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Underwater target recognition based on adaptive multi-feature fusion network

Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Helicity-Controlled Spin Hall Angle in 2D Altermagnets with Rashba Spin-Orbit Coupling

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Chinese Physics Letters 2025年第1期42卷 134-140页

作者： Weiwei Chen Longhai Zeng W.Zhu Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of SciencesChina Jiliang UniversityHangzhou 310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of ScienceChina Jiliang UniversityHangzhou 310018China School of Science Westlake UniversityHangzhou 310024China

We investigated the efficiency of charge-to-spin conversion in two-dimensional Rashba altermagnets,a class of materials that combines the characteristics of both ferromagnets and *** quantum linear response theory,we quantified the longitudinal and spin Hall conductivities in this system and demonstrated a substantial enhancement in the spin Hall angle below the band crossing point through the dual effects of relativistic spin–orbit interaction and nonrelativistic altermagnetic exchange ***,the results showed that the skew scattering and intrinsic mechanisms arising from Fermi sea states are almost negligible in this system,in contrast to conventional ferromagnetic Rashba *** findings not only elucidate the spin dynamics in Rashba altermagnets but also pave the way for developing novel strategies for manipulating charge-to-spin conversion via sophisticated control of noncollinear and collinear out-of-plane spin textures.

关键词： interaction magnets Rashba

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A data representation method using distance correlation

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Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Screening and Artifact Detection of RFI in Sentinel-1A Time-Series Images Combining Change Detection Techniques With Structural Similarity Index

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10864-10881页

作者： Zhang, Zhizheng Shu, Gaofeng Huang, Yabo Wu, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China

As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. Given that RFI significantly impacts data processing and image interpretation, screening and artifact detection of RFI have become top priorities for spaceborne SAR systems, which generate massive data daily. However, current image-level methods mostly rely on the acquisition of relevant prior knowledge, which makes efficient screening of large-scale products difficult when the scenario changes. In this article, a novel screening and detection method based on change detection techniques is proposed. Due to the time-varying feature of RFI artifacts in time-series SAR images, local areas affected by RFI can be masked through change detection operator. The screening step is performed by quantifying and analyzing the feature differences between RFI artifacts and ground-truth information in local areas. Therefore, an RFI-free background is constructed based on the results, enabling effective artifacts detection of RFI-containing images. Experimental results of Sentinel-1A Level-2 products verify the effectiveness and robustness of the proposed method. © 2008-2012 IEEE.

关键词： Radio interference

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Wireless Charging Scheduling for Long-term Utility Optimization

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ACM Transactions on Sensor Networks 2025年第1期21卷 1-31页

作者： Xu, Jia Chen, Wenbin Dai, Haipeng Xu, Lijie Xiao, Fu Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inductive power transmission

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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment

Anima2: Cross-Species Animal Animation through Image-to-Vide...

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

作者： Xu, Yuanfeng Chen, Yuhao Huang, Zhongzhan He, Zijian Wang, Guangrun Lin, Liang Sun Yat-sen University China Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350368741

Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima2, a zero-shot diffusion-based video generator to address this issue, aiming to accurately ANIMAte ANIMAls while preserving the background. The key technique involves two-fold subject alignment. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. They capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing animals by introducing a scale-information remover and an adaptive rescaling module. They both enhance subject alignment for accurate cross-species animation. Additionally, we introduce two high-quality animal video datasets with diverse species to benchmark cross-species animation. Trained on these extensive datasets, our model directly generates videos with accurate movements, consistent appearances, and high-fidelity frames, eliminating the need for test-time training. Extensive experiments demonstrate our method's superiority in cross-species animation, showcasing robust adaptability and generality. © 2025 IEEE.

关键词： cross-species subject alignment video editing

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LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

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

作者： Ma, Junlong Yue, Conghan Peng, Zhengwei Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://***/M-Chase/LLDB. © 2025 IEEE.

关键词： Diffusion Bridge Diffusion Model Low-Light image Enhancement

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Robust primary quantization step estimation on resized and double JPEG compressed images

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Multimedia Tools and Applications 2025年第12期84卷 11097-11118页

作者： Zhang, Lei Chen, XuGuang Niu, YaKun Zuo, XianYu Wang, Huaqing School of Computer and Information Engineering Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng Henan Henan Kaifeng475004 China National Internet Emergency Center Henan Zhengzhou450000 China

As one of the most important forensic tasks, reconstruction of the original information in tampered images is a key step for tampering detection and localization. Currently, a number of methods have been designed to estimate the primary quantization steps of double compressed JPEG images. However, the estimation in the presence of resizing operation remains a challenge. In this paper, we propose a robust primary quantization steps estimation method on resized and double JPEG compressed images. Specifically, the distribution of Discrete Cosine Transform (DCT) coefficients is firstly analyzed on the inverse resized image. Then, a maximum likelihood function together with a filtering strategy is designed to obtain the primary quantization step on Alternating Current (AC) bands. In addition, we find the prominent peak in the Discrete Fourier Transform (DFT) spectrum of the distribution of Direct Current (DC) coefficients is nonlinearly related to the step. Based on this observation, a mapping function derived from the geometric fitting is proposed to estimate the step on DC band. Experimental results demonstrate the proposed method provides superior estimation performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image compression

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SymGaussian: Occluded Human Rendering with Multi-scale Symmetry Feature from Monocular Video

SymGaussian: Occluded Human Rendering with Multi-scale Symme...

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

作者： Jiang, Zekai Duan, Tong Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

The growing demand for high-quality 3D human rendering in real-world applications highlights significant challenges. These challenges are particularly evident in dealing with occlusion in monocular video. Previous methods often rely on controlled datasets and overlook the inherent symmetry of the human body, leading to incomplete rendering in occluded areas. To address these limitations, we propose SymGaussian, a novel Gaussian Splatting-based approach for rendering occluded human from monocular video. We introduce Multi-scale Symmetry Feature to compensate for lost information in occluded areas, along with a Projective Texture Mapping method that efficiently encodes 2D appearance while preserving 3D perception. Experiments show that SymGaussian outperforms state-of-the-art methods in rendering quality, while achieving rapid training speed and real-time rendering capability exceeding 200 FPS. © 2025 IEEE.

关键词： 3D Gaussian Splatting Multi-scale Symmetry Feature Occluded Human Rendering Texture Mapping

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An Iterated Local Search Algorithm for the Heterogeneous Fixed Multi-compartment Vehicle Routing Problem

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IAENG International Journal of Applied Mathematics 2025年第5期55卷 1084-1091页

作者： Ma, Hengrui Hou, Yan-e Xu, Haizhe Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

This paper addresses the multi-compartment vehicle routing problem with heterogeneous fleets with maximum number limitations. To solve this problem, we propose an iterated local search algorithm integrating with multiple strategies to find a solution with minimum total cost. The proposed algorithm starts by generating an initial solution using a cheapest insertion heuristic. Then, a load adjustment strategy is applied to transfer customers from larger vehicle to smaller one. Four neighborhood operators such as relocation, 2-opt, exchange and or-opt, are applied sequentially to refine the solution, allowing for vehicle type adjustments during the local search process. Additionally, a perturbation mechanism based on destroy-and-repair principle is introduced to explore a wider search space. Meanwhile, an acceptance strategy based on route load variance is designed to permit the acceptance of suboptimal solutions. Finally, the best global solution undergoes further optimization through a vehicle assignment strategy to improve vehicle allocation. Experimental results on benchmark instances demonstrate that the proposed algorithm outperforms existing methods. © (2025), (International Association of Engineers). All rights reserved.

关键词： Local search (optimization)

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