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LS-MambaNet: Integrating Large Strip Convolution and Mamba network for Remote Sensing Object Detection

Remote Sensing 2025年第10期17卷 1721-1721页

作者： Lingyu Yan Zijian He Zhiqi Zhang Guangqi Xie School of Computer Science Hubei University of Technology Wuhan 430068 China Key Laboratory of Green Intelligent Computing Network in Hubei Province Wuhan 430068 China

Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. However, in the presence of targets with high aspect ratios and significant scale variations in remotely sensed images, existing methods prefer CNN or transformer architectures but suffer from the limitations of overly fixed receptive fields or excessive computational complexity. Recently, Mamba-based methods have become hot in the field of target detection and show significant potential in capturing remote dependencies with linear complexity but lack in-depth customization for remote sensing targets. To address the above challenges, we propose a new target detection framework for complex remote sensing images, LS-MambaNet. Specifically, firstly, a group fusion strategy is combined with the introduction of large-band convolution to adaptively adjust the receptive domains of the features, which enhances the spatial context information extraction for objects with high aspect ratios. In addition, a Multi-Granularity Spatial Mamba Block is proposed, and this employs a multi-granularity scanning strategy to reduce the computational cost and feature redundancy on different scanning paths and is able to efficiently model the global contextual information of the target. Experimental results show that LS-MambaNet outperforms baselines on DOTA1.0 and HRSC2016 datasets. In particular, LS-MambaNet significantly improves the inference speed and achieves a higher FPS while maintaining state-of-the-art detection accuracy.

关键词： large strip convolution grouping fusion strategy state space model remote sensing images object detection

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Provably Secure Efficient key-Exchange Protocol for intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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A Robust key Exchange and Tamper-Resistant Protocol for HAN and NAN networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area networks (HAN) and Neighborhood Area networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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A cooperative hybrid breeding swarm intelligence algorithm for feature selection

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Pattern Recognition 2026年 169卷

作者： Mengqing Mei Songsong Zhang Zhiwei Ye Mingwei Wang Wen Zhou Jia Yang Jixin Zhang Lingyu Yan Jun Shen School of Computer Science Hubei University of Technology Wuhan 430000 Hubei China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan 430000 Hubei China School of Computing and Information Technology University of Wollongong Wollongong 2500 New South Wales Australia

Feature selection (FS) is critical for reducing dimensionality and enhancing model accuracy but remains challenging in high-dimensional datasets. Metaheuristic algorithms, especially swarm intelligence, are often applied to optimization issues like FS, however, they struggle with the local optima and slow convergence in high-dimensional space. To address these issues, this paper proposes a cooperative hybrid breeding swarm intelligence algorithm (CHBSI), which simulates a hybrid breeding optimization algorithm based on heterosis theory, where the population is divided into three subpopulations for cooperative evolution. In each subpopulation, solutions are updated using a well-established particle swarm optimization algorithm, with Lévy flights integrated to enhance the global search capabilities and avoid the local optima. To further accelerate convergence during local optimization, a hyperbolic dynamic adjustment mechanism is introduced. Moreover, a classical ant colony optimization algorithm is incorporated to guide the global search via pheromone trails, thus improving exploration in complex search space. The best solutions from subpopulations are shared periodically, facilitating efficient information exchange. For binary FS, a regularized binary encoding is used to adapt the algorithm. The effectiveness of CHBSI is validated on 26 benchmark functions, and its binary version (bCHBSI) is tested on FS tasks across 18 UCI datasets. The experimental results show that both CHBSI and bCHBSI excel in convergence speed, fitness, classification accuracy, F1-score, the size of feature subset, and stability, significantly outperforming 10 advanced algorithms, including the S-shaped and V-shaped variants of CHBSI. CHBSI demonstrates scalability and robustness, maintaining superior performance across datasets with varying dimensionalities and complexities.

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A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras

arXiv

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arXiv 2025年

作者： Xiao, Teng Hu, Qi Yan, Qingsong Liu, Wei Ye, Zhiwei Deng, Fei School of Computer Science Hubei University of Technology Wuhan China Hubei Key Laboratory of Green Intelligent Computing Power Network Wuhan China School of Surveying and Mapping Wuhan University Wuhan China Wuhan Tianjihang Information Technology Co. Ltd. Wuhan China

Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. This paper presents a Forward Distortion and Backward Warping network (FDBW-Net), a novel framework for wide-angle image rectification. It begins by using a forward distortion model to synthesize barrel-distorted images, reducing pixel redundancy and preventing blur. The network employs a pyramid context encoder with attention mechanisms to generate backward warping flows containing geometric details. Then, a multi-scale decoder is used to restore distorted features and output rectified images. FDBW-Net’s performance is validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net achieves SOTA performance in distortion rectification, boosting the adaptability of PTZ cameras for practical visual applications. © 2025, CC BY.

关键词： Nonlinear distortion

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A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and power Model with Decision Strategies

arXiv

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arXiv 2022年

作者： Sun, Jiaze Deng, Jiahui Li, Yang Han, Nan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China School of Information Science and Technology Northwest University Xi’an710127 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of economic cost, primary energy consumption, and pollutant emissions. The mathematical expression of the CCHP system is proposed, and a multi-objective optimization model with constraints is established. According to different usage requirements, two decision-making strategies are designed, which can adapt to different scenarios. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) algorithm is proposed to optimize the CCHP model for the first time. The algorithm provides the optimal energy scheduling scheme by optimizing the production capacity of different capacity equipment. The simulation is conducted in three application scenarios: hotels, offices, and residential buildings. The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%. Concurrently, the Wilcoxon signed-rank test shows that BCS-GDE is significantly better than OMOPSO, NSGA-II, and SPEA2 with greater than 95% confidence. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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FFSR: Feature focus network based on scale refinement for infrared and visible image fusion

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Infrared Physics & Technology 2025年 150卷

作者： Chuan Xu Longyan Zhao Xinglong Hu Jin Wang Linyao Gao Jiali Xia Cheng Lei Liye Mei School of Computer Science Hubei University of Technology Wuhan 430068 China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Hubei University of Technology Wuhan 430068 China State Grid Hubei Electric Power Co. Ltd. Huanggang Power Supply Company Huanggang 438000 China State Grid Hubei Electric Power Co. Ltd. Wuhan 430040 China The Institute of Technological Sciences Wuhan University Wuhan 430072 China

Infrared and visible image fusion aims to integrate the features of two source images into a single image, the essence of this method lies in the effective extraction and processing of source image features. The multi-scale feature extraction networks widely used in current image fusion are often unable to effectively capture spatial and contextual information at various scales, and this easily leads to the loss of crucial information. To address these issues, we propose a feature focus network based on scale refinement for infrared and visible image fusion, known as FFSR. The goal of this method is to acquire spatial information from image representations at different scales and to capture complementary contextual information from these representations. Specifically, this method does not confine itself to single-scale processing of image representations at various scales but employs cross-scale parallel convolutional streams to obtain spatial and contextual information for each scale. The method utilizes a feature information focus module to retain effective information, thereby enhancing the quality of the fused image. Additionally, a cross-scale information exchange module is used to fully integrate the feature streams processed in parallel, ensuring that feature information from different scales is effectively fused and complemented. To validate the performance of the network, we conducted extensive qualitative and quantitative experiments across four datasets. The results demonstrate that our proposed method exhibits significant advantages, particularly in its ability to effectively extract and fuse multi-scale features. Notably, in nighttime scenarios, our method outperforms other models, demonstrating superior performance in handling image fusion tasks under low-light conditions. The source code and pre-trained models are available at https://***/Avery-42/FFSRNet .

关键词： Electronic data interchange

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A two-phase approach for locally differentially private fuzzy co-clustering

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Information sciences 2025年 718卷

作者： Pengfei Zhang Jianlong An Xiang Cheng Youwen Zhu Ji Zhang Zhikun Zhang State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining No. 168 Shungeng Road Tianjiaan District 232001 Huainan China School of Computer Science and Engineering Anhui University of Science and Technology No. 168 Shungeng Road Tianjiaan District 232001 Huainan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics No. 237 Longquan Road Kunming 650221 Yunnan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) No. 3501 Daxue Road Jinan 255020 Shandong China Key Laboratory of Equipment Data Security and Guarantee Technology Guilin University of Electronic Technology No. 1 Jinji Road Guilin 541010 Guangxi China State Key Laboratory of Networking and Switching Technology No. 10 Xitucheng Road Haidian District 100876 Beijing China Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District 100876 Beijing China College of Computer and Data Science Nanjing University of Aeronautics and Astronautics No. 29 Yudao Street Qinhuai District Nanjing 210016 Jiangsu China School of Mathematics Physics and Computing University of Southern Queensland West Street Toowoomba 4350 Queensland Australia College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Xihu District 310027 Hangzhou China

Clustering analysis is important for providing personalized services across various user experience optimization. As an advanced clustering technique, Fuzzy Co-Clustering ( FCC ) excels at handling high-dimensional data by simultaneously grouping both samples and attributes while allowing objects to belong to multiple clusters. To alleviate the potential privacy risks posed by directly using raw data, researchers often employ local differential privacy (LDP) techniques for protection. However, current clustering studies under LDP either directly perform clustering on the high-dimensional data, which necessitates splitting the privacy budget heavily, leading to excessive noise injection, or fail to adequately address the impact of Laplacian noise on both objective function modeling and distance metric selection, resulting in reduced clustering accuracy. To address these limitations, we present a two-phase approach SONY , which allocates part of the privacy budget to evaluate attribute importance and perform dimensionality reduction accordingly, and then utilizes the remaining budget to conduct noise-aware clustering on the pruned data. Our key technical contributions include: (1) a Noise-aware Similarity Calculation method ( NSC ) that replaces traditional Euclidean distance with a specialized metric which accounts for the statistical properties of Laplacian noise; (2) an importance-driven dimension reduction and budget allocation method ( IDRBA ) which optimizes both attribute selection and privacy budget allocation; and (3) an outlier-aware objective function calculation method ( OOC ) which enhances robustness against potential noise-induced outliers. We provide theoretical guarantees for SONY 's convergence, privacy preservation, utility bounds, and computational complexity. Experimental evaluations conducted on two real-world datasets and one synthetic dataset, demonstrate that SONY outperforms the state-of-the-arts by at least 14% in F-Measure and 10% in Entrop

关键词： Fuzzy co-clustering Local differential privacy Privacy protection Two phase

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