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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Li, Jiacheng Wang, Wei School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology 52 Xuefu Road Heilongjiang Harbin150000 China

ISBN: (纸本)9781510691773

To address the challenges of insufficient spectral feature extraction and feature redundancy in hyperspectral image (HSI) classification, this paper proposes a Multi-Path Fusion Transformer Network (MPFT) that integrates Vision Transformer, hybrid residual convolution, and attention mechanisms. The proposed approach effectively utilizes both spatial and spectral information of pixels to overcome these limitations. The MPFT architecture comprises a Data Processing Module (DPM), a Dual-Stream Feature Extraction Module (DSFE), and a Vision Transformer (ViT) module. Specifically, the DPM performs dimensionality reduction and merges HSI information in three-dimensional space, while the DSFE extracts deeper features, which are subsequently processed by the ViT module for classification. Experiments conducted on three benchmark datasets—Indian Pines, University of Pavia, and Salinas—demonstrate that MPFT significantly outperforms mainstream models. Notably, MPFT achieved improvements in Overall Accuracy (OA) by up to 5.52%, Average Accuracy (AA) by up to 6.39%, and Kappa coefficient by up to 6.49% compared to state-of-the-art methods. These results highlight that MPFT not only efficiently extracts spatial-spectral features from HSI but also demonstrates superior effectiveness and robustness in various classification tasks. © 2025 SPIE.

关键词： Data streams

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DESIGN AND ANALYSIS OF A SLOTTED PLANAR ANTENNA WITH PARASITIC RING PATCH FOR EFFICIENT 5G MICROWAVE APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第1期84卷 71-82页

作者： Yadav, Swati Varun Yadav, Manish Varun Gupta, Ridhi Yadav, Dinesh Bhati, Kirti Panwar Ali, Tanweer Singh, Vikas Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India School of Electronics Devi Ahilya University M.P Indore India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Design and analysis of a slotted planar antenna with a parasitic ring patch for efficient 5G microwave applications is presented. The antenna design aims to strike a balance between complexity and size efficiency. Its front side features a rectangular patch at the center, comprising L-shaped, T-shaped, and inverse T-shaped elements, alongside four parasitic rings on top. The central feed utilizes a 50-ohm strip line connected to a microstrip line. On the back side, the antenna incorporates three parasitic rings on top, four horizontal strips arranged in an increasing order, and a ground plane with a small rectangular slot. To evaluate its performance, the antenna is simulated using CST Microwave Studio on an FR-4 substrate with overall dimensions measuring 13 × 15 × 1.5 mm3. The results demonstrate an impressive impedance bandwidth of 60.8% and a return loss of –28 dB within the frequency range of 3.2–6 GHz, with a central frequency of 4.6 GHz. Throughout its operational range, the radiation patterns remain constant, exhibiting stable polar patterns and efficient performance with a wide gain of 4.81 dBi and an efficiency of 82%;the proposed antenna proves to be suitable for 5G microwave applications. © 2025 by Begell House, Inc.

关键词： Microwave antennas

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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DOA Estimation Based on N-shape Array

DOA Estimation Based on N-shape Array

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Han, Chuang Gao, Mengran Heilongjiang Province Laboratory of Laser Spectroscopy Technology Application Harbin University of Science and Technology Harbin China School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

This paper combines the L-shaped array and two parallel linear arrays to propose an N-shape array configuration. For the N-shape array, the multiple signal classification (MUSIC) algorithm is utilized for DOA estimation under far-field conditions. When the signal source is in the far-field region, the signal arriving at the array elements can typically be represented as a plane wave. By utilizing the array manifold matrices of the L-shaped array and two parallel linear arrays, the array manifold matrix for the N-shape array is derived, which is then used for DOA estimation via the MUSIC algorithm. Simulation results indicate that the N-shape array can effectively achieve DOA estimation of signals in far-field conditions using the MUSIC algorithm. © 2025 SPIE.

关键词： Matrix algebra

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Research on Identification Method of Toroidal Propeller

Research on Identification Method of Toroidal Propeller

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Han, Chuang Hu, Aiai Wu, Haibin Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China School of Measurement - Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

With the continuous development of marine science and technology, propeller shapes are developing in a more complex direction. Among them, the toroidal propeller represents the latest research progress in propeller technology. This design features a ring-shaped geometry for the blades, which are similar to the catenary curve of a hanging chain. This propeller offers the advantages of higher efficiency and lower noise. In this paper, starting from the geometric shape analysis study of toroidal propeller, we introduce the geometric parameters such as wheelbase, outboard angle, lateral inclination angle among others, derive the mathematical formula of the coordinates of toroidal propeller in detail, simulate the modulation waveform and spectrogram of the toroidal propeller, and compare with those of the ordinary propeller, and according to the experimental data, the geometric shape of toroidal propeller can be recognized by using the proposed method. This lays a firm foundation for further research on the physical properties and scientific problems of toroidal propellers. © 2025 SPIE.

关键词： Geometry

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Data-Knowledge-Driven for UAV Swarm communication Interference Recognition

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IEEE Internet of Things Journal 2025年

作者： Wang, Bin Li, Aiping Wang, Anyi Sun, Yanjing Li, Song Xi'an University of Science and Technology School of Communication and Information Engineering Xi'an710054 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

Achieving high-precision interference recognition for unmanned aerial vehicle (UAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of UAV swarm communication. Although deep learning-based interference recognition methods for UAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labelled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this paper proposes a data-knowledge-driven method for UAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed UAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments. © 2014 IEEE.

关键词： Aircraft communication

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Transmission Schedule for Remote State Estimation in CPSs with Energy Harvesting and Sharing Two-Hop Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and Networking 2025年

作者： Xing, Wei Zhang, Junfeng Zong, Guangdeng Wang, Yuhan Zhao, Xudong Hainan University School of Information and Communication Engineering China Tiangong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Dalian University of Technology China

This paper investigates the problem of optimizing the transmission schedule between a sensor and a relay in cyber-physical systems to achieve accurate remote state estimation. The considered system model represents the plant as a linear time-invariant system, wherein the sensor measurements are transmitted to the remote estimator using an intelligent relay. Both the sensor and relay nodes are capable of harvesting energy from the external environment, and they are required to make transmission decisions at each time step to ensure the stability of system's operation. In addition, energy sharing between each other can yield significant advantages by capitalizing on the available battery power and maximizing energy harvesting from the external environment. We describe the problem of finding the optimal transmission schedule as a dynamic programming model, and establish structural properties on the optimal schedule of the sensor and relay nodes, more precisely, (i) for given battery levels and harvested energy, the optimal transmission strategy is to transmit the measurements when the error covariances at the relay and the remote estimator meet certain conditions, (ii) for given harvested energy and error covariances at the relay and the remote estimator, the optimal transmission strategy is a threshold-based strategy on the battery levels. © 2025 IEEE.

关键词： Dynamic programming

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th International Conference on Wireless communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Causal Attention Transformer for Video Text Retrieval

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IET Image Processing 2025年第1期19卷

作者： Lan, Hua Lv, Chaohui School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Interactive computer graphics

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