Modelling carbon membranes for gas and isotope separation

Molecular modelling has become a useful and widely applied tool to investigate separation and diffusion behavior of gas molecules through nano-porous low dimensional carbon materials, including quasi-1D carbon nanotubes and 2D graphene-like carbon allotropes. These simulations provide detailed, molecular level information about the carbon framework structure as well as dynamics and mechanistic insights, i.e. size sieving, quantum sieving, and chemical affinity sieving. In this perspective, we revisit recent advances in this field and summarize separation mechanisms for multicomponent systems from kinetic and equilibrium molecular simulations, elucidating also anomalous diffusion effects induced by the confining pore structure and outlining perspectives for future directions in this field.

A novel method for analysing lighting variance

Robust descriptor matching across varying lighting conditions is important for vision-based robotics. We present a novel strategy for quantifying the lighting variance of descriptors. The strategy works by utilising recovered low dimensional mappings from Isomap and our measure of the lighting variance of each of these mappings. The resultant metric allows different descriptors to be compared given a dataset and a set of keypoints. We demonstrate that the SIFT descriptor typically has lower lighting variance than other descriptors, although the result depends on semantic class and lighting conditions.

How thick SiO2 cap layer is needed to achieve strong visible photoluminescence from SiO2-buffered SiNx films?

The effect of a SiO2 nanolayer and annealing temperature on the UV/visible room-temperature photoluminescence (PL) from SiNx films synthesized by rf magnetron sputtering is studied. The PL intensity can be maximized when the SiO2 layer is 510 nm thick at 800 °C annealing temperature and only 2 nm at 1000 °C. A compositionstructureproperty analysis reveals that the PL intensity is directly related to both the surface chemical states and the content of the SiO and SiN bonds in the SiNx films. These results are relevant for the development of advanced optoelectronic and photonic emitters and sensors. © 2010 Elsevier B.V. All rights reserved.

Surface science of plasma exposed surfaces : a challenge for applied plasma science

This article introduces a deterministic approach to using low-temperature, thermally non-equilibrium plasmas to synthesize delicate low-dimensional nanostructures of a small number of atoms on plasma exposed surfaces. This approach is based on a set of plasma-related strategies to control elementary surface processes, an area traditionally covered by surface science. Major issues related to balanced delivery and consumption of building units, appropriate choice of process conditions, and account of plasma-related electric fields, electric charges and polarization effects are identified and discussed in the quantum dot nanoarray context. Examples of a suitable plasma-aided nanofabrication facility and specific effects of a plasma-based environment on self-organized growth of size- and position-uniform nanodot arrays are shown. These results suggest a very positive outlook for using low-temperature plasma-based nanotools in high-precision nanofabrication of self-assembled nanostructures and elements of nanodevices, one of the areas of continuously rising demand from academia and industry.

Plasma-Aided Nanofabrication : From Plasma Sources to Nanoassembly

In this single work to cover the use of plasma as nanofabrication tool in sufficient depth internationally renowned authors with much experience in this important method of nanofabrication look at reactive plasma as a nanofabrication tool, plasma production and development of plasma sources, as well as such applications as carbon-based nanostructures, low-dimensional quantum confinement structures and hydroxyapatite bioceramics. Written principally for solid state physicists and chemists, materials scientists, and plasma physicists, the book concludes with the outlook for such applications. © 2007 Wiley-VCH Verlag GmbH & Co. KGaA.

Reactive plasmas as a versatile nanofabrication tool

The underlying physics of the application of low-temperature, low-pressure reactive plasmas in various nanoassembly processes is described. From the viewpoint of the "cause and effect" approach, this Colloquium focuses on the benefits and challenges of using plasma-based systems in nanofabrication of nanostructured silicon films, low-dimensional semiconducting quantum structures, ordered carbon nanotip arrays, highly crystalline TiO2 coatings, and nanostructured hydroxyapatite bioceramics. Other examples and future prospects of plasma-aided nanofabrication are also discussed. © 2005 The American Physical Society.

SAIVT-ADMRG @ MediaEval 2014 social event detection

This paper outlines the approach taken by the Speech, Audio, Image and Video Technologies laboratory, and the Applied Data Mining Research Group (SAIVT-ADMRG) in the 2014 MediaEval Social Event Detection (SED) task. We participated in the event based clustering subtask (subtask 1), and focused on investigating the incorporation of image features as another source of data to aid clustering. In particular, we developed a descriptor based around the use of super-pixel segmentation, that allows a low dimensional feature that incorporates both colour and texture information to be extracted and used within the popular bag-of-visual-words (BoVW) approach.

Representing team behaviours from noisy data using player role

Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Influence of surface stress on elastic constants of nanohoneycombs

Surface effect on the four independent elastic constants of nanohoneycombs is investigated in this paper. The axial deformation of the horizontal cell wall is included, comparing to the Gibson's method, and the contributions of the two components of surface stress (i.e. surface residual stress and surface elasticity) are discussed. The result shows that the regular hexagonal honeycomb is not isotropic but orthotropic. An increase in the cell-wall thickness t leads to an increase in the discrepancy of the Young's moduli in both directions. Furthermore, the surface residual stress dominates the surface effect on the elastic constants when t < 15 nm (or the relative density <0.17), which is in contrast to that the surface elasticity does when t > 15 nm (or the relative density > 0.17) for metal Al. The present structure and theory may be useful in the design of future nanodevices.

Unsupervised Domain Adaptation by Domain Invariant Projection

Domain-invariant representations are key to addressing the domain shift problem where the training and test exam- ples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be di- rectly suitable for such a comparison, since some of the fea- tures may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and tar- get domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a stan- dard domain adaptation benchmark dataset

Multi-converter topology evaluation for connection of low voltage DC sources

A design for a cascaded multilevel DC-DC converter is proposed. The applications of a multilevel converter and the design issues involved in changing from a single converter to multiple converters are discussed. Implementation of the multilevel system using multiple Cuk converters is suggested and explanations of design decisions are given. The merits of the proposed design are discussed.

Diagnostics and two-dimensional simulation of low-frequency inductively coupled plasmas with neutral gas heating and electron heat fluxes

This article presents the results on the diagnostics and numerical modeling of low-frequency (∼460 KHz) inductively coupled plasmas generated in a cylindrical metal chamber by an external flat spiral coil. Experimental data on the electron number densities and temperatures, electron energy distribution functions, and optical emission intensities of the abundant plasma species in low/intermediate pressure argon discharges are included. The spatial profiles of the plasma density, electron temperature, and excited argon species are computed, for different rf powers and working gas pressures, using the two-dimensional fluid approach. The model allows one to achieve a reasonable agreement between the computed and experimental data. The effect of the neutral gas temperature on the plasma parameters is also investigated. It is shown that neutral gas heating (at rf powers≥0.55kW) is one of the key factors that control the electron number density and temperature. The dependence of the average rf power loss, per electron-ion pair created, on the working gas pressure shows that the electron heat flux to the walls appears to be a critical factor in the total power loss in the discharge.

A tri-dimensional approach for auditing brand loyalty

Over the past twenty years brand loyalty has been an important topic for both marketing practitioners and academics. While practitioners have produced proprietary brand loyalty audit models, there has been little academic research to make transparent the methodology that underpins these audits and to enable practitioners to understand, develop and conduct their own audits. In this paper, we propose a framework for a brand loyalty audit that uses a tri-dimensional approach to brand loyalty, which includes behavioural loyalty and the two components of attitudinal loyalty: emotional and cognitive loyalty. In allowing for different levels and intensity of brand loyalty, this tri-dimensional approach is important from a managerial perspective. It means that loyalty strategies that arise from a brand audit can be made more effective by targeting the market segments that demonstrate the most appropriate combination of brand loyalty components. We propose a matrix with three dimensions (emotional, cognitive and behavioural loyalty) and two levels (high and low loyalty) to facilitate a brand loyalty audit. To demonstrate this matrix, we use the example of financial services, in particular a rewards-based credit card.