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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Improved YOLOv8-Based Obstacle Detection Algorithm for Waterborne Transportation 2

Improved YOLOv8-Based Obstacle Detection Algorithm for Water...

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2nd International Conference on the Frontiers of Intelligent Control, Measurement, and Signal Systems, FICMSS 2025

作者： Zhang, Haozhi Yuan, Chang Wang, Ke Wang, Yudi Liu, Qinghua Shi, Jinlong College of Computer Science Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China College of Automation Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China

Waterborne obstacle detection plays a critical role in the environmental perception of intelligent cargo ship transportation, directly influencing both the efficiency and safety of the transportation process. However, current approaches still encounter challenges regarding detection accuracy, real-time performance, and model lightweighting. So this paper proposes a novel lightweight waterborne obstacle detection algorithm based on an improved YOLOv8 framework. The algorithm integrates an advanced Multi-Branch Fusion (MBF) and Attentional Feature (AF) module with the Cross Stage Feature Fusion (C2f) module to form a new C2fPPAC module, enhancing small-object feature representation while minimizing computational overhead. A feature fusion module, C2f-REPC, is introduced based on structural re-parameterization techniques, which reduces feature redundancy and improves detection accuracy. Additionally, a 160×160 detection head is added to enhance small object detection in challenging backgrounds. Experimental results on a ship-port logistics waterborne obstacle dataset show that the proposed algorithm outperforms the original YOLOv8 by achieving a 3.4% increase in mAP50 and a 3.6% increase in mAP50-95. The computational cost is reduced to 7.9G, and the model size is decreased by 2MB, allowing for real-time and precise detection of waterborne obstacles. The results highlight the effectiveness and advantages of the proposed approach. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Freight transportation

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 66-78页

作者： Liu, Siwen Zuo, Yi Li, Tieshan Wang, Huanqing Gao, Xiaoyang Xiao, Yang Navigation College Dalian Maritime University Dalian116026 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Yangtze Delta Region Institute University of Electronic Science and Technology of China Huzhou313000 China Bohai University College of Mathematical Sciences Jinzhou121000 China The University of Alabama Department of Computer Science TuscaloosaAL35487-0290 United States

In the article, an adaptive fixed-time reinforcement learning (RL) optimized control policy is given for nonlinear systems. Radial basis function neural networks (RBFNNs) are exploited to fit uncertain nonlinearities appeared in the considered systems and RL is applied under the critic-actor architecture by using RBFNNs. Specifically, a novel fixed-time smooth estimation system is proposed to improve the estimating performance of RBFNNs. The introduction of the hyperbolic tangent function effectively avoids the singularity problem of the derivative of the virtual controller. The stability analysis shows that the tracking error inclines to an adjustable region near the origin in a fixed-time interval and the boundedness of all signals is obtained. Finally, the intelligent ship autopilot is simulated to prove the utilizability of the obtained control way. © 2024 IEEE.

关键词： Radial basis function networks

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In vivo Research of the Blood Flow in a Mouse Pressure Ulcer Model by Using Laser Speckle Contrast Imaging 17

In vivo Research of the Blood Flow in a Mouse Pressure Ulcer...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Liu, Qimeng Wan, Min Tao, Ling Zhang, Yameng Li, Weitao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China School of Computer Engineering Nanjing Institute of Technology No.1 Hongjing Avenue Jiangsu Nanjing211167 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Pressure ulcer is a common condition for patients who are bedridden or have limited mobility. In this study, two models of pressure ulcer on mouse ears were established, and the blood flow after pressure ulcers was monitored using a laser speckle contrast imaging (LSCI) system. Maps of blood flow revealed that after a single 1.5-hour pressure ulcer on the mouse ear, the blood flow perfusion at the compressed area gradually recovered over time, and essentially returned to normal by the 5th day. Conversely, with continuous daily pressure ulcers for 1.5 hours, the compressed area showed almost no perfusion by the 4th day, and the ulceration was fully formed by the 5th day. Compared to white light imaging, LSCI offers more precise monitoring of blood flow at pressure ulcer sites. The research solves the problem of dynamic functional monitoring of the blood flow velocity changes at spatial resolution. It provides a new technology for dynamic assessment of pressure ulcers, and offers a new method for the care and treatment of clinical patients. © 2025 SPIE.

关键词： Clinical research

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A frequency-domain index for quickly selecting optimal down-sampling factor in photoacoustic imaging 17

A frequency-domain index for quickly selecting optimal down-...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Tang, Shihao Wan, Min Li, Jiani Zhang, Yameng Tao, Ling Li, Weitao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Department of Computer Engineering Nanjing Institute of Technology No.1 Hongjing Avenue Jiangsu Nanjing211167 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Photoacoustic microscopy (PAM) has emerged as a rapidly advancing non-invasive medical imaging method in recent years. However, slow imaging speed has impeded its widespread adoption in clinical applications. In certain scenarios, sparse spatial sampling is essential for PAM so there is a trade-off between spatial resolution and imaging speed. To address this limitation, we propose a frequency domain index based on cumulative power difference (CPD) to determine rapidly the optimal down-sampling factors. In this study, the structural images of mice ears were acquired by the PAM system. Subsequently, the optimal down-sampling factor was determined through CPD analysis of these images via interpolation. Finally, the correlation between cumulative power difference and the image quality loss curve were analyzed. The results indicate that the quality of the reconstructed images decreases with the increasing down-sampling factor. Moreover, the cumulative power difference is an effective tool for rapid assessment of reconstruction image quality degradation due to varying down-sampling factors. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Deep Learning Based Open Set Recognition: A Survey

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 828-863页

作者： Zhang, Qin Liu, Zi-Qi Zhang, Xiao-Lin Zhang, Peng Liu, Han Chen, Xiao-Jun College of Computer Science and Software Engineering ShenZhen University Guangdong ShenZhen518060 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

In recent years, machine learning research has continuously made breakthroughs, which has led to the maturity and implementation of a large number of intelligent systems. However, the current machine learning paradigm overly relies on complete prior knowledge, and its application scenarios are limited to static closed specialized systems. The emergence of new categories is one of the main challenges in the research of dynamic open environments, to which the conventional closed-world machine learning methods can not handle it well. In order to break the excessive dependence of existing machine learning on complete category information, the research on open set recognition problem has become a new trend, which extends the traditional closed-world classification methods to open-world applications. Open set recognition problem requires the classification models classify the learned known classes correctly, and effectively identify unknown class samples that appear in the testing phase simultaneously, to avoid the large number of misclassifications. This article systematically surveys the research on open set recognition in recent years, focusing on deep learning based open set recognition methods. It introduces the classic models by sorting out them into six categories based on the main ideas they employ, and horizontally compares various research achievements, and give the comparison details of their performances. Related paper and code links are collected and available online. © 2025 Science Press. All rights reserved.

关键词： Deep learning

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and Applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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Fully Distributed Model-Free Adaptive Sliding Mode Control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on automation Science and Engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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Conv-TasNet Adaptive Noise Cancellation Model Enhanced by WaveNet

Conv-TasNet Adaptive Noise Cancellation Model Enhanced by Wa...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Kun Zhao Yaojie Hou College of Computer Science and Technology (College of Big Data) Taiyuan University of Technology Jinzhong China College of Electrical and Automation Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Traditional speech denoising methods often struggle with low signal-to-noise ratios (SNR) and poor clarity in complex noise environments. To address these limitations, this study introduces an adaptive denoising model that integrates Conv-TasNet with WaveNet enhancements. The proposed model incorporates causal convolution within the Conv-TasNet separator to better capture long-term dependencies and employs WaveNet's gating activation mechanism to effectively handle nonlinear speech features. Additionally, a mask weighting strategy is implemented to emphasize target speech signals while suppressing background noise. Experimental results demonstrate that the enhanced model significantly outperforms the original Conv-TasNet, achieving an SNR improvement from –5 dB to 33.74 dB and reducing mean squared error (MSE) to nearly zero. These findings confirm the model's superior performance and robustness in complex noisy environments, highlighting its potential for advanced speech enhancement applications.

关键词： Adaptation models Convolution Computational modeling Particle separators Noise reduction Speech enhancement Feature extraction Computational efficiency Noise measurement Signal to noise ratio

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