From ideation to sculpture : digital sculpting as the primary 'ideation' tool for creature design

This research project examined the potential for circumventing drawing in the ideation process by adopting digital sculpture as the primary conceptual development and design tool for the digital sculpting of creature designs. Through a series of experimental research cycles, multiple frameworks were explored with the aim of identifying a methodology for creating '3D sculpted sketches' for the initial phases of the ideation process. This research project acknowledges that drawing still remains the predominant method of visualising design ideas for characters and creatures for many artists. However, alongside other ideation techniques digital sculpting can function as a rapid and responsive tool to visualize and explore forms in a digital sculpting environment for the conceptualisation of multiple creature design variations. The results of this study are significant for emerging digital sculptors who may not necessarily have a well-defined creative brief or initial concept.

Single-cell force spectroscopy, an emerging tool to quantify cell adhesion to biomaterials

Cell adhesion receptors play a central role in sensing and integrating signals provided by the cellular environment. Thus, understanding adhesive interactions at the cell-biomaterial interface is essential to improve the design of implants that should emulate certain characteristics of the cell's natural environment. Numerous cell adhesion assays have been developed; among these, atomic force microscopy-based single-cell force spectroscopy (AFM-SCFS) provides a versatile tool to quantify cell adhesion at physiological conditions. Here we discuss how AFM-SCFS can be used to quantify the adhesion of living cells to biomaterials and give examples of using AFM-SCFS in tissue engineering and regenerative medicine. We anticipate that in the near future, AFM-SCFS will be established in the biomaterial field as an important technique to quantify cell-biomaterial interactions and thereby will contribute to the optimization of implants, scaffolds, and medical devices.

Development of a climate assessment tool for hybrid air conditioner

Australian climate is highly suitable for using outdoor air for free building cooling. In order to evaluate the suitability of hybrid cooler for specific applications, a pre-design climate assessment tool is developed and presented in this paper. In addition to the consideration of the local climate, comfort zone proposed by ASHRAE handbook and specific design of building and operation of hybrid cooler, possible influence from environmental factors (e.g. air humidity and air velocity), as well as personal factors (e.g. activity level and clothing insulation) on occupant’s thermal comfort are also considered in this tool. It is demonstrated that with the input of climatic data for a particular location and the associated design data for a specific application, the developed climate assessment tool is able to not only sort outdoor air conditions into the different process regions but also project them onto the psychrometric chart. It can also be used to estimate the hours for an individual operational mode under various climate conditions and summarize them in a table “Results”.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

TriggerSync: A time synchronisation tool

This paper presents a framework for synchronising multiple triggered sensors with respect to a local clock using standard computing hardware. Providing sensor measurements with accurate and meaningful timestamps is important for many sensor fusion, state estimation and control applications. Accurately synchronising sensor timestamps can be performed with specialised hardware, however, performing sensor synchronisation using standard computing hardware and non-real-time operating systems is difficult due to inaccurate and temperature sensitive clocks, variable communication delays and operating system scheduling delays. Results show the ability of our framework to estimate time offsets to sub-millisecond accuracy. We also demonstrate how synchronising timestamps with our framework results in a tenfold reduction in image stabilisation error for a vehicle driving on rough terrain. The source code will be released as an open source tool for time synchronisation in ROS.

The challenge of developing a fleet driving risk assessment tool: What can be learned from the process?

One of the riskiest activities in the course of a person's work is driving. By developing and testing a new work driving risk assessment measurement tool for use by organisations this research will contribute to the safety of those who drive for work purposes. The research results highlighted limitations associated with current self-report measures and provided evidence that the work driving environment is extremely complex and involves constant interactions between humans, vehicles, the road environment, and the organisational context.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Evaluation of the performance of a dengue outbreak detection tool for China

An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.

Detection of rain in acoustic recordings of the environment using machine learning techniques

This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.

Direct measurement as a tool for assessing the accuracy of inverse dynamics

The understanding of the loads generated within the prosthetic leg can aid engineers in the design of components and clinicians in the process of rehabilitation. Traditional methods to assess these loads have relied on inverse dynamics. This indirect method estimates the applied load using video recordings and force-plates located at a distance from the region of interest, such as the base of the residuum. The well-known limitations of this method are related to the accuracy of this recursive model and the experimental conditions required (Frossard et al., 2003). Recent developments in sensors (Frossard et al., 2003) and prosthetic fixation (Brånemark et al., 2000) permit the direct measurement of the loads applied on the residuum of transfemoral amputees. In principle, direct measurement should be an appropriate tool for assessing the accuracy of inverse dynamics. The purpose of this paper is to determine the validity of this assumption. The comparative variable used in this study is the velocity of the relative body center of mass (VCOM(t)). The relativity is used to align the static (w.r.t. position) force plate measurement with the dynamic load cell measurement.

Psychometric properties of an Australian supportive care needs assessment tool for Indigenous patients with cancer

BACKGROUND There are significant disparities in cancer outcomes between Indigenous and non-Indigenous Australians. Identifying the unmet supportive care needs of Indigenous Australians with cancer is imperative to improve their cancer care. The purpose of this study was to test the psychometric properties of a supportive care needs assessment tool for Indigenous Australian (SCNAT-IP) cancer patients. METHODS The SCNAT-IP was administered to 248 Indigenous Australians diagnosed with a range of cancer types and stages, and received treatment in one of four Queensland hospitals. All 39 items were assessed for ceiling and floor effects and analysed using exploratory factor analysis (EFA) to determine construct validity. Identified factors were assessed for internal consistency and convergent validity to validated psychosocial tools. RESULTS EFA revealed a four-factor structure (physical and psychological, hospital care, information and communication, and practical and cultural needs) explaining 51% of the variance. Internal consistency of four subscales was good, with Cronbach Alpha reliability coefficients ranging from 0.70-0.89. Convergent validity was supported by significant correlations between the SCNAT-IP with the Distress Thermometer (r=0.60, p<0.001), and The Cancer Worry Chart (r=0.58, p<0.001) and a moderately strong negative correlation with Assessment of Quality of Life questionnaire (r=-0.56, p<0.001). CONCLUSION These data provide initial support for the SCNAT-IP a measure of multiple supportive care needs domains specific to Indigenous Australian cancer patients undergoing treatment.

Variations in proximity as a tool for audience engagement

The tool proximity and ways in which variations in audience-performer proximity can engage audiences of contemporary dance in a different way is discussed. The key aspects and features of the Voyeur, created by the author in 2009, a dance work that tested these theories in action and looked at how specifically changes in the traditional presentation paradigm affected engagement are highlighted.