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[Retracted] IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Security and Communication Networks 2023年第1期2023卷

作者： Liwa H. Al-Farhani Yahya Alqahtani Hamdan Ali Alshehri R. John Martin Sachin Lalar Rituraj Jain System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russiarudn.ru Faculty of Computer Science and Information Technology Jazan University Saudi Arabiajazanu.edu.sa Department of Computer Science and Applications Kurukshetra University Kurukshetra Indiakuk.ac.in Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopiawollegauniversity.edu.et

The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.

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control system DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND control PERFORMANCE

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Asian Journal of control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： control system design LMI BMI tradeoff curves

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HYBRID SATELLITE AND TERRESTRIAL NETWORKS

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第3期12卷 313-327页

作者： CHITRE, DM SHYY, DJ EPHREMIDES, A GUPTA, S COMSAT Laboratories 22300 Comsat Drive Clarksburg MD 20871–9475 USA. Received his B.Sc. from the University of Bombay India an M.A. in mathematics from the University of Cambridge U.K. and a Ph.D. in physics from the University of Maryland. He is currently an Associate Executive Director of the Network Technology Division at COMSAT Laboratories. He has been involved in research and development activities in ISDN VSAT networks data communications and network systems and architectures. Prior to his current positions Dr. Chitre was a Principal Scientist in the Network Technology Division at COMSAT Laboratories. Dr. Chitre joined COMSAT Laboratories in 1980. He has made major contributions to the analysis and architecture of data communication ISDN and BISDN via satellite. Dr. Chitre directs and participates in the international and national standards activities in ISDN BISDN and data communication as they apply to satellite communication. He was Chairman of the Working Group on Protocols and Network Timing Function of the CCIR/CCITT Joint Ad Hoc Group on ISDN/Satellite Matters during 1990–1992. Currently he is the Chairman of the Working Group on New Technologies in the ITU Intersector Coordinating Group (ICG) on Satellite Matters. Dr. Chitre was a programme manager during 1990 and 1991 on a contract from INTELSAT on systems studies on satellite communications systems architectures for ISDN and broadband ISDN systems. Currently he is the technical manager of the DoD Contract on ATM via satellite demonstration and the programme manager for the INTELSAT contract on analysis and top-level specification of INTELSAT ISDN subnetworks and SDH compatible transport network. Received the B.S. degree in electrical engineering from national Chiao-Tung University Hsin-Chu Taiwan in 1983 and the M.S. and Ph.D. degrees in electrical engineering from Georgia Institute of Technology Atlanta GA in 1986 and 1990 respectively. From June 1987 to October 1987 he worked for the Department of Neurology Emory Univers

The role of satellite communications in networks that provide new services, such as frame relay and multimedia, is investigated. Both passive and active (on-board switching/processing) satellite systems are considered. Novel techniques are developed for each system to demonstrate, via detailed analysis and simulation, how the communications bandwidth agility of multipoint/broadcast satellite channels, and the on-board switching/processing, makes it feasible to provide these new services via hybrid satellite and terrestrial networks in a resource-efficient manner.

关键词： HYBRID NETWORKS ON-BOARD processing FRAME RELAY

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COMMERCIAL APPLICATIONS OF ADVANCED MARINE VEHICLES FOR EXPRESS SHIPPING

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NAVAL ENGINEERS JOURNAL 1983年第3期95卷 283-300页

作者： LUEDEKE, G FARNHAM, RB JR. George Luedeke Jr.: received his BS degree in Mechanical Engineering from Massachusetts Institute of Technology and his MS degree in Product Design from Illinois Institute of Technology. Early in his career Mr. Luedeke joined General Motors Corporation as a designer responsible for development of people mover and rail rapid transit systems. From 1964 to 1974 he was with Hughes Aircraft Company. At Hughes he performed analyses and developed designs for a wide variety of program and proposal efforts such as: High Speed Ground Transportation (DOT) Task Force Command Center (NAVY) Panama Canal Marine Traffic Control Center (Panama Canal Co.) Royal Iranian Navy Command Center (Iran) Tactical Information Processing and Interpretation Center (Air Force) and WALLEYE CONDOR and PHOENIX Missile Systems (NAVY). He also had marketing development responsibilities related to the diversification of Hughes resources in civil business areas such as: Automatic train control (WMATA BARTD SCRTD) water/sewage treatment plant automation (Santa Clara County) Aqueduct Control (SWR) Hydrometeorological data collection (BPA WMO) and Salton Sea basin systems analysis (Dept. of the Interior). He was responsible for combat system integration for the Hughes 2000T Surface Effect Ship (SES) proposal. He also conducted detailed studies concerning ship flexure for the Improved Point Defense Target Acquisition System Program and for the definition of operational High Energy Laser weapon installations on a series of conventional monohulls (DLG DD and CVN). Since 1974 Mr. Luedeke has been employed at RMI Inc. (formerly Rohr Marine Inc.). During this time he has held several positions. His responsibilities have included directing a number of studies on advanced SES concepts managing activities defining mission/cost effectiveness of military and commercial SES's including defining the operational benefits and enhanced survivability characteristics of cargo SES's for high speed military sealiftfor NA TO and Southeast Asia

This paper will present the results of a marketing, engineering, and economic analysis of advanced marine vehicles done by IMA Resources, Inc. and RMI, Inc., in support of a Maritime Administration project to study “Multimode Express Shipping”. The study was conducted in 1981 and examined the economic benefits of using advanced marine vehicles as express cargo vessels in domestic and international service. Commodity characteristics, desirable express carrier rates, and potential high payoff service and route alternatives were identified. Advanced marine vehicles were surveyed and sized to meet desirable deadweight and block speed objectives. The costs of operating these craft on a variety of trade routes were calculated using an advanced marine vehicle economic analysis program. Revenues, expenses, break-even, profit and loss, cash flow requirements, tax summary and economic indicators (i.e., cost/ton – mile, etc.) were projected over the expected life of the vehicles as was return on investment. Traffic density and market penetration considerations narrowed the field of choice to smaller sized advanced marine vehicle carriers (i.e., 50 and 250 ton deadweight) and to three international and five domestic routes.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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