Rendering optimisations for stylised sketching

We present work that specifically pertains to the rendering stage of stylised, non-photorealistic sketching. While a substantial body of work has been published on geometric optimisations, surface topologies, space-algorithms and natural media simulation, rendering-specific issues are rarely discussed in-depth even though they are often acknowledged. We investigate the most common stylised sketching approaches and identify possible rendering optimisations. In particular, we define uncertainty-functions, which are used to describe a human-error component, discuss how these pertain to geometric perturbation and textured silhouette sketching and explain how they can be cached to improve performance. Temporal coherence, which poses a problem for textured silhouette sketching, is addressed by means of an easily computed visibility-function. Lastly, we produce an effective yet surprisingly simple solution to seamless hatching, which commonly presents a large computational overhead, by using 3-D textures in a novel fashion. All our optimisations are cost-effective, easy to implement and work in conjunction with most existing algorithms.

Engineering optimisations in query rewriting for OBDA

Ontology-based data access (OBDA) systems use ontologies to provide views over relational databases. Most of these systems work with ontologies implemented in description logic families of reduced expressiveness, what allows applying efficient query rewriting techniques for query answering. In this paper we describe a set of optimisations that are applicable with one of the most expressive families used in this context (ELHIO¬). Our resulting system exhibits a behaviour that is comparable to the one shown by systems that handle less expressive logics.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Reduction of environmental effects of civil aircraft through multi-objective flight plan optimisation

With rising environmental alarm, the reduction of critical aircraft emissions including carbon dioxides (CO2) and nitrogen oxides (NOx) is one of most important aeronautical problems. There can be many possible attempts to solve such problem by designing new wing/aircraft shape, new efficient engine, etc. The paper rather provides a set of acceptable flight plans as a first step besides replacing current aircrafts. The paper investigates a green aircraft design optimisation in terms of aircraft range, mission fuel weight (CO2) and NOx using advanced Evolutionary Algorithms coupled to flight optimisation system software. Two multi-objective design optimisations are conducted to find the best set of flight plans for current aircrafts considering discretised altitude and Mach numbers without designing aircraft shape and engine types. The objectives of first optimisation are to maximise range of aircraft while minimising NOx with constant mission fuel weight. The second optimisation considers minimisation of mission fuel weight and NOx with fixed aircraft range. Numerical results show that the method is able to capture a set of useful trade-offs that reduce NOx and CO2 (minimum mission fuel weight).

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Input-orientated data envelopment analysis framework for measuring and decomposing economic, environmental and ecological efficiency : an application to OECD agriculture

This paper presents an input-orientated data envelopment analysis (DEA) framework which allows the measurement and decomposition of economic, environmental and ecological efficiency levels in agricultural production across different countries. Economic, environmental and ecological optimisations search for optimal input combinations that minimise total costs, total amount of nutrients, and total amount of cumulative exergy contained in inputs respectively. The application of the framework to an agricultural dataset of 30 OECD countries revealed that (i) there was significant scope to make their agricultural production systemsmore environmentally and ecologically sustainable; (ii) the improvement in the environmental and ecological sustainability could be achieved by being more technically efficient and, even more significantly, by changing the input combinations; (iii) the rankings of sustainability varied significantly across OECD countries within frontier-based environmental and ecological efficiency measures and between frontier-based measures and indicators.

Fast formulas for computing cryptographic pairings

The most powerful known primitive in public-key cryptography is undoubtedly elliptic curve pairings. Upon their introduction just over ten years ago the computation of pairings was far too slow for them to be considered a practical option. This resulted in a vast amount of research from many mathematicians and computer scientists around the globe aiming to improve this computation speed. From the use of modern results in algebraic and arithmetic geometry to the application of foundational number theory that dates back to the days of Gauss and Euler, cryptographic pairings have since experienced a great deal of improvement. As a result, what was an extremely expensive computation that took several minutes is now a high-speed operation that takes less than a millisecond. This thesis presents a range of optimisations to the state-of-the-art in cryptographic pairing computation. Both through extending prior techniques, and introducing several novel ideas of our own, our work has contributed to recordbreaking pairing implementations.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

Polybridge Technical Report

This study examined the physical and chemical properties of a novel, fully-recirculated prawn and polychaete production system that incorporated polychaete-assisted sand filters (PASF). The aims were to assess and demonstrate the potential of this system for industrialisation, and to provide optimisations for wastewater treatment by PASF. Two successive seasons were studied at commercially-relevant scales in a prototype system constructed at the Bribie Island Research Centre in Southeast Queensland. The project produced over 5.4 tonnes of high quality black tiger prawns at rates up to 9.9 tonnes per hectare, with feed conversion of up to 1.1. Additionally, the project produced about 930 kg of high value polychaete biomass at rates up to 1.5 kg per square metre of PASF, with the worms feeding predominantly on waste nutrients. Importantly, this closed production system demonstrated rapid growth of healthy prawns at commercially relevant production levels, using methods that appear feasible for application at large scale. Deeper (23 cm) PASF beds provided similar but more reliable wastewater treatment efficacies compared with shallower (13 cm) beds, but did not demonstrate significantly greater polychaete productivity than (easier to harvest) shallow beds. The nutrient dynamics associated with seasonal and tidal operations of the system were studied in detail, providing technical and practical insights into how PASF could be optimised for the mitigation of nutrient discharge. The study also highlighted some of the other important advantages of this integrated system, including low sludge production, no water discharge during the culture phase, high ecosystem health, good prospects for biosecurity controls, and the sustainable production of a fishery-limited resource (polychaetes) that may be essential for the expansion of prawn farming industries throughout the world. Regarding nutrient discharge from this prototype mariculture system, when PASF was operating correctly it proved feasible to have no water (or nutrient) discharge during the entire prawn growing season. However, the final drain harvest and emptying of ponds that is necessary at the end of the prawn farming season released 58.4 kg ha-1 of nitrogen and 6 kg ha-1 of phosphorus (in Season 2). Whilst this is well below (i.e., one-third to one-half of) the current load-based licencing conditions for many prawn farms in Australia, the levels of nitrogen and chlorophyll a in the ponds remained higher than the more-stringent maximum limits at the Bribie Island study site. Zero-net-nutrient discharge was not achieved, but waste nutrients were low where 5.91 kg of nitrogen and 0.61 kg of phosphorus was discharged per tonne of prawns produced. This was from a system that deployed PASF at 14.4% of total ponded farm area which treated an average of 5.8% of pond water daily and did not use settlement ponds or other natural or artificial water remediation systems. Four supplemental appendices complement this research by studying several additional aspects that are central to the industrialisation of PASF. The first details an economic model and decision tool which allows potential users to interactively assess construction and operational variables of PASF at different scales. The second provides the qualitative results of a prawn maturation trial conducted collaboratively with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) to assess dietary inclusions of PASF-produced worms. The third provides the reproductive results from industry-based assessments of prawn broodstock produced using PASF. And the fourth appendix provides detailed elemental and nutritional analyses of bacterial biofilm produced by PASF and assesses its potential to improve the growth of prawns in recirculated culture systems.

Comparative and functional genome analysis of fungi for development of the protein production host Trichoderma reesei

Filamentous fungi of the subphylum Pezizomycotina are well known as protein and secondary metabolite producers. Various industries take advantage of these capabilities. However, the molecular biology of yeasts, i.e. Saccharomycotina and especially that of Saccharomyces cerevisiae, the baker's yeast, is much better known. In an effort to explain fungal phenotypes through their genotypes we have compared protein coding gene contents of Pezizomycotina and Saccharomycotina. Only biomass degradation and secondary metabolism related protein families seem to have expanded recently in Pezizomycotina. Of the protein families clearly diverged between Pezizomycotina and Saccharomycotina, those related to mitochondrial functions emerge as the most prominent. However, the primary metabolism as described in S. cerevisiae is largely conserved in all fungi. Apart from the known secondary metabolism, Pezizomycotina have pathways that could link secondary metabolism to primary metabolism and a wealth of undescribed enzymes. Previous studies of individual Pezizomycotina genomes have shown that regardless of the difference in production efficiency and diversity of secreted proteins, the content of the known secretion machinery genes in Pezizomycotina and Saccharomycotina appears very similar. Genome wide analysis of gene products is therefore needed to better understand the efficient secretion of Pezizomycotina. We have developed methods applicable to transcriptome analysis of non-sequenced organisms. TRAC (Transcriptional profiling with the aid of affinity capture) has been previously developed at VTT for fast, focused transcription analysis. We introduce a version of TRAC that allows more powerful signal amplification and multiplexing. We also present computational optimisations of transcriptome analysis of non-sequenced organism and TRAC analysis in general. Trichoderma reesei is one of the most commonly used Pezizomycotina in the protein production industry. In order to understand its secretion system better and find clues for improvement of its industrial performance, we have analysed its transcriptomic response to protein secretion stress conditions. In comparison to S. cerevisiae, the response of T. reesei appears different, but still impacts on the same cellular functions. We also discovered in T. reesei interesting similarities to mammalian protein secretion stress response. Together these findings highlight targets for more detailed studies.

Robust multiplexed model predictive control

This paper extends the recently developed multiplexed model predictive control (MMPC) concept to ensure satisfaction of hard constraints despite the action of persistent, unknown but bounded disturbances. MMPC uses asynchronous control moves on each input channel instead of synchronised moves on all channels. It offers reduced computation, by dividing the online optimisation into a smaller problem for each channel, and potential performance improvements, as the response to a disturbance is quicker, albeit via only one channel. Robustness to disturbances is introduced using the constraint tightening approach, tailored to suit the asynchronous updates of MMPC and the resulting time-varying optimisations. Numerical results are presented, involving a simple mechanical example and an aircraft control example, showing the potential computational and performance benefits of the new robust MMPC.

Passive suspensions incorporating inerters for railway vehicles

This paper investigates the possibility of improving the performance of railway vehicle suspensions by incorporating a newly developed mechanical device known as the inerter. A comparative study of several low-complexity passive suspension layouts is made. Improved performance for the lateral and vertical ride comfort, as well as lateral body movement when curving are demonstrated in comparison with the conventional suspension layout. The constraints imposed are to maintain the same level of other performance metrics. The calculations and optimisations are based on linearised plan-view and side-view high-speed train mathematical models. © 2012 Copyright Taylor and Francis Group, LLC.