Challenges in visible wavelength detection using optically transparent oxide semiconductors

We discuss the development of amorphous oxide semiconductor technology for optical sensor applications. In particular, we discuss the challenges of detecting visible wavelengths using this family of materials, which are known to be optically transparent due to their relatively large bandgap energy. One of the main issues with amorphous oxide semiconductors (AOS) is the ionization of the oxygen vacancies (VO) under illumination. While this can be beneficial in terms of optical absorption and high photoconductive gain, it can give rise to persistent photoconductivity (PPC). We will present techniques to overcome the PPC, and discuss how to achieve the high photoconductive gain for image sensor applications. © 2012 IEEE.

How to achieve ultra high photoconductive gain for transparent oxide semiconductor image sensors

We present quantitative analysis of the ultra-high photoconductivity in amorphous oxide semiconductor (AOS) thin film transistors (TFTs), taking into account the sub-gap optical absorption in oxygen deficiency defects. We analyze the basis of photoconductivity in AOSs, explained in terms of the extended electron lifetime due to retarded recombination as a result of hole localization. Also, photoconductive gain in AOS photo-TFTs can be maximized by reducing the transit time associated with short channel lengths, making device scaling favourable for high sensitivity operation. © 2012 IEEE.

Optimisation of passive and semi-active heavy vehicle suspensions

An investigation into the potential for reducing road damage by optimising the design of heavy vehicle suspensions is described. In the first part of the paper two simple mathematical models are used to study the optimisation of conventional passive suspensions. Simple modifications are made to the steel spring suspension of a tandem axle trailer and it is found experimentally that RMS dynamic tyre forces can be reduced by 15% and theoretical road damage by 5.2%. A mathematical model of an air-sprung articulated vehicle is validated, and its suspension is optimised according to the simple models. This vehicle generates about 9% less damage than the leaf-sprung vehicle in the unmodified state and it is predicted that, for the operating conditions examined, the road damage caused by this vehicle can be reduced by a further 5.4%. Finally, it is shown experimentally that computer-controlled semi-active dampers have the potential to reduce road damage by a further 5-6%, compared to an air suspension with optimum passive damping. © Copyright 1994 Society of Automotive Engineers, Inc.

A Bayesian framework to predict deformations during supported excavations based on a semi-empirical method

The ground movements induced by the construction of supported excavation systems are generally predicted by empirical/semi-empirical methods in the design stage. However, these methods cannot account for the site-specific conditions and for information that becomes available as an excavation proceeds. A Bayesian updating methodology is proposed to update the predictions of ground movements in the later stages of excavation based on recorded deformation measurements. As an application, the proposed framework is used to predict the three-dimensional deformation shapes at four incremental excavation stages of an actual supported excavation project. © 2011 Taylor & Francis Group, London.

Reliability analysis of deep excavation based on a semi-empirical approach

The design and construction of deep excavations in urban environment is often governed by serviceability limit state related to the risk of damage to adjacent buildings. In current practice, the assessment of excavation-induced building damage has focused on a deterministic approach. This paper presents a component/system reliability analysis framework to assess the probability that specified threshold design criteria for multiple serviceability limit states are exceeded. A recently developed Bayesian probabilistic framework is used to update the predictions of ground movements in the later stages of excavation based on the recorded deformation measurements. An example is presented to show how the serviceability performance for excavation problems can be assessed based on the component/system reliability analysis. © 2011 ASCE.

A Bayesian framework to predict deformations during supported excavations based on a semi-empirical method

The ground movements induced by the construction of supported excavation systems are generally predicted in the design stage by empirical/semi-empirical methods. However, these methods cannot account for the site-specific conditions and for information that become available as an excavation proceeds. A Bayesian updating methodology is proposed to update the predictions of ground movements in the later stages of excavation based on recorded deformation measurements. As an application, the proposed framework is used to predict the three-dimensional deformation shapes at four incremental excavation stages of an actual supported excavation project. Copyright © ASCE 2011.

Rank-preserving geometric means of positive semi-definite matrices

The generalization of the geometric mean of positive scalars to positive definite matrices has attracted considerable attention since the seminal work of Ando. The paper generalizes this framework of matrix means by proposing the definition of a rank-preserving mean for two or an arbitrary number of positive semi-definite matrices of fixed rank. The proposed mean is shown to be geometric in that it satisfies all the expected properties of a rank-preserving geometric mean. The work is motivated by operations on low-rank approximations of positive definite matrices in high-dimensional spaces.© 2012 Elsevier Inc. All rights reserved.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

A highly conductive and transparent solution processed AZO/MWCNT nanocomposite

A solution processed aluminum-doped zinc oxide (AZO)/multi-walled carbon nanotube (MWCNT) nanocomposite thin film has been developed offering simultaneously high optical transparency and low electrical resistivity, with a conductivity figure of merit (σDC/σopt) of ~75-better than PEDOT:PSS and many graphene derivatives. The reduction in sheet resistance of thin films of pristine MWCNTs is attributed to an increase in the conduction pathways within the sol-gel derived AZO matrix and reduced inter-MWCNT contact resistance. Films have been extensively characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), X-ray diffractometry (XRD), photoluminescence (PL), and ultraviolet-visible (UV-vis) spectroscopy. © 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Centrifuge coating for low-waste solution processing of transparent nanostructured electrodes

Centrifuge coating was implemented to fabricate nanostructured conductive layers through solution processing at room temperature. This coating procedure allows fast evaporation, thereby fixing the nanomaterials in their dispersed state onto a substrate by the centrifuge action. Material wastes were minimized by mitigating the effects of particle reaggregation. Using this method, we fabricate single-wall nanotube coatings on different substrates such as polyethylene terephthalate, polydimethylsiloxane, and an acrylic elastomer with no prior surface modification of the substrate. The effects of the choice of solvents on the morphology and subsequent performance of the coating network are studied. © 2002-2012 IEEE.