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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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Generic Software Design for Radar Basic Data Inspection

Generic Software Design for Radar Basic Data Inspection

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2024 Advanced Fiber Laser Conference, AFL 2024

作者： Chen, HongLi Fu, JianChuan Liu, RenJie Ding, Chao Wang, Mingyang Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology No. 51 North Muxidi Xicheng District Beijing100038 China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

A general data inspection software based on the basic data of multi-source radar was designed, which was the central part of the data pre-processing. In this paper, software requirements analysis, overall architecture design, workflow design and detailed function design were carried out. Detailed functions included radar basic data loading, data analysis and recombination, data inspection requirement analysis, preprocessing model loading and data inspection. Data checks included normalization, completeness, correctness, consistency, current status, and satisfaction. Eventually, the software can present the results of the data inspection in the form of visualizations or reports. This software provided more general radar data review and filtering services. © 2025 SPIE.

关键词： Inspection Data modeling Radar Design Databases Data processing Statistical analysis Data acquisition Data storage Reliability

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Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution

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IEEE Access 2025年 13卷 73124-73134页

作者： Yang, Xiang Fang, Chen Xie, Xiaolan Dong, Minggang Guilin University of Technology College of Computer Science and Engineering Guangxi Guilin China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin China Guilin University of Technology College of Physics and Electronic Information Engineering Guangxi Guilin China

Super-resolution (SR) imaging involves converting low-resolution images into corresponding high-resolution data in specific scenarios. Recent advances in intelligent image reconstruction algorithms have been accompanied by impressive visual outcomes in SR generative adversarial networks (GANs). However, existing research has methodological limitations owing to the constraints posed by the model architecture and loss guidance used for optimization. Consequently, unnatural artifacts often appear in the complex backgrounds of images, leading to unappealing visual results. To address these problems, we propose an enhanced U-Net, known as the handwritten W-Net (HW-Net) architecture, as well as a loss function that uses high-frequency components for guidance. The former improves the best approximate representation of image regions, whereas the latter guides the generation of SR images with more detailed features. We experimentally implemented a biased strategy called parameter transfer, which is suitable for hybrid parameter models. This approach facilitates the blending of weight parameters between similar but different models, significantly improving the peak signal-to-noise ratio and structural similarity index measure metrics without sacrificing the learned perceptual image patch similarity. Across nine SR benchmark datasets, the proposed combinations, with and without parameter transfer, demonstrated excellent performance across multiple evaluation metrics, surpassing the state-of-the-art SRGAN. The code is available at https://***/DaHuaSen/H2SR. © 2013 IEEE.

关键词： Image enhancement

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Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 163-179页

作者： Yang, Gaoxiang Li, Xingrong Xiong, Yuan He, Meng Zhang, Lei Jiang, Chongya Yao, Xia Zhu, Yan Cao, Weixing Cheng, Tao National Engineering and Technology Center for Information Agriculture MOE Engineering Research Center of Smart Agriculture MARA Key Laboratory of Crop System Analysis and Decision Making Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural University One Weigang Jiangsu Nanjing210095 China College of Agronomy Henan Agricultural University Henan Zhengzhou450046 China

Spatially explicit information on crop distribution over large areas and long timespans is essential for optimizing agricultural spatial allocation and promoting food security. Despite the emergence of numerous remote sensing-based approaches for crop type mapping in recent years, the generation of long-term and high-quality crop type maps still remains challenging due to the poor spatiotemporal scalability of existing algorithms in the absence of ground labels or satellite imagery sources. In this study, we proposed a knowledge-guided machine learning (KGML) approach for extracting year-to-year training data and producing long-term winter wheat products in China by integrating multi-source remote sensing and environmental datasets. Based on the crop development patterns, critical phenological domains were first retrieved by spectral or polarization variation characteristics, and then the corresponding spectral signatures were combined to strengthen the differentiation between crop types. Consequently, annual training samples were extracted and refined automatically from the candidate crop pixels and employed to train the machine learning classifier with harmonic features, thus producing winter wheat maps over China year by year. Based on the long-term dataset, the spatiotemporal dynamics of winter wheat planting areas at the national scale from 2000 to 2023 were revealed by spatial and trend analyses, and the driving forces were further quantified. With the KGML, the first long-term winter wheat products at 30-m spatial resolution were produced over China (ChinaWheat30L). Independent validation suggested that the overall accuracy and F1-score of ChinaWheat30L were 0.929 and 0.906, respectively, with weak variations across years. The mapped areas of winter wheat aligned well with agricultural statistics at provincial and municipal levels (R2 = 0.93 and 0.84). Furthermore, the ChinaWheat30L exhibited minimal classification bias across various landscapes and demonstra

关键词： Photomapping

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Multimodal Physiological Signal Analysis Using Attention-Based Feature and Model Fusion 17th

Multimodal Physiological Signal Analysis Using Attention-Bas...

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17th International Conference on Brain Informatics, BI 2024

作者： Liu, Wei Jia, Kebin Sun, Zhonghua Feng, Jinchao Zhao, Xiaohu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819632930

Disease analysis using multimodal physiological signals is currently a hot research area. Aiming at the problem that current multimodal feature fusion approaches neglect the correlation between different modalities, this paper proposes a hybrid multimodal fusion model based on the attention mechanism for depression recognition. The model selects two modalities, EEG and speech signals, for feature fusion. In the feature extraction stage of EEG signals, the correlation between features is analyzed using Spearman’s rank correlation coefficient to achieve dimensionality reduction. In the feature-level modal fusion stage, the attention mechanism is applied to automatically learn the contribution of different features and the complementarity between different modalities. In the model fusion layer, a multi-layer long and short-term memory network (ML-LSTM) is adopted, which makes full use of the complementarity between the features of EEG signals and audio signals. Finally, the outputs of feature-level fusion and model-level fusion are weighted and combined to obtain the final prediction results. Experimental results on the MODMA dataset show that the accuracy of the proposed multimodal fusion model for depression recognition reached 88.54%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Photonics-assisted intensity-modulation and direct-detection MIMO millimeter-wave system in the D-band

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Chinese Optics Letters 2025年第1期23卷 7-12页

作者： Jingwen Tan Peng Tian Jianjun Yu Xianming Zhao Xinglin Dou Long Zhang Xiongwei Yang YangHan Yi Wei Bing Zhang Qinyi Zhang Luhan Jiang Weiping Li Rahim Uddin Kaihui Wang Wen Zhou State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves(MoE)School of Information Science and TechnologyFudan UniversityShanghai 200433China Beijing Hongshan Information Technology Research Institute Co. Ltd.Beijing 100176China

We experimentally demonstrate the transmission of discrete multi-tone(DMT)millimeter-wave(mm-wave)signals over a 1.2-km distance at the D-band(110–170 GHz)in a cost-effective intensity-modulation and direct-detection(IM/DD)communication *** the experiment,we successfully achieve the transmission of DMT-QPSK and DMT-16QAM mm-wave signals over multiple-input multiple-output(MIMO)*** the 1.2-km free-space transmission,the bit error rate(BER)of the DMT-16QAM is below the 25%soft decision forward error correction(25%SD-FEC)threshold of 4×10-2,with a maximum net bit rate of 24.62 Gbit/s achieved in this system.

关键词： D-band discrete multi-tone intensity modulation and direct detection long-distance wireless transmission photonics-assisted technique

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A Valuable and Low-Budget Process Scheme of Equivalized 1 nm technology Node Based on 2D Materials

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Nano-Micro Letters 2025年第8期17卷 294-305页

作者： Yang Shen Zhejia Zhang Zhujun Yao Mengge Jin Jintian Gao Yuhan Zhao Wenzhong Bao Yabin Sun He Tian College of Integrated Circuit Science and Engineering Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241People’s Republic of China Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing 100084People’s Republic of China State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China Shaoxin Laboratory Shaoxing 312000People’s Republic of China

Emerging two-dimensional(2D)semiconductors are among the most promising materials for ultra-scaled transistors due to their intrinsic atomic-level *** the stacking process advances,the complexity and cost of nanosheet field-effect transistors(NSFETs)and complementary FET(CFET)continue to *** 1 nm technology node is going to be based on Si-CFET process according to international roadmap for devices and systems(IRDS)(2022,https://***/),but not publicly confirmed,indicating that more possibilities still *** miniaturization advantage of 2D semiconductors motivates us to explore their potential for reducing process costs while matching the performance of next-generation nodes in terms of area,power consumption and *** this study,a comprehensive framework is built.A set of MoS2 NSFETs were designed and fabricated to extract the key parameters and *** then for benchmarking,the sizes of 2D-NSFET are scaled to a extent that both of the Si-CFET and 2D-NSFET have the same average device *** these conditions,the frequency of ultra-scaled 2D-NSFET is found to improve by 36%at a fixed power *** work verifies the feasibility of replacing silicon-based CFETs of 1 nm node with 2D-NSFETs and proposes a 2D technology solution for 1 nm nodes,i.e.,“2D eq 1 nm”*** the same time,thanks to the lower characteristic length of 2D semiconductors,the miniaturized 2D-NSFET achieves a 28%frequency increase at a fixed power ***,developing a standard cell library,these devices obtain a similar trend in 16-bit RISC-V *** work quantifies and highlights the advantages of 2D semiconductors in advanced nodes,offering new possibilities for the application of 2D semiconductors in high-speed and low-power integrated circuits.

关键词： Two-dimensional semiconductors 1 nm technology node Nanosheet field-effect transistors Complementary field-effect transistors Horizontal scaling

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Listen with Seeing: Cross-modal Contrastive Learning for Audio-Visual Event Localization

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IEEE Transactions on Multimedia 2025年 27卷 2650-2665页

作者： Sun, Chao Chen, Min Zhu, Chuanbo Zhang, Sheng Lu, Ping Chen, Jincai Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Wuhan430074 China South China University of Technology School of Computer Science and Engineering Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

In real-world physiological and psychological scenarios, there often exists a robust complementary correlation between audio and visual signals. Audio-Visual Event Localization (AVEL) aims to identify segments with Audio-Visual Events (AVEs) that contain both audio and visual tracks in unconstrained videos. Prior studies have predominantly focused on audio-visual cross-modal fusion methods, overlooking the fine-grained exploration of the cross-modal information fusion mechanism. Moreover, due to the inherent heterogeneity of multi-modal data, inevitable new noise is introduced during the audio-visual fusion process. To address these challenges, we propose a novel Cross-modal Contrastive Learning Network (CCLN) for AVEL, comprising a backbone network and a branch network. In the backbone network, drawing inspiration from physiological theories of sensory integration, we elucidate the process of audio-visual information fusion, interaction, and integration from an information-flow perspective. Notably, the Self-constrained Bi-modal Interaction (SBI) module is a bi-modal attention structure integrated with audio-visual fusion information, and through gated processing of the audio-visual correlation matrix, it effectively captures inter-modal correlation. The Foreground Event Enhancement (FEE) module emphasizes the significance of event-level boundaries by elongating the distance between scene events during training through adaptive weights. Furthermore, we introduce weak video-level labels to constrain the cross-modal semantic alignment of audio-visual events and design a weakly supervised cross-modal contrastive learning loss (WCCL Loss) function, which enhances the quality of fusion representation in the dual-branch contrastive learning framework. Extensive experiments conducted on the AVE dataset for both fully supervised and weakly supervised event localization, as well as Cross-Modal Localization (CML) tasks, demonstrate the superior performance of our model compa

关键词： information fusion

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Comparison of gap-filling methods for generating landsat-like land surface temperatures under all-weather conditions

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 113-130页

作者： Guo, Jiali Quan, Jinling Zhan, Wenfeng Wen, Zhongguan State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100190 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Jiangsu Nanjing210046 China College of Environmental Sciences and Engineering Peking University Beijing100871 China

Thermal infrared remote sensors provide cost-effective and widespread land surface temperatures (LSTs) but often with spatiotemporal gaps due to discrete sampling and synoptic disturbance, greatly limiting their reliability and application. Current gap-filling methods have been primarily developed and validated for medium- to low-resolution LSTs;however, with rising demand for spatiotemporally continuous, high-resolution (tens of meters like Landsat) LSTs across disciplines, there is an urgent need to assess these methods’ applicability and uncertainty at higher spatial resolutions under a unified framework. In this study, we apply eight typical and hybrid methods, including temporal interpolation, spatiotemporal interpolation, weight-based fusion, learning-based fusion, and four standard annual temperature cycle (ATC)-based hybrid reconstructions, to fill gaps in irregularly spaced Landsat series over Weishan, Huairou, and Yulin, China. These sites represent cropland in a sub-humid plain, forest in a sub-humid mountain region, and grassland in the semi-arid Loess Plateau. We evaluate their performance in terms of spatiotemporal pattern, statistical accuracy, sensitivity to input data quality and distribution, and adaptability to different synoptic and surface conditions based on cloudy Landsat data and in-situ measurements. Results reveal that the enhanced ATC (EATC) method is optimal among these methods, capturing all-weather spatiotemporal dynamics at the Landsat scale with superior accuracy and robustness under various input, cloud, and ground conditions. In addition, the ATC-based hybrid methods do not necessarily improve the statistical accuracy over their respective typical ones. This comprehensive evaluation provides valuable insights into the selection of appropriate gap-filling methods for generating Landsat-like LSTs under all-weather conditions and highlights the need for further advancements especially in addressing abrupt changes in land cover types an

关键词： Surface stress

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Temperature Self-Reference Gas Pressure Sensor Based on Intracavity Sensing of Fiber Ring Laser

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Journal of Lightwave technology 2025年第9期43卷 4259-4265页

作者： Shi, Boya Huo, Xinjuan Wang, Shaona Niu, Pingjuan Tang, Longhuang Li, Hongmei Yao, Jianquan Shi, Jia Tiangong University Tianjin Key Laboratory of Optoelectronic Detection Technology and System School of Electronics and Information Engineering Tianjin300387 China China Academy of Engineering Physics Institute of Fluid Physics Mianyang621900 China Aero Engine Corporation of China Commercial Aircraft Engine Company Limited Shanghai200241 China Ministry of Education Tianjin University Key laboratory of Opto-Electronic Information Technology Tianjin300072 China

The temperature cross-sensitivity is always challenging for the design and application of fiber-optic gas pressure sensors. Here, a temperature self-reference gas pressure sensor based on intracavity sensing of fiber ring laser has been demonstrated. The Fabry-Perot interferometer and fiber Bragg grating are cascaded as reflection sensing head and coupled into a fiber ring laser. The Fabry-Perot interferometer shows high sensitivities of gas pressure and temperature because of its specific fiber micro cavity structure. The fiber Bragg grating has narrow-band filtering characteristics at a certain wavelength, which ensures the desired accuracy for intracavity sensing, and has good temperature response characteristics. In the temperature self-reference sensing system, the output wavelength of the fiber ring laser is only modulated by the sensor temperature, while the output intensity is simultaneously modulated by the sensor temperature and gas pressure. The sensing model of the temperature self-reference gas pressure sensor has been presented. The experiment verifies that the scheme can measure gas pressure and temperature simultaneously. The stability and measurement errors have been analyzed. The proposed sensing system provides a potential solution for solving temperature cross-sensitivity in gas pressure measurement and can be extensible to similar fiber-optic sensors. © 1983-2012 IEEE.

关键词： Fabry-Perot interferometers

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