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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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IMPULSE NOISE REMOVAL BY L1 WEIGHTED NUCLEAR NORM MINIMIZATION

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Journal of Computational Mathematics 2023年第6期41卷 1171-1191页

作者： Jian Lu Yuting Ye Yiqiu Dong Xiaoxia Liu Yuru Zou Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Pazhou LabGuangzhou 510335China Department of Applied Mathematics and Computer Science Technical University of Denmark2800 Kgs.LyngbyDenmark

In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many ***,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise *** this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from *** apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this *** exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse *** results show that our method can effectively remove impulse noise.

关键词： Image denoising Weighted nuclear norm minimization l 1-data-fidelity term Low rank analysis Impulse noise

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Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay MEC System with Energy Harvesting

Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay ME...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

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

ISBN: (纸本)9798350345384

In the mobile edge computing (MEC) scenarios, users outside the coverage area of the base station are unable to offload tasks during the offloading process. In this paper, a resource allocation strategy in unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) multi-relay MEC system with energy harvesting is proposed. Multiple UAVs are deployed around the edge of the base station (BS) coverage area. D2D link between users outside the BS coverage area and edge users of BS is controlled by the UAV. Edge users of BS can harvest energy from the UAV. Computing tasks of users outside the BS coverage area can be sent to edge users of BS by different D2D links. Then, edge users of BS can forward computing tasks to the MEC server of BS. Transmitting power, energy harvesting time, computing resources, and channel bandwidth are jointly optimized to minimize the total task completion time. The formulated problem is a non-convex optimization problem. By introducing a series of auxiliary variables, a perspective function is used to convert some non-convex constraints into convex constraints. An alternate iterative optimization algorithm is adopted to obtain the optimal solution to the original problem. Simulation results show that compared with other baseline schemes, the proposed strategy can reduce the total task completion time more effectively. © 2023 IEEE.

关键词： Base stations

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RIS-Aided Cell-Free Massive MIMO Systems With Low-Resolution ADCs: Uplink Performance Analysis and Optimization

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IEEE Internet of Things Journal 2025年第11期12卷 18249-18265页

作者： Xiong, Youzhi Tang, Liu Sun, Sanshan Yang, Songjie Liu, Li Mao, Sun Zhang, Zhongpei Wei, Ning Sichuan Normal University Laboratory of Information Intelligent Processing and Communications College of Physics and Electronic Engineering Chengdu610101 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Sichuan Normal University College of Computer Science Chengdu610101 China

This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization. © 2014 IEEE.

关键词： Mean square error

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th International Conference on intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

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InfoMat 2024年第7期6卷 62-75页

作者： Junjie Zou Yanan Ma Chenxu Liu Yimei Xie Xingyao Dai Xinhui Li Shuxuan Li Shaohui Peng Yang Yue Shuo Wang Ce-Wen Nan Xin Zhang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanthe People's Republic of China Hubei Key Laboratory of Energy Storage and Power Battery School of MathematicsPhysics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanthe People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeithe People's Republic of China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China

The simultaneous detection of multiple stimuli,such as pressure and temperature,has long been a persistent challenge for developing electronic skin(eskin)to emulate the functionality of human ***,the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and ***,we propose a self-powered dual temperature–pressure(SPDM)sensor,which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al *** SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation,while exhibiting a slow and hysteretic response to temperature *** distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning,resulting in an impressive accuracy rate of 99.1%.Furthermore,the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75C,encompassing the environmental conditions encountered in daily human *** dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination,showcasing its potential applications in wearable electronics,intelligent robots,human–machine interactions,and so on.

关键词： e-skin gel electrolyte machine learning self-powered dual-mode sensor

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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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AI-Based Text Detection and Classification for Smart City Monitoring Using BERT and TextCNN Models

AI-Based Text Detection and Classification for Smart City Mo...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Yunxia Li, Mingyuan Ye, Zhonglin Shi, Xiaoan Wang, Weijie Zhao, Haixing College of Computer Qinghai Normal University Xining810000 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9798331505516

This paper proposes an Artificial Intelligence (AI) text detection and classification model that combines Bidirectional Encoder Representations from Transformers (BERT) and Text Convolutional Neural Network (TextCNN), applicable for smart city social media monitoring. By leveraging BERT's powerful contextual semantic understanding capabilities and TextCNN's local feature extraction abilities, the model achieves efficient text detection and classification. The model first utilizes BERT to perform semantic representation of the input text, capturing rich contextual features. These features are then fed into the TextCNN model, where multiple convolutional and pooling operations extract and compress the features. Finally, a fully connected layer converts the extracted features into fixed-length vectors for precise classification prediction. Experimental results show that this hybrid model significantly outperforms traditional baseline models on two public datasets, Human ChatGPT Comparison Corpus (HC3) and sharegpt_gpt4. The hybrid model demonstrates notable improvements in key metrics such as Accuracy (ACC), Precision (PREC), Recall (REC), and F1-score (F1). For example, on the sharegpt_gpt4 dataset, the accuracy reaches 0.8490, making a significant improvement over the baseline model. This validates the effectiveness and superiority of combining BERT and TextCNN in text classification tasks. The innovation of this paper lies in the novel integration of BERT and TextCNN, harnessing the contextual semantic understanding of BERT with the local feature extraction of TextCNN, resulting in enhanced text classification performance. The use of diverse datasets such as HC3 and sharegpt_gpt4 showcases the model's robustness across various text types. Additionally, the model's application in smart city social media monitoring demonstrates its practical relevance, providing accurate and efficient text detection crucial for monitoring public sentiment and emerging issues in real-t

关键词： Smart city

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Observer-based Hierarchical Control of Heterogeneous Multiagent Systems under Attacks and Disturbances 43

Observer-based Hierarchical Control of Heterogeneous Multiag...

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43rd Chinese Control Conference, CCC 2024

作者： Zhi, Yongran Fan, Huijin Liu, Lei Wang, Bo Huazhong University of Science and Technology The National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

The resilient cooperative output regulation of heterogeneous leader-follower multiagent systems (MASs) under asynchronous DoS attacks and unknown external disturbances is investigated in this paper. An observer-based hierarchical control frame is constructed, which contains communication layer and controller layer. In the communication layer, based on an improved dynamic event-triggered mechanism, a novel fully distributed observer is proposed to estimate the leader's state under the asynchronous DoS attack. In the controller layer, an anti-disturbance state observer is firstly designed to estimate the follower's state in the existence of unknown external disturbances, then an output feedback controller is constructed to track the estimate leader's state. The proposed two observers has increased the tolerance to DoS attacks and external disturbances. Finally, simulation results have demonstrated the effectiveness of the proposed hierarchical control frame. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feedback control

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