Post-quantum key exchange for the TLS protocol from the ring learning with errors problem

Lattice-based cryptographic primitives are believed to offer resilience against attacks by quantum computers. We demonstrate the practicality of post-quantum key exchange by constructing cipher suites for the Transport Layer Security (TLS) protocol that provide key exchange based on the ring learning with errors (R-LWE) problem, we accompany these cipher suites with a rigorous proof of security. Our approach ties lattice-based key exchange together with traditional authentication using RSA or elliptic curve digital signatures: the post-quantum key exchange provides forward secrecy against future quantum attackers, while authentication can be provided using RSA keys that are issued by today's commercial certificate authorities, smoothing the path to adoption. Our cryptographically secure implementation, aimed at the 128-bit security level, reveals that the performance price when switching from non-quantum-safe key exchange is not too high. With our R-LWE cipher suites integrated into the Open SSL library and using the Apache web server on a 2-core desktop computer, we could serve 506 RLWE-ECDSA-AES128-GCM-SHA256 HTTPS connections per second for a 10 KiB payload. Compared to elliptic curve Diffie-Hellman, this means an 8 KiB increased handshake size and a reduction in throughput of only 21%. This demonstrates that provably secure post-quantum key-exchange can already be considered practical.

Modelling of Electric Fields

South Africa has an electrical transmission grid of over 25 000 km of overhead power lines with voltages of 132 kV to 765 kV. The grid has been largely designed and built by the power utility, Eskom. This book embodies the planning philosophies, design principles and construction practices of Eskom. It is the culmination of decades of thought, study, research and the practical experience of many overhead power line engineers and researchers. The book covers the main aspects of overhead power line design and construction, from electrical first principles, system planning, insulation co-ordination (including live line working), mechanical design through to environmental impact management and power line communications. The content emphasises the need for close interaction between all technical disciplines involved and the importance of optimising designs for economy and performance. Additional challenges in South Africa are the relatively high altitude of the interior plateau (1 000 m to 1 700 m above sea level), severe lightning in some areas and long transmission distances. The book explains how these factors are accommodated in modern designs. Other advanced work covered includes the use and understanding of polymeric insulators, the judicious reduction of phase-to-phase spacings and the adoption of guyed structures.

Groundwater balance research to improve the accuracy of drawdown predictions in the Surat Basin, Queensland

Groundwater modelling studies rely on an accurate determination of inputs and outputs that make up the water balance. Often there is large uncertainty associated with estimates of recharge and unmetered groundwater use. This can translate to equivalent uncertainty in the forecasting of sustainable yields, impacts of extraction, and susceptibility of groundwater dependent ecosystems. In the case of Coal Seam Gas, it is important to characterise the temporal and special distribution of depressurisation in the reservoir and how this may or may not extend to the adjacent aquifers. A regional groundwater flow model has been developed by the Queensland Government to predict drawdown impacts due to Coal Seam Gas activities in the Surat basin. This groundwater model is undergoing continued refinement and there is currently scope to address some of the key areas of uncertainty including better quantification of groundwater recharge and unmetered groundwater extractions. Research is currently underway to improve the accuracy of estimates of both of these components of the groundwater balance in order to reduce uncertainty in predicted groundwater drawdowns due to CSG activities.

A dynamic navigation model for unmanned aircraft systems and an application to autonomous front-on environmental sensing and photography using low-cost sensor systems

This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

A strong lightweight authentication protocol for low-cost RFID systems

RFID is an important technology that can be used to create the ubiquitous society. But an RFID system uses open radio frequency signal to transfer information and this leads to pose many serious threats to its privacy and security. In general, the computing and storage resources in an RFID tag are very limited and this makes it difficult to solve its secure and private problems, especially for low-cost RFID tags. In order to ensure the security and privacy of low-cost RFID systems we propose a lightweight authentication protocol based on Hash function. This protocol can ensure forward security and prevent information leakage, location tracing, eavesdropping, replay attack and spoofing. This protocol completes the strong authentication of the reader to the tag by twice authenticating and it only transfers part information of the encrypted tag’s identifier for each session so it is difficult for an adversary to intercept the whole identifier of a tag. This protocol is simple and it takes less computing and storage resources, it is very suitable to some low-cost RFID systems.

Effects of Tamoxifen and oestrogen on histology and radiographic density in high and low mammographic density human breast tissues maintained in murine tissue engineering chambers

Mammographic density (MD) is a strong risk factor for breast cancer. It is altered by exogenous endocrine treatments, including hormone replacement therapy and Tamoxifen. Such agents also modify breast cancer (BC) risk. However, the biomolecular basis of how systemic endocrine therapy modifies MD and MD-associated BC risk is poorly understood. This study aims to determine whether our xenograft biochamber model can be used to study the effectiveness of therapies aimed at modulating MD, by examine the effects of Tamoxifen and oestrogen on histologic and radiographic changes in high and low MD tissues maintained within the biochamber model. High and low MD human tissues were precisely sampled under radiographic guidance from prophylactic mastectomy fresh specimens of high-risk women, then inserted into separate vascularized murine biochambers. The murine hosts were concurrently implanted with Tamoxifen, oestrogen or placebo pellets, and the high and low MD biochamber tissues maintained in the murine host environment for 3 months, before the high and low MD biochamber tissues were harvested for histologic and radiographic analyses. The radiographic density of high MD tissue maintained in murine biochambers was decreased in Tamoxifen-treated mice compared to oestrogen-treated mice (p = 0.02). Tamoxifen treatment of high MD tissue in SCID mice led to a decrease in stromal (p = 0.009), and an increase in adipose (p = 0.023) percent areas, compared to placebo-treated mice. No histologic or radiographic differences were observed in low MD biochamber tissue with any treatment. High MD biochamber tissues maintained in mice implanted with Tamoxifen, oestrogen or placebo pellets had dynamic and measurable histologic compositional and radiographic changes. This further validates the dynamic nature of the MD xenograft model, and suggests the biochamber model may be useful for assessing the underlying molecular pathways of Tamoxifen-reduced MD, and in testing of other pharmacologic interventions in a preclinical model of high MD.

Study of robust H(infinity) filtering application in loosely coupled INS/GPS system

Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H(infinity) filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H(infinity) filter is first reviewed; next we utilize linear matrix inequalities (LMI) approach to design the robust H(infinity) filter. For the special INS/GPS model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H(infinity) filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H(infinity) filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

Multiphasic scaffolds for periodontal tissue engineering

For a successful clinical outcome, periodontal regeneration requires the coordinated response of multiple soft and hard tissues (periodontal ligament, gingiva, cementum, and bone) during the wound-healing process. Tissue-engineered constructs for regeneration of the periodontium must be of a complex 3-dimensional shape and adequate size and demonstrate biomechanical stability over time. A critical requirement is the ability to promote the formation of functional periodontal attachment between regenerated alveolar bone, and newly formed cementum on the root surface. This review outlines the current advances in multiphasic scaffold fabrication and how these scaffolds can be combined with cell- and growth factor-based approaches to form tissue-engineered constructs capable of recapitulating the complex temporal and spatial wound-healing events that will lead to predictable periodontal regeneration. This can be achieved through a variety of approaches, with promising strategies characterized by the use of scaffolds that can deliver and stabilize cells capable of cementogenesis onto the root surface, provide biomechanical cues that encourage perpendicular alignment of periodontal fibers to the root surface, and provide osteogenic cues and appropriate space to facilitate bone regeneration. Progress on the development of multiphasic constructs for periodontal tissue engineering is in the early stages of development, and these constructs need to be tested in large animal models and, ultimately, human clinical trials.

Increasing autonomy transparency through capability communication in multiple heterogeneous UAV management

Interest in the area of collaborative Unmanned Aerial Vehicles (UAVs) in a Multi-Agent System is growing to compliment the strengths and weaknesses of the human-machine relationship. To achieve effective management of multiple heterogeneous UAVs, the status model of the agents must be communicated to each other. This paper presents the effects on operator Cognitive Workload (CW), Situation Awareness (SA), trust and performance by increasing the autonomy capability transparency through text-based communication of the UAVs to the human agents. The results revealed a reduction in CW, increase in SA, increase in the Competence, Predictability and Reliability dimensions of trust, and the operator performance.

Design and flight testing of an integrated solar powered UAV and WSN for greenhouse gas monitoring emissions in agricultural farms

There is an increased interest in measuring the amount of greenhouse gases produced by farming practices . This paper describes an integrated solar powered Unmanned Air Vehicles (UAV) and Wireless Sensor Network (WSN) gas sensing system for greenhouse gas emissions in agricultural lands. The system uses a generic gas sensing system for CH4 and CO2 concentrations using metal oxide (MoX) and non-dispersive infrared sensors, and a new solar cell encapsulation method to power the unmanned aerial system (UAS)as well as a data management platform to store, analyze and share the information with operators and external users. The system was successfully field tested at ground and low altitudes, collecting, storing and transmitting data in real time to a central node for analysis and 3D mapping. The system can be used in a wide range of outdoor applications at a relatively low operational cost. In particular, agricultural environments are increasingly subject to emissions mitigation policies. Accurate measurements of CH4 and CO2 with its temporal and spatial variability can provide farm managers key information to plan agricultural practices. A video of the bench and flight test performed can be seen in the following link: https://www.youtube.com/watch?v=Bwas7stYIxQ

Submission to: Strategic Review of Health and Medical Research (Australia) otherwise known as the McKeon Review

Public submission # 247 to the McKeon Review. The submission addresses the terms of reference on: How can we optimise translation of health and medical research into better health and wellbeing? (Terms of Reference 4, 8, 9, 10 and 11)

Constructing abstract maps from spatial descriptions for goal-directed exploration

This paper describes ongoing work on a system using spatial descriptions to construct abstract maps that can be used for goal-directed exploration in an unfamiliar office environment. Abstract maps contain membership, connectivity, and spatial layout information extracted from symbolic spatial information. In goal-directed exploration, the robot would then link this information with observed symbolic information and its grounded world representation. We demonstrate the ability of the system to extract and represent membership, connectivity, and spatial layout information from spatial descriptions of an office environment. In the planned study, the robot will navigate to the goal location using the abstract map to inform the best direction to explore in.