Green Falcon IV: An Unmanned Aerial System and an integrated Wireless Sensor Network for remote sensing tasks

This report summarises the development of an Unmanned Aerial System and an integrated Wireless Sensor Network (WSN), suitable for the real world application in remote sensing tasks. Several aspects are discussed and analysed to provide improvements in flight duration, performance and mobility of the UAV, while ensuring the accuracy and range of data from the wireless sensor system.

Heritability of the network architecture of intrinsic brain functional connectivity

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

Heritability of brain network topology in 853 twins and siblings

Anatomical brain networks change throughout life and with diseases. Genetic analysis of these networks may help identify processes giving rise to heritable brain disorders, but we do not yet know which network measures are promising for genetic analyses. Many factors affect the downstream results, such as the tractography algorithm used to define structural connectivity. We tested nine different tractography algorithms and four normalization methods to compute brain networks for 853 young healthy adults (twins and their siblings). We fitted genetic structural equation models to all nine network measures, after a normalization step to increase network consistency across tractography algorithms. Probabilistic tractography algorithms with global optimization (such as Probtrackx and Hough) yielded higher heritability statistics than 'greedy' algorithms (such as FACT) which process small neighborhoods at each step. Some global network measures (probtrackx-derived GLOB and ST) showed significant genetic effects, making them attractive targets for genome-wide association studies.

Journalism, media & communication network: Final report 2015

The JoMeC Network project had three key objectives. These were to: 1. Benchmark the pedagogical elements of journalism, media and communication (JoMeC) programs at Australian universities in order to develop a set of minimum academic standards, to be known as Threshold Learning Outcomes (TLOs), which would applicable to the disciplines of Journalism, Communication and/or Media Studies, and Public Relations; 2. Build a learning and teaching network of scholars across the JoMeC disciplines to support collaboration, develop leadership potential among educators, and progress shared priorities; 3. Create an online resources hub to support learning and teaching excellence and foster leadership in learning and teaching in the JoMeC disciplines. In order to benchmark the pedagogical elements of the JoMeC disciplines, the project started with a comprehensive review of the disciplinary settings of journalism, media and communication-related programs within Higher Education in Australia plus an analysis of capstone units (or subjects) offered in JoMeC-related degrees. This audit revealed a diversity of degree titles, disciplinary foci, projected career outcomes and pedagogical styles in the 36 universities that offered JoMeC-related degrees in 2012, highlighting the difficulties of classifying the JoMeC disciplines collectively or singularly. Instead of attempting to map all disciplines related to journalism, media and communication, the project team opted to create generalised TLOs for these fields, coupled with detailed TLOs for bachelor-level qualifications in three selected JoMeC disciplines: Journalism, Communication and/or Media Studies, and Public Relations. The initial review’s outcomes shaped the methodology that was used to develop the TLOs. Given the complexity of the JoMeC disciplines and the diversity of degrees across the network, the project team deployed an issue-framing process to create TLO statements. This involved several phases, including discussions with an issue-framing team (an advisory group of representatives from different disciplinary areas); research into accreditation requirements and industry-produced materials about employment expectations; evaluation of learning outcomes from universities across Australia; reviews of scholarly literature; as well as input from disciplinary leaders in a variety of forms. Draft TLOs were refined after further consultation with industry stakeholders and the academic community via email, telephone interviews, and meetings and public forums at conferences. This process was used to create a set of common TLOs for JoMeC disciplines in general and extended TLO statements for the specific disciplines of Journalism and Public Relations. A TLO statement for Communication and/or Media Studies remains in draft form. The Australian and New Zealand Communication Association (ANZCA) and Journalism Education and Research Association of Australian (JERAA) have agreed to host meetings to review, revise and further develop the TLOs. The aim is to support the JoMeC Network’s sustainability and the TLOs’ future development and use.

System-level analysis of network interfaces for hierarchical MPSoCs

Network Interfaces (NIs) are used in Multiprocessor System-on-Chips (MPSoCs) to connect CPUs to a packet switched Network-on-Chip. In this work we introduce a new NI architecture for our hierarchical CoreVA-MPSoC. The CoreVA-MPSoC targets streaming applications in embedded systems. The main contribution of this paper is a system-level analysis of different NI configurations, considering both software and hardware costs for NoC communication. Different configurations of the NI are compared using a benchmark suite of 10 streaming applications. The best performing NI configuration shows an average speedup of 20 for a CoreVA-MPSoC with 32 CPUs compared to a single CPU. Furthermore, we present physical implementation results using a 28 nm FD-SOI standard cell technology. A hierarchical MPSoC with 8 CPU clusters and 4 CPUs in each cluster running at 800MHz requires an area of 4.56mm2.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Teacher peer support in social network sites

This paper describes the types of support that teachers are accessing through the Social Network Site (SNS) 'Facebook'. It describes six ways in which teachers support one another within online groups. It presents evidence from a study of a large, open group of teachers online over a twelve week period, repeated with multiple groups a year later over a one week period. The findings suggest that large open groups in SNSs can be a useful source of pragmatic advice for teachers but that these groups are rarely a place for reflection on or feedback about teaching practice.