An organic ionic plastic crystal electrolyte based on the triflate anion exhibiting high proton transport

A novel organic ionic plastic crystal (OIPC) electrolyte based on a quaternary ammonium cation and the triflate anion has been synthesized, which shows fast proton transport and high thermal stability in the solid state when doped with triflic acid.

Recognising faces in unseen modes : a tensor based approach

This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.

Efficient tensor based face recognition

This paper addresses the limitation of current multilinear PCA based techniques, in terms of pro- hibitive computational cost of testing and poor gen- eralisation in some scenarios, when applied to large training databases. We define person-specific eigen-modes to obtain a set of projection bases, wherein a particular basis captures variation across light- ings and viewpoints for a particular person. A new recognition approach is developed utilizing these bases. The proposed approach performs on a par with the existing multilinear approaches, whilst sig- nificantly reducing the complexity order of the testing algorithm.

Parametric investigation and measurement of near end and far end crosstalk in multiconductor microstrip transmission lines

This paper investigates and measures the near end and far end crosstalk in the multiconductor transmission line (MTL) mounted on the PCB by varying the parameters which are associated with physical dimension, characteristic of the substrate and the nature of input signal. With the variation of these factors, the coupling inductance and capacitance vary causing crosstalk. By using the method of moment (MoM), the per unit length parameters are calculated for microstrip lines. Subcircuit model is used to investigate the time domain and frequency domain analysis of near field and far field crosstalk. This parametric investigation is very useful for designing high speed interconnectors on PCB substrates. Some experimental results are presented to validate the analytical findings.

Plastic crystal phases with high proton conductivity

The addition of up to 4 mol% of the strong acids, trifluoromethane sulfonic acid (TfOH) and bis-trifluoromethanesulfonyl imide [HN(Tf) 2], to the organic ionic plastic crystal (OIPC) [Choline][DHP] has been shown to dramatically increase the ionic conductivity by up to three orders of magnitude whilst still retaining the crystalline structure of the OIPC matrix. This enhanced proton diffusivity led to a significant proton reduction reaction in the electrochemical measurements. Powder XRD and DSC thermal analyses strongly suggest that these mixtures are single phase, crystalline materials. The work here also confirms that an increase in TfOH acid concentration (8 mol% and 12 mol%) results in a higher content of the amorphous phase as previously observed for the H 3PO 4/[Choline][DHP] system. © 2012 The Royal Society of Chemistry.

Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection.

Multi-modal abnormality detection in video with unknown data segmentation

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

X-ray crystallography : applications in the biotechnology research

X-ray crystallography is essentially a form of very high resolution microscopy. It enables us to visualize protein structures at the atomic level and enhances our understanding of protein function. Specifically we can study how proteins interact with other molecules, how they undergo conformational changes, and how they perform catalysis in the case of enzymes. Armed with this information we can design novel drugs that target a particular protein, or rationally engineer an enzyme for a specific industrial process.

Biosensors, types and applications

Biosensor has rapidly become essential analytical tools, since they offer higher performance in terms of sensitivity and selectively than any other currently available diagnostic device. The development of biosensor technology represents a crucial task for environmental pollution management, there is a considerable need to project and realize biosensors with the best features for commercialization, such as selectivity, sensitivity, stability, reproducibility and low cost. With appropriate progress testing and commercialization, biosensors will have an important impact on environmental monitoring, reducing costs and increasing the efficiency of certain applications. The same multiple approach might be used for development of biosensor platforms suitable for use in fields as diverse as environmental and agrifood to industry, research security and defence, medical and clinical. This review paper focussed on the various types of biosensors and applications in environmental monitoring.

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.