Determining the detection capabilities of existing real time measurement techniques for different engineered nanoparticles

The application of nanotechnology products has increased significantly in recent years. With their broad range of applications, including electronics, food and agriculture, power and energy, scientific instruments, clothing, cosmetics, buildings, biomedical and health, etc (Catanzariti, 2008), nanomaterials are an indispensible part of human life.

Detection of right-to-left Atrial communication using agitated saline contrast imaging : experience with 1162 patients and recommendations for echocardiography

Background: Right-to-left shunting via a patent foramen ovale (PFO) has a recognized association with embolic events in younger patients. The use of agitated saline contrast imaging (ASCi) for detecting atrial shunting is well documented, however optimal technique is not well described. The purpose of this study is to assess the efficacy and safety of ASCi via TTE for assessment of right-to-left atrial communication in a large cohort of patients. Method: A retrospective review was undertaken of 1162 consecutive transthoracic (TTE) ASCi studies, of which 195 had also undergone clinically indicated transesophageal (TEE) echo. ASCi shunt results were compared with color flow imaging (CFI) and the role of provocative maneuvers (PM) assessed. Results: 403 TTE studies (35%) had paradoxical shunting seen during ASCi. Of these, 48% were positive with PM only. There was strong agreement between TTE ASCi and reported TEE findings (99% sensitivity, 85% specificity), with six false positive and two false negative results. In hindsight, the latter were likely due to suboptimal right atrial opacification, and the former due to transpulmonary shunting. TTE CFI was found to be insensitive (22%) for the detection of a PFO compared with TTE ASCi. Conclusions: TTE ASCi is minimally invasive and highly accurate for the detection of right-to-left atrial communication when PM are used. TTE CFI was found to be insensitive for PFO screening. It is recommended that TTE ASCi should be considered the initial diagnostic tool for the detection of PFO in clinical practice. A dedicated protocol should be followed to ensure adequate agitated saline contrast delivery and performance of provocative maneuvers.

Ensemble-based DDoS detection and mitigation model

This work-in-progress paper presents an ensemble-based model for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks, and its partial implementation. The model utilises network traffic analysis and MIB (Management Information Base) server load analysis features for detecting a wide range of network and application layer DDoS attacks and distinguishing them from Flash Events. The proposed model will be evaluated against realistic synthetic network traffic generated using a software-based traffic generator that we have developed as part of this research. In this paper, we summarise our previous work, highlight the current work being undertaken along with preliminary results obtained and outline the future directions of our work.

The Donald Mackay Churchill Fellowship to study methods of preventing and supporting victims of online fraud

Every day inboxes are being flooded with invitations to invest money in overseas schemes, notifications of overseas lottery wins and inheritances, as well as emails from banks and other institutions asking for customers to confirm information about their identity and account details. While these requests may seem outrageous, many believe the request to be true and respond, through the sending of money or personal details. This can have devastating consequences, financially, emotionally and physically. While enforcement action is important, greater success is likely to come in the area of prevention, which avoids victim losses in the first place. Considerable victim support is also required by victims who have suffered significant losses, in trying to get their lives back on track. This project examined fraud prevention strategies and support services for victims of online fraud across the United Kingdom, United States of America and Canada. While much work has already been undertaken in Queensland, there is considerable room for improvement and a great deal can be learnt from these overseas jurisdictions. There are several examples of innovative and effective responses, particularly in the area of victim support, that are highlighted throughout this report. It is advocated that Australia can continue to improve its position regarding the prevention and support of online fraud victims, by applying the knowledge and expertise learnt overseas to a local context.

Spectrum sensing, detection and optimisation in cognitive radio for non-stationary primary user signals

Cognitive radio is an emerging technology proposing the concept of dynamic spec- trum access as a solution to the looming problem of spectrum scarcity caused by the growth in wireless communication systems. Under the proposed concept, non- licensed, secondary users (SU) can access spectrum owned by licensed, primary users (PU) so long as interference to PU are kept minimal. Spectrum sensing is a crucial task in cognitive radio whereby the SU senses the spectrum to detect the presence or absence of any PU signal. Conventional spectrum sensing assumes the PU signal as ‘stationary’ and remains in the same activity state during the sensing cycle, while an emerging trend models PU as ‘non-stationary’ and undergoes state changes. Existing studies have focused on non-stationary PU during the transmission period, however very little research considered the impact on spectrum sensing when the PU is non-stationary during the sensing period. The concept of PU duty cycle is developed as a tool to analyse the performance of spectrum sensing detectors when detecting non-stationary PU signals. New detectors are also proposed to optimise detection with respect to duty cycle ex- hibited by the PU. This research consists of two major investigations. The first stage investigates the impact of duty cycle on the performance of existing detec- tors and the extent of the problem in existing studies. The second stage develops new detection models and frameworks to ensure the integrity of spectrum sensing when detecting non-stationary PU signals. The first investigation demonstrates that conventional signal model formulated for stationary PU does not accurately reflect the behaviour of a non-stationary PU. Therefore the performance calculated and assumed to be achievable by the conventional detector does not reflect actual performance achieved. Through analysing the statistical properties of duty cycle, performance degradation is proved to be a problem that cannot be easily neglected in existing sensing studies when PU is modelled as non-stationary. The second investigation presents detectors that are aware of the duty cycle ex- hibited by a non-stationary PU. A two stage detection model is proposed to improve the detection performance and robustness to changes in duty cycle. This detector is most suitable for applications that require long sensing periods. A second detector, the duty cycle based energy detector is formulated by integrat- ing the distribution of duty cycle into the test statistic of the energy detector and suitable for short sensing periods. The decision threshold is optimised with respect to the traffic model of the PU, hence the proposed detector can calculate average detection performance that reflect realistic results. A detection framework for the application of spectrum sensing optimisation is proposed to provide clear guidance on the constraints on sensing and detection model. Following this framework will ensure the signal model accurately reflects practical behaviour while the detection model implemented is also suitable for the desired detection assumption. Based on this framework, a spectrum sensing optimisation algorithm is further developed to maximise the sensing efficiency for non-stationary PU. New optimisation constraints are derived to account for any PU state changes within the sensing cycle while implementing the proposed duty cycle based detector.

Corporate fraud and misconduct : investigative and regulatory priorities

This article investigates the profile of the companies that have been investigated for corporate fraud and misconduct. Our definition of fraud includes financial statement fraud, market misconduct fraud such as insider trading or false disclosures, and managerial fraud. The particular evidence presented relates to those instances of corporate fraud and misconduct investigated by the Australian corporate regulatory, Australian Securities and Investments Commission (ASIC), and relates to sanctions for fraud, misconduct or compliance breaches. Using data compiled from the public announcements in the ASIC reports over the period 2004-2008, we categorise the type of fraud and misconduct breaches ASIC chooses to report and investigate.

Cameron's hot air. Pollution, fraud and the politics of carbon emissions

It is widely recognised that exposure to air pollutants affect pulmonary and lung dysfunction as well as a range of neurological and vascular disorders. The rapid increase of worldwide carbon emissions continues to compromise environmental sustainability whilst contributing to premature death. Moreover, the harms caused by air pollution have a more pernicious reach, such as being the major source of climate change and ‘natural disasters’, which reportedly kills millions of people each year (World Health Organization, 2012). The opening quotations tell a story of the UK government's complacency towards the devastation of toxic and contaminating air emissions. The above headlines greeted the British public earlier this year after its government was taken to the Court of Appeal for an appalling air pollution record that continues to cause the premature deaths of 30,000 British people each year at a health cost estimated at £20 billion per annum. This combined with pending legal proceedings against the UK government for air pollution violations by the European Commission, point to a Cameron government that prioritises hot air and profit margins over human lives. The UK's legal air pollution regimes are an industry dominated process that relies on negotiation and partnership between regulators and polluters. The entire model seeks to assist business compliance rather than punish corporate offenders. There is no language of ‘crime’ in relation to UK air pollution violations but rather a discourse of ‘exceedence’ (Walters, 2010). It is a regulatory system not premised on the ‘polluter pay’ principle but instead the ‘polluter profit’ principle.

Characterization of sky-region morphological-temporal airborne collision detection

Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.

Comparison of PCR, nested PCR, and random amplified polymorphic DNA PCR for detection and typing of Ureaplasma urealyticum in specimens from pregnant women

A PCR assay, using three primer pairs, was developed for the detection of Ureaplasma urealyticum, parvo biovar, mba types 1, 3, and 6, in cultured clinical specimens. The primer pairs were designed by using the polymorphic base positions within a 310- to 311-bp fragment of the 5* end and upstream control region of the mba gene. The specificity of the assay was confirmed with reference serovars 1, 3, 6, and 14 and by the amplified-fragment sizes (81 bp for mba 1, 262 bp for mba 3, and 193 bp for mba 6). A more sensitive nested PCR was also developed. This involved a first-step PCR, using the primers UMS-125 and UMA226, followed by the nested mba-type PCR described above. This nested PCR enabled the detection and typing of small numbers of U. urealyticum cells, including mixtures, directly in original clinical specimens. By using random amplified polymorphic DNA (RAPD) PCR with seven arbitrary primers, we were also able to differentiate the two biovars of U. urealyticum and to identify 13 RAPD-PCR subtypes. By applying these subtyping techniques to clinical samples collected from pregnant women, we established that (i) U. urealyticum is often a persistent colonizer of the lower genital tract from early midtrimester until the third trimester of pregnancy, (ii) mba type 6 was isolated significantly more often (P 5 0.048) from women who delivered preterm than from women who delivered at term, (iii) no particular ureaplasma subtype(s) was associated with placental infections and/or adverse pregnancy outcomes, and (iv) the ureaplasma subtypes most frequently isolated from women were the same subtypes most often isolated from infected placentas.

Risks of Carbon Fraud

Carbon credit markets are in the early stages of development and media headlines such as these illustrate emerging levels of concern and foreboding over the potential for fraudulent crime within these markets. Australian companies are continuing to venture into the largely unregulated voluntary carbon credit market to offset their emissions and / or give their customers the opportunity to be ‘carbon neutral’. Accordingly, the voluntary market has seen a proliferation of carbon brokers that offer tailored offset carbon products according to need and taste. With the instigation of the Australian compliance market and with pressure increasing for political responses to combat climate change, we would expect Australian companies to experience greater exposure to carbon products in both compliance and voluntary markets. This paper examines the risks of carbon fraud in these markets by reviewing cases of actual fraud and analysing and identifying contexts where risks of carbon fraud are most likely.

Damage detection in slab-on-girder bridges using vibration characteristics

This paper develops and applies a multi-criteria procedure, incorporating changes in natural frequencies, modal flexibility and the modal strain energy, for damage detection in slab-on-girder bridges. The proposed procedure is first validated through experimental testing of a model bridge. Numerically simulated modal data obtained through finite element analyses are then used to evaluate the vibration parameters before and after damage and used as the indices for assessment of the state of structural health. The procedure is illustrated by its application to full scale slab-on-girder bridges under different damage scenarios involving single and multiple damages on the deck and girders.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Comparison of antigen detection and quantitative PCR in the detection of chlamydial infection in koalas (Phascolarctos cinereus)

The gold standard method for detecting chlamydial infection in domestic and wild animals is PCR, but the technique is not suited to testing animals in the field when a rapid diagnosis is frequently required. The objective of this study was to compare the results of a commercially available enzyme immunoassay test for Chlamydia against a quantitative Chlamydia pecorum-specific PCR performed on swabs collected from the conjunctival sac, nasal cavity and urogenital sinuses of naturally infected koalas (Phascolarctos cinereus). The level of agreement for positive results between the two assays was low (43.2%). The immunoassay detection cut-off was determined as approximately 400 C. pecorum copies, indicating that the test was sufficiently sensitive to be used for the rapid diagnosis of active chlamydial infections.

Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.