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UAV-assisted full-duplex ISAC: Joint communication scheduling, beamforming, and trajectory optimization

Digital Communications and Networks 2025年

作者： Yuanshuo Gang Yuexia Zhang Xinyi Wang Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing 102206 China

This paper proposes the Unmanned Aerial Vehicle (UAV)-assisted Full-Duplex (FD) Integrated Sensing And Communication (ISAC) system. In this system, the UAV integrates sensing and communication functions, capable of receiving transmission signals from Uplink (UL) users and echo signal from target, while communicating with Downlink (DL) users and simultaneously detecting target. With the objective of maximizing the Average Sum Rate (ASR) for both UL and DL users, a composite non-convex optimization problem is established, which is decomposed into sub-problems of communication scheduling optimization, transceiver beamforming design, and UAV trajectory optimization. An alternating iterative algorithm is proposed, employing relaxation optimization, extremum traversal search, augmented weighted minimum mean square error, and successive convex approximation methods to solve the aforementioned sub-problems. Simulation results demonstrate that, compared to the traditional UAV-assisted HD ISAC scheme, the proposed FD ISAC scheme effectively improves the ASR.

关键词： Integrated sensing and communication Unmanned aerial vehicle Full-duplex communication Beamforming Trajectory optimization

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Based on an improved Zero-DCE++ low-light enhanced object detection algorithm

Based on an improved Zero-DCE++ low-light enhanced object de...

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Bai, Xuecheng Gao, Hongwei Zhao, Zhanpeng Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China Xi'an Modern Control Technology Research Institute Xi'an710100 China

ISBN: (纸本)9781510689176

Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information loss. This study presents an enhanced low-light image detection model based on an improved Zero-DCE++ algorithm, integrated with YOLOv8 for high-speed, accurate detection. key improvements include the CBAM attention mechanism to enhance feature extraction in low-light areas, DCNv2 for capturing complex object details, and a Local Region Brightness Curve Enhancement Module to prevent overexposure in well-lit areas. The proposed model was tested on the Night Drone dataset, where it achieved superior performance in image quality and detection accuracy compared to state-of-the-art methods. This approach demonstrates significant potential for applications requiring reliable object detection in challenging low-light conditions. © 2025 SPIE.

关键词： Semantic Segmentation

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Development of a spatial accuracy compensation method based on multi-beam laser interferometer

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Scientific Reports 2025年第1期15卷 1-12页

作者： Lei, Lihua Sun, Changjian Xie, Zhangning Liang, Lijie Fu, Yunxia Zhang, Bo Shanghai Institute of Measurement and Testing Technology Shanghai 201203 China Shanghai Key Laboratory of Online Test and Control Technology Shanghai 201203 China Renishaw (Shanghai) Trading Co Ltd Shanghai 200436 China

A mathematical model has been established for the error associated with the degree of freedom for each motion axis, with a correction applied based on the 12-line method in three-dimensional space. A multi-beam laser interferometer is proposed, which comprises four lasers, where different laser combinations can be used to measure different parameters, ultimately achieving simultaneous measurement of errors for the six degrees of freedom. This has informed the development of a spatial error measurement and accuracy compensation system. The error in the X/Y/Z axis data of the computer numerical control (CNC) machine tool is determined using the multi-beam laser interferometer. Spatial accuracy compensation is achieved by applying a software algorithm, and the positioning accuracy is obtained by measuring the same axis at different spatial coordinate positions to verify correction of the angular error. The experimental results have confirmed that after spatial error compensation, linear positioning accuracy, horizontal and vertical straightness errors were improved by nearly 96%, 89% and 55% respectively;the average and maximum deviations of positioning accuracy were improved by nearly 88% and 91% respectively. © The Author(s) 2025.

关键词： Accuracy Error compensation Laser interferometer Spatial positioning

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FDCA-DSTGCN: A Wind Farm Cluster Power Day-ahead Prediction Model Based on Frequency Domain Information Gain and Dynamic Trend Sensing

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IEEE Transactions on Sustainable Energy 2025年

作者： Yang, Mao Niu, Jiajun Wang, Bo Huang, Dawei Su, Xin Ma, Chenglian Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing100912 China

Accurate wind farm cluster power prediction (WFCPP) is of vital significance for new power systems with large-scale wind power integration. The current WFCPP modeling method ignores the important role of wind direction and frequency domain information, resulting in insufficient use of spatial information and difficult to improve the prediction accuracy. In order to solve the above problems, a day-ahead power prediction model for wind farm cluster power is proposed based on information gain in frequency domain and dynamic trend sensing. Firstly, a cluster division method based on graph theory and progressive fusion of multiple information is proposed, and a virtual information node is set up. Secondly, a method to identify prevailing wind direction under time window is proposed for subsets, and a dynamic weighted directed graph structure is designed based on prevailing wind direction and wind speed. Thirdly, a dynamic spatio-temporal graph convolutional network model with frequency domain gain channel attention mechanism (FDCA-DSTGCN) is proposed to integrate the above information and complete the prediction. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia of China. Compared with the existing and traditional prediction methods, the normalized root mean square error of the proposed method is reduced by 1.13% -2.15%, which verifies the effectiveness of the proposed method. © 2010-2012 IEEE.

关键词： Mean square error

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Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Shifting Spotlight for Co-supervision: A Simple yet Efficien...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yang Hu Jinxia Zhang Kaihua Zhang Yin Yuan Jiale Huang Zechao Zhan Xin Wang Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China Advanced Ocean Institute of Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS 3 Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS 3 Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS 3 Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness.

关键词： Computer vision Image edge detection Object detection Signal processing Feature extraction Acoustics Decoding Computational efficiency Speech processing Light sources

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Strategies for Enhancing Direct Interspecies Electron Transfer Induced by Granular Activated Carbon During Anaerobic Digestion of Fat, Oil, and Grease

SSRN

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SSRN 2025年

作者： He, Xia Chen, Jieying Xu, Weijia Lu, Yanqin College of Environmental Science and Engineering Guilin University of Technology Guangxi541006 China Modern Industry College of Ecology and Environmental Protection Guilin University of Technology Guilin541006 China Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control Guilin University of Technology Guilin541006 China

Adsorption of long chain fatty acids (LCFAs) on granular activated carbon (GAC) limits the establishment of direct interspecies electron transfer (DIET) via GAC during anaerobic digestion of fat, oil, and grease (FOG). Exploration of strategies for enhancing GAC-induced DIET for LCFAs degradation is vital for the application of GAC in FOG digestion. In this study, the influence of different strategies, combined with pre-mixing GAC with inoculum, inoculum acclimation, and inoculum size differentiation, on the enhancement of GAC-induced DIET was investigated. The results show that pre-mixing GAC with the large size acclimatized inoculum was the optimal strategy to reduce LCFAs adsorption on GAC and enhance GAC-induced DIET for LCFAs degradation, leading to 63.4% increment of methane production compared to the control. Using the optimal strategy, Exilispira, Geobacter, Methanosaeta, Methanolinea, and RBG-16-49-21 were highly enriched and cooperatively interacted on GAC, contributing to the enhanced acetate/propionate degradation and nitrogen conversion to accelerate LCFAs degradation. This simultaneously reduced the inhibitory effect of LCFAs in the digester, changed the microbial composition and the network of the suspended sludge, benefiting the methane production by suspended sludge. Overall, this work provides an in-depth understanding of the enhancement of GAC-induced DIET during FOG digestion. © 2025, The Authors. All rights reserved.

关键词： Anaerobic digestion

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A Monitoring Method for the Ice Shape and the Freeze-Thaw Process of Ice Accretion on Transmission Lines Based on Circular FBG Plane Principal Strain Sensor

arXiv

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arXiv 2025年

作者： Qin, Zhuoke Jia, Bin Shen, Xiahui Zhang, Lizhen Lu, Honggang Du, Chao Cui, Liqin Zhang, Li Deng, Xiao The College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China The Scientific Research Office of Shanxi Electric Power Technical College Taiyuan030021 China The College of Physics and Optoelectronics The Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education The Shanxi Key Laboratory of Precision Measurement Physics Taiyuan University of Technology Taiyuan030024 China

As a key infrastructure for China's "West-to-East Power Transmission" project, transmission lines (TL) face the threat of ice accretion under complex microclimatic conditions. This study proposes a plane principal strain sensing method based on a fiber Bragg grating circular array, achieving synchronous monitoring of 6 strains (ranging from -2000 to 2000 μΕ) across the TL cross-section. Through finite element simulation experiments, a mapping relationship between the bending of TL and the plane principal strain has been established. After completing the sensor calibration, an experimental platform for the freeze-thaw process of ice accretion on the TL was built. The relationships between ice mass and bending strain, as well as the ice shape on the TL cross-section (C-shaped and circular ice) and plane principal strain, were studied. Furthermore, a BP neural network model was developed to determine the 4 states of the icing process (no ice/freeze/stable/thaw), achieving an accuracy of 91.23%. This study provides effective monitoring of the freeze-thaw process of ice accretion on the TL, offering important technical support for the prevention and control of ice accretion in power grid. Copyright © 2025, The Authors. All rights reserved.

关键词： Thawing

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ASTARS Aided NOMA Covert Communication Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Yue, Xinwei Song, Xinlong Zhao, Jingjing Huang, Chongwen Jin, Hongxu Xiang, Wei Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing102206 China Beijing Information Science and Technology University Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing102206 China Beihang University School of Electronic and Information Engineering Beijing100191 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Hangzhou310027 China Zunyi Normal University School of Information Engineering Zunyi563006 China La Trobe University School of Computing Engineering and Mathmatical Sciences MelbourneVIC3086 Australia

The active simultaneously transmitting and reflecting surface (STARS) has been proposed as a complement of passive STARS (PSTARS) to inhibit the double path-loss. This paper applies the active STARS (ASTARS) to aid in non-orthogonal multiple access (NOMA) covert communications over cascaded rice fading channels, where two wardens are located on the opposite sides of ASTARS to monitor the transmission information. More specifically, we derive approximate expressions for detection error probability of two wardens. The optimal detection threshold and minimum detection error probability are presented in the worst covert scenarios. The covert transmission rate expressions for a coupe of covert users are further deduced by taking into account imperfect/perfect successive interference cancellation. The experimental simulation results are presented to confirm that: 1) The detection error probability of ASTARS-NOMA networks is superior to that of ASTARS assisted-orthogonal multiple access (ASTARS-OMA) and PSTARS aided NOMA networks;2) As the number of active elements increases, the covert transmission rates of ASTARS-NOMA networks become larger relative to ASTARS-OMA;and 3) ASTARS-NOMA has ability to provide the enhanced system throughput in delay-tolerant transmission mode. © 2025 IEEE.

关键词： Fading channels

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Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces

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Computers in Biology and Medicine 2025年第Pt A期192卷 110231-110231页

作者： Li, Xiao Dong, Xianxin Wang, Jun Mao, Haodong Tu, Xikai Li, Wei He, Jiping Li, Qiang Zhang, Peng Hubei Key Laboratory of Modern Manufacturing Quantity Engineering School of Mechanical Engineering Hubei University of Technology Wuhan430068 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Science and Technology Hubei Wuhan China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs. © 2025

关键词： Adversarial machine learning

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A Short-Term Power Prediction Method for Wind Farm Cluster Based on Time Dynamic Graph Network and Improved Transformer Model

A Short-Term Power Prediction Method for Wind Farm Cluster B...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Chao Han Zihang Chen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jiangmen Power Supply Bureau of Guangdong Power Grid Co. Ltd Jiangmen China

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

In recent years, wind power has gradually replaced thermal power as the main source of new energy generation. Wind power prediction in advance can help formulate scheduling strategies and improve the stability of the power system. With the advent of the Industry 4.0 and the widespread application of artificial intelligence technology, artificial algorithms have been extensively used in the field of wind power prediction. To further explore the spatiotemporal correlation of every wind farm in the wind farm cluster, a temporal dynamic graph network is used to divide the wind farm farm into several sub-clusters. Then, the KAN-BiLSTM-Transformer algorithm is used to predict wind power of each wind sub-cluster. Finally, the wind power of each sub-cluster is added to attain the final predicted power of the wind farm cluster. After verification, the RMSE and MAE of the proposed method is 0.06849 and 0.05805 respectively, with an accuracy of 93.15%, which is superior to the compared algorithms. Proposed method can provide more accurate prediction results.

关键词： Accuracy Heuristic algorithms Clustering algorithms Wind power generation Wind farms Predictive models Prediction algorithms Transformers Spatiotemporal phenomena Wind forecasting

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