Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Obligatory dangerousness criteria in the involuntary commitment and treatment provisions of Australian mental health legislation

Abstract Objective Involuntary commitment and treatment (IC&T) of people affected by mental illness may have reference to considerations of dangerousness and/or need for care. While attempts have been made to classify mental health legislation according to whether IC&T has obligatory dangerousness criteria, there is no standardised procedure for making classification decisions. The aim of this study was to develop and trial a classification procedure and apply it to Australia's mental health legislation. Method We developed benchmarks for ‘need for care’ and ‘dangerousness’ and applied these benchmarks to classify the mental health legislation of Australia's 8 states and territories. Our focus was on civil commitment legislation rather than criminal commitment legislation. Results One state changed its legislation during the course of the study resulting in two classificatory exercises. In our initial classification, we were able to classify IC&T provisions in legislation from 6 of the 8 jurisdictions as being based on either ‘need for care’ or ‘dangerousness’. Two jurisdictions used a terminology that was outside the established benchmarks. In our second classification, we were also able to successfully classify IC&T provisions in 6 of the 8 jurisdictions. Of the 6 Acts that could be classified, all based IC&T on ‘need for care’ and none contained mandatory ‘dangerousness’ criteria. Conclusions The classification system developed for this study provided a transparent and probably reliable means of classifying 75% of Australia's mental health legislation. The inherent ambiguity of the terminology used in two jurisdictions means that further development of classification may not be possible until the meaning of the terms used has been addressed in case law. With respect to the 6 jurisdictions for which classification was possible, the findings suggest that Australia's mental health legislation relies on ‘need for care’ and not on ‘dangerousness’ as the guiding principle for IC&T. Keywords: Involuntary commitment; Mental health legislation; Dangerousness; Australia

Enhanced locomotion control for a planetary rover

This article presents an approach to improve and monitor the behavior of a skid-steering rover on rough terrains. An adaptive locomotion control generates speeds references to avoid slipping situations. An enhanced odometry provides a better estimation of the distance travelled. A probabilistic classification procedure provides an evaluation of the locomotion efficiency on-line, with a detection of locomotion faults. Results obtained with a Marsokhod rover are presented throughout the paper

A model of social work classification in health care

The introduction of casemix funding for Australian acute health care services has challenged Social Work to demonstrate clear reporting mechanisms, demonstrate effective practice and to justify interventions provided. The term 'casemix' is used to describe the mix and type of patients treated by a hospital or other health care services. There is wide acknowledgement that the procedure-based system of Diagnosis Related Groupings (DRGs) is grounded in a medical/illness perspective and is unsatisfactory in describing and predicting the activity of Social Work and other allied health professions in health care service delivery. The National Allied Health Casemix Committee was established in 1991 as the peak body to represent allied health professions in matters related to casemix classification. This Committee has pioneered a nationally consistent, patient-centred information system for allied health. This paper describes the classification systems and codes developed for Social Work, which includes a minimum data set, a classification hierarchy, the set of activity (input) codes and 'indicator for intervention' codes. The advantages and limitations of the system are also discussed.