Birmingham, United Kingdom

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School of Biosciences
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School of Geography, Earth and Environmental Sciences
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School of Psychology
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    ABSTRACT: We are investigating the problem of predicting how objects behave under manipulative actions. In particular, we wish to predict the workpiece motions which will result from simple pushing manipulations by a single robotic fingertip. Such interactions are themselves fundamental components of multi-fingered grasping and other complex interactions. Physics simulators can be used to do this, but they model many kinds of object interactions poorly, being dependent on detailed scene descriptions and parameters, which in practice are often difficult to tune. Additionally, we have previously investigated ways of learning to predict, by employing density estimation techniques to learn, from many example pushes, a probabilistic mapping between applied pushing motions and resulting work-piece motions. In contrast, this paper presents an alternative approach to prediction, which does not rely on learning but infers the likelihood of possible workpiece motions by using the simple physics principle of minimum energy. This approach is advantageous in situations where insufficient prior knowledge is available for training our learned predictors. In such situations, possible strategies include either training learned predictors on unrealistic simulation data, or making use of the simple physics approach which requires no training. We show that the second of these strategies performs significantly better, and approaches the performance of learned predictors are trained on observations of real object motions.
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    ABSTRACT: This paper describes the potential for optimising a resonant gas sensor by using nano-textured surfaces. Initial empirical calculations determined that a device with a nano-textured active layer could have its surface area increased by an order of magnitude when compared to a device with an ideal theoretical smooth surface. These nano-textured surfaces are achieved when using the author's one step DRIE process. By carefully choosing the parameters when using the one step process the underside can be made to mimic porous silicon giving the user this vastly increased surface area on a micro and nano scale. After explaining how these surfaces are obtained this paper goes on to detail modelling work carried out to demonstrate the possible effects this new textured surface could have on the performance of future resonant gas sensors. A simple generic resonator is modelled with and without the new surface to give a clear indication of the improvement in signal performance that can be expected from future devices utilizing this new pseudo porous silicon as its active layer.
    Nanotec; 04/2015
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    ABSTRACT: In this work we investigate how judgments of perceived duration are influenced by the properties of the signals that define the intervals. Participants compared two auditory intervals that could be any combination of the following four types: intervals filled with continuous tones (filled intervals), intervals filled with regularly-timed short tones (isochronous intervals), intervals filled with irregularly-timed short tones (anisochronous intervals), and intervals demarcated by two short tones (empty intervals). Results indicate that the type of intervals to be compared affects discrimination performance and induces distortions in perceived duration. In particular, we find that duration judgments are most precise when comparing two isochronous and two continuous intervals, while the comparison of two anisochronous intervals leads to the worst performance. Moreover, we determined that the magnitude of the distortions in perceived duration (an effect akin to the filled duration illusion) is higher for tone sequences (no matter whether isochronous or anisochronous) than for continuous tones. Further analysis of how duration distortions depend on the type of filling suggests that distortions are not only due to the perceived duration of the two individual intervals, but they may also be due to the comparison of two different filling types.
    Frontiers in Psychology 03/2015; 6(114):1-8. DOI:10.3389/fpsyg.2015.00114


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    Edgbaston, B15 2TT, Birmingham, United Kingdom
  • Head of Institution
    Lord Bilimoria of Chelsea CBE, DL
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MLEARN 2004: Learning Anytime, EverywhereMLEARN 2004: Learning Anytime, Everywhere; 01/2004
Sociology 01/1972; 6(1):1-22. DOI:10.1177/003803857200600101

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