Hardness of silicon nitride thin films characterised by nanoindentation and nanoscratch deconvolution methods

Plasma enhanced chemical vapour deposition silicon nitride thin films are widely used in microelectromechanical system devices as structural materials because the mechanical properties of those films can be tailored by adjusting deposition conditions. However, accurate measurement of the mechanical properties, such as hardness, of films with thicknesses at nanometric scale is challenging. In the present study, the hardness of the silicon nitride films deposited on silicon substrate under different deposit conditions was characterised using nanoindentation and nanoscratch deconvolution methods. The hardness values obtained from the two methods were compared. The effect of substrate on the measured results was discussed.

Et tu, George? Campaign journalism and the politics of compromise

There is a category of film about journalism in which journalism is not the star, but the supporting player, and journalists not the protagonists but the Greek chorus, commenting on and also changing the realities they report. In such films the news media are a structuring presence driving the plot, shaping the narrative, constructing what we might think of as a pseudo-reality. Like Daniel Boorstin’s notion of the pseudo-event (introduced in his still-relevant book The Image, 1962), this pseudo-reality is so-named because it would not exist were it not for the demands of the news media’s hunger for stories, and knowledge of the damage they can do with those stories, on the calculations and actions of the key actors. Pseudo-realities form as responses to what political actors think journalists and their organisations need and want, or as efforts to shape journalistic accounts in ways favourable to themselves. Films about politics often feature pseudorealities of this kind, in which the events and actions driving the plot have only a tenuous relationship with important things going on in the everyday world beyond the political arena. Everything we see is about image, perception, appearance.

Conductometric gas sensors based on nanostructured WO3 thin films

Nanostructured WO3 thin films have been prepared by thermal evaporation to detect hydrogen at low temperatures. The influence of heat treatment on the physical, chemical and electronic properties of these films has been investigated. The films were annealed at 400oC for 2 hours in air. AFM and TEM analysis revealed that the as-deposited WO3 film is high amorphous and made up of cluster of particles. Annealing at 400oC for 2 hours in air resulted in very fine grain size of the order of 5 nm and porous structure. GIXRD and Raman analysis revealed that annealing improved the crystallinity of WO3 film. Gas sensors based on annealed WO3 films have shown a high response towards various concentrations (10-10000 ppm) H2 at an operating temperature of 150oC. The improved sensing performance at low operating temperature is due to the optimum physical, chemical and electronic properties achieved in the WO3 film through annealing.

The backfilled GEI : a cross-capture modality gait feature for frontal and side-view gait recognition

In this paper, we propose a novel direction for gait recognition research by proposing a new capture-modality independent, appearance-based feature which we call the Back-filled Gait Energy Image (BGEI). It can can be constructed from both frontal depth images, as well as the more commonly used side-view silhouettes, allowing the feature to be applied across these two differing capturing systems using the same enrolled database. To evaluate this new feature, a frontally captured depth-based gait dataset was created containing 37 unique subjects, a subset of which also contained sequences captured from the side. The results demonstrate that the BGEI can effectively be used to identify subjects through their gait across these two differing input devices, achieving rank-1 match rate of 100%, in our experiments. We also compare the BGEI against the GEI and GEV in their respective domains, using the CASIA dataset and our depth dataset, showing that it compares favourably against them. The experiments conducted were performed using a sparse representation based classifier with a locally discriminating input feature space, which show significant improvement in performance over other classifiers used in gait recognition literature, achieving state of the art results with the GEI on the CASIA dataset.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

Asian representations in films for the Australian Curriculum : an annotated guide

In this annotated guide we offer a reference list, with brief synposes, of possible films for inclusion in schools and linked to the Australian Curriculum: English (AC:E). These films meet one of the three cross curriculum priorities in the Australian Curriculum, which is Studies of Asia, specifically Australia’s contribution to Asia and Asia’s impact on Australia. This priority was recently introduced to curriculum policy in the 2008 Melbourne Declaration (Ministerial Council for Education Early Childhood Development and Youth Affairs, 2008). In this guide we include Australians films made by Asian Australian filmmakers, as well as films about people from Asian countries in Australia, where representations of Asia are a significant part of the film’s content.

The effect of Yb addition in bi2sr2ca 1-xybxcu2oy partial melted thick films

To study the phase relations in the Bi-2212 and Yb2O3 system, Bi2Sr2Ca1-xYbxCu 2Oy thick films are prepared by partial melt processing via an intermediate reaction between Bi-2212 and Yb2O3. When Bi-2212 and Yb2O3 are partially melted and then slowly cooled, solid solutions of Bi2Sr2Ca 1-xYbxCu2Oy, form by reactions between liquid and solid phases which contain Yb. Following these reactions, Ca is partially replaced in Bi-2212 matrix and participates in the formation of secondary phases, such as Bi-free, (Ca, Sr)Ox and CaO. Variation of the Bi-2212-Yb2O3 ratios and processing parameters changes the balance between the phases and leads to different Yb:Ca ratios in the Bi-2212 matrix of processed thick films. When the partial melting process is optimized for each sample to minimize the growth of secondary phases, x = 0.42-0.46 for the samples prepared at pO2 = 0.01 atm, x = 0.24-0.29 for the samples prepared at pO2 = 0.21 atm, x = 0.18-0.23 for the samples prepared at pO2 = 0.99 atm are obtained regardless to the starting compositions. It is found that superconducting properties of Bi 2Sr2Ca1-xYbxCu2O y thick films strongly depend on the processing conditions, because the conditions result in different Yb content in the Bi-2212 matrix and the volume fraction of the secondary phases. The highest Tc(0) of 77, 90 and 91 K were obtained for the samples processed at 0.01, 0.21 and 0.99 atm of O2, respectively.

Synthesis and characterisation of nanoscale magnesium oxide powders and their application in thick films of Bi2Sr2CaCu2O8

Nanoscale MgO powder was synthesized from magnesite ore by a wet chemical method. Acid dissolution was used to obtain a solution from which magnesium containing complexes were precipitated by either oxalic acid or ammonium hydroxide, The transformation of precipitates to the oxide was monitored by thermal analysis and XRD and the transformed powders were studied by electron microscopy. The MgO powders were added as dopants to Bi2SrCa2CuO8 powders and high temperature superconductor thick films were deposited on silver. Addition of suitable MgO powder resulted in increase of critical current density, J(c), from 8,900 Acm(-2) to 13,900 Acm(-2) measured at 77 K and 0 T. The effect of MgO addition was evaluated by XRD, electron microscopy and critical current density measurements. (C) 1998 Elsevier Science B.V.

Partial melt processing and electrical properties of Bi-Sr-Ca-Cu-o superconducting thick films on (100) MgO substrates

Superconducting thick films of Bi2Sr2CaCu2Oy (Bi-2212) on single-crystalline (100) MgO substrates have been prepared using a doctor-blade technique and a partial-melt process. It is found that the phase composition and the amount of Ag addition to the paste affect the structure and superconducting properties of the partially melted thick films. The optimum heat treatment schedule for obtaining high Jc has been determined for each paste. The heat treatment ensures attainment of high purity for the crystalline Bi-2212 phase and high orientation of Bi-2212 crystals, in which the c-axis is perpendicular to the substrate. The highest Tc, obtained by resistivity measurement, is 92.2 K. The best value for Jct (transport) of these thick films, measured at 77 K in self-field, is 8 × 10 3 Acm -2.

Reaction products between Bi-Sr-Ca-Cu-oxide thick films and alumina substrates

The structure and composition of reaction products between Bi-Sr-Ca-Cu-oxide (BSCCO) thick films and alumina substrates have been characterized using a combination of electron diffraction, scanning electron microscopy and energy dispersive X-ray spectrometry (EDX). Sr and Ca are found to be the most reactive cations with alumina. Sr4Al6O12SO4 is formed between the alumina substrates and BSCCO thick films prepared from paste with composition close to Bi-2212 (and Bi-2212 + 10 wt.% Ag). For paste with composition close to Bi(Pb)-2223 + 20 wt.% Ag, a new phase with f.c.c. structure, lattice parameter about a = 24.5 A and approximate composition Al3Sr2CaBi2CuOx has been identified in the interface region. Understanding and control of these reactions is essential for growth of high quality BSCCO thick films on alumina. (C) 1997 Elsevier Science S.A.

Microstructural investigation of Bi-Sr-Ca-Cu-oxide thick films on alumina substrates

The microstructure of Bi-Sr-Ca-Cu-oxide (BSCCO) thick films on alumina substrates has been characterized using a combination of X-ray diffractometry, scanning electron microscopy, transmission electron microscopy of sections across the film/substrate interface and energy-dispersive X-ray spectrometry. A reaction layer formed between the BSCCO films and the alumina substrates. This chemical interaction is largely responsible for off-stoichiometry of the films and is more significant after partial melting of the films. A new phase with fee structure, lattice parameter a = 2.45 nm and approximate composition Al3Sr2CaBi2CuOx has been identified as reaction product between BSCCO and Al2O3.

A boy and his toys : technology and gadgetry in the James Bond films

This chapter provides an indepth examination of the history of product placement in the James Bond film series, specifically focusing on the emergence of technology and gadgetry in the series and the impact this had on the number and types of products that were placed in the films.

Adopting relevance feature to learn personalized ontologies

Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.