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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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Preface to Special Issue on Optoelectronic Neuromorphic Devices

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Journal of Semiconductors 2025年第2期46卷 2-4页

作者： Zhenyi Ni Zhongqiang Wang Jia Huang Xiaodong Pi State key Laboratory of Silicon and Advanced Semiconductor Materials&School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China Shangyu Institute of Semiconductor Materials Shaoxing 312300China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China School of Materials Science and Engineering&National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji UniversityShanghai 201804China Institute of Advanced Semiconductors&Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJ-UHangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou 311200China

The rapid advancement of artificial intelligence(AI),recognized with Nobel Prizes in both Physics and Chemistry in 2024,has been revolutionizing countless aspects of modern life,driving innovations across diverse fields and reshaping how people interact with *** its great advances,the computing of AI systems is now approaching the limit of traditional computing based on the von Neumann architecture,which is characterized by separated memory and process units-a challenge known as the"von Neumann bottleneck".

关键词： approaching separated Neumann

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Characteristics of catalytic destruction of dichloromethane and ethyl acetate mixture over H_(x)PO_(4)-RuO_(x)/CeO_(2) catalyst

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Journal of Environmental Sciences 2025年第2期148卷 336-349页

作者： Jiawen Chen Qingjie Meng Feng Bi Jingkun Chen Xiaole Weng Zhongbiao Wu Key Laboratory of Environment Remediation and Ecological Health Ministry of EducationCollege of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China School of Civil&Environmental Engineering and Geography Science Ningbo UniversityNingbo 315211China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200China Zhejiang Provincial Engineering Research Centre of Industrial Boiler&Furnace Flue Gas Pollution Control Hangzhou 310058China

Catalytic destruction is an ascendant technology for the abatement of volatile organic compounds(VOCs)originating fromsolvent-based industrial *** varied composition tends to influence each VOC’s catalytic behavior in the reaction *** investigated the catalytic destruction of multi-component VOCs including dichloromethane(DCM)and ethyl acetate(EA),as representatives from pharmaceutical waste gases,over co-supported HxPO_(4)-RuOx/CeO_(2) catalyst.A mutual inhibitory effect relating to concentrations because of competitive adsorption was verified in the binary VOCs oxidation and EA posed a more negative effect on DCM oxidation owing to EA’s superior adsorption *** adsorption of EA on acidic sites(HxPO_(4)/CeO_(2))promoted DCM activation on basic sites(O^(2−))and the dominating EA oxidation blocked DCM’s access to oxidation centers(RuOx/CeO_(2)),resulting in boosted monochloromethane yield and increased chlorine deposition for DCM *** impaired redox ability of Ru species owing to chlorine deposition in turn jeopardized deep oxidation of EA and its by-products,leading to increased gaseous by-products such as acetic acid originating fromEA ***,DCM at low concentration slightly promoted EA conversion at low temperatures with or without water,consistent with the enhanced EA adsorption in co-adsorption *** was mainly due to that DCM impeded the shielding effect of hydrolysate deposition from rapid EA hydrolysis depending on the decreased ***,water benefited EA hydrolysis but decreased CO_(2) selectivity while the generated water derived from EA was likely to affect DCM *** work may provide theoretical guidance for the promotion of applied catalysts toward industrial applications.

关键词： Catalytic destruction Multi-component VOCs Mixture effect Interaction mechanism

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 IEEE.

关键词： Contrastive learning Graph representation Self-supervised learning

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Optical fiber integrated photoelectrochemical sensing patch: towards non-invasive blood glucose detection

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IEEE Sensors Journal 2025年第11期25卷 18741-18749页

作者： Wen, Xingyue Ge, Zhongxuan Ma, Minghua Yang, Xinghua Wang, Shengjia Tian, Fengjun Liu, Zhihai Zhang, Yu Teng, Pingping Gao, Shuai Zhu, Zheng Zhang, Yang Jones, Adam Zhang, Bo Sivanathan, Sivagunalan Li, Kang Harbin Engineering University Key Laboratory of In-Fiber Integrated Optics Ministry of Education College of Science Harbin150001 China Department of Anesthesiology First Hospital of Harbin Harbin150000 China University of South Wales Faculty of Computing Engineering & Science CF37 1DL United Kingdom Henan Academy of Special Optics Ltd Xinxiang453000 China

In recent years, non-invasive blood glucose detection has received increasing attention. Among them, sweat glucose detection is expected to become an alternative to blood glucose detection due to advantages such as its samples easily to obtain. This study was proposed and preliminarily verified a prototype of photoelectrochemical sensing patch based on an optical fiber modified by ITO/ZnO/polydopamine/glucose oxidase and printed electrode, enabling close contact with human skin. In this innovative design, the optical fiber to serve both as a light transmission medium and a working electrode, while the printed electrode functions as the counter electrode and reference electrode. The electrode materials were characterized by SEM, EDS, XRD and XPS, and the reaction kinetics of the optical fiber electrode studied through SWV, EIS, and Mott-Schottky analysis. A linear relationship was established between the photocurrent and the logarithm of glucose concentration in the concentration range from 10 μM to 1 mM, with a detection limit of 3.5 μM, and a response time of less than 1 s. In addition, the specificity and stability of the sensing patch were evaluated, and an attempt was made to detect real sweat glucose in situ. This work presents a novel prototype for the development of wearable sensors, with promising applications in biochemical analysis and health monitoring. © 2001-2012 IEEE.

关键词： Glucose sensors

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design

arXiv

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arXiv 2025年

作者： Fan, Xilai Jiang, Bo Liu, Ya-Feng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China

In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an ϵ-stationary point within O(ϵ−2) iterations for nonconvex objectives and an ϵ-optimal solution within O(ϵ−1) iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP. © 2025, CC BY.

关键词： Beamforming

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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