A compact interferometric sensor design using three waveguide coupling

The use of metal stripes for the guiding of plasmons is a well established technique for the infrared regime and has resulted in the development of a myriad of passive optical components and sensing devices. However, the plasmons suffer from large losses around sharp bends, making the compact design of nanoscale sensors and circuits problematic. A compact alternative would be to use evanescent coupling between two sufficiently close stripes, and thus we propose a compact interferometer design using evanescent coupling. The sensitivity of the design is compared with that achieved using a hand-held sensor based on the Kretschmann style surface plasmon resonance technique. Modeling of the new interferometric sensor is performed for various structural parameters using finite-difference time-domain and COMSOL Multiphysics. The physical mechanisms behind the coupling and propagation of plasmons in this structure are explained in terms of the allowed modes in each section of the device.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Graphene-like nano-sheets for surface acoustic wave gas sensor applications

The gas sensing properties of graphene-like nano-sheets deposited on 36° YX lithium tantalate (LiTaO3) surface acoustic wave (SAW) transducers are reported. The thin graphene-like nano-sheets were produced via the reduction of graphite oxide which was deposited on SAW interdigitated transducers (IDTs). Their sensing performance was assessed towards hydrogen (H2) and carbon monoxide (CO) in a synthetic air carrier gas at room temperature (25 °C) and 40 °C. Raman and X-ray photoelectron spectroscopy (XPS) revealed that the deposited graphite oxide (GO) was not completely reduced creating small, graphitic nanocrystals ∼2.7 nm in size. © 2008 Elsevier B.V.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Novel corrosion sensor based on carbon nanotube composites for structural health monitoring

Bridges are important infrastructures of all nations and are required for transportation of goods as well as human. A catastrophic failure can result in loss of lives and enormous financial hardship to the nation. Hence, there is an urgent need to monitor our infrastructures to prolong their life span, at the same time catering for heavier and faster moving traffics. Although various kinds of sensors are now available to monitor the health of the structures due to corrosion, they do not provide permanent and long term measurements. This paper investigates the fabrication of Carbon Nanotube (CNT) based composite sensors for structural health monitoring. The CNTs, a key material in nanotechnology has aroused great interest in the research community due to their remarkable mechanical, electrochemical, piezoresistive and other physical properties. Multi-wall CNT (MWCNT)/Nafion composite sensors were fabricated to evaluate their electrical properties when subjected to chemical solutions, to simulate a chemical reaction due to corrosion and real life corrosion experimental tests. The electrical resistance of the sensor electrode was dramatically changed due to corrosion. The novel sensor is expected to effectively detect corrosion in structures based on the measurement of electrical impedances of the CNT composite.

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on “patch occupancy” metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the “asymmetry” and “average path strength” of the connectivity pattern, and the “centrality” of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation’s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

A hydrogen gas sensor based on Pt/nanostructured WO3/SiC Schottky diode

Pt/nanostructured WO3/SiC Schottky diodes were fabricated and applied for hydrogen gas sensing applications. The nanostructured WO3 films were synthesized from tungsten coated SiC substrates via an acid-etching method using a 1.5 M HNO3 solution for 1 hr, 2 hrs and 3 hrs duration. Scanning electron microscopy of the developed films revealed platelet crystals with thicknesses in the order of 20-60 nm and lengths between 100-700 nm. X-ray diffraction analysis revealed that the rate of oxidation of tungsten increases as the duration of acid-etching increases. The devices were tested towards hydrogen gas balanced in air at different temperatures from 25°C to 200°C. At 200°C, voltage shifts of 0.45 V, 0.93 V and 2.37 V were recorded for devices acid-etched for 1 hr, 2 hrs and 3 hrs duration, respectively upon exposure to 1% hydrogen, under a constant forward bias current of 500 µA.

Issues and concerns of microscopic calibration process at different network levels : case study of Pacific Motorway

Calibration process in micro-simulation is an extremely complicated phenomenon. The difficulties are more prevalent if the process encompasses fitting aggregate and disaggregate parameters e.g. travel time and headway. The current practice in calibration is more at aggregate level, for example travel time comparison. Such practices are popular to assess network performance. Though these applications are significant there is another stream of micro-simulated calibration, at disaggregate level. This study will focus on such microcalibration exercise-key to better comprehend motorway traffic risk level, management of variable speed limit (VSL) and ramp metering (RM) techniques. Selected section of Pacific Motorway in Brisbane will be used as a case study. The discussion will primarily incorporate the critical issues encountered during parameter adjustment exercise (e.g. vehicular, driving behaviour) with reference to key traffic performance indicators like speed, lane distribution and headway; at specific motorway points. The endeavour is to highlight the utility and implications of such disaggregate level simulation for improved traffic prediction studies. The aspects of calibrating for points in comparison to that for whole of the network will also be briefly addressed to examine the critical issues such as the suitability of local calibration at global scale. The paper will be of interest to transport professionals in Australia/New Zealand where micro-simulation in particular at point level, is still comparatively a less explored territory in motorway management.

Issues and concerns of microscopic calibration process at different network levels : case study of pacific motorway

Calibration process in micro-simulation is an extremely complicated phenomenon. The difficulties are more prevalent if the process encompasses fitting aggregate and disaggregate parameters e.g. travel time and headway. The current practice in calibration is more at aggregate level, for example travel time comparison. Such practices are popular to assess network performance. Though these applications are significant there is another stream of micro-simulated calibration, at disaggregate level. This study will focus on such micro-calibration exercise-key to better comprehend motorway traffic risk level, management of variable speed limit (VSL) and ramp metering (RM) techniques. Selected section of Pacific Motorway in Brisbane will be used as a case study. The discussion will primarily incorporate the critical issues encountered during parameter adjustment exercise (e.g. vehicular, driving behaviour) with reference to key traffic performance indicators like speed, land distribution and headway; at specific motorway points. The endeavour is to highlight the utility and implications of such disaggregate level simulation for improved traffic prediction studies. The aspects of calibrating for points in comparison to that for whole of the network will also be briefly addressed to examine the critical issues such as the suitability of local calibration at global scale. The paper will be of interest to transport professionals in Australia/New Zealand where micro-simulation in particular at point level, is still comparatively a less explored territory in motorway management.

The future of the internet economy : addressing challenges facing the implementation of the Australian national broadband network

Innovation is vital for the future of Australia.s internet economy. Innovations rely on businesses. ability to innovate. Businesses. ability to innovate relies on their employees. The more these individual end users engage in the internet economy, the better businesses. engagement will be. The less these individual end users engage, the less likely a business is to engage and innovate. This means, for the internet economy to function at its fullest potential, it is essential that individual Australians have the capacity to engage with it and participate in it. The Australian federal government is working to facilitate the internet economy through policies, legislation and practices that implement high-speed broadband. The National Broadband Network will be a vital tool for Australia.s internet economy. Its .chief importance¡® is that it will provide faster internet access speeds that will facilitate access to internet services and content. However, an appropriate infrastructure and internet speed is only part of the picture. As the Organisation for Economic Co-operation and Development identified, appropriate government policies are also needed to ensure that vital services are more accessible by consumers. The thesis identifies essential theories and principles underpinning the internet economy and from which the concept of connectedness is developed. Connectedness is defined as the ability of end users to connect with internet content and services, other individuals and organisations, and government. That is, their ability to operate in the internet economy. The NBN will be vital in ensuring connectedness into the future. What is not currently addressed by existing access regimes is how to facilitate end user access capacity and participation. The thesis concludes by making recommendations to the federal government as to what the governing principles of the Australian internet economy should include in order to enable individual end user access capacity.