) to QR decompose the merged matrices, sorting and selecting TAC candidates with higher transmission possibilities for complexity reduction, performing symbol estimation for each TAC candidate, and computing Euclidean distances (EDs) to find the final solution. Based on the steps of the proposed algorithm, five corresponding pipeline architectures are designed for a PQSM MIMO system with four transmit antennas, four receive antennas, and 16-QAM symbol modulation. Finally, the results of simulations, complexity analysis, and hardware implementation show that the proposed detector can provide a near-optimal bit error rate performance as well as a high throughput rate, but with lower complexity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bit error rate

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Dynamic Analysis and Stability Enhancement of a Weak Grid-Tied Hybrid System Integrating PV, Full-Scale Wind Turbine, and Battery Storage

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2025年 6卷 325-349页

作者： Magableh, Mohammad Adnan K. Radwan, Amr Mohamed, Yasser Abdel-Rady I. Jordan University of Science and Technology Electrical Engineering Department Irbid22110 Jordan University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G2V4 Canada Western Washington University Engineering and Design Department BellinghamWA98225 United States

Hybrid generation systems integrating photovoltaics (PV), full-scale wind turbines (WT), and battery energy storage systems (BESS) have garnered substantial interest for utility-scale grid-tied applications due to their enhanced efficiency and reliability. However, in a hybrid system integrating these resources at the dc-side of a voltage-source converter, the dc-link stability remains largely unexplored in existing literature, particularly under weak grid conditions and across different operational regions of the PV, WT, and BESS. This article presents a comprehensive small-signal stability analysis of a hybrid PV-WT-BESS system using detailed state-space and incremental impedance models. The analysis reveals that the hybrid system exhibits low-frequency oscillation instability in weak grid conditions when the PV operates in the current-limited region. A novel active damping strategy is introduced to mitigate these instabilities, effectively suppressing negative interaction dynamics and improving overall system stability;this is achieved by repositioning unstable eigenmodes and reshaping the dc-link dynamics to comply with the Nyquist stability criterion. The proposed compensation technique offers several key advantages: 1) it is simple yet effective and can be designed using linear analysis methods;2) it ensures stable operation under varying grid conditions without altering steady-state performance;3) it enhances the low-voltage ride-through capability;and 4) it eliminates the need for additional measurement sensors, thereby simplifying the implementation and reducing costs compared to alternative stabilization methods. Offline and real-time software-in-the-loop simulations validate the accuracy of the developed models and dynamic interactions, as well as the effectiveness of the proposed active stabilization approach across typical operating conditions. © 2020 IEEE.

关键词： Battery storage

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Accurate Distinction of Weak and Strong AC Grids for Emerging Hierarchical-Infeed LCC-UHVDC Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 772-777页

作者： Hao Xiao Xianzhong Duan Yi Zhang Department of Electrical and Computer Engineering University of ManitobaWinnipeg R3T 2N2ManitobaCanada State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina RTDS Technologies Inc. Winnipeg R3T 2E1ManitobaCanada

Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.

关键词： Hierarchical-Infeedinteractive effective short-circuit ratio hierarchical-infeed LCC-UHVDC systems inter-inverter interactions strong AC grids weak AC grids

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Deep Learning for MANET Routing

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2023年 1卷 412-424页

作者： Danilchenko, Kiril Azoulay, Rina Reches, Shulamit Haddad, Yoram University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada Jerusalem College of Technology Department of Computer Science Jerusalem91160 Israel

Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNetR. SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNetR solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNetR are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNetR can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNetR reach efficient solutions for large problem instances, even though they were trained on small problems. © 2023 CCBY.

关键词： Time division multiple access

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

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computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo Search cable news network(CNN) IoT ANOVA F-test

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