The navigation and visualisation of environmental audio using zooming spectrograms

Acoustic recordings play an increasingly important role in monitoring terrestrial and aquatic environments. However, rapid advances in technology make it possible to accumulate thousands of hours of recordings, more than ecologists can ever listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings on multiple scales, from minutes, hours, days to years. The visualization should facilitate navigation and yield ecologically meaningful information prior to listening to the audio. To construct images, we calculate acoustic indices, statistics that describe the distribution of acoustic energy and reflect content of ecological interest. We combine various indices to produce false-color spectrogram images that reveal acoustic content and facilitate navigation. The technical challenge we investigate in this work is how to navigate recordings that are days or even months in duration. We introduce a method of zooming through multiple temporal scales, analogous to Google Maps. However, the “landscape” to be navigated is not geographical and not therefore intrinsically visual, but rather a graphical representation of the underlying audio. We describe solutions to navigating spectrograms that range over three orders of magnitude of temporal scale. We make three sets of observations: 1. We determine that at least ten intermediate scale steps are required to zoom over three orders of magnitude of temporal scale; 2. We determine that three different visual representations are required to cover the range of temporal scales; 3. We present a solution to the problem of maintaining visual continuity when stepping between different visual representations. Finally, we demonstrate the utility of the approach with four case studies.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Passengers' intuitive navigation in airports

This thesis examined passengers' intuitive navigation in airports. It aims to ensure that passengers can navigate fast and efficiently through these complex environments. Field research was conducted at two Australian international airports. Participants wore eye-tracking glasses while finding their way through the terminal. Insight was gained into the intuitive use of navigation elements in the airport environment. With a detailed understanding of how passengers' navigate, the findings from this research can be used to improve airport design and planning. This will assist passengers who don't regularly fly as well as those who are frequent flyers.

The potential benefits of divergent thinking and metacognitive skills in STEAM learning: A discussion paper

In the wake of an almost decade long economic downturn and increasing competition from developing economies, a new agenda in the Australian Government for science, technology, engineering, and mathematics (STEM) education and research has emerged as a national priority. However, to art and design educators, the pervasiveness and apparent exclusivity of STEM can be viewed as another instance of art and design education being relegated to the margins of curriculum (Greene, 1995). In the spirit of interdisciplinarity, there have been some recent calls to expand STEM education to include the arts and design, transforming STEM into STEAM in education (Maeda, 2013). As with STEM, STEAM education emphasises the connections between previously disparate disciplines, meaning that education has been conceptualised in different ways, such as focusing on the creative design thinking process that is fundamental to engineering and art (Bequette & Bequette, 2012). In this article, we discuss divergent creative design thinking process and metacognitive skills, how, and why they may enhance learning in STEM and STEAM.

Pilot-scale cellulosic ethanol production using eucalyptus biomass pre-treated by dilute acid and steam explosion

The widespread deployment of commercial-scale cellulosic ethanol currently hinges on developing and evaluating scalable processes whilst broadening feedstock options. This study investigates whole Eucalyptus grandis trees as a potential feedstock and demonstrates dilute acid pre-treatment (with steam explosion) followed by pre-saccharification simultaneous saccharification fermentation process (PSSF) as a suitable, scalable strategy for the production of bioethanol. Biomass was pre-treated in dilute H2SO4 at laboratory scale (0.1 kg) and pilot scale (10 kg) to evaluate the effect of combined severity factor (CSF) on pre-treatment effectiveness. Subsequently, pilot-scale pre-treated residues (15 wt.%) were converted to ethanol in a PSSF process at 2 L and 300 L scales. Good polynomial correlations (n = 2) of CSF with hemicellulose removal and glucan digestibility with a minimum R2 of 0.91 were recorded. The laboratory-scale 72 h glucan digestibility and glucose yield was 68.0% and 51.3%, respectively, from biomass pre-treated at 190 °C /15 min/ 4.8 wt.% H2SO4. Pilot-scale pre-treatment (180 °C/ 15 min/2.4 wt.% H2SO4 followed by steam explosion) delivered higher glucan digestibility (71.8%) and glucose yield (63.6%). However, the ethanol yields using PSSF were calculated at 82.5 and 113 kg/ton of dry biomass for the pilot and the laboratory scales, respectively. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd