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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Iranpour, Mohammadreza Narimani, Mohammad Rasoul Department of Electrical and Computer Engineering Los Angels United States

ISBN: (纸本)9798331541125

False Data Injection (FDI) attacks are a significant threat to modern power systems. Although numerous research studies have focused on FD I attacks on power systems, these studies have primarily concentrated on designing or detecting DC FDI attacks, with less attention given to the impact analysis of AC FDI attacks. AC FDI attacks are potentially more harmful as they can easily bypass bad data detection (BDD) algorithms. In this paper, we present a unified approach to investigate the impact of AC FDI attacks on power transmission lines using the PowerWorld simulator. We also investigate the impact of different FDI attack designs, including those optimally designed to evade BDD algorithms and compare them accordingly. Our findings demonstrate that in designing optimal AC FDI attacks, a trade-off between the residuals of state variables and the cor-responding impacts of the proposed attack should be considered. This is because optimal attacks result in fewer changes in the attacked variable states and their estimated residuals compared to arbitrary AC FDI attacks. Moreover, the impacts of optimal AC FDI attacks can be less severe than those of arbitrary attacks. We implement and analyze the proposed approach on the IEEE 39 bus test systems using PowerWorld simulator. © 2025 IEEE.

关键词： AC False Data Injection (FDI) attacks Bad Data Detection (BDD) Optimal Power Flow (OPF) Power-World

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Digital Twin Enabled Runtime Verification for Autonomous Mobile Robots under Uncertainty 28

Digital Twin Enabled Runtime Verification for Autonomous Mob...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Betzer, Joakim Schack Boudjadar, Jalil Frasheri, Mirgita Talasila, Prasad Aarhus University Department of Electrical and Computer Engineering Denmark

ISBN: (纸本)9798331527211

As autonomous robots increasingly navigate complex and unpredictable environments, ensuring their reliable behavior under uncertainty becomes a critical challenge. This paper introduces a Digital Twin as a service approach to enable runtime monitoring and verification of an autonomous mobile robot and mitigate the impact posed by uncertainty in the deployment environment. The safety and performance properties are specified and synthesized as runtime monitors using TeSSLa. The integration of the executable digital twin, via the MQTT protocol, enables continuous monitoring and validation of the robot's behavior in real-time. We explore different sources of uncertainties and analyze their impact on the robot safety and performance. Equipped with high computation resources, the cloud-located digital twin serves as a watch-dog model to estimate the actual state, checking the consistency of the robot's actuations and approving or denying such actuations depending on the safety and performance properties. The experimental analysis demonstrated high efficiency of the proposed approach in ensuring the reliability and robustness of the autonomous robot behavior in uncertain environments by securing high alignment between the actual and expected speeds where the difference is reduced by up to 41% compared to the default robot navigation control. © 2024 IEEE.

关键词： Mobile robots

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Channel selection and wavelet transformation-based data compression preserve motor unit information 46

Channel selection and wavelet transformation-based data comp...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Siyu Quinn, Kiara N. Slepyan, Ariel Qin, Lang Thakor, Nitish V. Johns Hopkins Whiting School of Engineering Department of Biomedical Engineering BaltimoreMD21218 United States Johns Hopkins Whiting School of Engineering Department of Electrical and Computer Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350371499

Both motor unit-based control algorithms and implantable interfaces are promising approaches to improve current prosthetic limbs by allowing control of more movements. However, combining the two methods poses a challenge due to the current wireless transmission capabilities which limits the amount of data that can be processed. Capturing the motor unit requires a higher sampling rate and higher channel counts. Thus, this paper investigates a data compression strategy comprised of two independent stages: recording data reduction by selecting fewer channels to be 'active' and minimization of the transmitting data through compression with wavelet transformation. We have demonstrated a promising wavelet compression pipeline using db4, which can compress the raw EMG signal 0.63% of its original size while maintaining low WMSE error (0.109) and high heatmap reconstruction correlation coefficient (0.9741±0.972). In addition, placing high-density electrodes along the muscle fibers during channel selection while utilizing wavelet transformation can compress 99.82% of transmitted data while preserving a MCC of 0.706 ± 0.169. © 2024 IEEE.

关键词： Data compression

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Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces 37

Balancing Memorization and Generalization in RNNs for High P...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Costello, Joseph T. Temmar, Hisham Cubillos, Luis H. Mender, Matthew J. Wallace, Dylan M. Willsey, Matthew S. Patil, Parag G. Chestek, Cynthia A. Department of Electrical and Computer Engineering Biomedical Engineering Robotics and Neurosurgery University of Michigan Ann ArborMI United States

ISBN: (纸本)9781713899921

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Mammals

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Harnessing dielectric metasurfaces for light-matter interactions in the infrared region 15

Harnessing dielectric metasurfaces for light-matter interact...

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Photonic and Phononic Properties of Engineered Nanostructures XV 2025

作者： Biswas, Shovasis Kumar Adi, Wihan Beisenova, Aidana Rosas, Samir Arvelo, Eduardo Romero Yesilkoy, Filiz Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States

ISBN: (纸本)9781510685024

Resonant metasurfaces exhibit exceptional subwavelength light-trapping capabilities crucial for advanced biochemical sensors and surface-enhanced spectroscopy. While mid-infrared light-matter interactions have predominantly operated in the weak coupling regime, exploring the strong coupling regime—marked by hybrid polariton states—remains underexplored. Notably, low-loss dielectric metasurfaces with quasi-bound states in the continuum (q-BIC) offer ideal platforms for coherent energy exchange between their high-Q cavities and molecular resonances. This study delves into q-BIC resonances at mid-infrared wavelengths identifying key parameters governing the transition from weak to strong coupling. Moreover, we introduce a novel transitional coupling zone where polaritons are challenging to spectrally distinguish. Additionally, we analyze molecules' transition dipole strength, damping rate, and the total number of molecules coupled to a single cavity, offering insights to optimize dielectric metasurfaces for VSC. These findings provide crucial guidelines for enhancing VSC across diverse molecular transitions, anticipating transformative impacts on polaritonic chemistry. © 2025 SPIE.

关键词： Light sensitive materials

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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On Optimizing Resource Allocation:A Comparative Review of Resource Allocation Strategies in HetNets

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computer Modeling in engineering & Sciences 2025年第3期142卷 2211-2245页

作者： Jeta Dobruna Zana Limani Fazliu Iztok Humar Mojca Volk Faculty of Electrical Engineering Laboratory for TelecommunicationsUniversity of LjubljanaKongresni trg 12Ljubljana1000Slovenia Faculty of Electrical and Computer Engineering Telecommunications DepartmentUniversity of PrishtinaStr.“George Bush”No.31Prishtina10000Kosova

Resource allocation remains a challenging issue in communication networks,and its complexity is continuously increasing with the densification of the *** the evolution of new wireless technologies such as Fifth Generation(5G)and Sixth Generation(6G)mobile networks,the service level requirements have become stricter and more heterogeneous depending on the use *** this paper,we review a large body of literature on various resource allocation schemes that are used in particular in mobile wireless communication networks and compare the proposed schemes in terms of performance indicators as well as techniques *** review shows that among the strategies proposed in the literature,there is a wide variety of optimization targets and combinations thereof,focusing mainly on performance indicators such as energy efficiency,spectral efficiency,and network *** addition,in this paper,selected algorithms for resource allocation are numerically analyzed through simulations to compare and highlight the importance of how the resource algorithms are implemented to achieve efficient usage of the available *** performance of selected algorithms is evaluated in a multi-cell heterogeneous network and compared to proportional fair and eICIC,a widely-used combination of resource allocation and interferencemitigation techniques used by communication *** results show that one approach may performbetter when looking at the individual average user data rate but worse when looking at the overall spectral or energy efficiency,depending on the category of *** results,therefore,confirm that theremay not be a single algorithmthat visibly outperforms other candidates in terms of all performance ***,their efficiency is always a consequence of a strategic choice of goals,and the targeted parameters are optimized at a ***,the development and implementation of resource allocation algorithms must follow concrete usage scenarios and ne

关键词： Resource allocation wireless communication heterogeneous networks resource allocation algorithms

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National Science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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