Application of podcasting in online engineering education

Applications of mobile technologies for engineering education can be found in the literature, but, many of the reported applications are aimed at the online (wirelessly), on-campus, synchronous and proximal use of mobile technologies. Mobile technologies in engineering education can encompass more than the proximal teaching and learning environment-they can be offline, asynchronous and at a distance from the classroom. This paper reports on the initial application of `podcasting' in a wholly online engineering study unit. It presents the rationale for, technical development details of, and, limited evaluation of this initial podcasting trial.

Generalised 2D-correlation NMR analysis of a wine fermentation

A wine fermentation has been monitored on a daily basis by 1H NMR spectroscopy. Following data pre-processing that includes synthesis of the spectra to ensure all peaks are of constant half-width, the series of spectra were examined using generalised two-dimensional correlation techniques. Synchronous and asynchronous data maps have been generated and employed to interpret the changes in the fermentation process as a function of time. The results illustrate the potential of high resolution NMR with multivariate data analysis as a tool for process monitoring and the manner in which two-dimensional correlation mapping can aid in data interpretation.

Research into cyberbullying : student perspectives on cybersafe learning environments

This paper reports a qualitative study designed to investigate the issues of cybersafety and cyberbullying and report how students are coping with them. Through discussion with 74 students, aged from 10 to 17, in focus groups divided into three age levels, data were gathered in three schools in Victoria, Australia, where few such studies had been set. Social networking sites and synchronous chat sites were found to be the places where cyberbullying most commonly occurred, with email and texting on mobile phones also used for bullying. Grades 8 and 9 most often reported cyberbullying and also reported behaviours and internet contacts that were cybersafety risks. Most groups preferred to handle these issues themselves or with their friends rather then alert parents and teachers who may limit their technology access. They supported education about these issues for both adults and school students and favoured a structured mediation group of their peers to counsel and advise victims.

Bearing-only localization using geometrically constrained optimization

We examine the problem of optimal bearing-only localization of a single target using synchronous measurements from multiple sensors. We approach the problem by forming geometric relationships between the measured parameters and their corresponding errors in the relevant emitter localization scenarios. Specifically, we derive a geometric constraint equation on the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry. We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with traditional maximum likelihood (TML) estimation.

Population structure and life history characteristics of euastacus bispinosus and cherax destructor (Parastacidae) in the Grampians National Park, Australia

Despite being the most widely distributed and cultured freshwater crayfish species in Australia, relatively little is known about the ecology of Cherax destructor outside of captivity. Similarly, few ecological studies have been conducted on the large and threatened stream dwelling Australian freshwater crayfish Euastacus bispinosus. A series of seasonal sampling surveys over two years investigated the population structure, life history and reproductive timing of C. destructor in fire dam habitats, and of E. bispinosus in channel (stream) habitats, in the Grampians National Park in south-west Victoria, Australia. Cherax destructor individuals in the largest size class (50 – 59.95 mm OCL) were not abundant during the study, while those belonging to the 0 – 9.95 mm OCL size class were more frequent in summer than at other times of year, suggesting synchronous recruitment in fire dam habitats. Individuals in the reproductively active mid-size classes were also frequent in summer. For E. bispinosus, sex ratios in spring always favoured females, although there were no clear trends for other times of year. Gravid E. bispinosus females were found in winter and spring throughout the study, and were sexually mature at a smaller size than has previously been reported.

Using virtual meeting spaces for work integrated learning

Work integrated learning activities provide students with the opportunity to apply the knowledge and skills they have developed through their tertiary education to authentic work place problems. This paper reports on the outcome of a virtual work integrated learning activity undertaken by third year IT students. Students used a synchronous communication tool to participate in meetings with their virtual teammates. They were required to produce minutes and a report of their meeting. The majority of students completed the exercise successfully with some student groups using the meeting facility for subsequent collaboration during the remainder of the unit.

Late palaeozoic global changes affecting high-latitude environments and biotas : an introduction

The Late Palaeozoic Ice Age (LPIA), spanning approximately from ~320 Ma (Serpukhovian, late Mississippian) to 290 Ma (mid-Sakmarian, Early Permian), represents the vegetated Earth’s largest and most long-lasting regime of severe and multiple glaciations, involving processes and patterns probably comparable to those of the Last Ice Age. Accompanying the LPIA occurred a number of broadly synchronous global environmental and biotic changes. These global changes, as briefly reviewed and summarized in this introductory paper, comprised (but are not limited to) the following: massive continental reorganization in the lead up to the final assembly of Pangea resulting in profound changes in global palaeogeography, palaeoceanography and palaeobiogeogarphy; substantially lowered global atmospheric carbon dioxide concentrations (pCO₂), coupled with an unprecedented increase in atmospheric oxygen concentrations reaching Earth's all-time high in its last 600 million year history; sharp global temperature and sea-level drops (albeit with considerable spatial and temporal variability throughout the ice age); and apparently a prolonged period of global sluggish macro-evolution with both low extinction and origination rates compared to other times. In the aftermath of the LPIA, the world's climate entered into a transitional climate phase through the late Early to Middle Permian before its transformation into a greenhouse state towards the end-Permian. In recent years, considerable amount of data and interpretations have been published concerning the physical evidence in support of the LPIA, its broad timeframe and eustatic and ecosystem responses from the lower latitudes, but relatively less attention has been drawn to the impact of the ice age on late Palaeozoic high-latitude environments and biotas. It is with this mission in mind that we have organized this special issue, with the central focus on late Palaeozoic high latitude regions of both hemispheres, that is, Gondwana and northern Eurasia. Our aim is to gather a set of papers that not only document the physical environmental changes that had occurred in the polar regions of Gondwana and northern Eurasia during the LPIA, but also review on the biotic responses at different taxonomic, ecological and spatial scales to these physical changes in a refined chronological timeframe.

This introductory paper is designed to provide a global context for the special issue, with a brief review of key late Palaeozoic global environmental changes (including: changes in global land-sea configurations, atmospheric chemistry, global climate regimes, global ocean circulation patterns and sea levels) and large -scale biotic (biogeographic and evolutionary) responses, followed by a summary of what we see as unresolved scientific issues and various working hypotheses concerning late Palaeozoic global changes and, in particular, the LPIA, as a possible reference to future research.

Investigating Ways of Enhancing Online Research

We live in an era characterized as ‘the Digital Age’ and the ways in which we engage in teaching, learning and research are evolving with the increased use of digital technologies. This paper describes a study that investigated the ways in which a cohort of Education students in Victoria, Australia engaged in online research projects using Information Communication Technologies (ICT) as the main form of communication during the research process. When an array of technologies, related resources and training are made available to staff and University students, what are the key influences that effect their adoption and application of the selected mediums? Understanding the answer to this question is important in informing instruction and technological pedagogies for distance education and research. Data was gathered from students and their research supervisors via the use of online surveys. The research identified a number of key factors that influenced people’s preferences for using certain digital technologies. The study revealed that there was a tendency for people to prefer the use of asynchronous forms of digital communication. It is argued that more research is needed in this area in order to improve the application of online modes of communication and ensure that those researching via distance/technological modes are not disadvantaged in their research and learning experiences.

Human action recognition based on pyramid histogram of oriented gradients

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

The hidden permutation model and location-based activity recognition

Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.

A smith-waterman local alignment approach for spatial activity recognition

In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise.