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digital engineering school on the way to digital production

Journal of Physics: Conference Series 2020年第1期1691卷

作者： E E Kovshov S M Lesin V S Kuvshinnikov JSC 'Research and Development Institute of Construction Technology–Atomstroy' Scientific Engineering and Educational Laboratory of Digital Computer Systems and Automation Moscow Russian Federation Moscow City University Laboratory of research of educational policy of Department of the strategic growth Institute of system projects Laboratory of professional development in education Moscow Russian Federation

Identifies approaches for improving and enhancing the quality of pre, basic, and additional school' engineering education according to key paradigm 'Industry 4.0' based on the development and wide application of information and communication technology with opportunities in research areas of Industrial Informatics. As an example of implementation, innovative research and educational project of the digital engineering school is considered, including designed for the practical implementation of continuous engineering education 'School-University-Enterprise'. The main objectives of the project 'digital engineering school' are formulated and disclosed, the approach to teaching programming in high-level languages using microcontrollers and microprocessor technology is highlighted, practical recommendations are given for its implementation in the structure of school education for both students and teachers.

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Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing systems with Multi-Access Schemes

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Journal of Communications and Information Networks 2018年第4期3卷 33-44页

作者： Meng Hua Yongming Huang Yi Wang Qingqing Wu Haibo Dai Lyuxi Yang School of Information Science and Engineering Southeast UniversityNanjing 210096China School of Electronics and Communication Engineering Zhengzhou University of AeronauticsZhengzhou 450046China National Digital Switching System Engineering and Technological Research Center Zhengzhou 450002China Department of Electrical and Computer Engineering National University of SingaporeSingapore School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China

In this paper,a cellular-connected unmanned aerial vehicle(UAV)mobile edge computing system is studied where several UAVs are associated to a terrestrial base station(TBS)for computation *** compute the large amount of data bits,a part of computation task is migrated to TBS and the other part is locally handled at *** goal is to minimize the total energy consumption of all UAVs by jointly adjusting the bit allocation,power allocation,resource partitioning as well as UAV trajectory under TBS’s energy *** deeply comprehending the impact of multi-UAV access strategy on the system performance,four access schemes in the uplink transmission is considered,i.e.,time division multiple access,orthogonal frequency division multiple access,one-by-one access and non-orthogonal multiple *** involved problems under different access schemes are all formulated in non-convex forms,which are difficult to be tackled *** solve this class of problem,the successive convex approximation technique is employed to obtain the suboptimal *** numerical results show that the proposed scheme save significant energy consumption compared with the benchmark schemes.

关键词： mobile edge computing UAV trajectory bit allocation power allocation resource partitioning energy minimization

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting system (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fan Mou Rui Kong Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Zhigang Liu Ankai Hou Ling Jiang Shengli Wang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

关键词： Biological system modeling Deep learning Probabilistic logic Roads Urban areas Laboratories Data mining

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A virtual service placement approach based on improved quantum genetic algorithm

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Frontiers of Information Technology & Electronic engineering 2016年第7期17卷 661-671页

作者： Gang XIONG Yu-xiang HU Le TIAN Ju-long LAN Jun-fei LI Qiao ZHOU National Digital Switching System Engineering&Technological Research Center Zhengzhou 450002China Department of Mathematics and Computer Science University of AntwerpAntwerp 2020Belgium

Despite the critical role that middleboxes play in introducing new network functionality,management and innovation of them are still severe challenges for network operators,since traditional middleboxes based on hardware lack service flexibility and ***,though new networking technologies,such as network function virtualization(NFV) and softwaredefined networking(SDN),are considered as very promising drivers to design cost-efficient middlebox service architectures,how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for ***,we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and ***,a framework is designed for service placement decision,and an integer linear programming model is proposed to resolve the service placement and minimize the network transport *** a heuristic solution is designed based on the improved quantum genetic *** results show that our proposed method can calculate automatically the optimal placement *** scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.

关键词： Software-defined networking(SDN) Network function virtualization Quantum genetic algorithm Middlebox

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CLASSIFICATION BASED ON CAPSULE NETWORK WITH HYPERSPECTRAL IMAGE

CLASSIFICATION BASED ON CAPSULE NETWORK WITH HYPERSPECTRAL I...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yi Me Zezhong Zheng Zhengqiang Guo Fan Mou Fangrong Zhou Rui Kong Ankai Hou Mingcang Zhu Yong He Juan Ren Huaixin Chen Zhigang Liu Guoqing Zhou Jiang Li Yunnan Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming Yunnan PRC School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan 611731 Department of Natural Resources of Sichuan Province Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.

关键词： Hyperspectral imaging Training Image classification Principal component analysis Logistics

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High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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Low-complexity message passing based massive MIMO channel estimation by exploiting unknown sparse common support with dirichlet process

arXiv

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arXiv 2017年

作者： Yuan, Zhengdao Zhang, Chuanzong Wang, Zhongyong Guo, Qinghua National Digital Switching System Engineering and Technological Research and Development Center Zhengzhou Institute of Information Science and Technology Zhengzhou450001 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia School of Electrical Electronic and Computer Engineering University of Western Australia CrawleyWA6009 Australia

This paper investigates the problem of estimating sparse channels in massive MIMO systems. Most wireless channels are sparse with large delay spread, while some channels can be observed having sparse common support (SCS) within a certain area of the antenna array, i.e., the antenna array can be grouped into several clusters according to the sparse supports of channels. The SCS property is attractive when it comes to the estimation of large number of channels in massive MIMO systems. Using the SCS of channels, one expects better performance, but the number of clusters and the elements for each cluster are always unknown in the receiver. In this paper, the Dirichlet process is exploited to model such sparse channels where those in each cluster have SCS. We proposed a low complexity message passing based sparse Bayesian learning to perform channel estimation in massive MIMO systems by using combined BP with MF on a factor graph. Simulation results demonstrate that the proposed massive MIMO sparse channel estimation outperforms the state-of-the-art algorithms. Especially, it even shows better performance than the variational Bayesian method applied for massive MIMO channel estimation. Copyright © 2017, The Authors. All rights reserved.

关键词： MIMO systems

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