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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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Qinghai Embroidery Classification System and intelligent Classification Research 9th

Qinghai Embroidery Classification System and Intelligent Cla...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Lin, Xiaofei Ye, Zhonglin Zhao, Haixing College of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

ISBN: (纸本)9789819959709

Qinghai embroidery is an artistic treasure of folk embroidery in Qinghai Province. Classifying them to understand the differences between them is an important task. However, currently, there is a lack of a systematic classification method for Qinghai embroidery. First, by studying the history of Qinghai embroidery and a large number of Qinghai embroidery patterns, this paper divides Qinghai embroidery patterns into three categories: animals and plants, auspicious meanings, and geometric decoration. This method breaks through the regional classification system for the Qinghai embroidery. Second, this article utilizes four CNN models to classify the Qinghai embroidery datasets, exploring the differences in the classification of Qinghai embroidery by different models and finding the optimal classification model. The results show that the GoogLeNet model performs the best in the classification of Qinghai embroidery images, achieving the highest accuracy rate. This is mainly due to the small size of the Qinghai embroidery datasets and the application of the Inception structure and batch normalization technology in the GoogLeNet model, enabling it to better extract features and classify Qinghai embroidery images. Through this research, we can provide a certain reference and assistance for the classification of Qinghai embroidery images and provide technical support for the protection and inheritance of cultural heritage. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

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Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Coupling Co-Ni phosphides for energy-saving alkaline seawater splitting

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Nano Research 2024年第6期17卷 4797-4806页

作者： Weijia Liu Wenxian Liu Tong Hou Junyang Ding Zhigui Wang Ruilian Yin Xingyuan San Ligang Feng Jun Luo Xijun Liu State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Guangxi Key Laboratory of Processing for Nonferrous Metals and Featured MaterialsMOE Key Laboratory of New Processing Technology for Nonferrous Metals and MaterialsSchool of ResourcesEnvironment and MaterialsGuangxi UniversityNanning 530004China College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014China Hebei Key Laboratory of Optic-electronic Information and Materials the College of Physics Science and TechnologyHebei UniversityBaoding 071002China School of Chemistry and Chemical Engineering Yangzhou UniversityYangzhou 225002China ShenSi Lab Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of ChinaShenzhen 518110China

The coupling of energy-saving small molecule conversion reactions and hydrogen evolution reaction(HER)in seawater electrolytes can reduce the energy consumption of seawater electrolysis and mitigate chlorine corrosion ***,the fabrication of efficient multifunctional catalysts for this promising technology is of great ***,a heterostructured catalyst comprising CoP and Ni_(2)P on nickel foam(CoP/Ni_(2)P@NF)is reported for hydrazine oxidation(HzOR)-assisted alkaline seawater *** coupling of CoP and Ni_(2)P optimizes the electronic structure of the active sites and endows excellent electrocatalytic performance for HzOR and ***,the two-electrode HzOR-assisted alkaline seawater splitting(OHzS)cell based on the CoP/Ni_(2)P@NF required only 0.108 V to deliver 100 mA·cm^(−2),much lower than 1.695 V for alkaline seawater electrolysis ***,the OHzS cell exhibits satisfactory stability over 48 h at a high current density of 500 mA·cm^(−2).Furthermore,the CoP/Ni_(2)P@NF heterostructured catalyst also efficiently catalyzed glucose oxidation,methanol oxidation,and urea oxidation in alkaline seawater *** work paves a path for high-performance heterostructured catalyst preparation for energy-saving seawater electrolysis for H_(2) production.

关键词： seawater splitting heterostructure electrocatalysis hydrazine oxidation reaction hydrogen evolution reaction

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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A tracking filter algorithm for migratory birds based on group situation constraints 2

A tracking filter algorithm for migratory birds based on gro...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Li, Biao Wang, Rui Cai, Jiong Beijing Key Laboratory of Embedded Real-Time Information Processing Technology The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Invertebrates

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Lie algebra-based multi-validation model for 3D human pose estimation

Lie algebra-based multi-validation model for 3D human pose e...

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2024 International Conference on Computer Application and information Security, ICCAIS 2024

作者： Wang, Zhenrong He, Zanic Huang, Jiang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Guangdong Shenzhen618060 China Huamei International School Guangdong Guangzhou512520 China

ISBN: (纸本)9781510689312

We investigate 3D stereo view human posture estimation. We investigate a straightforward architecture that uses intermediate 2D pose predictions to reason, in contrast to many methods that attempt to directly predict 3D stance from picture data. Our strategy is founded on two important findings. (1) 2D posture estimation has been transformed by deep neural networks, which can now provide precise 2D predictions even for poses with self-occlusions. (2) 2D stereo vision is fruitful enough to reconstruct 3D vision, making it tempting to "lift" predicted 2D poses to 3D through stereo triangulation. In this paper, we propose to model skeleton parts as the trajectory in the Lie group SE(n)×...×SE(n). The 3D pose estimation is performed based on the resulting trajectories mapped to a vector space, the Lie algebra, where the 3D pose of the human body is obtained by pose construction in Lie algebra space from a series of raw images from a different view, thus making sense how it reaches good performance. Our method is compared with other stereo-vision-based methods and outperforms them. Especially, our method shows good performance compared with the Microsoft Kinect sensor. © The Authors.

关键词： Vector spaces

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Dense Convolution Siamese Network for Hyperspectral Image Target Detection 5

Dense Convolution Siamese Network for Hyperspectral Image Ta...

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5th International Conference on information Communication and Signal processing, ICICSP 2022

作者： Shen, Kun Xie, Weixin Tang, Haojin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors. © 2022 IEEE.

关键词： Convolution

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SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1717-1719页

作者： Jiufang Chen Ye Yuan Xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

关键词： representation preserving undirected

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Bagging and Boosting Fine-Tuning for Ensemble Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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