Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US

Summarizing the state of the terrestrial biosphere in few dimensions

In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems – i.e. the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data

Dynamic bayesian networks for learning interactions between assistive robotic walker and human users

Detection of individuals intentions and actions from a stream of human behaviour is an open problem. Yet for robotic agents to be truly perceived as human-friendly entities they need to respond naturally to the physical interactions with the surrounding environment, most notably with the user. This paper proposes a generative probabilistic approach in the form of Dynamic Bayesian Networks (DBN) to seamlessly account for users attitudes. A model is presented which can learn to recognize a subset of possible actions by the user of a gait stability support power rollator walker, such as standing up, sitting down or assistive strolling, and adapt the behaviour of the device accordingly. The communication between the user and the device is implicit, without any explicit intention such as a keypad or voice.The end result is a decision making mechanism that best matches the users cognitive attitude towards a set of assistive tasks, effectively incorporating the evolving activity model of the user in the process. The proposed framework is evaluated in real-life condition. © 2010 Springer-Verlag Berlin Heidelberg

Planning stable and efficient paths for articulated mobile robots on challenging terrains

An analytical strategy to generate stable paths for a reconfigurable vehicle while also meeting additional navigational objectives is herein proposed. The work is motivated by robots traversing over challenging terrains during search and rescue operations, such as those equipped with manipulator arms and/or flippers. The proposed solution looks at minimizing the length of the traversed path and the energy expenditure in changing postures, yet also accounts for additional constraints in terms of sensor visibility (i.e arm configurations close to those orthogonal to the horizontal global plane which can afford a wider sensor view) and traction (i.e. flipper angles that provide the largest trackterrain interaction area). The validity of the proposed planning approach is evaluated with a multitracked robot fitted with flippers and a range camera at the end of a manipulator arm while navigating over two challenging 3D terrain data sets: one in a mock-up urban search and rescue arena (USAR), and a second one from a publicly available quasi-outdoor rover testing facility (UTIAS)

Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains

© 2015, Springer Science+Business Media New York. A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility

Superconducting proximity effects in metals with a repulsive pairing interaction

Studies of the superconducting proximity effect in normal conductor/superconductor $(N/S)$ junctions almost universally assume no effective electron-electron coupling in the $N$ region. While such an approximation leads to a simple description of the proximity effect, it is unclear how it could be rigorously justified. We reveal a much more complex picture of the proximity effect in $N/S$ bilayers, where $S$ is a clean s-wave BCS superconductor and $N$ is a simple metal with a repulsive effective electron coupling. We elucidate the proximity effect behavior using a highly accurate method to self-consistently solve the Bogoliubov-deGennes equations. We present our results for a wide range of values of the interface scattering, the Fermi wave vector mismatch, the temperature, and the ratio $g$ of the effective interaction strengths in the $N$ and $S$ region. We find that the repulsive interaction, represented by a negative $g$, strongly alters the signatures of the proximity effect as can be seen in the spatial dependence of the Cooper pair amplitude and the pair potential, as well as in the local density of states near the interface.Comment: 12 pages, including 10 figures. To appear in Phys. Rev.

Cytogenetic evidences on the evolutionary relationships between the tetraploids of the section Rhizomatosae and related diploid species (Arachis, Leguminosae).

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Supra-oscillatory critical temperature dependence of Nb-Ho bilayers

We investigate the critical temperature Tc of a thin s-wave superconductor (Nb) proximity coupled to a helical rare earth ferromagnet (Ho). As a function of the Ho layer thickness, we observe multiple oscillations of Tc superimposed on a slow decay, that we attribute to the influence of the Ho on the Nb proximity effect. Because of Ho inhomogeneous magnetization, singlet and triplet pair correlations are present in the bilayers. We take both into consideration when solving the self consistent Bogoliubov-de Gennes equations, and we observe a reasonable agreement. We also observe non-trivial transitions into the superconducting state, the zero resistance state being attained after two successive transitions which appear to be associated with the magnetic structure of Ho.Comment: Main article: 5 pages, 4 figures; Supplementary materials: 4 pages, 5 figure