Flying Spore Trap Airborne Based Surveillance : towards a Biosecure Australia

The main limitations with existing fungal spore traps are that they are stationary and cannot be used in inaccessible or remote areas of Australia. This may result in delayed assessment, possible spread of harmful crop infestations and loss of crop yield and productivity. Fitted with the developed smart spore trap the UAV can fly, detect and monitor spores of plant pathogens in areas which previously were almost impossible to monitor. The technology will allow for earlier detection of emergency plant pests (EPPs) incursions by providing efficient and effective airborne surveillance, helping to protect Australia’s crops, pastures and the environment. The project is led by the Cooperative Research Centre for National Plant Biosecurity, with ARCAA/ QUT, CSIRO and the Queensland Government also providing resources. The prototype airplane was exhibited at the Innovation in Australia event December 7.

A prospective surveillance model for rehabilitation for women with breast cancer

Background: The current model of care for breast cancer is focused on disease treatment followed by ongoing recurrence surveillance. This approach lacks attention to the patients’ physical and functional well-being. Breast cancer treatment sequelae can lead to physical impairments and functional limitations. Common impairments include pain, fatigue, upper extremity dysfunction, lymphedema, weakness, joint arthralgia, neuropathy, weight gain, cardiovascular effects, and osteoporosis. Evidence supports prospective surveillance for early identification and treatment as a means to prevent or mitigate many of these concerns. Purpose: This paper proposes a prospective surveillance model for physical rehabilitation and exercise that can be integrated with disease treatment to create a more comprehensive approach to survivorship health care. The goals of the model are to promote surveillance for common physical impairments and functional limitations associated with breast cancer treatment, to provide education to facilitate early identification of impairments, to introduce rehabilitation and exercise intervention when physical impairments are identified and to promote and support physical activity and exercise behaviors through the trajectory of disease treatment and survivorship. Methods: The model is the result of a multi-disciplinary meeting of research and clinical experts in breast cancer survivorship and representatives of relevant professional and advocacy organizations. Outcomes: The proposed model identifies time points during breast cancer care for assessment of and education about physical impairments. Ultimately, implementation of the model may influence incidence and severity of breast cancer treatment related physical impairments. As such, the model seeks to optimize function during and following treatment and positively influence a growing survivorship community.

Weighted LDA techniques for I-vector based speaker verification

This paper introduces the Weighted Linear Discriminant Analysis (WLDA) technique, based upon the weighted pairwise Fisher criterion, for the purposes of improving i-vector speaker verification in the presence of high intersession variability. By taking advantage of the speaker discriminative information that is available in the distances between pairs of speakers clustered in the development i-vector space, the WLDA technique is shown to provide an improvement in speaker verification performance over traditional Linear Discriminant Analysis (LDA) approaches. A similar approach is also taken to extend the recently developed Source Normalised LDA (SNLDA) into Weighted SNLDA (WSNLDA) which, similarly, shows an improvement in speaker verification performance in both matched and mismatched enrolment/verification conditions. Based upon the results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset, we believe that both WLDA and WSNLDA are viable as replacement techniques to improve the performance of LDA and SNLDA-based i-vector speaker verification.

A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.

Surveillance and slander : Eleanor Dark in the 1940s and 1950s

Examines the effects of national surveillance and local right-wing intimidation on the literary works of author Eleanor Dark during the 1940s and 1950s in Australia. Reason Dark was subjected to national surveillance and right-wing intimidation; Relationship of Dark with local and national security forces; Accusations against the Dark family; Censorship faced by writers.

Improved Subject Identification in Surveillance Video using Super Resolution

The time consuming and labour intensive task of identifying individuals in surveillance video is often challenged by poor resolution and the sheer volume of stored video. Faces or identifying marks such as tattoos are often too coarse for direct matching by machine or human vision. Object tracking and super-resolution can then be combined to facilitate the automated detection and enhancement of areas of interest. The object tracking process enables the automatic detection of people of interest, greatly reducing the amount of data for super-resolution. Smaller regions such as faces can also be tracked. A number of instances of such regions can then be utilized to obtain a super-resolved version for matching. Performance improvement from super-resolution is demonstrated using a face verification task. It is shown that there is a consistent improvement of approximately 7% in verification accuracy, using both Eigenface and Elastic Bunch Graph Matching approaches for automatic face verification, starting from faces with an eye to eye distance of 14 pixels. Visual improvement in image fidelity from super-resolved images over low-resolution and interpolated images is demonstrated on a small database. Current research and future directions in this area are also summarized.

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

A database for person re-identification in multi-camera surveillance networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.

Client adaptiveness in entrepreneurship and its effect on sustained competitive advantage in project-orientated service firms

Important differences exist in how service firms operate in comparison with manufacturing firms (c.f. Johne & Storey, 1998; Tether, 2002). Despite these significant differences, not much is known whether these differences extrapolate to entrepreneurship in the services industry. This study seeks to address this gap by investigating how value creation occurs when project-oriented firms1 adopt client adaptiveness as part of their entrepreneurial posture. Specifically, we examine the effect of client adaptiveness on sustained competitive advantage. Client adaptiveness is conceptualized as the extent to which an organization engages in identifying and responding to perceived client needs and wants which reflects the service firm’s propensity to dynamically synchronize with the project/client requirements.