Sitooter-i

As part of the process of renewing its City Centre Master Plan, Brisbane City Council and its CBD are hosting an Ideas Fiesta between April 11th- 3rd May 2013. It hopes to generate new ideas, showcase design concepts, and stimulate interest in imagining the future of the city centre. Events will be held in a variety of city outdoor spaces, streets, laneways and venues to identify catalyst projects and explore design ideas for the city centre. In the City Botanic Gardens ‘Sunday reserved for you’ on 21st April and ‘A shot of green’ on Wednesday 24th April are some of the events planned and are the setting for innovative items of park furniture and other activities. A sitooterie (Scottish) is an outdoor space to sit… a place to enjoy nature. This Sitooter-i has been digitally designed for CNC fabrication with the ergonomics of reclining, lounging, just sitting or jumping around in mind. It was assembled by staff and students from Queensland University of Technology and is made from locally sourced plywood components which are easily dismantled for re-use elsewhere. But this Sitooter-i inspired by natural forms is also both physically and technologically interactive. Sensors record sound, light and temperature in their interactions with users. This data may be relayed as LED lights played in rhythm along frame edges or used by Brisbane City Council to assess frequency and style of use, perhaps revealing the effectiveness of its performance and the preferences of its users. See: http://sitooteri.wordpress.com/

Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

Highly sensitive electrochemical sensor for nitric oxide using the self-assembled monolayer of 1,8,15,22-tetraaminophthalo-cyanatocobalt(II) on glassy carbon electrode

Glassy carbon (GC) electrode modified with a self-assembled monolayer (SAM) of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) was used for the selective and highly sensitive determination of nitric oxide (NO). The SAM of 4α-CoIITAPc was formed on GC electrode by spontaneous adsorption from DMF containing 1 mM 4α-CoIITAPc. The SAM showed two pairs of well-defined redox peaks corresponding to CoIII/CoII and CoIIIPc−1/CoIIIPc−2 in 0.2 M phosphate buffer (PB) solution (pH 2.5). The SAM modified electrode showed excellent electrocatalytic activity towards the oxidation of nitric oxide (NO) by enhancing its oxidation current with 310 mV less positive potential shift when compared to bare GC electrode. In amperometric measurements, the current response for NO oxidation was linearly increased in the concentration range of 3×10−9 to 30×10−9 M with a detection limit of 1.4×10−10 M (S/N=3). The proposed method showed a better recovery for NO in human blood serum samples.

Selective electrochemical epinephrine sensor using self-assembled monomolecular film of 1,8,15,22-tetraaminophthalocyanatonickel(II) on gold electrode

This article describes the highly sensitive and selective determination of epinephrine (EP) using self-assembled monomolecular film (SAMF) of 1,8,15,22-tetraamino-phthalocyanatonickel(II) (4α-NiIITAPc) on Au electrode. The 4α-NiIITAPc SAMF modified electrode was prepared by spontaneous adsorption of 4α-NiIITAPc from dimethylformamide solution. The modified electrode oxidizes EP at less over potential with enhanced current response in contrast to the bare Au electrode. The standard heterogeneous rate constant (k°) for the oxidation of EP at 4α-NiIITAPc SAMF modified electrode was found to be 1.94×10−2 cm s−1 which was much higher than that at the bare Au electrode. Further, it was found that 4α-NiIITAPc SAMF modified electrode separates the voltammetric signals of ascorbic acid (AA) and EP with a peak separation of 250 mV. Using amperometric method the lowest detection limit of 50 nM of EP was achieved at SAMF modified electrode. Simultaneous amperometric determination of AA and EP was also achieved at the SAMF modified electrode. Common physiological interferents such as uric acid, glucose, urea and NaCl do not interfere within the potential window of EP oxidation. The present 4α-NiIITAPc SAMF modified electrode was also successfully applied to determine the concentration of EP in commercially available injection.

Practical analysis of big acoustic sensor data for environmental monitoring

Monitoring the environment with acoustic sensors is an effective method for understanding changes in ecosystems. Through extensive monitoring, large-scale, ecologically relevant, datasets can be produced that can inform environmental policy. The collection of acoustic sensor data is a solved problem; the current challenge is the management and analysis of raw audio data to produce useful datasets for ecologists. This paper presents the applied research we use to analyze big acoustic datasets. Its core contribution is the presentation of practical large-scale acoustic data analysis methodologies. We describe details of the data workflows we use to provide both citizen scientists and researchers practical access to large volumes of ecoacoustic data. Finally, we propose a work in progress large-scale architecture for analysis driven by a hybrid cloud-and-local production-grade website.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.