Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions

This paper presents large, accurately calibrated and time-synchronised datasets, gathered outdoors in controlled environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. It discusses how the data collection process was designed, the conditions in which these datasets have been gathered, and some possible outcomes of their exploitation, in particular for the evaluation of performance of sensors and perception algorithms for UGVs.

Reflections on talk about natural disasters by early childhood educators and directors

Facilitated discussion with early childhood staff working with children and families affected by natural disasters in Queensland, Australia, raises issues regarding educational communication in emergencies. This paper reports on these discussions as ‘reflections on talk’. It examines discrepancies between the literature and staff talk, gaps in the literature, and the inaccessible style of some literature-demanded collaborative debate and information re-interpretation. Reframing of the discourse style was used to support staff de-briefing, mutual encouragement, and sharing of insights on promoting resilience in children and families. Formal investigation is required regarding effective emergency-situation talk between staff, as well as with children and families.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.

The emergence of Leptospira borgpetersenii serovar Arborea as the dominant infecting serovar following the summer of natural disasters in Queensland, Australia 2011

The following research reports the emergence of Leptospira borgpetersenii serovar Arborea as the dominant infecting serovar following the summer of disasters and the ensuing clean up in Queensland, Australia during 2011. For the 12 month period (1 January to 31 December) L. borgpetersenii serovar Arborea accounted for over 49% of infections. In response to a flooding event public health officials need to issue community wide announcements warning the population about the dangers of leptospirosis and other water borne diseases. Communication with physicians working in the affected community should also be increased to update physicians with information such as clinical presentation of leptospirosis and other waterborne diseases. These recommendations will furnish public health officials with considerations for disease management when dealing with future disaster management programs.

The environmental enigma : why do producers professing stewardship continue to practice poor natural resource management?

Despite a wide acceptance that primary producers in Australia subscribe to a stewardship ethic, land and water degradation remains an ongoing problem. Recent calculations suggest that the economic cost of Australia's environmental degradation is amounting to more than $A3.5 billion a year with an estimated cost of managing (not overcoming) problems of salinity, acidification, soil erosion totalling $A60 billion over the next decade. This paper argues that stewardship itself is an unsatisfactory concept when looking to landholders to respond to environmental problems, for rarely does the attitude of stewardship translate into behaviours of improving natural resource management practices on private land. Whilst there is some acceptance of the environmental problem among primary producers, a number of external constraints may also impede the uptake of conservation-orientated practices. In light of the prevailing accounts of poor adoption of sustainable practices a number of policy options are reviewed in this paper, including formal regional partnerships, regulatory frameworks and market-based measures. It is concluded that the contentious nature of some of these new opportunities for change will mean that any moves aimed at reversing environmental degradation in Australia will be slow.

Genuine community engagement in remote dryland regions : natural resource management in Lake Eyre Basin

Remote dryland regions are characterised by sparse populations and socially marginalised voices which pose particular challenges to natural resource management. This paper considers the issue of how to achieve community engagement in regions with these characteristics. In doing so, the paper contributes to an expanding international research agenda focusing on the distinct characteristics of arid and semi-arid regions under the heading of 'dryland syndrome'. The paper draws on government liaison officer and local community perspectives of successful engagement in the case-study region of Lake Eyre Basin, Australia. The results demonstrate that widely recognised characteristics of successful engagement are required but insufficient for genuine engagement in remote dryland regions. In addition to building trust through community ownership, being inclusive, effective communication, and adequate resources, genuine community engagement in drylands also requires respecting the extreme conditions and extraordinary variability of these areas. Residents of dryland regions seek genuine engagement yet engage opportunistically when seasons are conducive and when tangible outcomes are visible. © 2011 The Authors. Geographical Research © 2011 Institute of Australian Geographers.

Vaccination of koalas with a recombinant Chlamydia pecorum major outer membrane protein induces antibodies of different specificity compared to those following a natural live infection

Chlamydial infection in koalas is common across the east coast of Australia and causes significant morbidity, infertility and mortality. An effective vaccine to prevent the adverse consequences of chlamydial infections in koalas (particularly blindness and infertility in females) would provide an important management tool to prevent further population decline of this species. An important step towards developing a vaccine in koalas is to understand the host immune response to chlamydial infection. In this study, we used the Pepscan methodology to identify B cell epitopes across the Major Outer Membrane Protein (MOMP) of four C. pecorum strains/genotypes that are recognized, either following (a) natural live infection or (b) administration of a recombinant MOMP vaccine. Plasma antibodies from the koalas naturally infected with a C. pecorum G genotype strain recognised the epitopes located in the variable domain (VD) four of MOMP G and also VD4 of MOMP H. By comparison, plasma antibodies from an animal infected with a C. pecorum F genotype strain recognised epitopes in VD1, 2 and 4 of MOMP F, but not from other genotype MOMPs. When Chlamydia-free koalas were immunised with recombinant MOMP protein they produced antibodies not only against epitopes in the VDs but also in conserved domains of MOMP. Naturally infected koalas immunised with recombinant MOMP protein also produced antibodies against epitopes in the conserved domains. This work paves the way for further refinement of a MOMP-based Chlamydia vaccine that will offer wide cross-protection against the variety of chlamydial infections circulating in wild koala populations.

Cell image classification using histograms, higher order statistics and adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.

Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle

Real-time image analysis and classification onboard robotic marine vehicles, such as AUVs, is a key step in the realisation of adaptive mission planning for large-scale habitat mapping in previously unexplored environments. This paper describes a novel technique to train, process, and classify images collected onboard an AUV used in relatively shallow waters with poor visibility and non-uniform lighting. The approach utilises Förstner feature detectors and Laws texture energy masks for image characterisation, and a bag of words approach for feature recognition. To improve classification performance we propose a usefulness gain to learn the importance of each histogram component for each class. Experimental results illustrate the performance of the system in characterisation of a variety of marine habitats and its ability to operate onboard an AUV's main processor suitable for real-time mission planning.

The impact of OCR accuracy on automated cancer classification of pathology reports

Objective To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Method Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. Results The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. Conclusions The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Xanthones from fungi, lichens, and bacteria : the natural products and their synthesis

Many fungi, lichens, and bacteria produce xanthones (derivatives of 9H-xanthen-9-one, “xanthone” from the Greek “xanthos”, for “yellow”) as secondary metabolites. Xanthones are typically polysubstituted and occur as either fully aromatized, dihydro-, tetrahydro-, or, more rarely, hexahydro-derivatives. This family of compounds appeals to medicinal chemists because of their pronounced biological activity within a notably broad spectrum of disease states, a result of their interaction with a correspondingly diverse range of target biomolecules. This has led to the description of xanthones as “privileged structures”.(1) Historically, the total synthesis of the natural products has mostly been limited to fully aromatized targets. Syntheses of the more challenging partially saturated xanthones have less frequently been reported, although the development in recent times of novel and reliable methods for the construction of the (polysubstituted) unsaturated xanthone core holds promise for future endeavors. In particular, the fascinating structural and biological properties of xanthone dimers and heterodimers may excite the synthetic or natural product chemist.