42,650 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Discrete event simulation and virtual reality use in industry: new opportunities and future trends
This paper reviews the area of combined discrete
event simulation (DES) and virtual reality (VR) use within industry.
While establishing a state of the art for progress in this
area, this paper makes the case for VR DES as the vehicle of choice
for complex data analysis through interactive simulation models,
highlighting both its advantages and current limitations. This paper
reviews active research topics such as VR and DES real-time
integration, communication protocols, system design considerations,
model validation, and applications of VR and DES. While
summarizing future research directions for this technology combination,
the case is made for smart factory adoption of VR DES as
a new platform for scenario testing and decision making. It is put
that in order for VR DES to fully meet the visualization requirements
of both Industry 4.0 and Industrial Internet visions of digital
manufacturing, further research is required in the areas of lower
latency image processing, DES delivery as a service, gesture recognition
for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets
Recommended from our members
Designing for change: mash-up personal learning environments
Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down.
The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed.
In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services.
Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems
Rebels Leading London: the mayoralties of Ken Livingstone and Boris Johnson compared
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link
- …