2,975 research outputs found
Footprints of information foragers: Behaviour semantics of visual exploration
Social navigation exploits the knowledge and experience of peer users of information resources. A wide variety of visual–spatial approaches become increasingly popular as a means to optimize information access as well as to foster and sustain a virtual community among geographically distributed users. An information landscape is among the most appealing design options of representing and communicating the essence of distributed information resources to users. A fundamental and challenging issue is how an information landscape can be designed such that it will not only preserve the essence of the underlying information structure, but also accommodate the diversity of individual users. The majority of research in social navigation has been focusing on how to extract useful information from what is in common between users' profiles, their interests and preferences. In this article, we explore the role of modelling sequential behaviour patterns of users in augmenting social navigation in thematic landscapes. In particular, we compare and analyse the trails of individual users in thematic spaces along with their cognitive ability measures. We are interested in whether such trails can provide useful guidance for social navigation if they are embedded in a visual–spatial environment. Furthermore, we are interested in whether such information can help users to learn from each other, for example, from the ones who have been successful in retrieving documents. In this article, we first describe how users' trails in sessions of an experimental study of visual information retrieval can be characterized by Hidden Markov Models. Trails of users with the most successful retrieval performance are used to estimate parameters of such models. Optimal virtual trails generated from the models are visualized and animated as if they were actual trails of individual users in order to highlight behavioural patterns that may foster social navigation. The findings of the research will provide direct input to the design of social navigation systems as well as to enrich theories of social navigation in a wider context. These findings will lead to the further development and consolidation of a tightly coupled paradigm of spatial, semantic and social navigation
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks
Energy harvester based cognitive radio is a promising solution to address the
shortage of both spectrum and energy. Since the spectrum access and power
consumption patterns are interdependent, and the power value harvested from
certain environmental sources are spatially correlated, the new power dimension
could provide additional information to enhance the spectrum sensing accuracy.
In this paper, the Markovian behavior of the primary users is considered, based
on which we adopt a hidden input Markov model to specify the primary vs.
secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and
power (harvested) sensing scheme to improve the primary user detection
performance, which is also capable of estimating the primary transmit power
level. Theoretical and simulated results demonstrate the effectiveness of the
proposed scheme, in term of the performance gain achieved by considering the
new power dimension. To the best of our knowledge, this is the first work to
jointly consider the spectrum and power dimensions for the cognitive primary
user detection problem
HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks
A recent approach in modeling and analysis of the supply and demand in
heterogeneous wireless cellular networks has been the use of two independent
Poisson point processes (PPPs) for the locations of base stations (BSs) and
user equipments (UEs). This popular approach has two major shortcomings. First,
although the PPP model may be a fitting one for the BS locations, it is less
adequate for the UE locations mainly due to the fact that the model is not
adjustable (tunable) to represent the severity of the heterogeneity
(non-uniformity) in the UE locations. Besides, the independence assumption
between the two PPPs does not capture the often-observed correlation between
the UE and BS locations.
This paper presents a novel heterogeneous spatial traffic modeling which
allows statistical adjustment. Simple and non-parameterized, yet sufficiently
accurate, measures for capturing the traffic characteristics in space are
introduced. Only two statistical parameters related to the UE distribution,
namely, the coefficient of variation (the normalized second-moment), of an
appropriately defined inter-UE distance measure, and correlation coefficient
(the normalized cross-moment) between UE and BS locations, are adjusted to
control the degree of heterogeneity and the bias towards the BS locations,
respectively. This model is used in heterogeneous wireless cellular networks
(HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic
on the network performance. This network is called HetHetNet since it has two
types of heterogeneity: heterogeneity in the infrastructure (supply), and
heterogeneity in the spatial traffic distribution (demand).Comment: JSA
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