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Intelligent Distributed Computing XIII
This book gathers research contributions on recent advances in intelligent and distributed computing. A major focus is placed on new techniques and applications for several highlydemanded research directions: Internet of Things, Cloud Computing and Big Data, Data Mining and Machine Learning, Multi-agent and Service-Based Distributed Systems, Distributed Algorithms and Optimization, Modeling Operational Processes, Social Network Analysis and Inappropriate Content Counteraction, Cyber-Physical Security and Safety, Intelligent Distributed Decision Support Systems, Intelligent Human-Machine Interfaces, VisualAnalytics and others. The book represents the peer-reviewed proceedings of the 13thInternational Symposium on Intelligent Distributed Computing (IDC 2019), which was held in St. Petersburg, Russia, from October 7 to 9, 2019.
Proceedings
None
2016 Intl IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC ATC ScalCom CBDCom IoP SmartWorld)
Ubiquitous sensors, devices, networks and information are paving the way towards a smart world in which computational intelligence is distributed throughout the physical environment to provide reliable and relevant services to people This ubiquitous intelligence will change the computing landscape because it will enable new breeds of applications and systems to be developed and the realm of computing possibilities will be significantly extended By enhancing everyday objects with intelligence, many tasks and processes could be simplified, the physical spaces where people interact like the workplaces and homes, could become more efficient, safer and more enjoyable Ubiquitous computing, or pervasive computing, uses these many smart things or u things to create smart environments, services and applications
Parallel, Distributed and Network-Based Processing
"IEEE Computer Society Order Number P3544"--T.p. verso.
Lessons Learned in Analytics from the COVID-19 Pandemic
At the beginning of 2020, as the COVID-19 pandemic swept across the US in multiple waves, health systems had to rapidly develop systems for tracking various aspects related to managing the pandemic. This included not just overall trends in incidence, hospitalizations, and outcomes; but also metrics related to the response. COVID-19 was the first pandemic in the United States since the widespread adoption of electronic health records incentivized by the Meaningful Use program. As a result, the availability of health information was much broader than in any previous pandemic. The widespread impact of COVID-19 also meant that every healthcare institution was affected, and was tracking data related to the pandemic in some form. There has been more focused activity with data and analytics regarding COVID-19 than we have ever had with any other disease, including important advances as well as technical and regulatory obstacles.
Parallel Computing is Everywhere
The most powerful computers work by harnessing the combined computational power of millions of processors, and exploiting the full potential of such large-scale systems is something which becomes more difficult with each succeeding generation of parallel computers. Alternative architectures and computer paradigms are increasingly being investigated in an attempt to address these difficulties. Added to this, the pervasive presence of heterogeneous and parallel devices in consumer products such as mobile phones, tablets, personal computers and servers also demands efficient programming environments and applications aimed at small-scale parallel systems as opposed to large-scale supercomputers....
High performance computing for solving large sparse systems. Optical diffraction tomography as a case of study
This thesis, entitled €High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study€ investigates the computational issues related to the resolution of linear systems of equations which come from the discretization of physical models described by means of Partial Differential Equations (PDEs). These physical models are conceived for the description of the space-temporary behavior of some physical phenomena f(x, y, z, t) in terms of their variations (partial derivative) with respect to the dependent variables of the phenomena. There is a wide variety of discretization methods for PDEs. Two of the most well-known methods are the Finite Diff...
European Control Conference 1991
Proceedings of the European Control Conference 1991, July 2-5, 1991, Grenoble, France
