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Active control of target scattered sound fields in ocean environments using virtual sensing

Journal of the Acoustical Society of America 2025年第5期157卷 3610-3623页

作者： Mei, Manjie Zhang, Lihong An, Buchao Li, Jianlong College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Systems Engineering Research Institute Beijing100036 China Ocean College Zhejiang University Zhoushan316021 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China

Active control of scattered sound fields is essential for low-frequency acoustic stealth of underwater targets. However, the ocean waveguide environment introduces challenges, such as multipath effects and non-uniform sound speed distribution, that degrade the performance of existing active scattering control methods. This paper proposes an active scattering control method applicable to ocean waveguide environments. The method employs an improved virtual sensing technique suitable for multi-incidence scenarios to obtain free-field far-field predictions, which are then combined with ocean environmental parameters to estimate the far-field pressure distribution in the waveguide. These distributions are subsequently used to design the weight vector for the secondary source array. Additionally, the paper explores active control strategy when environmental parameters are unknown. Finally, the proposed method is validated through finite element method simulations, performing active control of the scattered field of a cylinder in both isovelocity and depth-dependent channels. The simulation results demonstrate the feasibility and effectiveness of the proposed method. © 2025 Acoustical Society of America.

关键词： Acoustic fields

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Foreground Prediction for Image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th International Conference on Graphics and Image processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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Achieving High Efficiency in Schnorr-Based Multi-Signature Applications in Blockchain

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Electronics (Switzerland) 2025年第9期14卷 1883-1883页

作者： Zhang, Peng Ge, Fa Tang, Zujie Xie, Weixin Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Multi-signature applications allow multiple signers to collaboratively generate a single signature on the same message, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The k-sum attacks are one of the major challenges in designing secure multi-signature schemes. In this work, we address k-sum attacks from a novel angle by defining a Public Third Party (PTP), which is an automatic process that can be verifiable by the public and restricts the signing phase from continuing until receiving commitments from all signers. Further, a two-round multi-signature scheme HEMS with PTP is proposed, which is secure based on the discrete logarithm assumption in the random oracle model. As each signer communicates directly with the PTP instead of other co-signers, the total amount of communication is significantly reduced. In addition, as PTP participates in the computation of the aggregation and signing algorithms, the computation cost left for each signer and verifier remains the same as the basis Schnorr signature. To the best of our knowledge, this is the high efficiency that a Schnorr-based multi-signature scheme can achieve. Further, HEMS is applied in a blockchain platform, e.g., Fabric, to improve transaction efficiency. © 2025 by the authors.

关键词： multi-signature Schnorr signature k-sum attacks Public Third Party blockchain

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RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents 31

RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ji, Changkai Zhao, Bowen Wang, Zhuoyao Wang, Yingwen Zhang, Yuejie Cheng, Ying Feng, Rui Zhang, Xiaobo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China National Children's Medical Center Children's Hospital of Fudan University Shanghai China

ISBN: (纸本)9798891761964

Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have employed Large Language Models (LLMs) to automate this process. However, they typically focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. Inspired by the human bias assessment process, we propose RoBGuard, a novel framework for enhancing LLMs to assess the risk of bias in RCTs. Specifically, RoBGuard integrates medical knowledge-enhanced question reformulation, multimodal document parsing, and multi-expert collaboration to ensure both completeness and accuracy. Additionally, to address the lack of suitable datasets, we introduce two new datasets: RoB-Item and RoB-Domain. Experimental results demonstrate RoBGuard's effectiveness on the RoB-Item dataset, outperforming existing methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Optimization algorithm for global k-means algorithm 13

Optimization algorithm for global k-means algorithm

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Sen Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes. © 2025 IEEE.

关键词： Optimization algorithms

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Enhancing 3D medical image registration with cross attention, residual skips, and cascade attention

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intelligent Data Analysis 2025年第1期29卷 186-201页

作者： Anwar, Muhammad He, Zhiquan Cao, Wenming College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

At the core of Deep Learning-based Deformable Medical Image Registration (DMIR) lies a strong foundation. Essentially, this network compares features in two images to identify their mutual correspondence, which is necessary for precise image registration. In this paper, we use three novel techniques to increase the registration process and enhance the alignment accuracy between medical images. First, we propose cross attention over multi-layers of pairs of images, allowing us to take out the correspondences between them at different levels and improve registration accuracy. Second, we introduce a skip connection with residual blocks between the encoder and decoder, helping information flow and enhancing overall performance. Third, we propose the utilization of cascade attention with residual block skip connections, which enhances information flow and empowers feature representation. Experimental results on the OASIS data set and the LPBA40 data set show the effectiveness and superiority of our proposed mechanism. These novelties contribute to the enhancement of 3D DMIR-based on unsupervised learning with potential implications in clinical practice and research. © 2024 – IOS Press. All rights reserved.

关键词： convolutional neural networks deep learning Deformable medical image registration similarity measures

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Scalable Algorithms for Forest-Based Centrality on Large Graphs 34

Scalable Algorithms for Forest-Based Centrality on Large Gra...

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34th ACM Web Conference, WWW 2025

作者： Sun, Yubo Sun, Haoxin Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai 200433 China Institute of Intelligent Complex Systems Fudan University Shanghai 200433 China

ISBN: (纸本)9798400712746

Centrality measures are essential for identifying important nodes and edges in networks. In this paper, we focus on two forest-based centrality measures on undirected graphs: forest node centrality (FNC) and forest edge centrality (FEC), which capture the influence of nodes and edges through their participation in spanning forests. Both centrality measures can be represented using entries of the forest matrix. To address the challenge of computing the two measures on large networks, we propose two scalable algorithms from different perspectives. The first algorithm IFGN combines two variance reduction techniques to approximate the entries of the forest matrix, applicable to both FNC and FEC. The second algorithm FECE incorporates a new physical interpretation of FEC, allowing for a better overall estimation. We provide error guarantees for both algorithms and demonstrate their efficiency and effectiveness through extensive experiments on various real-world networks. © 2025 Copyright held by the owner/author(s).

关键词： approximation algorithms Forest-based centrality variance reduction technique

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on pattern recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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