Adsorption und phase equilibria: completely without diffusion?

In this work, we will present experimental and theoretical results concerning adsorption und phase equilibria being influenced by kinetic effects

Towards an Energy-Aware Cloud Architecture for Smart Grids

Energy consumption in Cloud computing is a significant issue in regards to aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Smart grids offers the prospect of dynamic costs for a data center’s energy usage. These dynamic costs can be passed on to Cloud users providing incentives for users to moderate their load while also ensuring the Cloud providers are insulated from fluctuations in the cost of energy. The first step towards this is an architecture that focuses on energy monitoring and usage prediction. We provide such an architecture at both the PaaS and IaaS layers, resulting in energy metrics for applications, VMs and physical hosts, which is key to enabling active demand in cloud data centers. This architecture is demonstrated through our initial results utilising a generic use case, providing energy consumption information at the PaaS and IaaS layers. Such monitoring and prediction provides the groundwork for providers passing on energy consumption costs to end users. It is envisaged that the resulting varying price associated with energy consumption can help motivate the formation of methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint of Cloud applications

Data-Oriented Characterization of Application-Level Energy Optimization

Abstract. Empowering application programmers to make energy-aware decisions is a critical dimension of energy optimization for computer systems. In this paper, we study the energy impact of alternative data management choices by programmers, such as data access patterns, data precision choices, and data organization. Second, we attempt to build a bridge between application-level energy management and hardware-level energy management, by elucidating how various application-level data management features respond to Dynamic Voltage and Frequency Scal-ing (DVFS). Finally, we apply our findings to real-world applications, demonstrating their potential for guiding application-level energy opti-mization. The empirical study is particularly relevant in the Big Data era, where data-intensive applications are large energy consumers, and their energy efficiency is strongly correlated to how data are maintained and handled in programs

Orthorhombic to tetragonal transition of SrRuO3 layers in Pr0.7Ca0.3MnO3/SrRuO3 superlattices

High-quality Pr0.7Ca0.3MnO3/SrRuO3 superlattices with ultrathin layers were fabricated by pulsed laser deposition on SrTiO3 substrates. The superlattices were studied by atomically resolved scanning transmission electron microscopy, high-resolution transmission electron microscopy, resistivity and magnetoresistance measurements. The superlattices grew coherently without growth defects. Viewed along the growth direction, SrRuO3 and Pr0.7Ca0.3MnO3 layers were terminated by RuO2 and MnO2, respectively, which imposes a unique structure to their interfaces. Superlattices with a constant thickness of the SrRuO3 layers, but varying thickness of the Pr0.7Ca0.3MnO3 layers showed a change of crystalline symmetry of the SrRuO3 layers. At a low Pr0.7Ca0.3MnO3 layer thickness of 1.5 nm transmission electron microscopy proved the SrRuO3 layers to be orthorhombic, whereas these were non-orthorhombic for a Pr0.7Ca0.3MnO3 layer thickness of 4.0 nm. Angular magnetoresistance measurements showed orthorhombic (with small monoclinic distortion) symmetry in the first case and tetragonal symmetry of the SrRuO3 layers in the second case. Mechanisms driving this orthorhombic to tetragonal transition are briefly discussed.Comment: 23 pages, 12 figure

Towards Exploiting the Advantages of Colour in Scan Matching

© Springer International Publishing Switzerland 2014. Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached

SLAM algorithm applied to robotics assistance for navigation in unknown environments

<p>Abstract</p> <p>Background</p> <p>The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).</p> <p>Methods</p> <p>In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents.</p> <p>Results</p> <p>The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.</p> <p>Conclusions</p> <p>The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.</p