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Consumer Technology (ICCT-Pacific), International Conference on

作者： Naoyasu Kaneko Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper considers a cloud streaming game using WebRTC. Since the cloud streaming game consists of a server and a client, the network quality between them affects the game. Among the network quality degradation factors, we focus on delay jitter. We reduce the effect of the jitter by buffering. We conduct a subjective experiment of a single-player action game and evaluate application-level QoS and QoE. We investigate the impact of buffering on the video and audio quality of the user playing the game.

关键词： Degradation Games Quality of service Jitter Streaming media Cloud gaming Delays Quality of experience Servers WebRTC

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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Advanced technologies in InGaN micro-LED fabrication to mitigate the sidewall effect

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光：科学与应用（英文版） 2025年第2期14卷 307-332页

作者： Zhiyuan Liu Haicheng Cao Xiao Tang Tingang Liu Yi Lu Zixian Jiang Na Xiao Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering ProgramCEMSE DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The size of InGaN micro-LEDs is continuously decreasing to meet the demands of various emerging applications,especially in tiny micro-displays such as AR/***,the conventional pixel definition based on plasma etching significantly damages the mesa sidewalls,leading to a severe reduction in efficiency as the micro-LED size *** seriously impedes the development and application of *** this work,we comprehensively explain the origin of micro-LED sidewall effects and corresponding physical ***,we systematically review recent progress in micro-LED fabrication aiming at suppressing sidewall ***,we discuss advancements in micro-LED fabrication with"damage-free"techniques,which hold the potential to fundamentally address the issue of plasma damage in the micro-LED *** believe this review will deepen the understanding of micro-LED sidewall effects and provide a better insight into the latest associated fabrication technologies for high-efficient InGaN micro-LEDs.

关键词： technologies micro-led advanced ingan effect fabrication mitigate sidewall

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A Time-Domain Thin-Sheet Volume Integral Equation for Non-Dispersive Bi-Isotropic Media

A Time-Domain Thin-Sheet Volume Integral Equation for Non-Di...

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European Conference on Antennas and Propagation, EuCAP

作者： S. Celis Sierra R. Zhao M. Khamitova H. Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Bi-isotropic and bi-anisotropic materials are expected to revolutionize wireless communications through the extended use of metasurfaces. This paper proposes a time-domain integral equation solver to simulate non-dispersive biisotropic materials under the thin-sheet approximation. Thin-sheet approximation is used to convert the time-domain volume integral equation for non-dispersive bi-isotropic media into a surface integral equation. This surface equation is discretized in space using Rao-Wilton-Glisson (RWG) basis functions for the tangential components of the electric and magnetic fluxes and pulse basis functions for their normal components (relative to the surface of the thin sheet). In time, shifted Lagrange polynomials are used for discretization. Applying Galerkin testing in space and point matching in time yields a matrix system. This matrix system is solved for the unknown basis expansion coefficients at each time step via time-marching. Numerical results demonstrate the accuracy of the proposed method. Future work includes extending this solver to dispersive bi-anisotropic media and use it in the simulation of metasurface represented by generalized sheet transition conditions (GSTCs).

关键词： Wireless communication Accuracy Integral equations Media Metasurfaces Polynomials Time-domain analysis Dispersion Method of moments Testing

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Elevating Robust ASR By Decoupling Multi-Channel Speaker Separation and Speech Recognition

Elevating Robust ASR By Decoupling Multi-Channel Speaker Sep...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yufeng Yang Hassan Taherian Vahid Ahmadi Kalkhorani DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Center for Cognitive and Brain Sciences The Ohio State University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the multi-channel speaker separation frontend and the ASR backend, with the latter trained only on clean speech. On SMS-WSJ, the proposed approach achieves a word error rate (WER) of 5.74%, outperforming the previous best by 14.3%. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 3.86%, outperforming the previous best system trained on the same data by 24.8%. These state-of-the-art results suggest that decoupling speech separation and recognition is a potentially effective approach to robust ASR.

关键词： Training Deep learning Error analysis Signal processing Acoustics Noise measurement Speech processing Automatic speech recognition

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Enhancing Video Action Recognition with Spatial–Temporal Convolutional Attention Networks 15th

Enhancing Video Action Recognition with Spatial–Temporal Co...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Novel Via-Based Magnetic Coupling Solution to Improving Illumination of Artificial Magnetic Conductor by Antenna-on-Chip

A Novel Via-Based Magnetic Coupling Solution to Improving Il...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Yiyang Yu Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

Antenna-on-Chip (AoC) is one of the most promising solutions in upcoming 6G era. However, AoC suffers from low gain usually due to the highly conductive silicon substrate in mainstream complementary-metal-oxide-semiconductor (CMOS) processes. Artificial magnetic conductors (AMC) embedded within the stack-up of standard CMOS processes have been widely used to enhance the gain by isolating the AoC from the Si substrate and providing constructive reflection. Nonetheless, the ultra-thin stack-up of standard CMOS processes causes that only the AMC unit cells nearby the AoC can be illuminated well while the outermost part is illuminated poorly, which prevents the AMC from its optimal reflection performance. To mitigate the issue, this paper proposes a novel via-based magnetic coupling enhancement structures (H-CES- V) to improve the AMC illumination by coupling the energy from well-illuminated unit cells to outermost unit cells magnetically. Compared to conventional AMC, the gain can be improved by 1.5 dBi with the proposed structure. Finally, the proposed AMC-backed AoC with H-CES- V shows a boresight gain of 7 dBi (12 dBi better than a standalone AoC without AMC), and a radiation efficiency of 59 % at 94 GHz.

关键词： Couplings Lighting Conductors CMOS process Silicon Gain Antennas Substrates Magnetic circuits Standards

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Reward invigorates isometric gripping actions

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Journal of Neurophysiology 2025年第4期133卷 1282-1294页

作者： Marbaker, Rachel M. Schmad, Ryan C. Al-Ghamdi, Razan A. Sukumar, Shruthi Ahmed, Alaa A. Department of Mechanical Engineering University of Colorado Boulder Boulder CO United States Department of Electrical Engineering University of Colorado Boulder Boulder CO United States Department of Computer Science University of Colorado Boulder Boulder CO United States Biomedical Engineering Program University of Colorado Boulder Boulder CO United States

Individuals exhibit a propensity to move faster toward more rewarding stimuli. Although this phenomenon has been observed in movements, the effect of reward on implicit control of isometric actions, such as gripping or grasping, is relatively unknown. How reward-related invigoration generalizes to other effortful actions is an important question. Reward invigorates reaching movements and saccades, supporting the idea that reward pays the additional effort cost of moving faster. Effort in isometric force generation is less understood, so here we ask whether and how reward-related invigoration generalizes to isometric force gripping. And if so, what implicit characteristics of gripping change when there is a prospect of reward? Participants (n = 19) gripped a force transducer and the force applied was mapped to radial position of an onscreen cursor. Each trial, a target appeared in one of four locations;increasing grip force moved the cursor toward the target. The gripping action was interchangeable for all target positions. In each block of 100 trials, one target was consistently rewarded, whereas the other targets were not. When gripping to acquire the rewarded target, participants reacted faster, generated force more rapidly and to a greater extent, without increasing variance and without increasing the rising force-time integral. These findings support the generalization of reward-related invigoration in isometric force tasks, and that the brain exquisitely trades-off reward and effort costs to obtain reward more rapidly without increasing variance and without more effort costs than necessary. NEW & NOTEWORTHY Gripping actions are important for day-to-day tasks, for medical diagnostics like strength and force control, and for choice selection in decision-making experiments. Comparing isometric gripping responses to reward and nonreward cues, we observed reward-based invigoration mediated by selective increases in effort. These findings can be leveraged to provide

关键词： decision making effort grasping movement vigor speed-accuracy tradeoff

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Privacy-Preserving Distributed Accelerated Resource Allocation in General Directed Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Keke Lu, Qingguo Liao, Xiaofeng Li, Huaqing Huang, Tingwen Chongqing University College of Computer Science China Southwest University College of Electronic and Information Engineering China Texas A and M University Science Program Doha23874 Qatar

With the proliferation of consumer electronics applications, related devices usually entail allocating system resources to process large-scale distributed data in efficient ways. Effective allocation of system resources is crucial for improving device performance, optimizing energy efficiency, and safeguarding user experience. In this article, we study a distributed constraint-coupled resource allocation problem. Each node in directed networks maintains a private cost function and achieves a solution via only local communication. Problems of this nature considering efficiency and privacy usually emerge in consumer electronics (energy management, intelligent dispatch, energy conservation and utility optimization, etc.), which deserve to be addressed in depth. To tackle this problem, we propose an efficient privacy-preserving distributed accelerated resource allocation algorithm based on state decomposition with added noises. In one aspect, by applying the parametric distributed momentum method to the push-pull distributed resource allocation algorithm, we obtain accelerated allocation in directed networks. Given privacy concerns, our algorithm employs the differential privacy strategy along with a state decomposition scheme to avoid disclosing individual state information in communication. It is proven that our algorithm with a constant stepsize can linearly converge to a neighborhood of the optimal allocation while preserving privacy. The trade-off between convergence accuracy and privacy level is also shown. Extensive simulations further illustrate the efficacy of our algorithm and validate our theoretical discoveries. © 1975-2011 IEEE.

关键词： Cost functions

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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