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University of Sussex, United Kingdom

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2 Papers

Taxonomy for Internet of Things - Tools for Monitoring Personal Effects

Topics: Ubiquitous Computing Systems and Services; Human and Computer Interaction

Authors: Leon Barker , Martin White , Natalia Beloff , Tudor Pascu , Mairead Curran , Benji Huggins and Zeeshan Patoli

Affiliation: University of Sussex, United Kingdom

Digital Object Identifier: 10.5220/0004809500670071

Proceedings of PECCS 2014, ISBN: 978-989-758-000-0

Quick Abstract

Human capacities to track and identify peers, products and activities have benefited from the wide proliferation of networked electronic devices; developments in smart-materials, near-field communication and computervision technology further advance these capabilities. During this investigation into The Internet of Things (IoT) a new taxonomy was created together with a set of prototype applications and accompanying architecture. In addition to presenting these taxonomies the creation of a peer-to-peer network with a distributed database is discussed. The development of a system that enables users to track and exchange objects or services using a secure and robust data repository model is also proposed.

Related Ontology Subjects/Areas/Topics (8)

Applications and Services	Human and Computer Interaction	Telecommunications
Computer Vision, Visualization and Computer Graphics	Human-Computer Interaction	Ubiquitous Computing Systems and Services
Enterprise Information Systems	Mobile and Pervasive Computing

Experience-based Fuzzy Control of an Anthropomimetic Robot

Topics: Fuzzy Control in Robotics: Sensors, Navigation and Coordination; Fuzzy Systems Design, Modeling and Control

Authors: Veljko Potkonjak ¹ , Nenad Bascarevic ¹ , Predrag Milosavljevic ¹ , Kosta Jovanovic ¹ and Owen Holland ²

Affiliations: ¹ University of Belgrade, Serbia , ² University of Sussex, United Kingdom

Digital Object Identifier: 10.5220/0004117503890394

Proceedings of FCTA 2012/IJCCI, ISBN: 978-989-8565-33-4

Quick Abstract

This paper aims to present a novel experience-based solution for a black-box control problem, applied to an anthropomimetic robot. The control method is tested on a point to point control problem of a multi-jointed robot arm. The model characteristics – dynamics, kinematics, and control parameters – are considered as unspecified, and therefore we deal with a machine learning approach that follows the cybernetic concept of black-box. The only available data of the system are those obtained from measuring inputs and outputs. The control algorithm involves two levels: feedforward and feedback. The main focus is, however, on feedback level where the algorithm for experience-based estimation of kinematic coefficients is combined with fuzzy logic control in order to relate the control inputs with the robot arm motion in the global frame.

Related Ontology Subjects/Areas/Topics (6)