Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Enabling Social Interaction Through Embodiment in ExCITE

No abstract availableThe emerging demographic trends toward an aging population involve an unflagging research of ways of assisting elderly people to stay independent for as long as possible. This means to be active at home and in the labour market, to prevent social isolation and promote societal inclusion. Both ICT and robotics technologies can contribute to help achieving these goals. This paper introduces the aims of the Ambient Assisted Living project ExCITE whose main objective is to enhance a robotic platform for telepresence with features enabling social interaction from a domestic environment to the outside world. The whole ExCITE project uses a user-centered approach hence it evolves around an intensive evaluation to be performed in situ, on a PanEuropean scale. An existing prototype, called Giraff, is to be deployed to targeted end-users, and refined taking into account outcome of the evaluation. This paper introduces the objectives of ExCITE and offers a description of its initial activities particularly focused on the user evaluation

Tangible user interfaces : past, present and future directions

In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research

AI Watch: Assessing Technology Readiness Levels for Artificial Intelligence

Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are when and how this is going to happen. Not only do we lack the tools to determine what achievements will be attained in the near future, but we even underestimate what various technologies in AI are capable of today. Many so-called breakthroughs in AI are simply associated with highly-cited research papers or good performance on some particular benchmarks. Certainly, the translation from papers and benchmark performance to products is faster in AI than in other non-digital sectors. However, it is still the case that research breakthroughs do not directly translate to a technology that is ready to use in real-world environments. This document describes an exemplar-based methodology to categorise and assess several AI research and development technologies, by mapping them into Technology Readiness Levels (TRL) (e.g., maturity and availability levels). We first interpret the nine TRLs in the context of AI and identify different categories in AI to which they can be assigned. We then introduce new bidimensional plots, called readiness-vs-generality charts, where we see that higher TRLs are achievable for low-generality technologies focusing on narrow or specific abilities, while low TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we use the dynamics of several AI technology exemplars at different generality layers and moments of time to forecast some short-term and mid-term trends for AI.JRC.B.6-Digital Econom

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving

Movement ecology of the Greenland shark (Somniosus microcephalus): Identifying tools, management considerations, and horizontal movement behaviours using multi-year acoustic telemetry

Arctic ecosystems are highly seasonally dynamic, and as such, mobile Arctic species have adopted movement patterns that correspond to the occurrence of productivity hotspots. As polar regions continue to warm at an unprecedented rate, the predictable occurrence of these hotspots of may be reduced, resulting in dire consequences for long-lived or slow-adapting species. Effective marine management approaches will therefore rely on an understanding of the ability of Arctic predators to confer community stability by linking disparate food webs and by responding flexibly to environmental change. This thesis describes the use of static acoustic telemetry to examine the long-term movement patterns of a model mobile predator, the Greenland shark (Somniosus microcephalus) within two distinct habitat types (coastal and offshore waters) and across multiple years (7 y). Movement records for 155 tagged Greenland sharks revealed strong seasonality in coastal and offshore residency driven by fluctuations in sea-ice cover, with evidence of site fidelity to specific sites (receivers) in both regions. Juvenile sharks remained in coastal regions for longer durations than subadults, however, no size-based spatial segregation was observed. At a localized scale, sharks used deep-water channels to direct movements between a coastal fjord system and offshore waters, where they exhibited transient behaviour near offshore moorings located outside of identified hotspot regions. Ultimately, this research provides novel insight into the long-term movement dynamics of this potentially vulnerable Arctic predator and will inform future management practices that promote the longevity of this species

Occam's Razor For Big Data?

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor