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MST-Net: Multispectral transformer network with geo-guided query for multi-label remote sensing image scene classification

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Pan, Yukai Jin, Wei Fu, Randi Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

Multi-label scene classification (MLSC) in remote sensing (RS) plays a crucial role in recognizing the intricate contents of RS images. However, the MLSC task is challenged by the complexity of label combinations and the diversity of visual content. Multispectral (MS) data provide valuable information for scene classification. To achieve accurate classification results, it is important to fully utilize the diverse information contained in MS data. To this end, we propose a multispectral transformer network (MST-Net), which leverages transformer-based architectures to capture the diverse information within MS images and the complex relationships between labels and features. Specifically, MST-Net consists of a feature fusion encoder and a semantic query decoder. Within the encoder, we have developed an MS deformable attention module based on a sampling strategy that reduces focus on redundant spectral areas, allowing for better integration of complementary MS information. In the decoder, geographic information is introduced as an inductive bias, utilizing the unique spatiotemporal characteristics of RS images to learn better class-related features. Extensive experiments were conducted on two RS multi-label datasets, namely, LSCIDMRv2 and BigEarthNet. Comparisons with several state-of-the-art multi-label classification methods demonstrate the effectiveness and superiority of MST-Net. © 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Deep learning

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CNN-Transformer-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1576-1580页

作者： Fang, Xiangqian Jin, Ming Guo, Qinghua Jiang, Tao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Recently, deep learning (DL)-based data-driven spectrum sensing has attracted much attention due to its powerful capabilities to exploit underlying signal features. Existing DL-based cooperative spectrum sensing (CSS) detectors usually extract the signal features from each secondary user (SU) individually, followed by feature fusion at a central fusion center (FC). However, this cannot extract correlation features across SUs. To address this limitation, this letter proposes a CNN-Transformer neural network to exploit the correlations among the received signals across and within SUs. Leveraging the CNN-Transformer, we propose a posteriori probability ratio detector, which is optimal for the binary hypothesis testing problem under the Neyman-Pearson criterion. Moreover, unlike most DL-based CSS detectors, the proposed detector considers imperfect and independent reporting channels between SUs and the FC, and exploits the temporal correlation of signals among neighboring samples. Numerical results are provided to demonstrate the superior performance of the proposed detector, showing an improvement of approximately 6 dB over traditional model-driven energy detectors. Moreover, the proposed detector improves the detection probability to 0.99 from 0.88 of data-driven detectors under Rician fading channels. © 2012 IEEE.

关键词： Fading channels

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JC-GN: multi-scene joint conditional network for remote sensing sequence imagery generation under spatio-temporal variation

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Journal of King Saud university - computer and Information sciences 2025年第3期37卷 1-13页

作者： Zhao, Zi-Yi Jin, Xing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China

Remote sensing imagery plays an important role in the research and application of environmental change, such as phenology changes, land surface parameter relationships, land degradation, etc. However, remote sensing observation data are often missing due to sensor resolution limitations and environmental factors. The missing images may occur randomly or continuously, which is difficult to deal with this situation with conventional methods. To address this issue, we perform the multi-scene joint conditional generation network (JC-GN) based on radiation properties to learn the complex mapping between phase images. The proposed model introduces multi-scene joint conditional loss to constrain the spatio-temporal characteristics of the generated image. Our experiments, conducted utilizing unmanned aerial vehicle (UAV) datasets and the Landsat-8 datasets, which are located in the southeast of Gansu Province (34.73∘N, 105.50∘E), demonstrate that the proposed methodology achieves promising performance in terms of both precision (RMSE decreases by about 2.5 points, PSNR increases by about 10 points, and SSIM increases by about 0.03 points) and effectiveness (time cost savings of about 50 percent), and data-driven model exhibits a superior capacity to simulate intricate information with greater fidelity. © The Author(s) 2025.

关键词： Data driven model Image generation Landsat-8 Multi-scene joint conditional network Unmanned Aerial Vehicle (UAV)

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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 2025 IEEE.

关键词： Convex optimization

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Receiver-Agnostic Radio Frequency Fingerprinting Using a Prototypical Contrastive Domain Adaptation Method

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IEEE Signal Processing Letters 2025年 32卷 1910-1914页

作者： Li, Wenhan Wang, Jiangong Liu, Taijun Xu, Gaoming Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Coast Guard Academy Ningbo315801 China

Radio frequency (RF) fingerprinting is a technique used to identify different transmitters by analyzing the unique hardware impairments of RF transmitters, known as RF fingerprints. However, many existing studies have primarily focused on transmitter impairments, while the impact of receiver hardware impairments on RF signals has often been overlooked. To alleviate this issue, this letter proposes a receiver-agnostic RF fingerprinting method using unsupervised domain adaptation. The method employs prototypical contrastive learning to align the features of source domain and target domain data, while simultaneously learning the features of both domains. Experimental results on the real-world dataset (WiSig) demonstrate that the proposed method outperforms other receiver-agnostic RF fingerprinting methods. © 1994-2012 IEEE.

关键词： Transmitters

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Integrated Microfluidic Rapid Response and Highly Sensitive Electrochemical Aptasensor for Simultaneous Detection of Aflatoxin B1 and Deoxynivalenol 38

Integrated Microfluidic Rapid Response and Highly Sensitive ...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wu, Jinlei Jin, Qinghui Yang, Ping Gao, Wanlei Ningbo University The Faculty of Electrical Engineering and Computer Science China

ISBN: (纸本)9798331508890

A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provides high sensitivity, with detection limits of 7.74 pg/mL for AFB1 and 0.172 ng/mL for DON across broad ranges (0.03 - 300 ng/mL for AFB1, 0.3 - 3000 ng/mL for DON). Results can be obtained within 20 minutes, and the platform exhibits excellent reproducibility, selectivity, and stability. Validation with corn extract highlights its potential for on-site food safety monitoring. © 2025 IEEE.

关键词： Aflatoxins

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

A Novel Audio-Visual Multimodal Semi-Supervised Model Based ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yaqin Sun, Chenjian Dong, Yihong Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (纸本)9798350368741

There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients' audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics. © 2025 IEEE.

关键词： audio-visual multimodal depression detection graph neural networks semi-supervised

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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators

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IEEE Access 2025年 13卷 81507-81523页

作者： Huang, Xiaowei Shi, Xuhua Wang, Peiyao Xu, Hongzan Tang, Xiaojun Zhang, Gaoran Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China CO.Ltd Ningbo315800 China

The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant enhancements. First, transform the black-box simulator environment into a differentiable simulator environment that provides gradient information at each time step and use this gradient information to directly update the control policy. Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator’s motion reference control signal sequences for the same duration. Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. As the prediction horizon increases, the algorithm demonstrates superior computational efficiency, which helps reduce the incidence of motion sickness during the use of the driving simulator. Third, a loss function specifically designed for the motion simulator is proposed. This function incorporates constraints derived from the MPC framework to address workspace limitations and applies them to workspace management. These constraints restrict the platform’s acceleration and speed near the workspace boundaries, allowing for better utilization of the available space. The algorithm is validated using Carla’s autonomous driving simulation software as the dataset generator. During the training process, the proposed algorithm in this paper achieves an order-of-magnitude improvement in convergence speed compared to conventional training methods of PPO and DDPG. Simulations with a 10-step prediction horizon indicate that the Root Mean Square Error (RMSE) produced by this algorithm is comparable to that of the MCA based on MPC (MPC-

关键词： Automobile driver simulators

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Resource Scheduling and Delay Optimization of IoT Devices in UAV-Assisted Multi-Access Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 16998-17011页

作者： Qu, Long Wang, Jiming Assi, Chadi Faculty of Electrical Engineering and Computer Science of Ningbo University Ningbo315211 China MontrealQCH3G 2W1 Canada

Multi-access edge computing (MEC) plays a crucial role in providing low-latency and high-data transmission services to Internet of Things (IoT) devices. However, in remote areas where deploying edge devices is challenging, optimizing delay remains a significant research focus. To address this issue, our research investigates a multi-unmanned aerial vehicle (UAV)-assisted IoT task offloading model. In this model, tasks generated by IoT devices equipped with energy harvesting (EH) capabilities are offloaded to MEC servers with the assistance of multiple UAVs. In order to monitor and manage the energy consumption of IoT devices and the task backlog of edge servers, the energy consumption and task update queues are established. We formulate a mixed integer non-linear programming (MINLP) problem, which aims to optimize the allocation of communication and computation resources to minimize the execution latency of IoT devices. To ensure the stability of each queue, we employ the weighted perturbation method within the Lyapunov optimization framework to decompose the original problem. And a low complexity Multi-UAV Assisted Offloading (MUAO) algorithm is designed. Simulation results show that MUAO consistently exhibits lower latency and energy consumption compared to the baseline scheme and other existing algorithms, while maintaining a low packet loss rate of only 5%. © 2025 IEEE.

关键词： Resource allocation

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Wideband Slot Antenna Array with Stable Low Sidelobe Performance

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1248-1252页

作者： Zhang, Fengyuan Zhang, Hanlin You, Qingchun Huang, Jifu Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Ningbo315211 China University of Birmingham School of Engineering B15 2TT United Kingdom

This letter presents a dispersion compensation method that integrates a comb-shaped slow-wave structure with a defected ground structure (DGS) to achieve wideband low-sidelobe performance. This method effectively ensures phase balance within the feed network, achieving a stable low sidelobe level over a wide frequency range. A prototype planar array antenna operating in the 21.6-30.8 GHz range was designed, fabricated, and measured. The experimental results demonstrate that the antenna achieves a fractional bandwidth (FBW) of 35.1%, with the first sidelobe level maintained below -21 dB across the entire band and a fluctuation confined within ±1.3 dB. © 2002-2011 IEEE.

关键词： Defected ground structures

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