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arXiv 2021年

作者： Guo, di Tu, Zhangren Guo, Yi Zhou, Yirong Wang, Jian Wang, Zi Qiu, Tianyu Xiao, Min Chen, Yinran Feng, Liubin Huang, Yuqing Lin, donghai Hong, Qing Goldbourt, Amir Lin, Meijin Qu, Xiaobo The School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Xiamen University Xiamen361102 China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China

—Nuclear Magnetic resonance (NMr) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMr. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to self-adapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMr spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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retracted: Artificial intelligence point-to-point signal communication network optimization based on ubiquitous clouds

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International Journal of Communication Systems 2020年第6期34卷

作者： Lin Hong Lianbing deng daming Li Harry Haoxiang Wang School of Management Beijing Normal University at Zhuhai Zhuhai China The Post-Doctoral Research Center Zhuhai Da Hengqin Science and Technology Development Co. Ltd Zhuhai China Department of R&D Guangdong Qinzhi Science and Technology Research Institute Zhuhai China Department of R&D Huazhong University of Science and Technology Wuhan China Institute of Data Science City University of Macau Macau China Department of R&D GoPerception Laboratory Ithaca New York USA

At present, the application of communication network has spread to every area of social life. The progress of network technology has driven the development of information technology industry and the advancement of cloud computing. With the application of artificial intelligence and deep learning technology, the network becomes more and more intelligent, so the application of artificial intelligence theory in communication network optimization modeling is more extensive. In this paper, we propose the ubiquitous clouds framework and apply the artificial intelligence optimization scheme to the point-to-point (P2P) signal transmission network optimization scheme. For different application scenarios, this paper analyzes and experiments the proposed method. The experimental results show that the proposed method makes full use of the advantages of communication network resources which improves the efficiency of communication network optimization and also reduces the optimization cost to a certain extent compared with the state-of-the-art approaches.

关键词： artificial intelligence communication network neural network optimization particle swarm optimization (PSO) point-to-point transmission ubiquitous clouds

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Benchmarking the variational quantum eigensolver through simulation of the ground state energy of prebiotic molecules on high-performance computers

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AIP Conference Proceedings 2021年第1期2362卷

作者： P. Lolur M. rahm M. Skogh L. García-Álvarez G. Wendin 1)Department of Chemistry and Chemical Engineering Chalmers University of Technology SE-412 96 Gothenburg Sweden 2)Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden 3)Applied Quantum Physics Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology SE-412 96 Gothenburg Sweden 4)Quantum Technology Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology SE-412 96 Gothenburg Sweden

We use the Variational Quantum Eigensolver (VQE) as implemented in the Qiskit software package to compute the ground state energy of small molecules derived from water, H2O, and hydrogen cyanide, HCN. The work aims to benchmark algorithms for calculating the electronic structure and energy surfaces of molecules of relevance to prebiotic chemistry, beginning with water and hydrogen cyanide, and to run them on the available simulated and physical quantum hardware. The numerical calculations of the algorithms for small quantum processors allow us to design more efficient protocols to be run in real hardware, as well as to analyze their performance. Future implementations on accessible quantum processing prototypes will benchmark quantum computers and provide tests of quantum advantage with heuristic quantum algorithms.

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Skin disease detection using artificial intelligence

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AIP Conference Proceedings 2022年第1期2393卷

作者： S. Kuzhaloli L. M. Varalakshmi Kamal Gulati Makarand Upadhyaya Narinder Kumar Bhasin Vijayakumar Peroumal 1EEE Department Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology Chennai India 2Dept of ICE Sri ManakulaVinayagar Engineering College Puducherry India 3Data Analytics Amity School of Insurance Banking and Actuarial Science Amity University Noida India 4Department of Management & Marketing College of Business Administration Kingdom of Bahrain Bahrain 5CSIT Amity School of Insurance Banking and Actuarial Science Amity University Noida India 6School of Electronics Engineering Vellore Institute of Technology Chennai India

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the consumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application for recognition of skin diseases. The AI algorithm classified the images giving 5 differential diagnoses, which were then compared to the diagnoses made clinically by the dermatologists and/or histological. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development. Input signs have been developed to classify the disorder. With the aid of experts in the area, we received symptoms of 10 skin diseases. The symptom data were trained by various classifiers. We observed that high quality AI-based support for clinical decision making enhances the accurate diagnosis of either AI or doctors alone and that less skilled physicians are better served by AI.

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Advancing covid-19 diagnosis with privacy-preserving collaboration in artificial intelligence

arXiv

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arXiv 2021年

作者： Bai, Xiang Wang, Hanchen Ma, Liya Xu, Yongchao Gan, Jiefeng Fan, Ziwei Yang, Fan Ma, Ke Yang, Jiehua Bai, Song Shu, Chang Zou, Xinyu Huang, renhao Zhang, Changzheng Liu, Xiaowu Tu, dandan Xu, Chuou Zhang, Wenqing Wang, Xi Chen, Anguo Zeng, Yu Yang, dehua Wang, Ming-Wei Holalkere, Nagaraj Halin, Neil J. Kamel, Ihab r. Wu, Jia Peng, Xuehua Wang, Xiang Shao, Jianbo Mongkolwat, Pattanasak Zhang, Jianjun Liu, Weiyang roberts, Michael Teng, Zhongzhao Beer, Lucian Sanchez, Lorena Escudero Sala, Evis rubin, daniel Weller, Adrian Lasenby, Joan Zheng, Chuangsheng Wang, Jianming Li, Zhen Schönlieb, Carola-Bibiane Xia, Tian Department of Radiology Tongji Hospital and Medical College Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge United Kingdom Department of Radiology Union Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan China HUST-HW Joint Innovation Lab Wuhan China CalmCar Inc Suzhou China Wuhan Blood Centre Wuhan China MSA Capital Beijing China The National Centre for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China CardioVascular and Interventional Radiology Radiology for Quality and Operations The Cardiovascular Centre at Tufts Medical Centre Radiology Tufts University School of Medicine Medford United States Russell H Morgan Department of Radiology & Radiologic Science Johns Hopkins Hospital & Medicine Institute Baltimore United States Department of Radiation Oncology School of Medicine Stanford University Palo Alto United States Department of Radiology Wuhan Central Hospital Wuhan China Department of Radiology Wuhan Children's Hospital Wuhan China Faculty of Information and Communication Technology Mahidol University Thailand Thoracic/Head and Neck Medical Oncology Translational Molecular Pathology University of Texas MD Anderson Cancer Centre Houston United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D at AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Department of Biomedical Data Science Radiology and Medicine Stanford University Palo Alto United States Alan Turing Institute London United Kingdom Department of Hepatobiliary Pancreatic Surgery Affiliated Tianyou Hospital Wuhan University of Science and Technology Wuhan China

Artificial intelligence (AI) provides a promising substitution for streamlining COVId-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVId AI diagnostic Initiative (UCAdI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. © 2021, CC BY-NC-SA.

关键词： diagnosis

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Sketch-based Medical Image retrieval

arXiv

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arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIr) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIr) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIr system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIr system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIr system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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OpenHI2 - Open source histopathological image platform

OpenHI2 - Open source histopathological image platform

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Pargorn Puttapirat Haichuan Zhang Jingyi deng Yuxin dong Jiangbo Shi Hongyu He Zeyu Gao Chunbao Wang Xiangrong Zhang Chen Li National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University Department of Power System and Automation School of Electrical Engineering Xi'an Jiaotong University Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781728118680

Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats;ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges including responsive region retrieval and viewing, virtual slide magnification, recording of diagnostic areas. These factors would promote OpenHI2 be used as a platform for histopathological images in real-world clinical settings. Furthermore, in research, OpenHI2 inherited the annotation functionality from the previous version, thus acquired annotations can be directly utilized by the newly added machine learning module which include popular machine learning models to perform tasks such as histology image classification and segmentation in the same environment. Addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at .

关键词： OpenHI digital pathology histopathological image analysis open source software open source software pathology Platform Software Tools open source religious Missions External factors images

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β-delayed One and Two Neutron Emission Probabilities Southeast of Sn132 and the Odd-Even Systematics in r-Process Nuclide Abundances

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Physical review Letters 2022年第17期129卷 172701-172701页

作者： V. H. Phong S. Nishimura G. Lorusso T. davinson A. Estrade O. Hall T. Kawano J. Liu F. Montes N. Nishimura r. Grzywacz K. P. rykaczewski J. Agramunt d. S. Ahn A. Algora J. M. Allmond H. Baba S. Bae N. T. Brewer C. G. Bruno r. Caballero-Folch F. Calviño P. J. Coleman-Smith G. Cortes I. dillmann C. domingo-Pardo A. Fijalkowska N. Fukuda S. Go C. J. Griffin J. Ha L. J. Harkness-Brennan T. Isobe d. Kahl L. H. Khiem G. G. Kiss A. Korgul S. Kubono M. Labiche I. Lazarus J. Liang Z. Liu K. Matsui K. Miernik B. Moon A. I. Morales P. Morrall N. Nepal r. d. Page M. Piersa-Siłkowska V. F. E. Pucknell B. C. rasco B. rubio H. Sakurai Y. Shimizu d. W. Stracener T. Sumikama H. Suzuki J. L. Tain H. Takeda A. Tarifeño-Saldivia A. Tolosa-delgado M. Wolińska-Cichocka P. J. Woods r. Yokoyama RIKEN Nishina Center Wako Saitama 351-0198 Japan University of Science Vietnam National University Hanoi 120062 Vietnam National Physical Laboratory Teddington TW11 0LW United Kingdom Department of Physics University of Surrey Guildford GU2 7XH United Kingdom School of Physics and Astronomy University of Edinburgh Edinburgh EH9 3FD United Kingdom Department of Physics Central Michigan University Mount Pleasant Michigan 48859 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of Hong Kong Pokfulman Road Hong Kong National Superconducting Cyclotron Laboratory East Lansing Michigan 48824 USA Astrophysical Big-Bang Laboratory Cluster for Pioneering Research RIKEN Wako Saitama 351-0198 Japan Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Physics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Instituto de Fsica Corpuscular CSIC and Universitat de Valencia E-46980 Paterna Spain Center for Exotic Nuclear Studies Institute for Basic Science Daejeon 34126 Republic of Korea TRIUMF Vancouver British Columbia V6T 2A3 Canada Universitat Politecnica de Catalunya E-08028 Barcelona Spain STFC Daresbury Laboratory Daresbury Warrington WA4 4AD United Kingdom Department of Physics and Astronomy University of Victoria Victoria British Columbia V8P 5C2 Canada Faculty of Physics University of Warsaw PL02-093 Warsaw Poland Seoul National University Department of Physics and Astronomy Seoul 08826 Republic of Korea Department of Physics University of Liverpool Liverpool L69 7ZE United Kingdom Extreme Light Infrastructure—Nuclear Physics Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering (IFIN-HH) 077125 Bucharest-Măgurele Romania Institute of Physics Vietnam Academy of Science and Technology Ba Dinh 118011 Hanoi Vietnam Graduate University of Science and Technology Vietnam Academy of Science and Technology

The β-delayed one- and two-neutron emission probabilities (P1n and P2n) of 20 neutron-rich nuclei with N≥82 have been measured at the rIBF facility of the rIKEN Nishina Center. P1n of Ag130,131, Cd133,134, In135,136, and Sn138,139 were determined for the first time, and stringent upper limits were placed on P2n for nearly all cases. β-delayed two-neutron emission (β2n) was unambiguously identified in Cd133 and In135,136, and their P2n were measured. Weak β2n was also detected from Sn137,138. Our results highlight the effect of the N=82 and Z=50 shell closures on β-delayed neutron emission probability and provide stringent benchmarks for newly developed macroscopic-microscopic and self-consistent global models with the inclusion of a statistical treatment of neutron and γ emission. The impact of our measurements on r-process nucleosynthesis was studied in a neutron star merger scenario. Our P1n and P2n have a direct impact on the odd-even staggering of the final abundance, improving the agreement between calculated and observed Solar System abundances. The odd isotope fraction of Ba in r-process-enhanced (r−II) stars is also better reproduced using our new data.

关键词： Beta decay Nuclear astrophysics r process 90 ≤ A ≤ 149 radiation detectors radioactive beams

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Epidemiological and public health requirements for COVId-19 contact tracing apps and their evaluation

arXiv

引用

arXiv 2021年

作者： Colizza, Vittoria Grill, Eva Mikolajczyk, rafael Cattuto, Ciro Kucharski, Adam riley, Steven Kendall, Michelle Lythgoe, Katrina Abeler-dörner, Lucie Wymant, Chris Bonsall, david Ferretti, Luca Fraser, Christophe INSERM Sorbonne Université Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP Paris France Tokyo Tech World Research Hub Initiative Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan Institute für Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians University Muenchen Muenchen Germany Institute for Medical Epidemiology Biometrics and Informatics Interdisciplinary Center for Health Sciences Martin Luther University Halle-Wittenberg Halle Germany University of Turin Turin Italy ISI Foundation Turin Italy Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford United Kingdom Department of Statistics University of Warwick Warwick United Kingdom

digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust. © 2021, CC BY-NC-SA.

关键词： Public health

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Enabling AI in future wireless networks: A data life cycle perspective

arXiv

引用

arXiv 2020年

作者： Nguyen, dinh C. Cheng, Peng ding, Ming Lopez-Perez, david Pathirana, Pubudu N. Li, Jun Seneviratne, Aruna Li, Yonghui Poor, H. Vincent School of Engineering Deakin University Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Data61 CSIRO Australia Huawei France R&D Boulogne-Billancourt France School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk634050 Russia NSW Australia School of Electrical and Information Engineering University of Sydney Australia Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network device AI, Access AI, User device AI and data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area. © 2020, CC BY.

关键词： Wireless networks

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