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arXiv 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Australia The Information Technology Department Hunan Police Academy Changsha410100 China Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai200240 China DSLAB School of Information Science & Engineering Lanzhou University 730000 China Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, such methods primarily emphasize broadening the receptive field in textureless areas, while neglecting deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions. To address this, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate segmentation image with monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS and Strecha datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Restoration

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Phenotype Segmentation Method Based on Spectral Reconstruction for Uav Field Vegetation

SSRN

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SSRN 2023年

作者： Pei, Zhixun Wu, Xingcai Chen, Panfeng Wang, Qi Gao, Zhenran Wu, Xue Hao, Gefei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Province Guiyang550025 China National Key Laboratory of Green Pesticide Guizhou University Guizhou Province Guiyang550025 China New Rural Development Institute Guizhou University Guizhou Province Guiyang550025 China

Field phenotyping can show plant growth in real-time, and segmented vegetation data can facilitate the visualization and analysis of vegetation information by reducing the influence of background noise. Field phenotype segmentation is considered a difficult task due to the extremely complex environment. Currently, spectral-based methods with deep learning for field vegetation segmentation are developing to overcome complex environments and poor generalization, but there are still two limitations. One is that the equipment for real-field data collection is extremely expensive. The other is that the datasets in the field are scarce, which is time-consuming and laborious for data annotation. To solve these problems, this study aims to propose a weakly supervised field vegetation segmentation method by introducing multispectral images as a priori information and referring to the theory of vegetation index (VI). In detail, we first adopt the visible light images to reconstruct corresponding multispectral images and then perform feature fusion based on the reconstructed image. Furthermore, a VI-based threshold segmentation is achieved on the reconstructed multispectral images to obtain the field vegetation segmentation map. In addition, we provide an unmanned aerial vehicle (UAV) RGB-multispectral image dataset, including 2358 pairs of RGB-multispectral images. We introduce a variety of spectral reconstruction (SR) methods as the baseline model and train different SR methods on our dataset, which are applied to different crop vegetations and environments, showing satisfactory experimental results. © 2023, The Authors. All rights reserved.

关键词： Vegetation

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Pre-ictal causal connectivity reveals the epileptic network characteristics for deep brain stimulation

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Neuroscience 2025年 579卷 329-343页

作者： Yifan Wang Tao Yu Yulan Ma Weigang Cui Xueyuan Wang Liankun Ren Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Beijing Institute of Functional Neurosurgery Department of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing 100053 China School of Engineering Medicine Beihang University Beijing 100191 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

Deep brain stimulation of anterior nucleus of the thalamus (ANT-DBS) is an effective clinical treatment for drug-resistant focal epilepsy. However, the complex epileptic network characteristics underlying ANT-stimulation effectiveness remain unknown, owing to currently unclear connectivity between ANT and seizure-related cortex in pre-ictal periods. Here, we developed a novel individualized pre-ictal ANT-cortical tripartite connectivity network (PANT-CTCNet), aiming to reveal epileptic network characteristics using intracranial stereo-electroencephalography (sEEG) recordings in five patients with focal epilepsy for 90 trials. Each trial represented a pre-ictal or post-stimulus sEEG duration, which was used to construct the epileptic network. By employing conditional Granger causality, we constructed individualized ANT-cortical connectivity networks and found a common epileptic network centred on ANT closely connected with seizure-related cortex in pre-ictal periods. After ANT stimulation for clinical validation, strengthened pre-ictal connectivity between ANT and epileptogenic zones led to significant decline in the causal intensity of epileptic networks. The PANT-CTCNet can give a quantitative reference for clinical preoperative evaluation of patient suitability for ANT-DBS treatment. These findings regarding epileptic network characteristics provide theoretical basis in the selection of optimal surgical candidates for personalized ANT-DBS.

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Modeling and Performance Analysis for Semantic Communications Based on Empirical Results

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IEEE Transactions on Communications 2025年

作者： Ma, Shuai Shen, Bin Zhang, Chuanhui Wu, Youlong Li, Hang Li, Shiyin Shi, Guangming Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi’an710071 China Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, we propose an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, which can be applied for both image reconstruction tasks and inference tasks. Specifically, for image reconstruction tasks, the proposed ABG formula can well fit the commonly used DL networks, such as SCUNet, and Vision Transformer, for semantic encoding with the multi scale-structural similarity index measure (MS-SSIM) measurement. Furthermore, we find that the upper bound of the MS-SSIM depends on the number of quantized output bits of semantic encoders, and we also propose a closed-form expression to fit the relationship between the MS-SSIM and quantized output bits. To the best of our knowledge, this is the first theoretical expression between end-to-end performance metrics and SNR for semantic communications. Based on the proposed ABG formula, we investigate an adaptive power control scheme for semantic communications over random fading channels, which can effectively guarantee quality of service (QoS) for semantic communications, and then design the optimal power allocation scheme to maximize the energy efficiency of the semantic communication system. Furthermore, by exploiting the bisection algorithm, we develop the power allocation scheme to maximize the minimum QoS of multiple users for OFDMA downlink semantic communication Extensive simulations verify the effectiveness and superiority of the proposed ABG formula and power allocation schemes. © 1972-2012 IEEE.

关键词： Network coding

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On layer normalization in the transformer architecture 37

On layer normalization in the transformer architecture

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37th International Conference on Machine Learning, ICML 2020

作者： Xiong, Ruibin Yang, Yunchang He, Di Zheng, Kai Zheng, Shuxin Xing, Chen Zhang, Huishuai Lan, Yanyan Wang, Liwei Liu, Tie-Yan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technolog Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Data Science Peking University Beijing Institute of Big Data Research China Key Laboratory of Machine Perception MOE School of EECS Peking University China Microsoft Research College of Computer Science Nankai University China

ISBN: (纸本)9781713821120

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications. Copyright 2020 by the author(s).

关键词： Mean field theory

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Deformation Monitoring of Super High-Rise Buildings Based on the Tomosar Double-Tier Permanent Scatterer Network

Deformation Monitoring of Super High-Rise Buildings Based on...

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SAR in big data Era: Models, Methods and Applications (bigSARdata)

作者： Haoxuan Duan Hong Zhang Yuzhou Liu Zihuan Guo Chao Wang Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China International Research Center of Big Data for Sustainable Development Goals Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China Shenzhen Technology Institute of Urban Public Safety Shenzhen China

Urban building safety directly affects people's life and property and sustainable development, especially in the field of super high-rise buildings. This paper carries out the inversion of physical parameters such as height, linear deformation velocity and thermal amplitude of urban super high-rise buildings using a 5D-TomoSAR model. Taking Shenzhen City as the study area, the applicability and feasibility of the proposed method in the deformation monitoring of super high-rise buildings are verified through experiments on a number of super high-rise buildings. This research is of great significance to urban building safety and management, and helps to improve the level of urban safety governance and the construction of resilient cities.

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High power non-electrolytic capacitor LED driver with three ports based on energy distribution 5

High power non-electrolytic capacitor LED driver with three ...

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2022 5th International Conference on Power Electronics and Control Engineering, ICPECE 2022

作者： Li, Zejun Zhang, Junzhao Xu, Bingshe Han, Xiaoqing Li, Fan Lu, Xiaofeng College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Lighting Joint Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Culture and Tourism Industry Research Institute Shanxi Taiyuan030024 China

To eliminate the influence of electrolytic capacitors on the driving life of high-power LED, a three-port high-power LED without electrolytic capacitor driving power supply based on energy distribution is proposed. The energy difference between the input terminal and the output terminal is balanced by the auxiliary DC-DC circuit to reduce the ripple of the output current. The working principle of the topology and switching mode is analyzed in detail. According to the proposed circuit topology, the mechanism of the output current ripple of the low-frequency circuit is analyzed, and a low-frequency ripple control strategy suitable for the circuit topology is proposed. The specific implementation scheme and the key parameters of the circuit design are given, and the model is verified, which proves that the proposed model is feasible. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes

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Multi-task representation learning with multi-view graph convolutional networks

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： data mining

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Application of Improved YOLOV4 in Intelligent Driving Scenarios

Application of Improved YOLOV4 in Intelligent Driving Scenar...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Zicheng Zhang Quan Liang Zhihui Feng Wei Ji Hansong Wang Jinjing Hu School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China

ISBN: (纸本)9781665426565

With the development of unmanned technology, the technical innovation of invehicle vision detection system is also getting faster and faster, while the improvement of algorithm accuracy often brings an increase in the number of parameters and poor real-time performance. In his paper, the optimization of the algorithm structure of YoLoV4 target detection is achieved by using MobileNet-v3 instead of the CspDarkNet53 master Network, which has the inverse residual structure of linear bottleneck, while the lightweight attention mechanism is added to the feature extraction process, and the learning degree of feature channels is enhanced; due to the long computation time of sigmoid, it also uses ReLU6(x+3)/6 is used to approximate the original activation function due to the long computation time of sigmoid; the system parameters are reduced by constructing a depth-separable convolution instead of the normal convolution in PaNet. Meanwhile, this paper improves the original upsampling method by using dual cubic interpolation, which makes the image more smooth, less image loss and more accurate feature extraction during he upsampling method. The map% is improved from 79.1% to 81.2% on the voc dataset, reaching 58.14 FPS.

关键词： Technological innovation Interpolation Convolution Machine vision Conferences Object detection Feature extraction

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Improved YOLOv8 Algorithm for Small Target Detection in Drone Aerial Photography

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Computer Engineering and Applications 2025年第11期61卷 119-131页

作者： Xu, Jingke Suo, Xianglong Zhou, Lei School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang110168 China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang110168 China

Detecting small and densely distributed targets in UAV aerial images poses challenges such as complex backgrounds, imbalanced sample numbers, and limited computational power. To address these issues, an improved YOLOv8 algorithm, MFF-YOLOv8 (multi-feature fusion YOLOv8), is proposed. This algorithm integrates deformable convolution DCNv3 (deformable convolution v3) into the Bottleneck module of the C2f module to enhance feature extraction. A new MFFPN (multi-feature fusion pyramid network) is designed to add more feature fusion routes, retain low-level feature map details, and improve small target detection. Additionally, a P2 small target detection layer is added and the P5 detection layer is optimized to enhance accuracy and reduce parameters. The introduction of the Dyhead (dynamic head) further improves detection precision of the model. In the experiment on the Visdrone2019 dataset, MFF-YOLOv8s has a 10.2 percentage points and 7.1 percentage points increase in mAP50 and mAP50:95 respectively compared to YOLOv8s, with a 77.04% reduction in parameters. The model's detection accuracy surpasses that of YOLOv11, meeting the precision and lightweight requirements for UAV platforms. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.;science Press. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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