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Performance analysis of UAV-based mixed underwater PLC-RF systems

Digital Communications and Networks 2024年第5期10卷 1332-1340页

作者： Shenghai Chen Liang Yang Xianfu Lei College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua418000China School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031China

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)*** this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC *** assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician *** this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was ***,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

关键词： ABER Average bit-error-rate ACC Average channel capacity OP Outage probability UAV Unmanned aerial vehicle UPLC Underwater power line communication

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Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 308-316页

作者： Wang, Zhu Liu, Weisheng the College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China the College of Computer Science and Software Engineering the University of Science and Technology Liaoning CO Anshan114051 China

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

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Algorithms and statistical analysis for linear structured weighted total least squares problem

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Geodesy and Geodynamics 2024年第2期15卷 177-188页

作者： Jian Xie Tianwei Qiu Cui Zhou Dongfang Lin Sichun Long School of Earth Science and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China College of Science Central South University of Forestry and TechnologyChangsha 410018China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtan 411201China

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random ***,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient *** is called the linear structured EIV(LSEIV)*** kinds of methods are proposed for the LSEIV model from functional and stochastic *** the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)*** the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor *** algorithms are derived through the Lagrange multipliers method and linear *** estimation principles and iterative formula of the parameters are proven to be *** first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS ***,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and ***,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

关键词： Linear structured weighted total least squares Errors-in-variables Errors-in-observations Functional modelmodification Stochastic model modification Accuracyevaluation

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Dual-Branch Network with Mutual Guidance for Hyperspectral Image Super-Resolution

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14368-14381页

作者： Zhang, Tiejun Zhang, Boqun Hu, Nan Li, Xinyu Li, Peng Harbin University of Science and Technology College of Computer Science and Software Harbin150080 China Jilin University College of Computer Science and Technology Changchun130012 China

Fusion-based hyperspectral image super-resolution has recently attracted increasing interest due to its superior reconstruction quality. This approach enhances the spatial resolution of low-resolution hyperspectral images (LR-HSIs) by fusing high-resolution multispectral images (HR-MSIs) of the same scene. However, most existing deep learning-based methods have not sufficiently considered the huge modality differences between these two types of images before feature fusion, potentially resulting in the loss of valuable information. To tackle this challenge, we introduce a novel dual-branch network with mutual guidance (DBMGNet). Specifically, we employ a dual-branch architecture to process the input LR-HSI and HR-MSI in parallel, with the aim of reconciling their modality differences. For this purpose, we designed a mutually guided dual-stream Transformer block that performs bidirectional calibration between the branches, enhancing their spatial and spectral consistency. To prevent excessive coupling of information, we proposed a multiscale feature enhancement block, which independently refines fine-grained details within each branch. Finally, a weighted feature fusion block is developed to effectively integrate the features from both branches. Experiments on three widely used datasets indicate that the proposed DBMGNet achieves stable and superior performance with lower computational cost in comparison with several state-of-the-art approaches. © 2008-2012 IEEE.

关键词： Image enhancement

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Speech recognition

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Cloud Computing Security: Assured Deletion

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第3期48卷 485-496页

作者： Ahmed, Sarmad Mahmood Mahmood, Baban Ahmed Department of Computer Science College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq

With the advent of cloud computing, many organizations, institutions, and individuals have chosen to store their data in the cloud as a way to compensate for limited local storage capabilities and reduce expenses. However, the process of uploading data to the cloud results in relinquishing control over it, leaving the data owner unaware of the details of its storage and location. Hence, ensuring data confidentiality and integrity has emerged as a critical concern, especially with regard to cloud employees and other potential attacks. A significant amount of investigation has been conducted on the security aspects of cloud computing, with a particular focus on permanent removals. Hence This study investigates the deployment of deletion assurance in cloud computing on two separate cloud platforms, employing strategies such as cryptographic key partitioning, cryptographic-based random writing, and link deletion within confirmed deletion scenarios. The characteristics of this approach include security safeguards like asymmetric encryption (specifically Elliptic Curve Cryptography (ECC)) and multiple hashing algorithms like hash 256 are used to fortify the security and confidentiality of data stored in the public cloud and the secrecy of files kept in the cloud is unaffected by invasions from the outside or from within an encrypted file prevents a threat from reading it and the owner may easily encrypt each file with a fresh key by managing and storing the keys using the key management center also authorized users are able to request this key thanks to secure access enforcement and In the event of assure deletion, the file is overwritten with random encryption and uploaded to the cloud, updating all copies while deleting all links and keys from the key management. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Cloud platforms

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M-LSM:An Improved Multi-Liquid State Machine for Event-Based Vision Recognition

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Journal of computer science & technology 2023年第6期38卷 1288-1299页

作者：王蕾郭莎莎曲连华田烁徐炜遐 College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and ***,the existing event-based models for vision recogni-tion are faced with several issues,such as large network complexity and expensive training *** this paper,we propose an improved multi-liquid state machine(M-LSM)method for high-performance vision ***,we intro-duce two methods,namely multi-state fusion and multi-liquid search,to optimize the liquid state machine(LSM).Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal *** adapt network architecture search(NAS)to find the potential optimal architecture of the multi-liquid state *** also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity(STDP).Our M-LSM is evalu-ated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.

关键词： liquid state machine bio-inspired learning classification event-based vision

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PointNetV3:feature extraction with position encoding

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Optoelectronics Letters 2024年第8期20卷 483-489页

作者： WANG Jun WANG Xuefei ZHoU Boxiong GUO Dongyan College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China

Feature extraction of point clouds is a fundamental component of three-dimensional(3D)vision *** existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and overlook the crucial role played by *** instance,though two airplane wings share the same shape,it demands distinct feature representations due to their differing *** this paper,we introduce a novel module called position aware module(PAM)to leverage the coordinate features of points for positional encoding,and integrating this encoding into the feature extraction network to provide essential positional ***,we embed PAM into the Point Net++framework,and design a novel feature extraction network,named Point Net *** validate the effectiveness of Point Net V3,we conducted comprehensive experiments including classification,object tracking and object detection on point *** results of remarkable improvement in three tasks demonstrate the exceptional performance achieved by Point Net V3 in point cloud processing.

关键词： Net Point exceptional

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Path-Based Multicast Routing for Network-on-Chip of the Neuromorphic Processor

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Journal of computer science & technology 2023年第5期38卷 1098-1112页

作者：康子扬李石明王世英曲连华龚锐石伟徐炜遐王蕾 College of Computer Science and Technology National University of Defense TechnologyChangsha 410000China

Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic *** spiking packets may cause communication pressure on *** propose a path-based multicast routing method to alleviate the ***,all destination nodes of each source node on NoC are divided into several ***,multicast paths in the clusters are created based on the Hamiltonian path *** proposed routing can reduce the length of path and balance the communication load of each ***,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing *** use six datasets to verify the proposed multicast *** with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,*** maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,***,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.

关键词： neuromorphic processor spiking neural network(SNN) Network-on-Chip(NoC) path-based multicast

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PVDF-assisted pyrolysis strategy for corrugated plate oxygen electrocatalysis nanoreactor:Simultaneously realizing efficient active sites and rapid mass transfer

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Journal of Energy Chemistry 2024年第10期97卷 612-621,I0013页

作者： Chenxi Xu Liang Chen Haihui Zhou Shifeng Qin Zhaohui Hou Yangyang Chen Jiale Sun Junwei Xu Zhongyuan Huang Cllege of Chemistry and Chemical Engineering Hunan UniversityChangsha 410082HunanChina Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials School of Chemistry and Chemical EngineeringHunan Institute of Science and TechnologyYueyang 414006HunanChina College of Materials Science and Technology Central South University of Forestry and TechnologyChangsha 410004HunanChina

Though Zn-air batteries(ZABs)are one of the most promising system for energy storage and conversion,challenge still persists in its commercial application due to the sluggish kinetics of oxygen reduction/evolution reaction(ORR/OER).Hereby,a polyvinylidene fluoride(PVDF)-assisted pyrolysis strategy is proposed to develop a novel corrugated plate-like bifunctional electrocatalyst using two-dimensional zeolitic imidazolate frameworks(2D ZIF-67)as the *** employed PVDF plays an important role in inheriting the original 2D structure of ZIF-67 and modulating the composition of the final *** a result,a corrugated plate-like electrocatalyst,high-density Co nanoparticles decorated 2D Co,N,and F tri-doped carbon nanosheets,can be *** acquired electrocatalyst enables efficient active sites and rapid mass transfer simultaneously,thus showing appreciable electrocatalytic performance for rechargeable Zn-air ***,our proposed strategy offers a new perspective to the design of advanced oxygen electrocatalysts.

关键词： Active sites Mass transfer Corrugated plate Oxygen electrocatalyst Zn-air batteries

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