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arXiv 2025年

作者： Liu, Baiyang Tong, Kin-Fai Wong, Kai-Kit Chae, Chan-Byoung Wong, Hang College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering City University of Hong Kong Hong Kong School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electronic and Electrical Engineering University College London LondonWC1E7JE United Kingdom Yonsei Frontier Lab Yonsei University Korea Republic of

Interference and scattering, often deemed undesirable, are inevitable in wireless communications, especially when the current mobile networks and upcoming sixth generation (6G) have turned into ultra-dense networks. Current approaches relying on multiple-input multiple-output (MIMO) combined with artificial-intelligence-aided (AI) signal processing have drawbacks of being power-hungry and requiring wide bandwidth that raise scalability concerns. In this article, we take a radical approach and utilize the channel fading phenomenon to our advantage. Specifically, we propose a novel meta-fluid antenna architecture, referred to as the ‘fluid’ antenna system (FAS), that can freely surf on radio wave fluctuations, like ‘fluid’ figuratively speaking, with fine resolution in space to opportunistically avoid interference, eliminating the need for expensive signal processing. Our experimental results demonstrate that under rich scattering conditions, the proposed meta-fluidic architecture is able to exploit the natural ups and downs of radio waves in space for spatial multiplexing. These breakthrough results show that scattering can be desirable not harmful and interference can be dodged not suppressed, fundamentally changing our perception of fading and our understanding on how interference should be managed in wireless communications networks. © 2025, CC0.

关键词： Fading (radio)

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FPPNet: Fast Privacy-Preserving Neural Network via Three-Party Arithmetic Secret Sharing 15th

FPPNet: Fast Privacy-Preserving Neural Network via Three-Pa...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Bi, Renwan Xiong, Jinbo Li, Qi Liu, Ximeng Tian, Youliang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Hangzhou311121 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9783031239014

Jointing multi-source data for model training can improve the accuracy of neural network. To solve the raising privacy concerns caused by data sharing, data are generally encrypted and outsourced to a group of cloud servers for computing and processing. In this client-cloud architecture, we propose FPPNet, a fast and privacy-preserving neural network for secure inference on sensitive data. FPPNet is deployed in three cloud servers, who collaboratively execute privacy computing via three-party arithmetic secret sharing. We develop the secure conversion method between additive shares and multiplicative shares, and propose three secure protocols to calculate non-linear functions, such as comparison, exponent and division that are superior to prior three-party works. Some secure modules for running convolutional, ReLU, max-pooling and Sigmoid layers are designed to implement FPPNet. We theoretically analyze the security and complexity of the proposed protocols. With MNIST dataset and two types of neural networks, experimental results validate that our FPPNet is faster than the related works, and the accuracy is the same as that of plaintext neural network. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Sensitive data

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A Rapid Detection Method of Sewing Stitch Defects Based on Machine Vision

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SSRN 2023年

作者： Zheng, Xiaohu Chen, Yufan Xu, Xiuliang Liu, Bing Institute of Artificial Intelligence Donghua University Shanghai201620 China Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center Shanghai201620 China College of Information Science and Technology Donghua University Shanghai201620 China Hangzhou Zhongfu Technology&Innovation Research Institute Co. Ltd Shanghai201620 China Precise Machinery Scientific & Technology Co. Ltd Shanghai201599 China

In order to solve the problem of motion blur caused by sewing speed, an image DeblurGAN-BSV3 algorithm is proposed. Firstly, different attention fusion mechanisms are introduced to achieve accurate extraction of image features. Secondly, the depth-separable convolution of the trunk network is replaced by blueprint convolution to ensure the processing speed of the detection algorithm and the clarity of the output image. In order to solve the difficulty of rapid detection of pin defects, a student-teacher feature pyramid matching method was proposed. Firstly, the abnormal region interpretation technology was used to explicitly visualize the defect area of the identified abnormal instances, and then the abnormal detection score was performed on the detected images to make good judgment. The experimental results show that the combination of the above two methods has a good ability for rapid defect detection. Rapid detection of common pin defects is realized. © 2023, The Authors. All rights reserved.

关键词： Computer vision

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Improved Polar Complex Exponential Transform for Robust Local Image Description

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SSRN 2022年

作者： Yang, Zhanlong Yang, Linzhi Chen, Geng Yap, Pew-Thian School of Marine Science and Technology Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China University of North Carolina Chapel HillNC United States

Image description via robust local descriptors plays a vital role in a large number of image representation and matching applications. In this paper, we propose a novel distinctive local image descriptor that is based on the phase and amplitude information of Polar Complex Exponential Transform (PCET). The proposed descriptor, called IPCET (Improved PCET), is robust to the common photometric transformations (e.g., illumination, noise, JPEG compression, and blur) and geometric transformations (e.g., scaling, rotation, translation, and significant affine distortion). We perform extensive experiments to compare our IPCET descriptor with five most cutting-edge region descriptors (i.e., SIFT, Zernike Moment, GLOH, PCA-SIFT, and SURF). Experimental results demonstrate that our IPCET descriptor outperforms cutting-edge moment-based descriptors. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Modeling viral evolution:A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics

微生物与宿主健康（英文）

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微生物与宿主健康（英文） 2024年第5期2卷 227-245页

作者： Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangdongChina Respiratory Disease Al Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacau University of Science and TechnologyMacauChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Macau Center for Mathematical Sciences Macau University of Science and TechnologyMacauChina Respiratory Disease Al Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacau University of Science and TechnologyMacauChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Department of Engineering Science Faculty of Innovation EngineeringMacau University of Science and TechnologyMacauChina

Understanding evolutionary trends in emerging viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is crucial for effective public health management and ***,extensive debates have arisen concerning viral evolutionary trends,particularly the interplay between transmissibility,pathogenicity,and im-mune *** this context,we have developed a novel computational model named SIRSVIDE(Susceptible-In-fected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolu-tionary dynamics of viral *** simulation results indicate that under conditions of high mutation rates,elevated transmission rates,and larger susceptible host populations,viral populations exhibit prolonged increases in transmissibility and immune escape,accompanied by reductions in pathogenicity and noticeable short-term ***,when the total susceptible population size and mutation rate decrease,substantial uncertainty in the evolutionary trends of viral populations becomes *** summary,the SIRSVIDE model establishes a comprehen-sive framework for generating both short-and long-term viral epidemiological and evolutionary *** simula-tion outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened trans-missibility,decreased pathogenicity,and enhanced immune ***,the model sheds light on the possible evolutionary dynamics of other viruses.

关键词： viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape

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A WiFi-aware method for mobile data offloading with deadline constraints

A WiFi-aware method for mobile data offloading with deadline...

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作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

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Threshold Image Segmentation for Non-uniform Illumination Using Otsu Optimization Approach 6th

Threshold Image Segmentation for Non-uniform Illumination Us...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Quan, Liang Zicheng, Zhang Jinjing, Hu Chang, Kuo-Chi School of Computer Science and Mathematics Fujian University of Engineering Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology No3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

With an improved algorithm based on the Otsu method, this paper proposes solving the shadow region problem caused by non-uniform illumination. The recent image processing algorithms for handling the shaded area’s issue for the massive difference in the image grey value will not be practical. It is not easy to directly set the shaded area’s content to "black" based on this problem. This paper proposes improved threshold image segmentation for Non-uniform illumination based on the Otsu optimization Approach. It uses the local area threshold and window image pixel by sliding to enhance the image recognition rate of a shaded area. Experimental results verify that the proposed segmentation method has more robust adaptability to images with shadows. Compared with the classical global OTSU algorithm and genetic algorithm optimization, the image recognition rate is 96%. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image recognition

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Component Fault Diagnosability of Hierarchical Cubic Networks

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ACM Transactions on Design Automation of Electronic Systems 2021年第3期28卷 1-19页

作者： Huang, Yanze Wen, Kui Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology No. 3 Xueyuan Road Fujian Fuzhou China College of Computer and Cyber Security Fujian Normal University No. 8 Xuefu South Road Fujian Fuzhou350117 China Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan70101 Taiwan

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r-component diagnosability ctr(G) of a network G imposes the restriction that the number of components is at least r in the remaining network of G by deleting faulty set X, which enhances the diagnosability of G. In this article, we establish the r-component diagnosability for n-dimensional hierarchical cubic network HCNn, and we show that, under both PMC model and MM∗model, the r-component diagnosability of HCNn is rn-½(r-1)r+1 for n≥ 2 and 1≤ r≤ n-1. Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM∗subgraph of HCNn. Then, we make use of 0-PMC subgraph and 0-MM∗subgraph of HCNn to design two algorithms under PMC model and MM∗model, respectively, which are practical and efficient for component fault diagnosis of HCNn. Besides, we compare the r-component diagnosability of HCNn with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the r-component diagnosability of HCNn is higher than the other types of diagnosability. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Failure analysis

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Pixel-based agricultural drought forecasting based on deep learning approach: Considering the linear trend and residual feature of vegetation temperature condition index

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Computers and Electronics in Agriculture 2025年 237卷

作者： Guo, Fengwei Wang, Pengxin Tansey, Kevin Sun, Yuanfei Li, Mingqi Zhou, Ji College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China School of Geography Geology and the Environment University of Leicester LeicesterLE1 7RH United Kingdom School of Resources and Environment University of Electronic Science and Technology of China Chengdu611731 China

Drought is one of the most recurring natural hazards worldwide, causing serious damages on crop yield and economic development, so accurate agricultural drought forecasting in advance is essential for reducing disaster risk and enhancing agricultural management. Traditional statistical models, represented by autoregressive integrated moving average (ARIMA) model, demonstrate huge advantages in drought index based-forecasting, but overlook the complex underlying features inherent within the time series remotely sensed variables, such as residual feature. Long short-term memory (LSTM) is proven as an effective deep learning method for capturing nonlinear relationships, and is suitable for dealing with residual feature of time series drought index. With the study aim to explore the potential of a drought forecasting model constituted by a statistical model and a deep learning model, an advanced ARIMA-LSTM drought forecasting model was constructed by combining the linear expressiveness ability of the ARIMA model and the advantage in capturing the nonlinear relationship of the LSTM model. The proposed model considered both the linear trend and residual feature in the time series vegetation temperature condition index (VTCI) for improving the accuracy of drought forecasting in the Sichuan Basin, PR China. The results show that ARIMA-LSTM (RMSE = 0.06) achieved better forecast accuracy compared to the ARIMA model (RMSE = 0.10) without considering residual feature. In addition, the ARIMA-LSTM model more accurately expressed the severe drought condition in the east parallel ridge and valley and the wet condition in the central hilly area of the basin, presenting a more detailed spatial distribution that was closer to the drought monitoring results. From the quantitative analysis of forecasting errors, the absolute error range of the ARIMA-LSTM model was narrowed from [-0.6, 0.2] to [-0.2, 0.1] after the introduction of the residual feature. In summary, the ARIMA-LSTM drought

关键词： Time series

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IoT-Driven Predictive Analytics for Precision Harvest Timing with LightGBM Models

IoT-Driven Predictive Analytics for Precision Harvest Timing...

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Visual Analytics and data Visualization (ICVADV), International Conference on

作者： S. Lourdu Jame Chitra Sabapathy Ranganathan S.K. Saravanan Bharat Tidke E. Punarselvam M. Rajmohan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Mphasis Corporation Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Pune India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In agriculture, precise harvest time is essential to maximize production and quality. A predictive analytics system powered by the Internet of Things (IoT) and using LightGBM models for accurate harvest time determination is presented in this study. The framework incorporates IoT sensors to gather environmental data, such as soil moisture, light intensity, temperature, and humidity, which are important markers of crop maturity. The data is analyzed using machine learning models, namely LightGBM, to estimate when to harvest accurately. The proposed framework seeks to improve agricultural efficiency by using real-time data analytics. This would allow farmers to make better crop output, waste, and resource utilization choices. Farmers may dynamically adjust their harvesting plans in response to changing crop conditions due to the IoT-driven strategy that enables constant monitoring of environmental variables. By reducing input waste and environmental effects, this capacity enhances agricultural operations' total productivity while supporting sustainable farming practices. The proposed model can transform harvest management using advanced predictive analytics and IoT technologies, as shown by case studies and simulations confirming its efficacy in several agricultural settings.

关键词： Temperature sensors Analytical models Accuracy Humidity Predictive models Agriculture Real-time systems data models Sensors Predictive analytics

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