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research on Low Overhead Fast Repair Array Code Algorithm

International Journal of Network Security 2025年第3期27卷 594-604页

作者： Xiong, Pan Hong, Tieyuan Wang, Yuheng Tang, Dan Cai, Hongliang Zhang, Junyan School of Software Engineering Chengdu University of Information Technology Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application Chengdu610225 China

With the growth of exponential data, disk data storage is becoming increasingly important. In the storage system, single-disk and dual-disk loss are more and more serious. To ensure the efficiency and speed of data restoration is crucial to the reliability of the storage system. The Row Diagonal Parity Code (RDP) indicates that the combined recovery algorithm can reduce the total number of disks that are read when a single-disk fault is rectified by 25%. However, when a two-disk fault is rectified, all disk elements must work together. In order to improve the efficiency and speed of single and double disk fault repair, the RDP Code is extended, and a Rapid Rows Diagonal Parity Code (RRDP code) with local repair property is proposed. On the premise of global horizontal coding of RDP code, RRDP code is computed by XOR to generate a local horizontal check column, and it participates in global horizontal redundancy calculation. The local horizontal redundancy check column shortens the repair chain by constructing the"missing diagonal" and then it has the function of local repair. Through theoretical analysis, RRDP code has decent and fast decoding complexity and I/O reading cost of single and dual disk fault repair under the premise of constant coding cost, which greatly saves the fault repair cost. © (2025), (International Journal of Network Security). All rights reserved.

关键词： Restoration

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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Two Compact Hybrid Band-Pass Filters Using Eighth-Mode Substrate-Integrated Waveguide and Microstrip Resonators

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Progress in Electromagnetics research C 2025年 151卷 167-175页

作者： Tang, Luyao Han, Wei Wei, Hao Li, Yanbin The 54th Research Institute of China Electronics Technology Group Corporation China National Engineering Reasearch Center of Communication Software and Asic Design China

Two compact substrate-integrated waveguide (SIW) filters with hybrid coupling of eighth-mode substrate integrated waveg-uide (EMSIW) resonators and microstrip are proposed in this paper. Hybrid coupled filters were achieved by etching two half-wavelength microstrip resonators (BPF I) or two quarter-wavelength microstrip resonators (BPF II) on the top of traditional second-order EMSIW filters. The topology of the two filters was analyzed. Due to the cross coupling between resonator 1 and resonator 4, two transmission zeros were achieved outside the band of BPF I, which increased the selectivity of the filter. Due to the mixed electromagnetic coupling between resonator 2 and resonator 3, a transmission zero is realized in the stopband of BPF II. To confirm the validity of the two filter models, two filters were designed, produced, and measured. Based on the findings of the measurements, the central frequency of BPF I is recorded at 7.7 GHz, with a fractional bandwidth (FBW) of 18.2%. The insertion loss (IL) within the passband is minimal at 0.8 dB, and the size of the filter is only 8 mm * 4 mm (0.53λg *0.26λg). The filter exhibits enhanced out-of-band suppression due to the presence of two transmission zeros located at frequencies of 6.4 GHz and 10 GHz. The center frequency of BPF II is 19.5 GHz,; the FBW is 20.5%; the IL within the passband is only 0.49 dB; and the size of the filter is only 2 mm ∗ 2 mm (0.34λg *0.34λg). As a result of the mixed electromagnetic coupling effects, a transmission zero occurs at a frequency of 26.7 GHz. The simulation outcomes are consistent with the experimental findings. Compared with other reported SIW filters, the two filters introduced in this study exhibit favorable characteristics such as reduced insertion loss and compact dimensions. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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Scalable Motion Estimation and Temporal Context Reinforcement for Video Compression Using RGB Sensors

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IEEE Sensors Journal 2025年第10期25卷 18323-18333页

作者： Huang, Qian Lu, Hao Liu, Wenting Wang, Yiming Hohai University College of Computer Science and Software Engineering Nanjing211100 China Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process Changzhou213164 China

We propose a novel deep learned video compression technique, named scalable motion estimation (SME), which is designed for video data generated by sensor systems in smart devices. These devices face unique challenges due to limited bandwidth, constrained computational resources, and the complexity of real-world scenarios. Existing learned video compression methods primarily rely on optical flow for motion estimation;however, optical flow often suffers from inaccuracies and displacement errors, resulting in imprecise motion vectors, and the increasing complexity of models further limits their deployment in resource-constrained environments. To address these issues, the SME framework introduces a hierarchical motion vector enhancement strategy that progressively optimizes motion information in video captured by sensors. In addition, we propose the temporal context reinforcement (TCR) module, which partitions high-dimensional contextual information into three components. Compared to direct context encoding methods, the TCR module optimizes bit allocation, achieving a more balanced distribution of high- and low-frequency information. Extensive experiments on benchmark datasets demonstrate that the SME framework substantially enhances the efficiency and performance of video compression systems on RGB sensor-captured videos, making it particularly suitable for video applications. © 2001-2012 IEEE.

关键词： Optical flows

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Understanding interfacial engineering of surface functionalized boron nitride nanosheets within thermal interface materials

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Science China Materials 2025年第4期68卷 1300-1302页

作者： Gaojie Han Yuezhan Fengg Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China

The continuous miniaturization and high-power development of electronic devices have given rise to severe interface thermal issues,which urgently demand highly thermally conductive thermal interface materials(TIMs)to ... 详细信息

关键词： interfacial engineering heat accumulation interface thermal issueswhich thermal interface materials surface functionalized boron nitride nanosheets electronic devices thermal conductivity

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Simultaneous Solar-Driven Interfacial Evaporation and Photo-Fenton Oxidation by Semiconducting Metal–Organic Framework From Waste Polyimide

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Energy & Environmental Materials 2025年第2期8卷 246-258页

作者： Lijie Liu Huajian Liu Zifen Fan Jie Liu Xueying Wen Huiyue Wang Yan She Guixin Hu Ran Niu Jiang Gong Key Laboratory of Material Chemistry for Energy Conversion and Storage Ministry of Education Hubei Key Laboratory of Material Chemistry and Service Failure Hubei Engineering Research Center for Biomaterials and Medical Protective Materials School of Chemistry and Chemical EngineeringHuazhong University of Science and Technology

The integrated technology of interfacial solar steam generation and photo-Fenton oxidation has emerged as a promising way to simultaneously mitigate freshwater scarcity and degrade organic ***,fabricating low-cost,multi-functional evaporators with high water evaporation and catalytic ability still presents a significant ***,we report the functional upcycling of waste polyimide into semiconducting Fe-BTEC and subsequently construct Fe-BTEC-based composite evaporators for simultaneous freshwater production and photo-Fenton degradation of ***,through a two-step solvothermal-solution stirring method,Fe-BTEC nanoparticles with the size of 20-100 nm are massively produced from waste polyimide,with a band gap energy of 2.2 *** composite evaporator based on Fe-BTEC and graphene possesses wide solar-spectrum absorption capacity,high photothermal conversion capacity,rapid delivery of water,and low enthalpy of *** from the merits above,the composite evaporator achieves a high evaporation rate of 2.72kgm-2h-1from tetracycline solution,as well as the photothermal conversion efficiency of 97% when exposed to irradiation of 1 Sun,superior to many *** is more,the evaporator exhibits the tetracycline degradation rate of 99.6% with good recycling stability,ranking as one of the most powerful heterogeneous Fenton *** Multiphysics and density functional theory calculation results prove the synergistic effect of the concentrated heat produced by interfacial solar steam generation and catalytic active sites of Fe-BTEC on promoting H2O2activation to form reactive oxidation *** work not only provides a green strategy for upcycling waste polyimide,but also proposes a new approach to fabricate multi-functional evaporators.

关键词： freshwater production interfacial solar steam generation metal–organic framework photo-Fenton oxidation waste polyimide

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Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation

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Neural Computing and Applications 2025年第8期37卷 5891-5911页

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Wang, Xiangxiang Ukot, Akpanika Robert Cai, Jingye School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Medical image segmentation is pivotal in computer-aided diagnosis systems, demanding high precision and contextual understanding. Vision Transformer-based approaches have gained much attention recently due to their excellent performance and ability to capture long-range dependencies in medical images. However, research shows they suffer from inadequate multi-scale feature integration, poor object localization, and inconsistent mask predictions, leading to sub-optimal segmentation performance. This paper addresses these challenges by redefining semantic medical image segmentation through learnable object queries within an enhanced transformer framework with a masked hybrid attention querying mechanism, optimizing multi-scale feature fusion, object localization, and instance-specific segmentation. First, this study presents a novel transformer-based masked hybrid attention mechanism using Laplacian query fusion on learnable query features and incorporating a novel tripartite sequence matching technique as part of the enhanced decoder block to improve the consistency of mask predictions and optimize decoder queries. The designed hybrid multi-head self- and cross-attention mechanisms aim to selectively integrate multi-scale features, ensuring optimal feature combinations for precise segmentation. Secondly, multiple class tokens are incorporated to improve object localization and capture class-specific characteristics within the transformer framework, leveraging the transformer decoders’ ability to learn distinct instance representations. Experimental results and extensive ablation studies demonstrate the effectiveness of the proposed approach on three publicly available datasets, obtaining better segmentation results compared to various state-of-the-art approaches using various evaluation metrics. Specifically, the proposed model achieves a Dice Score of 95.25%, 92.75%, and 85.25% on LUNA, ISIC, and DRIVE datasets, respectively. © The Author(s), under exclusive licence

关键词： Laplace transforms

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