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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

RETRACTED ARTICLE: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2025.

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Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air

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arXiv 2025年

作者： Yao, Jiacheng Xu, Wei Zhu, Guangxu Yang, Zhaohui Huang, Kaibin Niyato, Dusit National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Shenzhen Research Institute of Big Data Shenzhen China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though AirComp leverages the superposition property of multiple access channels for computation, it inherently limits its ability to manage inter-task interference in multi-task computing. In this paper, we propose a quantized analog beamforming scheme at the receiver to enable simultaneous multi-task FL. Specifically, inspiring by the favorable propagation and channel hardening properties of large-scale antenna arrays, a targeted analog beamforming method in closed form is proposed for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of O (1/Nr) with the number of analog phase shifters, Nr, irrespective of their quantization precision. Numerical results demonstrate the effectiveness of the proposed analog beamforming method and show that the performance upper bound of ideal learning without errors can be achieved by increasing the number of low-precision analog phase shifters. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

Precoding Optimization for Rate Splitting Enabled Internet of Underwater Things over Optical Wireless Underwater Turbulent Channels

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IEEE Internet of Things Journal 2025年

作者： Xing, Fangyuan Tong, Fei Liu, Xiaonan Wang, Zhenduo Leung, Victor C. M. Southeast University School of Cyber Science and Engineering Nanjing211189 China School of Natural and Computing Science University of Aberdeen AberdeenAB24 3FX United Kingdom Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the booming development of the underwater Internet of Things, underwater devices and data volume are explosively increasing. Thanks to the advantages of high efficiency and low complexity, the emerging rate splitting multiple access (RSMA) can be integrated with underwater optical wireless communication (UOWC), which can help improve resource utilization and system performance. However, the oceanic characteristics are distinctive and pose great challenges for RSMA-enabled UOWC networks. Precoding design is crucial for benefitting from RSMA-enabled systems, and thus, we explore the precoding optimization for RSMA-enabled UOWC networks over underwater turbulent channels. Specifically, the RSMA-enabled UOWC network is modeled, where a combined turbulent channel involving the effects of absorption, scattering, and underwater turbulence is considered, and a message transmission scheme with rate splitting design is analyzed. A precoding problem is formulated to maximize the ergodic sum rate (ESR) under the constraint of total transmitted power. Since the accurate expression of ESR is a non-closed form and intractable, we first propose a consecutive Fenton-Wilkinson (CFW)-based ergodic rate approximation approach to solve this formulation problem. Fenton-Wilkinson moment matching is consecutively employed to achieve a closed-form and low-complexity expression of the ergodic rate, as only Fenton-Wilkinson moment matching method can offer closed-form solutions for underlying parameters of the approximating log-normal distributions. For further solving the non-convex precoding problem, the successive convex approximation (SCA) approach is adopted, which is particularly applicable for resource-limited underwater environments. The effectiveness of the proposed CFW approach and precoding strategy is evaluated by extensive simulation results for various user deployments and different network loads. © 2025 IEEE.

关键词： Normal distribution

Comparative Analysis of AES, RC and Twofish Algorithms for data Encryption and Decryption

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Comparative Analysis of AES, RC and Twofish Algorithms for D...

computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ahmad Faizal Bin Tajuddin Mohammad Hafiz Mohd Yusof Mohammed Balfaqih Akram A. Almohammedi Sam Darshi Nadhem Qaid College of Computing Informatics and Mathematics Universiti Teknologi MARA UiTM Tapah Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical and Electronic Engineering Department Karabük University Karabük Turkey Department of Electrical Engineering Indian Institute of Tehcnology Ropar Punjab India R&D Engineer Lead Marine Powertrain Controls & Electronics Caterpillar Inc Houstan Texas USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This paper presents a comparative analysis of three widely used encryption algorithms, namely AES (Advanced Encryption Standard), RC (Rivest Cipher), and Twofish, with a focus on their performance in data encryption and decryption. The study aims to evaluate the strengths and weaknesses of these algorithms in terms of security, speed, and efficiency. The analysis involves benchmarking the algorithms against various performance metrics, including encryption time, decryption time, key size, and resistance against cryptanalysis attacks. Additionally, the research explores the impact of different data sizes and configurations on the performance of each algorithm. The findings of this study will provide valuable insights for decision-makers and practitioners in selecting the most suitable encryption algorithm for their specific needs, considering the tradeoffs between security and performance.

关键词： Resistance Ciphers Three-dimensional displays data security Benchmark testing Encryption Timing Information technology Standards

E1 TTS: Simple and Fast Non-Autoregressive TTS

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E1 TTS: Simple and Fast Non-Autoregressive TTS

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

作者： Zhijun Liu Shuai Wang Pengcheng Zhu Mengxiao Bi Haizhou Li School of Data Science The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China Fuxi AI Lab NetEase Inc. Hangzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at ***.

关键词： Training Noise reduction Neural networks Signal processing Acoustics Text to speech Speech processing

High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality

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arXiv 2025年

作者： Brazaola-Vicario, Aitor Kouvakis, Vasileios Trevlakis, Stylianos E. Ruiz, Alejandra Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Niyato, Dusit Spain The Department of Research and Development InnoCube P.C. 79 17is Noemvriou Str. Thessaloniki55534 Greece The Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece The Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design. © 2025, CC BY.

关键词： Bit error rate

Adaptive Solar Energy Storage with Deep Learning for Improved Grid Resilience

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Adaptive Solar Energy Storage with Deep Learning for Improve...

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： C.Shobana Nageswari Pramod Pandey Sujitha R Sujatha M S. Senthilkumar S. Sujatha Department of ECE R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Centre for Artificial Intelligence & Data Science (CAI) Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation ( DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Implementing renewable energy sources, especially solar power, into the electrical grid has distinct difficulties and potential for improving system resilience. This research investigates an adaptive solar energy storage system using deep learning methodologies, particularly Long Short-Term Memory (LSTM) networks, to enhance energy management and grid stability. The proposed system utilizes past solar generating data and usage patterns to forecast future energy needs and generation capacities. LSTM allows the model to efficiently reflect temporal interdependence and variation in solar energy output, facilitating more precise forecasting. The adaptive storage system modifies its charging and discharging processes in real time, optimizing efficiency and reducing energy waste. LSTM-based forecasting improves the storage system’s response to variations in supply and demand, enhancing load balancing and grid resilience. This method facilitates a change in renewable energy sources while enhancing the resilience of grid infrastructure to sustain delays. Findings indicate that integrating flexible energy storage using advanced deep-learning methodologies may significantly improve the efficacy and dependability of solar energy systems. This paper challenges solar energy intermittency with LSTM-based adaptive storage, enhancing forecasting precision, grid resilience, and energy efficiency enhancing stable energy management and optimized battery use.

关键词： Deep learning Renewable energy sources Computational modeling Solar energy Predictive models Real-time systems Robustness Forecasting Long short term memory Resilience