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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhang, Xinyu Zhao, Yisheng You, Hongyi Liang, Li Jian, Kaige College of Physics and Information Engineering Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China

ISBN: (纸本)9798350317251

Aiming at the problem that the ground terminals (GTs) often suffer from difficulties in energy harvesting and data processing in remote areas, a resource allocation strategy for layered unmanned aerial vehicles (UAVs)-assisted mobile edge computing system is investigated in this paper. According to different functions, the UAVs are deployed in three layers. By using a magnetic coupling resonance wireless power transfer (MCR-WPT) technology, the GTs can obtain sufficient energy from the first layer of UAVs equipped with transmitting coils. The computational tasks of the GTs are divided into popular, private, and non-popular tasks. The popular and private tasks are both offloaded to the second layer of popular tasks UAVs (PT-UAVs), while the non-popular tasks are offloaded to the third layer of non-popular tasks UAV (NPT-UAV). The resource allocation problem is formulated as an optimization problem. The optimization objective is to minimize the system overhead by jointly optimizing the PT-UAVs caching policy, the GTs partial offloading factor, the charging time of the GTs, the trajectory of the NPT-UAV, and the bandwidth and computational resource of the system. The suboptimal solution is derived by introducing a social learning particle swarm optimization (SLPSO) algorithm. Simulation results show that the SLPSO algorithm outperforms other benchmark methods in terms of the system overhead. © 2023 IEEE.

关键词： Resource allocation

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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

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IEEE Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 IEEE.

关键词： Computational complexity

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Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

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IEEE Transactions on Automatic control 2024年第6期70卷 3540-3555页

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. © 1963-2012 IEEE.

关键词： Network topology

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Core-shell nanoparticle enhanced Raman spectroscopy in situ probing the composition and evolution of interfacial species on PtCo surfaces

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Nano Research 2024年第6期17卷 4687-4692页

作者： Jing Liu Han-Liang Zhong Xiangyu Li Mu-Fei Yue Wei-Min Yang Xueqiu You Jing-Hua Tian Yao-Hui Wang Jian-Feng Li Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing School of Ocean Information EngineeringJimei UniversityXiamen 361021China College of Chemistry Fuzhou UniversityFuzhou 350108China State Key Laboratory of Physical Chemistry of Solid Surfaces MOE Key Laboratory of Spectrochemical Analysis and InstrumentationiChEMCollege of Chemistry and Chemical EngineeringCollege of MaterialsCollege of EnergyXiamen UniversityXiamen 361005China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005China Shenzhen Research Institute of Xiamen University Shenzhen 518000China

The composition and evolution of interfacial species play a key role during electrocatalytic *** the structural evolution and intermediate during catalytic process by in situ characterization can shed new light on the electrocatalytic reaction mechanism and develop highly efficient ***,directly probing the interfacial species is extremely difficult for most spectroscopic techniques due to complicated interfacial environment and ultra-low surface ***,electrochemical core-shell nanoparticle enhanced Raman spectroscopy is utilized to probe the composition and evolution processes of interfacial species on Au@Pt,Au@Co,and Au@PtCo core-shell nanoparticle *** spectral evidences of interfacial intermediates including hydroxide radical(OH*),superoxide ion(O_(2)^(−)),as well as metal oxide species are directly captured by in situ Raman spectroscopy,which are further confirmed by the both isotopic experiment and density functional theory *** results provide a mechanistic guideline for the rational design of highly efficient electrocatalysts.

关键词： in situ Raman core-shell nanoparticle PtCo interfacial species composition and evolution

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Lightweight-Based Defect Detection for Small Target Insulators

Lightweight-Based Defect Detection for Small Target Insulato...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Shuxin Zhang, Lei Shi, Chengcheng Qin, Shuhan Ji, Guanjun Wang, Xiaodi School of Electrical Engineering Shanghai Dianji University Shanghai201306 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Fujian354300 China

ISBN: (纸本)9783031694820

Identifying defects in insulators, especially in small targets, remains a significant challenge due to the diverse types of insulators and complex transmission line backgrounds. This paper aims to enhance the accuracy of defect detection in insulators while minimizing the model’s parameter count for stable power system operation. To achieve this, we propose improvements to the feature fusion mechanism in the YOLOv8 network. These enhancements focus on better integrating multi-scale feature information, particularly emphasizing the detection sensitivity towards small targets. Additionally, these modifications lead to a reduction in the model’s parameter count while improving the detection accuracy of small targets. Furthermore, we introduce the SIOU loss function to the improved model, further enhancing its accuracy. Through experiments, our improved model achieves a mean average precision (mAP) of 89.2%, surpassing YOLOv8n by 2.7%. Remarkably, the model’s parameter count is significantly reduced to 2,796,249, demonstrating a balance between higher model accuracy and fewer parameters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Insulating materials

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An Enhanced Particle Swarm Algorithm Based on Fitness Landscape information 13

An Enhanced Particle Swarm Algorithm Based on Fitness Landsc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Diao, Zhenya Yu, Fei Guan, Jian Liu, Meitong School of Physics and Information Engineering Minnan Normal University Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China School of Physics and Information Engineering Minnan Normal University Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China

ISBN: (纸本)9798350308365

In the original Particle Swarm Optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields 6

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Fan, Yujie Luo, Huan Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes. © 2024 IEEE.

关键词： Semantic Segmentation

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Meibomian glands segmentation in infrared images with limited annotation

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 401-407页

作者： Jia-Wen Lin Ling-Jie Lin Feng Lu Tai-Chen Lai Jing Zou Lin-Ling Guo Zhi-Ming Lin Li Li College of Computer and Data Science Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Shengli Clinical Medical College of Fujian Medical University Fuzhou 350001Fujian ProvinceChina Department of Ophthalmology Fujian Provincial Hospital South BranchFujian Provincial HospitalFuzhou 350001Fujian ProvinceChina

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble *** viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,***,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for *** model maintains an exceptionally high level of segmentation accuracy while substantially reducing training *** holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

关键词： infrared meibomian glands images meibomian gland dysfunction meibomian glands segmentation weak supervision scribbled annotation

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