Decentralized load balancing in distributed systems

Neelakantan, P.; Reddy, A. Rama Mohana

doi:10.1556/pollack.9.2014.2.2

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…The combination and reconciliation of the different HL7 versions are handled successfully within the OTI-Hub logic. The conversion of the body-sensory smart device output data stream into meaningful HL7 interface information is a significant challenge [25]. The spirometry output interface file was emitted and processed by the OTI-Hub correctly:…”

Section: Resultsmentioning

confidence: 99%

Methodology for clinical integration of e-Health sensor-based smart device technology with cloud architecture

et al. 2017

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The international healthcare systems interoperability is an unresolved technological area at the moment. This paper demonstrates the results of the software engineering research for simplex syntactic and semantic technical interoperability of hospital information systems, eHealth smart device technology and clinical telemedicine instruments through the recently developed Telemedicine Interoperability Hub. Several similar experiments exist. This research is unique in building a prototype interconnecting not only healthcare information systems with each other, but aiming to establish a general healthcare interoperability scheme including also the eHealth smart devices and telemedicine instruments. The aim of this research is to establish cloud-based data interchange capability with the newly developed information technology system interconnected with the emerging eHealth Internet of Things solutions and the classical hospital information system architecture. Notwithstanding the international information technology medical data exchange standards, like Health Level Seven, the adoption of an industry-wide open telemedicine syntactic and semantic interoperability standard is necessary. The research studied varying simplex, duplex, full-duplex, data package-and file-based information technology modalities establishing stable system interconnection among clinical instruments, healthcare systems and eHealth smart devices. This research is the manifestation of the trilateral cooperation of the

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Section: Resultsmentioning

confidence: 99%

Methodology for clinical integration of e-Health sensor-based smart device technology with cloud architecture

et al. 2017

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“…From the resulting data that was suitable for the networks three main datasets were generated, which were training on a validation and a test dataset. The training dataset is suitable to train the features of prediction and configure the weights of the network's variables during the training process [7]. The validation dataset is used to stop the training process at the right time.…”

Section: About the Data Usedmentioning

confidence: 99%

Ai Based Detection of Gas Hydrate Formation in the Field

Bölkény

Czap

2020

Pollack

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During the production of natural gas one of the major problems is the formation of hydrate crystals in the pipeline. The forming hydrate crystals can form hydrate plugs in the pipeline. The hydrate plugs lengthen production outages and result in financial losses for the producer, because the removal of the plugs is a time consuming procedure. One of the solutions used to prevent hydrate formation is the injection of modern compositions to the gas flow. The modern compositions help to dehydrate the gas, thus, the size of hydrate crystals does not increase. The substances, used in low concentrations, have to be locally injected, at the gas well sites. Inhibitor dosing depends on the amount of gas hydrate present. In the article a neural network based predictive detection solution is presented, which uses four factors.

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“…The trackers can collect multiple information about the wearer and they could send them to a telemedicine system that can process the received data and send notification or alert if it is necessary [14]. These telemedicine systems could be integrated to any other health-related systems.…”

Section: Sensor-based Adaptive E-health Systemsmentioning

confidence: 99%

Revolutionizing Healthcare with IoT and Cognitive, Cloud-based Telemedicine

2019

APH

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Telemedicine instruments and e-Health mobile wearable devices are designed to enhance patients' quality of life. The adequate man-and-machine cognitive ecosystem is the missing link for that in healthcare. This research program is dedicated to deliver the suitable solution. This research's goal is the establishment of adaptive informatics framework for telemedicine. This is achieved through the deployed open telemedicine interoperability hub-system. The presented inter-cognitive sensor-sharing system solution augments the healthcare ecosystem through extended interconnection among the telemedicine, IoT e-Health and hospital information system domains. The general purpose of this experiment is building an augmented, adaptive, cognitive and also universal healthcare information technology ecosphere. This study structures the actual questions and answers regarding the missing links and gaps between the emerging Sensor Hub technology and the traditional hospital information systems. The Internet-of-Things space penetrated the personal and industrial environments. The e-Health smart devices are neither widely accepted nor deployed in the ordinary healthcare service. This paper reviews the major technological burdens and proposes necessary actions for enhancing the healthcare service level with Sensor Hub and Internet-of-Things technologies. Hereby we report the studies on varying simplex, duplex, full-duplex, data package-and file-based information technology modalities. We establish with that stable system interconnection among clinical instruments, healthcare systems and eHealth smart devices. Our research is based on the trilateral cooperation comprising the

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Decentralized load balancing in distributed systems

Cited by 8 publications

References 6 publications

Methodology for clinical integration of e-Health sensor-based smart device technology with cloud architecture

Methodology for clinical integration of e-Health sensor-based smart device technology with cloud architecture

Ai Based Detection of Gas Hydrate Formation in the Field

Revolutionizing Healthcare with IoT and Cognitive, Cloud-based Telemedicine

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