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Anomaly Correction for Radiation Patterns of Active Phased Array Antenna Based on a Neural Network

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Guo, Kegang Zhang, Rui Shen, Lifei Feng, Yiwen Guo, Qingze Xu, Rongqi Tian, Linbo Zhang, Sasa Shandong University School of Information Science and Engineering Qingdao266237 China AVIC Research Institute for Special Structures of Aeronautical Composites Jinan250000 China Shandong University Key Laboratory of Education Ministry for Laser and Infrared System Integration Technology Qingdao266237 China Tsinghua University State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instruments Beijing100084 China

Anomaly interference in the radiation pattern of an active phased array antenna (APAA) is an intractable problem, that affects the overall system performance. To overcome this problem, this paper proposes an antenna radiation pattern anomaly correction model (ARPACM) based on a neural network to compensate for the shortcomings of the antenna radiation pattern (ARP) under anomalous conditions. In addition, to overcome the problem of the scarce data, this study uses simulated anomalous ARPs that are consistent with practical experimental conditions to construct and train a pre-trained model, named simulated ARPACM (SARPACM). The weights of the last hidden layer of the SARPACM are optimized using experimental data by introducing transfer learning, yielding an optimized ARPACM. Extensive experiments are conducted to verify the proposed ARPACM. The results indicate that the proposed model can outperform widely used correction algorithms and other training strategies, in terms of a mean coefficient of determination (R2), a mean square error (MSE), a mean relative error (MRE), achieving improvements of 99.76%, 0.08 dB, and 1.29%, respectively. Finally, the experimental results demonstrate that the ARPACM can effectively correct the anomalies of ARPs caused by various interfering factors. © 2002-2011 IEEE.

关键词： Mean square error

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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Carbon emission peaks in countries worldwide and their national drivers

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Carbon Research 2025年第1期4卷 1-16页

作者： Wu, Qian Chen, Yujie Huang, Chaoqing Zhang, Lu He, Chao School of Resource and Environmental Sciences Wuhan University Wuhan China Collaborative Innovation Center for Emissions Trading System Co-Constructed By the Province and Ministry Wuhan China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Guangdong Eco-Engineering Polytechnic Guangdong Province Guangzhou China State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan China National Science Library (Wuhan) Chinese Academy of Sciences Wuhan China

Estimating the characteristics of CO2 emission peaks through decoupling relationships is crucial for understanding global CO2 emissions and mitigating climate change. This study investigated the spatiotemporal patterns and primary sources of global CO2 emissions from 1990 to 2020. Using the Mann–Kendall test and decoupling index, we identified emission peak characteristics across countries worldwide. Furthermore, the geographically temporally weighted regression (GTWR) model was employed to examine the effects of various factors on CO2 emissions. The results indicate that global CO2 emissions increased steadily over the study period, with the power industry contributing the most (34.54%–38.62%). Countries were categorized into four groups: no-declined, plateau, passively emission declined, and proactively peaked, comprising 99, 48, 20, and 26 countries, respectively. Notably, 65.4% of proactively peaked countries were developed nations, while 65% of passively emission declined countries were developing nations. CO2 emissions exhibited positive correlations with carbon intensity, GDP per capita, and the secondary industry but negative correlations with population density, the primary and tertiary industries. These findings provide valuable insights into global CO2 emission dynamics, highlighting emission peaks and decoupling relationships. This study also offers robust scientific support for policymakers to effectively design tailored strategies for reducing carbon emissions.

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Research on Lightweight Sensing technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel state information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Validation of Sea Surface Winds from the Space-borne Radiometer COWVR

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Zhou, Luo Wang, Zhixiong Zhang, Naiqiang Qu, Jianhua State Key Laboratory of Climate System Prediction and Risk Management Nanjing210044 China Nanjing University of Information Science and Technology School of Marine Sciences Nanjing210044 China Beijng Huayun Shinetek Science and Technology Co. Ltd Beijing China

This study aims to validate sea surface wind data derived from the Compact Ocean Wind Vector Radiometer (COWVR) onboard the International Space Station (ISS). The COWVR, a fully polarimetric and two-look microwave radiometer, provides wind direction and speed retrievals in rain-free conditions. Validation was performed by comparing COWVR data with traditional radiometer (AMSR-2, GMI), scatterometer (MetOp\ASCAT, HY-2\SCAT), numerical weather prediction (ERA5), and buoy data. The results show that COWVR wind speed retrievals are comparable to those of AMSR-2 and GMI, with an overall wind speed bias close to zero and a standard deviation of 1.18 m\s when compared to ERA5. The COWVR also demonstrates good accuracy in wind direction retrievals for wind speeds above 8 m\s, with RMS errors of 12.5° and 15.1° for ERA5 and buoy comparisons, respectively. These findings suggest that COWVR can provide good sea surface wind products. However, the inconsistencies of radiometer and scatteromers sea surface wind speeds are still significant, especially for winds above 15 m\s. © 2008-2012 IEEE.

关键词： Radiometry

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A multip-level additive distortion method for security improvement in palette image steganography

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Chen, Yi Wang, Hongxia Cui, Yunhe Shen, Guowei Guo, Chun Liu, Yong Wu, Hanzhou State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University Guiyang550025 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China School of Artificial Intelligence Hubei University of Automotive Technology Shiyan442002 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China School of Communication and Information Engineering Shanghai University Shanghai200444 China

With the rapid development of the Internet and communication technology, palette images have become a preferred media for steganography. However, the security of palette image steganography faces a big problem. To address this, we propose a multiple-level additive distortion method for security improvement in palette image steganography. The proposed multiple-level additive distortion method comprises an index-level cost method and a pixel-level cost method. The index-level and the pixel-level costs by the two methods can respectively reflect the relationship changes of adjacent indices and the pixels corresponding to the adjacent indices. Meanwhile, the index-level and the pixel-level costs can also reflect the modification impact of steganography. Therefore, the proposed method can improve the security of palette image steganography. We conducted extensive experiments on three datasets to verify the security improvement. Experiment results have shown our proposed multiple-level distortion method indeed has an advantage in security when compared with four state-of-the-art methods. © 2025 Elsevier Inc.

关键词： Additive costs Color table Index matrix Multiple-level distortions Palette images security Steganography

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Deep Learning Based Vehicular Millimeter-Wave Channel Prediction Using Visual information

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Wang, Shirui He, Ruisi Yang, Mi Ai, Bo Zhang, Xuejian Zhong, Zhangdui Zhang, Haoxiang Han, Jiahui Liu, Bingcheng Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation the School of Electronics and Information Engineering the Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Ministry of Industry and Information Technology China Academy of Industrial Internet Beijing100044 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China

Millimeter-wave channel prediction requires detailed and accurate environmental information due to challenges of non-line-of-sight transmission and severe reflection and scattering. Image information conveniently reflects real-time changes of physical environment and provides a promising solution for millimeter-wave channel modeling. However, research on millimeter-wave channel prediction based on RGB images is still in its exploratory stage. Meanwhile, RGB images introduce new influencing factors, such as visual angle changes, leading to inaccurate prediction. This article proposes a millimeter-wave channel prediction model based on propagation knowledge and visual information, which enhances original RGB image features while combining manual features from specific domain prior knowledge in propagation. Experimental results show that the proposed model outperforms traditional RGB image-based prediction for a multi-scenario, multiview fused dataset, achieving a symmetric mean absolute percentage error as low as 4.16%. In addition, the proposed model demonstrates enhanced stability and interpretability. © 2002-2011 IEEE.

关键词： Prediction models

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Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC systems

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IEEE Transactions on Network and Service Management 2025年

作者： Mei, Jing Zeng, Cuibin Tong, Zhao Yang, Zhibang Li, Keqin Hunan Normal University College of Information Science and Engineering Changsha410081 China Changsha University in Changsha Hunan Province Key Laboratory of Industrial Internet Technology and Security Hunan 410022 China Hunan University College of Information Science and Engineering Changsha410012 China National Supercomputing Center Hunan Changsha410082 China State University of New York Department of Computer Science New YorkNY12561 United States

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Several classes of linear codes with few weights derived from Weil sums

arXiv

引用

arXiv 2025年

作者： Hu, Zhao Qiu, Mingxiu Li, Nian Tang, Xiaohu Wu, Liwei Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Southwest Jiaotong University Chengdu610031 China Wuhan Maritime Communication Research Institute China

Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of t-weight linear codes over Fq are presented with the defining sets given by the intersection, difference and union of two certain sets, where t = 3, 4, 5, 6 and q is an odd prime power. By using Weil sums and Gauss sums, the parameters and weight distributions of these codes are determined completely. Moreover, three classes of optimal codes meeting the Griesmer bound are obtained, and computer experiments show that many (almost) optimal codes can be derived from our constructions. Copyright © 2025, The Authors. All rights reserved.

关键词： Weibull distribution

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