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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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DESIGN AND ANALYSIS OF A SLOTTED PLANAR ANTENNA WITH PARASITIC RING PATCH FOR EFFICIENT 5G MICROWAVE APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第1期84卷 71-82页

作者： Yadav, Swati Varun Yadav, Manish Varun Gupta, Ridhi Yadav, Dinesh Bhati, Kirti Panwar Ali, Tanweer Singh, Vikas Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India School of Electronics Devi Ahilya University M.P Indore India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Design and analysis of a slotted planar antenna with a parasitic ring patch for efficient 5G microwave applications is presented. The antenna design aims to strike a balance between complexity and size efficiency. Its front side features a rectangular patch at the center, comprising L-shaped, T-shaped, and inverse T-shaped elements, alongside four parasitic rings on top. The central feed utilizes a 50-ohm strip line connected to a microstrip line. On the back side, the antenna incorporates three parasitic rings on top, four horizontal strips arranged in an increasing order, and a ground plane with a small rectangular slot. To evaluate its performance, the antenna is simulated using CST Microwave Studio on an FR-4 substrate with overall dimensions measuring 13 × 15 × 1.5 mm3. The results demonstrate an impressive impedance bandwidth of 60.8% and a return loss of –28 dB within the frequency range of 3.2–6 GHz, with a central frequency of 4.6 GHz. Throughout its operational range, the radiation patterns remain constant, exhibiting stable polar patterns and efficient performance with a wide gain of 4.81 dBi and an efficiency of 82%;the proposed antenna proves to be suitable for 5G microwave applications. © 2025 by Begell House, Inc.

关键词： Microwave antennas

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 5

Combined Classification of Hyperspectral and LiDAR Data base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhou, Binbin Wang, Qingyan Zhang, Junping Wang, Yujing School Of: Measurement-Control And Communication Engineering Harbin University Of Science And Technology Harbin150080 China School Of: Electronics And Information Engineering Harbin Institute Of Technology Harbin150001 China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods. © 2023 ACM.

关键词： Classification (of information)

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Data Security Storage Mechanism Based on Blockchain Network

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Computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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Lighting control with Myo armband based on customized classifier

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The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 106-116页

作者： Jiang Yujian Yang Xue Zhang Junming Song Yang Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment Technology Communication University of ChinaBeijing 100024China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China

This paper focuses on gesture recognition and interactive lighting *** collection of gesture data adopts the Myo armband to obtain surface electromyography(sEMG).Considering that many factors affect sEMG,a customized classifier based on user calibration data is used for gesture *** this paper,machine learning classifiers k-nearest neighbor(KNN),support vector machines(SVM),and naive Bayesian(NB)classifier,which can be used in small sample sets,are selected to classify four gesture *** performance of the three classifiers under different training parameters,different input features,including root mean square(RMS),mean absolute value(MAV),waveform length(WL),slope sign change(SSC)number,zero crossing(ZC)number,and variance(VAR)are tested,and different input channels are also *** results show that:The NB classifier,which assumes that the prior probability of features is polynomial distribution,has the best performance,reaching more than 95%***,an interactive stage lighting control system based on Myo armband gesture recognition is implemented.

关键词： Myo armband gesture recognition surface electromyography customized classifier lighting control

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Remaining Life Prediction Method of Rolling Bearing Based on TD3 Optimization Model Updating Strategy 16

Remaining Life Prediction Method of Rolling Bearing Based on...

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16th IEEE International Conference on Electronic measurement and Instruments, ICEMI 2023

作者： Wang, Yujing Jiao, Guangpeng Kang, Shouqiang Xie, Jinbao Sun, Yulin School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Physics and Electronic Engineering Hainan Normal University Haikou China

ISBN: (纸本)9798350327144

To address the problems of model degradation and low prediction accuracy in the of update procedure the rolling bearing life prediction model, the updating strategy of twin delayed deep deterministic policy gradient(TD3) is suggested to optimize the prediction model, and it applied to rolling bearing remaining useful life prediction. Continuous wavelet transform is used to build a dataset by converting the rolling bearing vibration data into two-dimensional time-frequency spectrum diagram. Deep reinforcement learning is introduced to design the elements of state, action, reward value and loss function, which are employed to training of the prediction model, this model is composed convolutional neural network (CNN) and bidirectional long short-term memory. The delayed update and soft update techniques of the TD3 algorithm are used to reduce the update frequency of the CNN feature network. A new loss function is proposed to establish a new model updating strategy for solving the prediction model degradation problem. The experimental study using the PHM2012 dataset and the finding indicate that, in comparison to the compared methodologies, the suggested prediction method with model optimization strategy can reduce the root mean square error and mean square error by more than 23.8% and 20.8% respectively, reducing the prediction error by at least 18.6% and improve the scores by more than 0.213. © 2023 IEEE.

关键词： Mean square error

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A Method for Constructing an Olfactory Dataset Based on fNIRS 9

A Method for Constructing an Olfactory Dataset Based on fNIR...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Li, Wei He, Haobo Li, Guang Kuang, Lidan Zhang, Jin Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410076 China Zhejiang University School of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9798350383706

Biomimetic olfaction models have been applied to various fields such as image recognition, text classification, electroencephalography (EEG) recognition, and spatio-temporal sequence prediction. However, the challenge of obtaining olfactory data complicates the construction of olfactory datasets, ultimately resulting in a scarcity of applications for biomimetic olfaction models in olfactory tasks. In this work, we begin by conducting a comprehensive comparison of existing biomimetic olfactory models from various aspects such as structure, degree of biomimicry, and performance. Following this, prefrontal lobe olfactory data from ten subjects were collected for three different odors using functional Near-Infrared Spectroscopy (fNIRS). Finally, the data underwent denoising through the use of various data processing methods, and the quantity of the olfactory dataset was expanded. The olfactory dataset constructed in this study can be used to evaluate the performance of biomimetic olfactory models in processing olfactory tasks, which may aid in advancing the development of these models in real-world olfactory applications. © 2023 IEEE.

关键词： Image recognition

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Multipath planning based on multimodal optimization slime mold algorithm

Multipath planning based on multimodal optimization slime mo...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Gao, Luli Ren, Hui Communication University of China School of Information and Communication Engineering Bejing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China Ministry of Culture and Tourism Bejing China Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Bejing China

ISBN: (纸本)9798350380347

Multipath planning aims to find multiple optimal paths simultaneously, and its essence is a multimodal optimization problem. In recent years, swarm intelligence (SI) optimization algorithms have provided effective solutions for multimodal optimization. In this paper, the multimodal optimization slime mold algorithm (MOSMA) based on master-slave structure and local search strategy (LS) is proposed to solve multipath planning problems in small cultural complex. Firstly, a local search strategy is introduced to eliminate the influence of the 'centerbias' operator and improve the quality of solutions. Secondly, the master-salves structure is introduced to preserve the diversity of the population and enable it to simultaneously locate multiple solutions. Finally, the performance of MOSMA is analyzed on several multimodal functions, and an example of path planning in a small cultural complex is given. © 2024 IEEE.

关键词： Optimization algorithms

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Reliability Allocation of Cruise Ship Automation System Based on BBWM-AHP 6

Reliability Allocation of Cruise Ship Automation System Base...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Xiang, Jiaxin Sun, Yongquan Wang, Hao Chen, Hongke Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China China Institute of Marine Technology & Economy Beijing China Xiamen University Tan Kah Kee College School of Mechanical and Electrical Engineering & Automation Zhangzhou China

ISBN: (纸本)9798350356083

Limited reliability data during the development of cruise ship automation systems often leads to subjective weight allocation. This paper proposes an reliability allocation method based on the Bayesian best-worst method and analytic hierarchy process to address this challenge. By considering the system reliability indices, influencing factors, and units need to be allocated, a hierarchical model is established for reliability allocation based on the analytic hierarchy process. The Bayesian best-worst method compares elements in each layer, generating best and worst matrices based on decision-makers. These matrices determine optimal weights for each layer's elements. The analytic hierarchy process determines relative importance weights among the system reliability index, influencing factors, and units. Consequently, the derived optimal weights are converted into allocation factors, which enable the allocation of reliability for cruise ship automation systems. This fusion-based approach presents a novel and effective method for optimizing reliability allocation during the development stage of complex systems. © 2024 IEEE.

关键词： Decision making

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