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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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The Role of Mobile Computing in Adaptive Testing for English Language Learners: Personalizing Assessment to Improve Outcomes

The Role of Mobile Computing in Adaptive Testing for English...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Vimochana M Myagmarsuren Orosoo P Deepika Malik Bader Alazzam Giftsy Dorcas E Mustafa Zuhaer Nayef Al-Dabagh Department of English Rajalakshmi Institute of Technology Chennai India Mongolian National University of Education Mongolia Department of English Nandha Engineering College (Autonomous) Erode India Faculty Information Technology Jadara University Irbid Jordan Department of English Kristu Jayanti College (Autonomous) Bengaluru Karnataka Department of Computer Science College of Science Knowledge University Erbil Iraq

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Adaptive testing is considered a critical part of personalized assessment, especially for ELLs, since it assesses learners by changing the difficulty level of questions that are being presented to learners based on learner performance. Research studies in the past utilized rule-based or machine learning models, which could not fully capture the richness of linguistic variations and contextual understanding, therefore performing sub optimally. To overcome these limitations, this study introduces a novel BERT-based adaptive testing framework. BERT uses a bidirectional approach to understand the entire context of learner responses, which helps in more accurate assessment and personalization. The proposed method outperforms existing models in key metrics such as accuracy (98.6 %), showcasing its ability to dynamically adjust question difficulty while providing contextually aware feedback. The system operates efficiently to provide real-time, personalized assessments through a mobile interface. It is scalable and accessible for widespread use. It addresses the shortcomings of earlier approaches by improving test accuracy, learner engagement, and overall educational outcomes.

关键词： Adaptation models Accuracy Adaptive systems Bidirectional control Smart systems Encoding Real-time systems Mobile computing Testing Context modeling

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and Computing 2025年 v页

作者： Bhattacharyya, Debnath Ghosh, Rajib Department of Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

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Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce

Artificial Intelligence Algorithms for Robotic Harvesting of...

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AIML-Applications for engineering & technology (ICAET), International Conference on

作者： Nilesh R. Kolhalkar Anupama A. Pandit Shridhar Ashok Kedar G. Yedukondalu Department of Mechanical Engineering MKSSS's Cummins College of Engg. For Women Pune MH India Department of Computer Science & Engg. Defence Institute of Advanced Technology (D.I.A.T) Pune Pune MH India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada A.P. India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial Intelligence, and Machine Vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations.

关键词： Accuracy Costs Automation Machine vision Pipelines Lighting Training data Robot sensing systems Prediction algorithms Robots

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Trust Online Over-the-air Computation for Wireless Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Mingjie Zheng, Jie Du, Hongyang Zhang, Haijun Niyato, Dusit Kang, Jiawen Wang, Jiacheng Ren, Jie Gao, Ling Wang, Zheng Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi Xian710127 China University of Hong Kong department of electrical and electronic engineering Hong Kong University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology Automation of School Guangzhou510006 China Shaanxi Normal University School of Computer Science Xi'an710062 China University of Leeds School of Computing LS2 9JT United Kingdom

Using the wireless waveform superposition property, over-the-air computation (OAC) enables federated learning (FL) to achieve fast model aggregation. However, this computing paradigm is vulnerable to poisoning attacks due to the openness of a wireless channel over time, where malicious mobile devices can introduce cumulative errors for the global FL model in a time-varying wireless environment for each communication round. This article presents a trust online OAC (TO-OAC) scheme to minimize impacts on the global model introduced by malicious devices adjusting to dynamic attack and wireless channel fluctuations over time. TO-OAC achieves this by utilizing trustworthy security quantification of OAC for each FL training round. To optimize the cumulative training loss at the aggregation node with the long-term power and trust constraints of mobile devices, we propose a joint trust, power, and channel-aware algorithm to flexibly update local and global models in response to the dynamic changes in the wireless and secure environment. We analyze the performance limits for the aggregation of trust models, considering metrics for computation and communication through time. We then propose another trust online regularization over-the-air computation (TOR-OAC) as an improved version of the TO-OAC scheme to decrease convergence time while ensuring long-term trust and power limitation. Experimental results performed on real-life datasets show that the two proposed schemes (TO-OAC and TOR-OAC) outperform prior works, especially in noisy, time-varying wireless channels and malicious attacks. 2002-2012 IEEE.

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Optimizing Bidding Curves for Renewable Energy in Two-Settlement Electricity Markets

arXiv

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arXiv 2025年

作者： Zhao, Dongwei Delikaraoglou, Stefanos Dvorkin, Vladimir Lamadrid, Alberto J.L. Botterud, Audun Argonne National Laboratory LemontIL United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Department of Economics Department of Industrial and Systems Engineering Lehigh University BethlehemPA United States

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome. © 2025, CC BY.

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Detection of atmospheric carbon dioxide with air-lasing-based coherent Raman spectroscopy

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Optics Express 2025年第11期33卷 23873-23882页

作者： Ning Zhang Zelong Li Rongyu Gong Pengyu Xue Xu Lu Bo Shen Quanjun Wang Jinping Yao State Key Laboratory of Ultra-Intense Laser Science and Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Microelectronics Shanghai University Shanghai 200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Optics and Optical Engineering University of Science and Technology of China Hefei 230026 China Institute of Modern Optics Nankai University Tianjin 300350 China

Coherent anti-Stokes Raman scattering (CARS) is recognized as a powerful technique for chemical sensing, biological imaging, and combustion diagnostics, but it is rarely used in atmospheric detection due to complex multi-beam configuration and fine spatio-temporal control. Air lasing provides an attractive probe source for CARS, largely simplifying its implementation. Herein, we successfully detected atmospheric carbon dioxide (CO 2 ) using air-lasing-based CARS spectroscopy and quantified its concentration to be 485.5 ppm and 502.0 ppm based on two CARS signals. The measured concentrations exhibit relative errors of 0.6% and 2.7% compared to the result obtained from a commercial gas analyzer. The detection limit reaches 43 ppm, which is one order of magnitude lower than previous results. The precision exhibits a nearly quadratic decay, gradually stabilizing at a constant value as the concentration increases. The precision for atmospheric CO 2 detection ranges from 9% to 10%. The developed spectroscopy holds great potential in standoff detection of air pollutants, biological agents, and radioactive substances.

关键词： Biomedical imaging Dual comb spectroscopy Femtosecond lasers Frequency combs Laser beams Raman scattering

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Vector Register Sharing Mechanism for High Performance Hardware Acceleration 30th

Vector Register Sharing Mechanism for High Performance Hard...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Tanaka, Tomoaki Kato, Michiya Osana, Yasunori Miyoshi, Takefumi Tada, Jubee Tanaka, Kiyofumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Graduate School of Science and Engineering Yamagata University 1-4-12 Kojirakawa-machi Yamagata Yamagata-shi990-8560 Japan Japan Advanced Institute of Science and Technology 1-1 Asahidai Ishikawa Nomi923-1292 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, demand for data-parallel processing has been growing, and this parallelism often appears in AI processes. One method to accelerate these processes is using DSA, domain-specific architecture. A common data transfer method on DSA is DMA, which is direct memory access. There are several studies on DMA-based accelerators. However, few studies focus on data transfer methods. In this paper, a vector register-sharing mechanism has been proposed as a new data transfer method. Our proposed mechanism is named "SHAVER". In this mechanism, a part of vector registers is directly shared with an accelerator. An open-source RISC-V vector co-processor is used to evaluate the mechanism’s potential. It has been implemented on an FPGA and a simulator for the evaluations. The results indicate the possibility of the proposal mechanism to achieve a maximum of 7.13% speedup over DMA transfer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Vectors

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An Asymmetric Approach to Three-Way Approximation of Fuzzy Sets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhao, Xuerong Miao, Duoqian Yao, Yiyu Pedrycz, Witold Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai201418 China Tongji University Department of Computer Science and Technology Shanghai201804 China University of Regina Department of Computer Science ReginaSKS4S 0A2 Canada University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute of the Polish Academy of Sciences Warsaw Poland

The three-way approximation of fuzzy sets represents membership values using a three-valued set {1,m,0} , where 1 indicates total belongingness, 0 total non-belongingness, and m an intermediate state. This approach elevates values of membership function above a threshold α to 1, reduces those below β to 0, and assigns the remaining ones to an intermediate value m. A key challenge lies in determining the thresholds α and β and selecting the value of m, as existing models often lack analytical solutions and fail to fully explore the relationship between m and membership structures. This study introduces an asymmetric three-way approximation model for fuzzy sets, removing the constraint α + β = 1. An analytical formula is derived for the thresholds (α,β) by minimizing information loss, and the relationship between m and membership structures is thoroughly examined. An adaptive optimizer is proposed to learn the approximate optimal value of m by minimizing the information loss. The experimental results show that information loss decreases initially before increasing as m grows. Besides, our model achieves the best classification across most datasets. © 1993-2012 IEEE.

关键词： Membership functions

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