Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19144
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dc.contributor.authorChen, J-
dc.contributor.authorAbbod, M-
dc.contributor.authorShieh, JS-
dc.date.accessioned2019-09-16T14:10:04Z-
dc.date.available2019-09-10-
dc.date.available2019-09-16T14:10:04Z-
dc.date.issued2019-08-29-
dc.identifier.citationSensors, 2019en_US
dc.identifier.issn1424-8220-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/19144-
dc.description.abstractThe emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectautonomousen_US
dc.subjectintelligent control systemen_US
dc.subjectmachine learningen_US
dc.subjectloTen_US
dc.subjectbig dataen_US
dc.titleIntegrations between Autonomous System and Modern Computing Techniques: A Mini-reviewen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/s19183897-
dc.relation.isPartOfSensors-
pubs.publication-statusPublished-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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