P-16: Light-bias induced instability and persistent photoconductivity in in-zn-o/ga-in-zn-o thin film transistors

Stress/recovery measurements demonstrate that even highperformance passivated In-Zn-O/ Ga-In-Zn-O thin film transistors with excellent in-dark stability suffer from light-bias induced threshold voltage shift (ΔV T) and defect density changes. Visible light stress leads to ionisation of oxygen vacancy sites, causing persistent photoconductivity. This makes the material act as though it was n-doped, always causing a negative threshold voltage shift under strong illumination, regardless of the magnitude and polarity of the gate bias. © 2011 SID.

Light-matter interaction in a microcavity-controlled graphene transistor

Graphene has extraordinary electronic and optical properties and holds great promise for applications in photonics and optoelectronics. Demonstrations including high-speed photodetectors, optical modulators, plasmonic devices, and ultrafast lasers have now been reported. More advanced device concepts would involve photonic elements such as cavities to control light-matter interaction in graphene. Here we report the first monolithic integration of a graphene transistor and a planar, optical microcavity. We find that the microcavity-induced optical confinement controls the efficiency and spectral selection of photocurrent generation in the integrated graphene device. A twenty-fold enhancement of photocurrent is demonstrated. The optical cavity also determines the spectral properties of the electrically excited thermal radiation of graphene. Most interestingly, we find that the cavity confinement modifies the electrical transport characteristics of the integrated graphene transistor. Our experimental approach opens up a route towards cavity-quantum electrodynamics on the nanometre scale with graphene as a current-carrying intra-cavity medium of atomic thickness. © 2012 Macmillan Publishers Limited. All rights reserved.

Seafloor-riser interaction model

Fatigue stresses associated with extreme storms, vessel movements, and vortex-induced vibrations are critical to the performance of steel catenary risers. The critical location for fatigue damage often occurs within the touchdown zone, where cyclic interaction of the riser with the seabed occurs. Developing a model for seabed stiffness requires characterization of a number of complex nonlinear processes including trench formation, nonlinear soil stiffness, soil suction, and breakaway of the riser from the seafloor. The analytical framework utilized in this research considers the riser-seafloor interaction problem in terms of a pipe resting on a bed of springs, the stiffness characteristics of which are described by nonlinear load-deflection (P-y) curves. The P-y model allows for first penetration and uplift, as well as repenetration and small range motions within the bounding loop defined by extreme loading. The backbone curve is constructed from knowledge of the soil strength, the rate of strength increase with depth, trench width, and two additional parameters, while three parameters are necessary for the cyclic response. © ASCE 2009.

Seafloor interaction with steel catenary risers

This paper describes the key features of a seafloor-riser interaction model. The soil is represented in terms of non-linear load-deflection (P- y) relationships, which are also able to account for soil stiffness degradation due to cyclic loading. The analytical framework considers the riser-seafloor interaction problem in terms of a pipe resting on a bed of springs, and requires the iterative solution of a fourth-order ordinary differential equation. A series of simulations is used to illustrate the capabilities of the model. Thanks to the non-linear soil springs with stiffness degradation it is possible to simulate the trench formation process and estimate moments in a riser. Copyright © 2008 by The International Society of Offshore and Polar Engineers (ISOPE).

Robotics and neuroscience: A rhythmic interaction

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup. © 2008 Elsevier Ltd. All rights reserved.

Exploring prior experience and the effects of age on product interaction and Learning

The purpose of this research was to investigate the extent to which prior technological experience of products is related to age, and if this has implications for the success of subsequent product interaction. The contribution of this work is to provide the design community with new knowledge and a greater awareness of the diversity of user needs, and particularly the needs and skills of older people. The focus of this paper is to present how individual's mental models of products and interaction were developed through experiential learning; what new knowledge was acquired, and how this contributed to the development of mental models and product understanding. © 2013 Springer-Verlag Berlin Heidelberg.

On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy. © 2013 IEEE.