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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image Inpainting

arXiv

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arXiv 2024年

作者： Zeng, Jianshun Li, Wang Lv, Yanjie Gao, Shuai Qin, YuChu Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

Street-view image has been widely applied as a crucial mobile mapping data source. The inpainting of street-view images is a critical step for street-view image processing, not only for the privacy protection, but also for the urban environment mapping applications. This paper presents a novel Deep Neural Network (DNN), multi-scale semantic prior Feature guided image inpainting Network (MFN) for inpainting street-view images, which generate static street-view images without moving objects (e.g., pedestrians, vehicles). To enhance global context understanding, a semantic prior prompter is introduced to learn rich semantic priors from large pre-trained model. We design the prompter by stacking multiple Semantic Pyramid Aggregation (SPA) modules, capturing a broad range of visual feature patterns. A semantic-enhanced image generator with a decoder is proposed that incorporates a novel cascaded Learnable Prior Transferring (LPT) module at each scale level. For each decoder block, an attention transfer mechanism is applied to capture long-term dependencies, and the semantic prior features are fused with the image features to restore plausible structure in an adaptive manner. Additionally, a background-aware data processing scheme is adopted to prevent the generation of hallucinated objects within holes. Experiments on Apolloscapes and Cityscapes datasets demonstrate better performance than state-of-the-art methods, with MAE, and LPIPS showing improvements of about 9.5% and 41.07% respectively. Visual comparison survey among multi-group person is also conducted to provide performance evaluation, and the results suggest that the proposed MFN offers a promising solution for privacy protection and generate more reliable scene for urban applications with street-view images. © 2024, CC BY.

关键词： Deep neural networks

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Multi-Semantic Markov Random Field Model for Semantic Segmentation of High-Resolution Remote Sensing Images

Multi-Semantic Markov Random Field Model for Semantic Segmen...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Haoyu Fu Chen Zheng Weiheng Xu Yili Zhao Leiguang Wang Faculty of Forestry Southwest Forestry University Kunming China School of Mathematics and Statistics Henan University Kaifeng China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Landscape Architecture and Horticulture Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Remote sensing images have a large imaging field, covering a wide range of land surfaces and encompassing complex geographical scenes. More diverse land cover types and detailed structural information within geographical scenes can be observed with improved image spatial resolution. However, due to the high cost of annotating fine semantic scales, only a few semantic segmentation methods can fully utilize multi-level semantic information. This paper introduces a novel multi-semantic Markov Random Field (MRF-MS) model for semantic segmentation of high-resolution remote sensing images. This method extends the classical object-level Markov Random Field model by introducing a sub-label field to capture semantic hierarchical information in high-resolution images. Assuming the initial categories follow a mixture of Gaussian distribution, this method models the land cover types or structural details of geographical objects within the scene by decomposing the mixture of Gaussian distribution into sub-components. Furthermore, it establishes an interactive contextual structure between the sub-label and initial label fields to model the interaction across different semantic scales. Experimental results on multi-scene high-resolution remote sensing images demonstrate that this approach yields more accurate classification results compared to the latest MRF methods.

关键词： Costs Semantic segmentation Semantics Land surface Imaging Gaussian distribution Spatial resolution

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Learn to Rectify Label Through Kernel Extreme Learning Machine 16th

Learn to Rectify Label Through Kernel Extreme Learning Machi...

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16th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2020

作者： Cai, Qiang Li, Fenghai Li, Haisheng Cao, Jian Li, Shanshan School of Computer Beijing Technology and Business University Beijing China National Engineering Laboratory for Agri-product Quality Traceability Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China

ISBN: (纸本)9783030775681

Recent studies attempt to construct complicated and redundant Convolutional Neural Networks (CNNs) to improve image classification performance. In this paper, instead of painstakingly designing a CNN’s architecture, we consider promoting classification performance by revising CNN’s classification results. We therefore propose a novel image classification approach that Learns to Rectify Label (LRL) through Kernel Extreme Learning Machine (KELM). It includes two phases: (1) Pre classification, we put images into a trained CNN to generate corresponding incomplete labels. (2) Label Rectification, the incomplete labels are rectified by the KELM’s high-dimensional mapping, so final classification results are acquired. Extensive experiments conducted on public datasets demonstrate the effectiveness of our method. At the meantime, our method has well generalizability that can be integrated with many popular networks. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Image classification

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Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

SSRN

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SSRN 2024年

作者： Fan, Yu Jing, Siyu Zhan, Min Zhang, Jian School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory of Big Data Decision Making for Green Development Beijing100192 China School of Digital Media Engineering and Humanities Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory School of Business Central South University Changsha410083 China Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application Beijing100192 China

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results show the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOX (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOX and CO2 emissions were stronger driven by final effects structure and types, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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CIMI4D: A Large Multimodal Climbing Motion dataset under Human-scene Interactions

arXiv

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arXiv 2023年

作者： Yan, Ming Wang, Xin Dai, Yudi Shen, Siqi Wen, Chenglu Xu, Lan Ma, Yuexin Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University China

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock ClImbing MotIon dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://***/cimi4d/. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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Developing a new red band- SEVI-blue band (RSB) enhancement method for recognition the extra-high-voltage transmission line corridor in green mountains

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International Journal of Digital Earth 2023年第1期16卷 806-824页

作者： Hong Jiang Yong Zhang Jinglan Lin Xiaogan Zheng Hui Yue Yunzhi Chen Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education National&Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyAcademy of Digital China(Fujian)Fuzhou UniversityFuzhouPeople’s Republic of China Fujian Soil and Water Conservation Experimental Station FuzhouPeople’s Republic of China Lab of Live Working Tech for Power Transmission Fuzhou Power Supply Company of State Grid Fujian Electric Power Co.Ltd.FuzhouPeople’s Republic of China Fujian(Changting)Soil and Water Conservation Research Center Soil and Water Conservation Center of Changting CountyLongyanPeople’s Republic of China

Monitoring the extra-high-voltage transmission line corridor(EHVTLC)in mountains is critical for safe smart-grid ***,the transmission lines are so narrow that they are difficult to recognize using multispectral satellite images with a spatial resolution of 10 *** this study,we developed a new method using the red band–shadow-eliminated vegetation index(SEVI)–blue band(RSB)composite image to enhance the EHVTLC in green mountains(named RSB-enhancement method).Using this method,the EHVTLC becomes evident in the false-color synthesis of the RSB composite of the Sentinel-2 ***,we recognized and extracted approximately 342.45 km of the EHVTLC in a mountainous region of Fuzhou City,China,including a 46.73 km three-parallel-lane segment of 1000 kV and a 295.72 km two-parallel-lane segment of 500 *** analysis shows that the SEVI mean difference between the EHVTLC and the buffer zone reaches approximately 10%,and three landslides and 2.66 km^(2) soil erosion reside in the buffer zone which area is approximately 73.67 km^(2).Finally,the RSB-enhancement method can be used in other satellite images with spatial resolutions of greater than 10 m for enhancement and recognition the transmission line corridors in green mountains.

关键词： Enhancement method shadow-eliminated vegetation index(SEVI) transmission line lane(TLL) green mountains soil erosion

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Used-Car Price Evaluation Using Mean Encoding and PCA based DeepFM

Used-Car Price Evaluation Using Mean Encoding and PCA based ...

引用

2021 China Automation Congress, CAC 2021

作者： Yin, Xuyang Liu, Luyao Xu, Xuefan Xiao, Wendong School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Guangdong Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the used-car industry, the demand for used-car price evaluation is increasing. Existing studies combine simple data preprocessing method with the traditional machine learning model, which cannot effectively model the complex interactions between features. In this paper, we propose a deep learning approach for used-car price evaluation which combines Mean Encoding, Principal Component Analysis (PCA) and Deep Factorization Machine (DeepFM). Firstly, the approach preprocesses the used-car data through Mean Encoding and PCA to avoid the high dimension of features. Then, the approach utilizes DeepFM to learn low-order and high-order interactions of the preprocessed features, and the output of network is taken as the price of used-car. We use real used-car trading data from Aliyun for experiments. Compared with LR, FM and DNN, the proposed approach can achieve superior performance of used-car price evaluation more efficiently. © 2021 IEEE

关键词： Principal component analysis

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative database

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, Xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative dataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models 39

Expensive Multi-Objective Bayesian Optimization Based on Dif...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Bingdong Di, Zixiang Lu, Yongfan Qian, Hong Wang, Feng Yang, Peng Tang, Ke Zhou, Aimin Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai 200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Computer Science Wuhan University Wuhan 430072 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

ISBN: (纸本)157735897X

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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